1
|
Buch MH, Mallat Z, Dweck MR, Tarkin JM, O'Regan DP, Ferreira V, Youngstein T, Plein S. Current understanding and management of cardiovascular involvement in rheumatic immune-mediated inflammatory diseases. Nat Rev Rheumatol 2024; 20:614-634. [PMID: 39232242 DOI: 10.1038/s41584-024-01149-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2024] [Indexed: 09/06/2024]
Abstract
Immune-mediated inflammatory diseases (IMIDs) are a spectrum of disorders of overlapping immunopathogenesis, with a prevalence of up to 10% in Western populations and increasing incidence in developing countries. Although targeted treatments have revolutionized the management of rheumatic IMIDs, cardiovascular involvement confers an increased risk of mortality and remains clinically under-recognized. Cardiovascular pathology is diverse across rheumatic IMIDs, ranging from premature atherosclerotic cardiovascular disease (ASCVD) to inflammatory cardiomyopathy, which comprises myocardial microvascular dysfunction, vasculitis, myocarditis and pericarditis, and heart failure. Epidemiological and clinical data imply that rheumatic IMIDs and associated cardiovascular disease share common inflammatory mechanisms. This concept is strengthened by emergent trials that indicate improved cardiovascular outcomes with immune modulators in the general population with ASCVD. However, not all disease-modifying therapies that reduce inflammation in IMIDs such as rheumatoid arthritis demonstrate equally beneficial cardiovascular effects, and the evidence base for treatment of inflammatory cardiomyopathy in patients with rheumatic IMIDs is lacking. Specific diagnostic protocols for the early detection and monitoring of cardiovascular involvement in patients with IMIDs are emerging but are in need of ongoing development. This Review summarizes current concepts on the potentially targetable inflammatory mechanisms of cardiovascular pathology in rheumatic IMIDs and discusses how these concepts can be considered for the diagnosis and management of cardiovascular involvement across rheumatic IMIDs, with an emphasis on the potential of cardiovascular imaging for risk stratification, early detection and prognostication.
Collapse
Affiliation(s)
- Maya H Buch
- Centre for Musculoskeletal Research, Division of Musculoskeletal & Dermatological Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester, UK.
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
| | - Ziad Mallat
- Section of Cardiorespiratory Medicine, Victor Phillip Dahdaleh Heart & Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Marc R Dweck
- Centre for Cardiovascular Science, Chancellors Building, Little France Crescent, University of Edinburgh, Edinburgh, UK
| | - Jason M Tarkin
- Section of Cardiorespiratory Medicine, Victor Phillip Dahdaleh Heart & Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK
| | - Vanessa Ferreira
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Taryn Youngstein
- National Heart & Lung Institute, Imperial College London, London, UK
- Department of Rheumatology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Sven Plein
- Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK
| |
Collapse
|
2
|
Huang R, Kong X, Geng R, Wu J, Chen T, Li J, Li C, Wu Y, You D, Zhao Y, Zhong Z, Ni S, Bai J. Joint and interactive associations of body mass index and genetic factors with cardiovascular disease: a prospective study in UK Biobank. BMC Public Health 2024; 24:2371. [PMID: 39223569 PMCID: PMC11367834 DOI: 10.1186/s12889-024-19916-6] [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: 01/15/2024] [Accepted: 08/28/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Both body mass index (BMI) and genetic factors independently contribute to cardiovascular disease (CVD). However, it is unclear whether genetic risk modifies the association between BMI and the risk of incident CVD. This study aimed to investigate whether BMI categories and genetic risk jointly and interactively contribute to incident CVD events, including hypertension (HTN), atrial fibrillation (AF), coronary heart disease (CHD), stroke, and heart failure (HF). METHODS A total of 496,851 participants from the UK Biobank with one or more new-onset CVD events were included in the analyses. BMI was categorized as normal weight (< 25.0 kg/m2), overweight (25.0-29.9 kg/m2), and obesity (≥ 30.0 kg/m2). Genetic risk for each outcome was defined as low (lowest tertile), intermediate (second tertile), and high (highest tertile) using polygenic risk score. The joint associations of BMI categories and genetic risk with incident CVD were investigated using Cox proportional hazard models. Additionally, additive interactions were evaluated. RESULTS Among the 496,851 participants, 270,726 (54.5%) were female, with a mean (SD) age was 56.5 (8.1) years. Over a median follow-up (IQR) of 12.4 (11.5-13.1) years, 102,131 (22.9%) participants developed HTN, 26,301 (5.4%) developed AF, 32,222 (6.9%) developed CHD, 10,684 (2.2%) developed stroke, and 13,304 (2.7%) developed HF. Compared with the normal weight with low genetic risk, the obesity with high genetic risk had the highest risk of CVD: HTN (HR: 3.96; 95%CI: 3.84-4.09), AF (HR: 3.60; 95%CI: 3.38-3.83), CHD (HR: 2.76; 95%CI: 2.61-2.91), stroke (HR: 1.44; 95%CI: 1.31-1.57), and HF (HR: 2.47; 95%CI: 2.27-2.69). There were significant additive interactions between BMI categories and genetic risk for HTN, AF, and CHD, with relative excess risk of 0.53 (95%CI: 0.43-0.62), 0.67 (95%CI: 0.51-0.83), and 0.37 (95%CI: 0.25-0.49), respectively. CONCLUSIONS BMI and genetic factors jointly and interactively contribute to incident CVD, especially among participants with high genetic risk. These findings have public health implications for identifying populations more likely to have cardiovascular benefit from weight loss interventions.
Collapse
Affiliation(s)
- Ruyu Huang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xinxin Kong
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Rui Geng
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jingwei Wu
- Department of Epidemiology and Biostatistics, College of Public Health, Temple University, Philadelphia, PA, 19122, USA
| | - Tao Chen
- Center for Health Economics, University of York, York, YO105DD, UK
| | - Jiong Li
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Chunjian Li
- Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yaqian Wu
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Dongfang You
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zihang Zhong
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Senmiao Ni
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Jianling Bai
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| |
Collapse
|
3
|
Yang K, Li J, Hui X, Wang W, Liu Y. Assessing the causal relationship between metabolic biomarkers and coronary artery disease by Mendelian randomization studies. Sci Rep 2024; 14:19034. [PMID: 39152174 PMCID: PMC11329738 DOI: 10.1038/s41598-024-69879-2] [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: 02/27/2024] [Accepted: 08/09/2024] [Indexed: 08/19/2024] Open
Abstract
The development of coronary artery disease (CAD) is significantly affected by impaired endocrine and metabolic status. Under this circumstance, improved prevention and treatment of CAD may result from knowing the connection between metabolites and CAD. This study aims to delve into the causal relationship between human metabolic biomarkers and CAD by using two-sample Mendelian randomization (MR). Utilizing two-sample bidirectional MR analysis, we assessed the correlation between 1400 blood metabolites and CAD, and the metabolites data from the CLSA, encompassing 8299 participants. Metabolite analysis identified 1091 plasma metabolites and 309 ratios as instrumental variables. To evaluate the causal link between metabolites and CAD, we analyzed three datasets: ebi-a-GCST005195 (547,261 European & East Asian samples), bbj-a-159 (29,319 East Asian CAD cases & 183,134 East Asian controls), and ebi-a-GCST005194 (296,525 European & East Asian samples). To estimate causal links, we utilized the IVW method. To conduct sensitivity analysis, we used MR-Egger, Weighted Median, and MR-PRESSO. Additionally, we employed MR-Egger interception and Cochran's Q statistic to assess potential heterogeneity and pleiotropy. What's more, replication and reverse analyses were performed to verify the reliability of the results and the causal order between metabolites and disease. Furthermore, we conducted a pathway analysis to identify potential metabolic pathways. 59 blood metabolites and 27 metabolite ratios nominally associated with CAD (P < 0.05) were identified by IVW analysis method. A total of four known blood metabolites, namely beta-hydroxyisovaleroylcarnitine (OR 1.06, 95% CI 1.027-1.094, FDR 0.07), 1-palmitoyl-2-arachidonoyl (OR 1.07, 95% CI 1.029-1.110, FDR 0.09), 1-stearoyl-2- docosahexaenoyl (OR 1.07, 95% CI 1.034-1.113, FDR 0.07) and Linoleoyl-arachidonoyl-glycerol, (OR 1.07, 95% CI 1.036-1.105, FDR 0.05), and two metabolite ratios, namely spermidine to N-acetylputrescine ratio (OR 0.94, 95% CI 0.903-0.972, FDR 0.09) and benzoate to linoleoyl-arachidonoyl-glycerol ratio (OR 0.87, 95% CI 0.879-0.962, FDR 0.07), were confirmed as having a significant causal relationship with CAD, after correcting for the FDR method (p < 0. 1). A causal relationship was found to be established between beta -hydroxyisovalerylcarnitine and CAD with the validation in other two datasets. Moreover, multiple metabolic pathways were discovered to be associated with CAD. Our study supports the hypothesis that metabolites have an impact on CAD by demonstrating a causal relationship between human metabolites and CAD. This study is important for new strategies for the prevention and treatment of CAD.
Collapse
Affiliation(s)
- Kai Yang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, People's Republic of China
- Shandong University of Traditional Chinese Medicine, Jinan, 250355, People's Republic of China
| | - Jixin Li
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, People's Republic of China
| | - Xiaoshan Hui
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, People's Republic of China
| | - Wenru Wang
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, People's Republic of China
| | - Yongmei Liu
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, People's Republic of China.
| |
Collapse
|
4
|
Nurmohamed NS, Shim I, Gaillard EL, Ibrahim S, Bom MJ, Earls JP, Min JK, Planken RN, Choi AD, Natarajan P, Stroes ESG, Knaapen P, Reeskamp LF, Fahed AC. Polygenic Risk Is Associated With Long-Term Coronary Plaque Progression and High-Risk Plaque. JACC Cardiovasc Imaging 2024:S1936-878X(24)00253-5. [PMID: 39152960 DOI: 10.1016/j.jcmg.2024.06.015] [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: 03/26/2024] [Revised: 06/24/2024] [Accepted: 06/28/2024] [Indexed: 08/19/2024]
Abstract
BACKGROUND The longitudinal relation between coronary artery disease (CAD) polygenic risk score (PRS) and long-term plaque progression and high-risk plaque (HRP) features is unknown. OBJECTIVES The goal of this study was to investigate the impact of CAD PRS on long-term coronary plaque progression and HRP. METHODS Patients underwent CAD PRS measurement and prospective serial coronary computed tomography angiography (CTA) imaging. Coronary CTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography imaging). The relationship between CAD PRS and change in percent atheroma volume (PAV), percent noncalcified plaque progression, and HRP prevalence was investigated in linear mixed-effect models adjusted for baseline plaque volume and conventional risk factors. RESULTS A total of 288 subjects (mean age 58 ± 7 years; 60% male) were included in this study with a median scan interval of 10.2 years. At baseline, patients with a high CAD PRS had a more than 5-fold higher PAV than those with a low CAD PRS (10.4% vs 1.9%; P < 0.001). Per 10 years of follow-up, a 1 SD increase in CAD PRS was associated with a 0.69% increase in PAV progression in the multivariable adjusted model. CAD PRS provided additional discriminatory benefit for above-median noncalcified plaque progression during follow-up when added to a model with conventional risk factors (AUC: 0.73 vs 0.69; P = 0.039). Patients with high CAD PRS had an OR of 2.85 (95% CI: 1.14-7.14; P = 0.026) and 6.16 (95% CI: 2.55-14.91; P < 0.001) for having HRP at baseline and follow-up compared with those with low CAD PRS. CONCLUSIONS Polygenic risk is strongly associated with future long-term plaque progression and HRP in patients suspected of having CAD.
Collapse
Affiliation(s)
- Nick S Nurmohamed
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Injeong Shim
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of South Korea
| | - Emilie L Gaillard
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Shirin Ibrahim
- Department of Vascular Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Michiel J Bom
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | | | - R Nils Planken
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Universiteit van Amsterdam, Amsterdam, the Netherlands
| | - Andrew D Choi
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Pradeep Natarajan
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Erik S G Stroes
- Department of Vascular Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Paul Knaapen
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Laurens F Reeskamp
- Department of Vascular Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Akl C Fahed
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| |
Collapse
|
5
|
Singh S, Stocco G, Theken KN, Dickson A, Feng Q, Karnes JH, Mosley JD, El Rouby N. Pharmacogenomics polygenic risk score: Ready or not for prime time? Clin Transl Sci 2024; 17:e13893. [PMID: 39078255 PMCID: PMC11287822 DOI: 10.1111/cts.13893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/11/2024] [Accepted: 06/25/2024] [Indexed: 07/31/2024] Open
Abstract
Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.
Collapse
Affiliation(s)
- Sonal Singh
- Merck & Co., IncSouth San FranciscoCaliforniaUSA
| | - Gabriele Stocco
- Department of Medical, Surgical and Health SciencesUniversity of TriesteTriesteItaly
- Institute for Maternal and Child Health IRCCS Burlo GarofoloTriesteItaly
| | - Katherine N. Theken
- Department of Oral and Maxillofacial Surgery and Pharmacology, School of Dental MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alyson Dickson
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - QiPing Feng
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jason H. Karnes
- Department of Pharmacy Practice and Science, R. Ken Coit College of PharmacyUniversity of ArizonaTucsonArizonaUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jonathan D. Mosley
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Nihal El Rouby
- Division of Pharmacy Practice and Adminstrative Sciences, James L Winkle College of PharmacyUniversity of CincinnatiCincinnatiOhioUSA
- St. Elizabeth HealthcareEdgewoodKentuckyUSA
| |
Collapse
|
6
|
Yu Z, Coorens THH, Uddin MM, Ardlie KG, Lennon N, Natarajan P. Genetic variation across and within individuals. Nat Rev Genet 2024; 25:548-562. [PMID: 38548833 DOI: 10.1038/s41576-024-00709-x] [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] [Accepted: 02/09/2024] [Indexed: 04/12/2024]
Abstract
Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.
Collapse
Affiliation(s)
- Zhi Yu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Md Mesbah Uddin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, 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.
| |
Collapse
|
7
|
Barkas F, Sener YZ, Golforoush PA, Kheirkhah A, Rodriguez-Sanchez E, Novak J, Apellaniz-Ruiz M, Akyea RK, Bianconi V, Ceasovschih A, Chee YJ, Cherska M, Chora JR, D'Oria M, Demikhova N, Kocyigit Burunkaya D, Rimbert A, Macchi C, Rathod K, Roth L, Sukhorukov V, Stoica S, Scicali R, Storozhenko T, Uzokov J, Lupo MG, van der Vorst EPC, Porsch F. Advancements in risk stratification and management strategies in primary cardiovascular prevention. Atherosclerosis 2024; 395:117579. [PMID: 38824844 DOI: 10.1016/j.atherosclerosis.2024.117579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/29/2024] [Accepted: 05/14/2024] [Indexed: 06/04/2024]
Abstract
Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide, highlighting the urgent need for advancements in risk assessment and management strategies. Although significant progress has been made recently, identifying and managing apparently healthy individuals at a higher risk of developing atherosclerosis and those with subclinical atherosclerosis still poses significant challenges. Traditional risk assessment tools have limitations in accurately predicting future events and fail to encompass the complexity of the atherosclerosis trajectory. In this review, we describe novel approaches in biomarkers, genetics, advanced imaging techniques, and artificial intelligence that have emerged to address this gap. Moreover, polygenic risk scores and imaging modalities such as coronary artery calcium scoring, and coronary computed tomography angiography offer promising avenues for enhancing primary cardiovascular risk stratification and personalised intervention strategies. On the other hand, interventions aiming against atherosclerosis development or promoting plaque regression have gained attention in primary ASCVD prevention. Therefore, the potential role of drugs like statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, omega-3 fatty acids, antihypertensive agents, as well as glucose-lowering and anti-inflammatory drugs are also discussed. Since findings regarding the efficacy of these interventions vary, further research is still required to elucidate their mechanisms of action, optimize treatment regimens, and determine their long-term effects on ASCVD outcomes. In conclusion, advancements in strategies addressing atherosclerosis prevention and plaque regression present promising avenues for enhancing primary ASCVD prevention through personalised approaches tailored to individual risk profiles. Nevertheless, ongoing research efforts are imperative to refine these strategies further and maximise their effectiveness in safeguarding cardiovascular health.
Collapse
Affiliation(s)
- Fotios Barkas
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece.
| | - Yusuf Ziya Sener
- Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | | | - Azin Kheirkhah
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Elena Rodriguez-Sanchez
- Division of Cardiology, Department of Medicine, Department of Physiology, and Molecular Biology Institute, UCLA, Los Angeles, CA, USA
| | - Jan Novak
- 2(nd) Department of Internal Medicine, St. Anne's University Hospital in Brno and Faculty of Medicine of Masaryk University, Brno, Czech Republic; Department of Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Maria Apellaniz-Ruiz
- Genomics Medicine Unit, Navarra Institute for Health Research - IdiSNA, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | - Ralph Kwame Akyea
- Centre for Academic Primary Care, School of Medicine, University of Nottingham, United Kingdom
| | - Vanessa Bianconi
- Department of Medicine and Surgery, University of Perugia, Italy
| | - Alexandr Ceasovschih
- Internal Medicine Department, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania
| | - Ying Jie Chee
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore
| | - Mariia Cherska
- Cardiology Department, Institute of Endocrinology and Metabolism, Kyiv, Ukraine
| | - Joana Rita Chora
- Unidade I&D, Grupo de Investigação Cardiovascular, Departamento de Promoção da Saúde e Doenças Não Transmissíveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal; Universidade de Lisboa, Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Lisboa, Portugal
| | - Mario D'Oria
- Division of Vascular and Endovascular Surgery, Department of Medical Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Nadiia Demikhova
- Sumy State University, Sumy, Ukraine; Tallinn University of Technology, Tallinn, Estonia
| | | | - Antoine Rimbert
- Nantes Université, CNRS, INSERM, l'institut du Thorax, Nantes, France
| | - Chiara Macchi
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", Università Degli Studi di Milano, Milan, Italy
| | - Krishnaraj Rathod
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom; Barts Interventional Group, Barts Heart Centre, St. Bartholomew's Hospital, London, United Kingdom
| | - Lynn Roth
- Laboratory of Physiopharmacology, University of Antwerp, Antwerp, Belgium
| | - Vasily Sukhorukov
- Laboratory of Cellular and Molecular Pathology of Cardiovascular System, Petrovsky National Research Centre of Surgery, Moscow, Russia
| | - Svetlana Stoica
- "Victor Babes" University of Medicine and Pharmacy, Timisoara, Romania; Institute of Cardiovascular Diseases Timisoara, Timisoara, Romania
| | - Roberto Scicali
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Tatyana Storozhenko
- Cardiovascular Center Aalst, OLV Clinic, Aalst, Belgium; Department of Prevention and Treatment of Emergency Conditions, L.T. Malaya Therapy National Institute NAMSU, Kharkiv, Ukraine
| | - Jamol Uzokov
- Republican Specialized Scientific Practical Medical Center of Therapy and Medical Rehabilitation, Tashkent, Uzbekistan
| | | | - Emiel P C van der Vorst
- Institute for Molecular Cardiovascular Research (IMCAR), RWTH Aachen University, 52074, Aachen, Germany; Aachen-Maastricht Institute for CardioRenal Disease (AMICARE), RWTH Aachen University, 52074, Aachen, Germany; Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians-University Munich, 80336, Munich, Germany; Interdisciplinary Center for Clinical Research (IZKF), RWTH Aachen University, 52074, Aachen, Germany
| | - Florentina Porsch
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
8
|
Abramowitz SA, Boulier K, Keat K, Cardone KM, Shivakumar M, DePaolo J, Judy R, Kim D, Rader DJ, Ritchie, Voight BF, Pasaniuc B, Levin MG, Damrauer SM. Population Performance and Individual Agreement of Coronary Artery Disease Polygenic Risk Scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.25.24310931. [PMID: 39108513 PMCID: PMC11302700 DOI: 10.1101/2024.07.25.24310931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/12/2024]
Abstract
Importance Polygenic risk scores (PRSs) for coronary artery disease (CAD) are a growing clinical and commercial reality. Whether existing scores provide similar individual-level assessments of disease liability is a critical consideration for clinical implementation that remains uncharacterized. Objective Characterize the reliability of CAD PRSs that perform equivalently at the population level at predicting individual-level risk. Design Cross-sectional Study. Setting All of Us Research Program (AOU), Penn Medicine Biobank (PMBB), and UCLA ATLAS Precision Health Biobank. Participants Volunteers of diverse genetic backgrounds enrolled in AOU, PMBB, and UCLA with available electronic health record and genotyping data. Exposures Polygenic risk for CAD from previously published PRSs and new PRSs developed separately from the testing cohorts. Main Outcomes and Measures Sets of CAD PRSs that perform population prediction equivalently were identified by comparing calibration and discrimination (Brier score and AUROC) of generalized linear models of prevalent CAD using Bayesian analysis of variance. Among equivalently performing scores, individual-level agreement between risk estimates was tested with intraclass correlation (ICC) and Light's Kappa, measures of inter-rater reliability. Results 50 PRSs were calculated for 171,095 AOU participants. When included in a model of prevalent CAD, 48 scores had practically equivalent Brier scores and AUROCs (region of practical equivalence = 0.02). Across these scores, 84% of participants had at least one score in both the top and bottom risk quintile. Continuous agreement of individual risk predictions from the 48 scores was poor, with an ICC of 0.351 (95% CI; 0.349, 0.352). Agreement between two statistically equivalent scores was moderate, with an ICC of 0.649 (95% CI; 0.646, 0.652). Light's Kappa, used to evaluate consistency of assignment to high-risk thresholds, did not exceed 0.56 (interpreted as 'fair') across statistically and practically equivalent scores. Repeating the analysis among 41,193 PMBB and 50,748 UCLA participants yielded different sets of statistically and practically equivalent scores which also lacked strong individual agreement. Conclusions and Relevance Across three diverse biobanks, CAD PRSs that performed equivalently at the population level produced unreliable individual risk estimates. Approaches to clinical implementation of CAD PRSs must consider the potential for discordant individual risk estimates from otherwise indistinguishable scores.
Collapse
Affiliation(s)
- Sarah A. Abramowitz
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell
| | - Kristin Boulier
- Department of Computational Medicine, University of California, Los Angeles
| | - Karl Keat
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Katie M. Cardone
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Manu Shivakumar
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - John DePaolo
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
| | - Renae Judy
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
| | - Dokyoon Kim
- Institute of Biomedical Informatics, University of Pennsylvania
| | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Benjamin F. Voight
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine
| | - Bogdan Pasaniuc
- Department of Computational Medicine, University of California, Los Angeles
| | - Michael G. Levin
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine
- Corporal Michael J. Crescenz VA Medical Center
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine
| | - Scott M. Damrauer
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine
- Corporal Michael J. Crescenz VA Medical Center
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Tubbs JD, Chen Y, Duan R, Huang H, Ge T. Real-time dynamic polygenic prediction for streaming data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.12.24310357. [PMID: 39040195 PMCID: PMC11261927 DOI: 10.1101/2024.07.12.24310357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Polygenic risk scores (PRSs) are promising tools for advancing precision medicine. However, existing PRS construction methods rely on static summary statistics derived from genome-wide association studies (GWASs), which are often updated at lengthy intervals. As genetic data and health outcomes are continuously being generated at an ever-increasing pace, the current PRS training and deployment paradigm is suboptimal in maximizing the prediction accuracy of PRSs for incoming patients in healthcare settings. Here, we introduce real-time PRS-CS (rtPRS-CS), which enables online, dynamic refinement and calibration of PRS as each new sample is collected, without the need to perform intermediate GWASs. Through extensive simulation studies, we evaluate the performance of rtPRS-CS across various genetic architectures and training sample sizes. Leveraging quantitative traits from the Mass General Brigham Biobank and UK Biobank, we show that rtPRS-CS can integrate massive streaming data to enhance PRS prediction over time. We further apply rtPRS-CS to 22 schizophrenia cohorts in 7 Asian regions, demonstrating the clinical utility of rtPRS-CS in dynamically predicting and stratifying disease risk across diverse genetic ancestries.
Collapse
Affiliation(s)
- Justin D. Tubbs
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Yu Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| |
Collapse
|
11
|
Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 84:97-114. [PMID: 38925729 DOI: 10.1016/j.jacc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.
Collapse
Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), New Haven, Connecticut, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Girish N Nadkarni
- The Samuel Bronfman Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica R Morley
- Digital Ethics Center, Yale University, New Haven, Connecticut, USA
| | - Jenna Wiens
- Electrical Engineering and Computer Science, Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA; Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California, USA
| | - Eric J Topol
- Molecular Medicine, Scripps Research Translational Institute, Scripps Research, La Jolla, California, USA
| |
Collapse
|
12
|
Jermy B, Läll K, Wolford BN, Wang Y, Zguro K, Cheng Y, Kanai M, Kanoni S, Yang Z, Hartonen T, Monti R, Wanner J, Youssef O, Lippert C, van Heel D, Okada Y, McCartney DL, Hayward C, Marioni RE, Furini S, Renieri A, Martin AR, Neale BM, Hveem K, Mägi R, Palotie A, Heyne H, Mars N, Ganna A, Ripatti S. A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk. Nat Commun 2024; 15:5007. [PMID: 38866767 PMCID: PMC11169548 DOI: 10.1038/s41467-024-48938-2] [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: 05/31/2023] [Accepted: 05/17/2024] [Indexed: 06/14/2024] Open
Abstract
Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.
Collapse
Affiliation(s)
- Bradley Jermy
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Brooke N Wolford
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kristina Zguro
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Remo Monti
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Julian Wanner
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Omar Youssef
- Helsinki Biobank, Hospital District of Helsinki and Uusimaa (HUS), Helsinki, Finland
- Pathology Department, University of Helsinki, Helsinki, Finland
| | - Christoph Lippert
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Simone Furini
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy
| | - Alessandra Renieri
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Medical Genetics, University of Siena, Siena, Italy
- Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Henrike Heyne
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Massachusetts General Hospital, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Massachusetts General Hospital, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Public Health, University of Helsinki, Helsinki, Finland.
| |
Collapse
|
13
|
Le A, Peng H, Golinsky D, Di Scipio M, Lali R, Paré G. What Causes Premature Coronary Artery Disease? Curr Atheroscler Rep 2024; 26:189-203. [PMID: 38573470 DOI: 10.1007/s11883-024-01200-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE OF REVIEW This review provides an overview of genetic and non-genetic causes of premature coronary artery disease (pCAD). RECENT FINDINGS pCAD refers to coronary artery disease (CAD) occurring before the age of 65 years in women and 55 years in men. Both genetic and non-genetic risk factors may contribute to the onset of pCAD. Recent advances in the genetic epidemiology of pCAD have revealed the importance of both monogenic and polygenic contributions to pCAD. Familial hypercholesterolemia (FH) is the most common monogenic disorder associated with atherosclerotic pCAD. However, clinical overreliance on monogenic genes can result in overlooked genetic causes of pCAD, especially polygenic contributions. Non-genetic factors, notably smoking and drug use, are also important contributors to pCAD. Cigarette smoking has been observed in 25.5% of pCAD patients relative to 12.2% of non-pCAD patients. Finally, myocardial infarction (MI) associated with spontaneous coronary artery dissection (SCAD) may result in similar clinical presentations as atherosclerotic pCAD. Recognizing the genetic and non-genetic causes underlying pCAD is important for appropriate prevention and treatment. Despite recent progress, pCAD remains incompletely understood, highlighting the need for both awareness and research.
Collapse
Affiliation(s)
- Ann Le
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Medical Sciences, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
| | - Helen Peng
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada
| | - Danielle Golinsky
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- School of Nursing, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada
| | - Matteo Di Scipio
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Medical Sciences, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
- Department of Medicine, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada
| | - Ricky Lali
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada.
- Department of Medical Sciences, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Department of Biochemistry and Biomedical Sciences, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada.
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada.
| |
Collapse
|
14
|
Schwantes-An TH, Whitfield JB, Aithal GP, Atkinson SR, Bataller R, Botwin G, Chalasani NP, Cordell HJ, Daly AK, Darlay R, Day CP, Eyer F, Foroud T, Gawrieh S, Gleeson D, Goldman D, Haber PS, Jacquet JM, Lammert CS, Liang T, Liangpunsakul S, Masson S, Mathurin P, Moirand R, McQuillin A, Moreno C, Morgan MY, Mueller S, Müllhaupt B, Nagy LE, Nahon P, Nalpas B, Naveau S, Perney P, Pirmohamed M, Seitz HK, Soyka M, Stickel F, Thompson A, Thursz MR, Trépo E, Morgan TR, Seth D. A polygenic risk score for alcohol-associated cirrhosis among heavy drinkers with European ancestry. Hepatol Commun 2024; 8:e0431. [PMID: 38727677 PMCID: PMC11093576 DOI: 10.1097/hc9.0000000000000431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 11/01/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Polygenic Risk Scores (PRS) based on results from genome-wide association studies offer the prospect of risk stratification for many common and complex diseases. We developed a PRS for alcohol-associated cirrhosis by comparing single-nucleotide polymorphisms among patients with alcohol-associated cirrhosis (ALC) versus drinkers who did not have evidence of liver fibrosis/cirrhosis. METHODS Using a data-driven approach, a PRS for ALC was generated using a meta-genome-wide association study of ALC (N=4305) and an independent cohort of heavy drinkers with ALC and without significant liver disease (N=3037). It was validated in 2 additional independent cohorts from the UK Biobank with diagnosed ALC (N=467) and high-risk drinking controls (N=8981) and participants in the Indiana Biobank Liver cohort with alcohol-associated liver disease (N=121) and controls without liver disease (N=3239). RESULTS A 20-single-nucleotide polymorphisms PRS for ALC (PRSALC) was generated that stratified risk for ALC comparing the top and bottom deciles of PRS in the 2 validation cohorts (ORs: 2.83 [95% CI: 1.82 -4.39] in UK Biobank; 4.40 [1.56 -12.44] in Indiana Biobank Liver cohort). Furthermore, PRSALC improved the prediction of ALC risk when added to the models of clinically known predictors of ALC risk. It also stratified the risk for metabolic dysfunction -associated steatotic liver disease -cirrhosis (3.94 [2.23 -6.95]) in the Indiana Biobank Liver cohort -based exploratory analysis. CONCLUSIONS PRSALC incorporates 20 single-nucleotide polymorphisms, predicts increased risk for ALC, and improves risk stratification for ALC compared with the models that only include clinical risk factors. This new score has the potential for early detection of heavy drinking patients who are at high risk for ALC.
Collapse
Affiliation(s)
- Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis IN, USA
| | - John B. Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Queensland 4029, Australia
| | - Guruprasad P. Aithal
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals and the University of Nottingham, Nottingham NG7 2UH, UK
| | - Stephen R. Atkinson
- Department of Metabolism, Digestion & Reproduction, Imperial College London, UK
| | - Ramon Bataller
- Center for Liver Diseases, University of Pittsburgh Medical Center, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Greg Botwin
- Department of Veterans Affairs, VA Long Beach Healthcare System, 5901 East Seventh Street, Long Beach, CA 90822, USA
- F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California CA 90048, USA
| | - Naga P. Chalasani
- Department of Medicine, Indiana University, Indianapolis, IN 46202-5175, USA
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK
| | - Ann K. Daly
- Faculty of Medical Sciences, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Rebecca Darlay
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK
| | - Christopher P. Day
- Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Florian Eyer
- Division of Clinical Toxicology, Department of Internal Medicine 2, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis IN, USA
| | - Samer Gawrieh
- Department of Medicine, Indiana University, Indianapolis, IN 46202-5175, USA
| | - Dermot Gleeson
- Liver Unit, Sheffield Teaching Hospitals, AO Floor Robert Hadfield Building, Northern General Hospital, Sheffield S5 7AU, UK
| | - David Goldman
- Office of the Clinical Director and Laboratory of Neurogenetics, NIAAA, Bethesda, MD 20952, USA
| | - Paul S. Haber
- Edith Collins Centre (Translational Research in Alcohol Drugs and Toxicology), Sydney Local Health District, Missenden Road, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Sydney, NSW 2006, Australia
| | | | - Craig S. Lammert
- Department of Medicine, Indiana University, Indianapolis, IN 46202-5175, USA
| | - Tiebing Liang
- Department of Medicine, Indiana University, Indianapolis, IN 46202-5175, USA
| | - Suthat Liangpunsakul
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University and Roudebush Veterans Administration Medical Center, Indianapolis, USA
| | - Steven Masson
- Faculty of Medical Sciences, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Philippe Mathurin
- CHRU de Lille, Hôpital Claude Huriez, Rue M. Polonovski CS 70001, 59 037 Lille Cedex, France
| | - Romain Moirand
- Univ Rennes, INRA, INSERM, CHU Rennes, Institut NUMECAN (Nutrition Metabolisms and Cancer), F-35000 Rennes, France
| | - Andrew McQuillin
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London WC1E 6DE, UK
| | - Christophe Moreno
- CUB Hôpital Erasme, Université Libre de Bruxelles, clinique d’Hépatologie, Brussels, Belgium; Laboratory of Experimental Gastroenterology, Université Libre de Bruxelles, Brussels, Belgium
| | - Marsha Y. Morgan
- UCL Institute for Liver & Digestive Health, Division of Medicine, Royal Free Campus, University College London, London NW3 2PF, UK
| | - Sebastian Mueller
- Department of Internal Medicine, Salem Medical Center and Center for Alcohol Research, University of Heidelberg, Zeppelinstraße 11-33, 69121 Heidelberg, Germany
| | - Beat Müllhaupt
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Rämistrasse 100, CH-8901 Zurich, Switzerland
| | - Laura E. Nagy
- Lerner Research Institute, 9500 Euclid Avenue, Cleveland, Ohio, OH 44195, USA
| | - Pierre Nahon
- Service d'Hépatologie, APHP Hôpital Avicenne et Université Paris 13, Bobigny, France
- University Paris 13, Bobigny, France
- Inserm U1162 Génomique fonctionnelle des tumeurs solides, Paris, France
| | - Bertrand Nalpas
- Service Addictologie, CHRU Caremeau, 30029 Nîmes, France
- DISC, Inserm, 75013 Paris, France
| | - Sylvie Naveau
- Hôpital Antoine-Béclère, 157 Rue de la Porte de Trivaux, 92140 Clamart, France
| | - Pascal Perney
- Hôpital Universitaire Caremeau, Place du Pr. Robert Debre, 30029 Nîmes, France
| | - Munir Pirmohamed
- MRC Centre for Drug Safety Science, Liverpool Centre for Alcohol Research, University of Liverpool, The Royal Liverpool and Broadgreen University Hospitals NHS Trust, and Liverpool Health Partners, Liverpool, L69 3GL, UK
| | - Helmut K. Seitz
- Department of Internal Medicine, Salem Medical Center and Center for Alcohol Research, University of Heidelberg, Zeppelinstraße 11-33, 69121 Heidelberg, Germany
| | - Michael Soyka
- Psychiatric Hospital University of Munich, Nussbaumsstr.7, 80336 Munich, Germany
| | - Felix Stickel
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Rämistrasse 100, CH-8901 Zurich, Switzerland
| | - Andrew Thompson
- MRC Centre for Drug Safety Science, Liverpool Centre for Alcohol Research, University of Liverpool, The Royal Liverpool and Broadgreen University Hospitals NHS Trust, and Liverpool Health Partners, Liverpool, L69 3GL, UK
- Health Analytics, Lane Clark & Peacock LLP, London, UK
| | - Mark R. Thursz
- Department of Metabolism, Digestion & Reproduction, Imperial College London, UK
| | - Eric Trépo
- CUB Hôpital Erasme, Université Libre de Bruxelles, clinique d’Hépatologie, Brussels, Belgium; Laboratory of Experimental Gastroenterology, Université Libre de Bruxelles, Brussels, Belgium
| | - Timothy R. Morgan
- Department of Medicine, University of California, Irvine, USA
- Department of Veterans Affairs, VA Long Beach Healthcare System, 5901 East Seventh Street, Long Beach, CA 90822, USA
| | - Devanshi Seth
- Edith Collins Centre (Translational Research in Alcohol Drugs and Toxicology), Sydney Local Health District, Missenden Road, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Sydney, NSW 2006, Australia
- Centenary Institute of Cancer Medicine and Cell Biology, the University of Sydney, Sydney, NSW 2006, Australia
| |
Collapse
|
15
|
Park H, Kim D, You SC, Jang E, Yu HT, Kim T, Kim D, Sung J, Pak H, Lee M, Yang P, Joung B. European and US Guideline-Based Statin Eligibility, Genetically Predicted Coronary Artery Disease, and the Risk of Major Coronary Events. J Am Heart Assoc 2024; 13:e032831. [PMID: 38639378 PMCID: PMC11179899 DOI: 10.1161/jaha.123.032831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 02/28/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND A study was designed to investigate whether the coronary artery disease polygenic risk score (CAD-PRS) may guide lipid-lowering treatment initiation as well as deferral in primary prevention beyond established clinical risk scores. METHODS AND RESULTS Participants were 311 799 individuals from the UK Biobank free of atherosclerotic cardiovascular disease, diabetes, chronic kidney disease, and lipid-lowering treatment at baseline. Participants were categorized as statin indicated, statin indication unclear, or statin not indicated as defined by the European and US guidelines on statin use. For a median of 11.9 (11.2-12.6) years, 8196 major coronary events developed. CAD-PRS added to European-Systematic Coronary Risk Evaluation 2 (European-SCORE2) and US-Pooled Cohort Equation (US-PCE) identified 18% and 12% of statin-indication-unclear individuals whose risk of major coronary events were the same as or higher than the average risk of statin-indicated individuals and 16% and 12% of statin-indicated individuals whose major coronary event risks were the same as or lower than the average risk of statin-indication-unclear individuals. For major coronary and atherosclerotic cardiovascular disease events, CAD-PRS improved C-statistics greater among statin-indicated or statin-indication-unclear than statin-not-indicated individuals. For atherosclerotic cardiovascular disease events, CAD-PRS added to the European evaluation and US equation resulted in a net reclassification improvement of 13.6% (95% CI, 11.8-15.5) and 14.7% (95% CI, 13.1-16.3) among statin-indicated, 10.8% (95% CI, 9.6-12.0) and 15.3% (95% CI, 13.2-17.5) among statin-indication-unclear, and 0.9% (95% CI, 0.6-1.3) and 3.6% (95% CI, 3.0-4.2) among statin-not-indicated individuals. CONCLUSIONS CAD-PRS may guide statin initiation as well as deferral among statin-indication-unclear or statin-indicated individuals as defined by the European and US guidelines. CAD-PRS had little clinical utility among statin-not-indicated individuals.
Collapse
Affiliation(s)
- Hanjin Park
- Division of Cardiology, Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Daehoon Kim
- Division of Cardiology, Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Seng Chan You
- Department of Biomedical Systems InformaticsYonsei University College of MedicineSeoulRepublic of Korea
| | - Eunsun Jang
- Division of Cardiology, Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Hee Tae Yu
- Division of Cardiology, Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Tae‐Hoon Kim
- Division of Cardiology, Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Dong‐min Kim
- Division of Cardiology, Department of Internal Medicine, College of MedicineDankook UniversityCheonanRepublic of Korea
| | - Jung‐Hoon Sung
- Division of Cardiology, CHA Bundang Medical CenterCHA UniversitySeongnamRepublic of Korea
| | - Hui‐Nam Pak
- Division of Cardiology, Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Moon‐Hyoung Lee
- Division of Cardiology, Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Pil‐Sung Yang
- Division of Cardiology, CHA Bundang Medical CenterCHA UniversitySeongnamRepublic of Korea
| | - Boyoung Joung
- Division of Cardiology, Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| |
Collapse
|
16
|
Abou-Karam R, Cheng F, Gady S, Fahed AC. The Role of Genetics in Advancing Cardiometabolic Drug Development. Curr Atheroscler Rep 2024; 26:153-162. [PMID: 38451435 DOI: 10.1007/s11883-024-01195-6] [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] [Accepted: 02/22/2024] [Indexed: 03/08/2024]
Abstract
PURPOSE OF REVIEW The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. RECENT FINDINGS Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.
Collapse
Affiliation(s)
- Roukoz Abou-Karam
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fangzhou Cheng
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shoshana Gady
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akl C Fahed
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
17
|
Holla B, Mahadevan J, Ganesh S, Sud R, Janardhanan M, Balachander S, Strom N, Mattheisen M, Sullivan PF, Huang H, Zandi P, Benegal V, Reddy YJ, Jain S, Purushottam M, Viswanath B. A cross ancestry genetic study of psychiatric disorders from India. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.25.24306377. [PMID: 38712191 PMCID: PMC11071591 DOI: 10.1101/2024.04.25.24306377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Genome-wide association studies across diverse populations may help validate and confirm genetic contributions to risk of disease. We estimated the extent of population stratification as well as the predictive accuracy of polygenic scores (PGS) derived from European samples to a data set from India. We analysed 2685 samples from two data sets, a population neurodevelopmental study (cVEDA) and a hospital-based sample of bipolar affective disorder (BD) and obsessive-compulsive disorder (OCD). Genotyping was conducted using Illumina's Global Screening Array. Population structure was examined with principal component analysis (PCA), uniform manifold approximation and projection (UMAP), support vector machine (SVM) ancestry predictions, and admixture analysis. PGS were calculated from the largest available European discovery GWAS summary statistics for BD, OCD, and externalizing traits using two Bayesian methods that incorporate local linkage disequilibrium structures (PGS-CS-auto) and functional genomic annotations (SBayesRC). Our analyses reveal global and continental PCA overlap with other South Asian populations. Admixture analysis revealed a north-south genetic axis within India (FST 1.6%). The UMAP partially reconstructed the contours of the Indian subcontinent. The Bayesian PGS analyses indicates moderate-to-high predictive power for BD. This was despite the cross-ancestry bias of the discovery GWAS dataset, with the currently available data. However, accuracy for OCD and externalizing traits was much lower. The predictive accuracy was perhaps influenced by the sample size of the discovery GWAS and phenotypic heterogeneity across the syndromes and traits studied. Our study results highlight the accuracy and generalizability of newer PGS models across ancestries. Further research, across diverse populations, would help understand causal mechanisms that contribute to psychiatric syndromes and traits.
Collapse
Affiliation(s)
- Bharath Holla
- Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Jayant Mahadevan
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Suhas Ganesh
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Reeteka Sud
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Meghana Janardhanan
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Srinivas Balachander
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Nora Strom
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Manuel Mattheisen
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Dalhousie University, Department of Community Health and Epidemiology & Faculty of Computer Science, Halifax, Nova Scotia, Canada
- University Hospital of Psychiatry and Psychotherapy, University of Bern
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA02114, USA
| | - Peter Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Yc Janardhan Reddy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Sanjeev Jain
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Meera Purushottam
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Biju Viswanath
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| |
Collapse
|
18
|
Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. CELL GENOMICS 2024; 4:100523. [PMID: 38508198 PMCID: PMC11019356 DOI: 10.1016/j.xgen.2024.100523] [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: 08/14/2023] [Revised: 09/15/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
Collapse
Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Leland E Hull
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia R Martin
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
| |
Collapse
|
19
|
Zou RS, Ruan Y, Truong B, Bhattacharya R, Lu MT, Karády J, Bernardo R, Finneran P, Hornsby W, Fitch KV, Ribaudo HJ, Zanni MV, Douglas PS, Grinspoon SK, Patel AP, Natarajan P. Polygenic Scores and Preclinical Cardiovascular Disease in Individuals With HIV: Insights From the REPRIEVE Trial. J Am Heart Assoc 2024; 13:e033413. [PMID: 38533953 PMCID: PMC11179771 DOI: 10.1161/jaha.123.033413] [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: 11/13/2023] [Accepted: 01/23/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Coronary artery disease (CAD) is a leading cause of death among the 38.4 million people with HIV globally. The extent to which cardiovascular polygenic risk scores (PRSs) derived in non-HIV populations generalize to people with HIV is not well understood. METHODS AND RESULTS PRSs for CAD (GPSMult) and lipid traits were calculated in a global cohort of people with HIV treated with antiretroviral therapy with low-to-moderate atherosclerotic cardiovascular disease risk enrolled in REPRIEVE (Randomized Trial to Prevent Vascular Events in HIV). The PRSs were associated with baseline lipid traits in 4495 genotyped participants, and with subclinical CAD in a subset of 662 who underwent coronary computed tomography angiography. Among participants who underwent coronary computed tomography angiography (mean age, 50.9 [SD, 5.8] years; 16.1% women; 41.8% African, 57.3% European, 1.1% Asian), GPSMult was associated with plaque presence with odds ratio (OR) per SD in GPSMult of 1.42 (95% CI, 1.20-1.68; P=3.8×10-5), stenosis >50% (OR, 2.39 [95% CI, 1.48-3.85]; P=3.4×10-4), and noncalcified/vulnerable plaque (OR, 1.45 [95% CI, 1.23-1.72]; P=9.6×10-6). Effects were consistent in subgroups of age, sex, 10-year atherosclerotic cardiovascular disease risk, ancestry, and CD4 count. Adding GPSMult to established risk factors increased the C-statistic for predicting plaque presence from 0.718 to 0.734 (P=0.02). Furthermore, a PRS for low-density lipoprotein cholesterol was associated with plaque presence with OR of 1.21 (95% CI, 1.01-1.44; P=0.04), and partially calcified plaque with OR of 1.21 (95% CI, 1.01-1.45; P=0.04) per SD. CONCLUSIONS Among people with HIV treated with antiretroviral therapy without documented atherosclerotic cardiovascular disease and at low-to-moderate calculated risk in REPRIEVE, an externally developed CAD PRS was predictive of subclinical atherosclerosis. PRS for low-density lipoprotein cholesterol was also associated with subclinical atherosclerosis, supporting a role for low-density lipoprotein cholesterol in HIV-associated CAD. REGISTRATION URL: https://www.reprievetrial.org; Unique identifier: NCT02344290.
Collapse
Affiliation(s)
- Roger S. Zou
- Department of MedicineMassachusetts General HospitalBostonMAUSA
- Cardiovascular Disease InitiativeBroad Institute of MIT and HarvardCambridgeMAUSA
- Harvard Medical SchoolBostonMAUSA
| | - Yunfeng Ruan
- Cardiovascular Disease InitiativeBroad Institute of MIT and HarvardCambridgeMAUSA
| | - Buu Truong
- Cardiovascular Disease InitiativeBroad Institute of MIT and HarvardCambridgeMAUSA
| | - Romit Bhattacharya
- Cardiovascular Disease InitiativeBroad Institute of MIT and HarvardCambridgeMAUSA
- Harvard Medical SchoolBostonMAUSA
- Division of Cardiology, Department of Medicine, Center for Genomic MedicineMassachusetts General HospitalBostonMAUSA
| | - Michael T. Lu
- Harvard Medical SchoolBostonMAUSA
- Cardiovascular Imaging Research CenterMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Júlia Karády
- Harvard Medical SchoolBostonMAUSA
- Cardiovascular Imaging Research CenterMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Rachel Bernardo
- Division of Cardiology, Department of Medicine, Center for Genomic MedicineMassachusetts General HospitalBostonMAUSA
| | - Phoebe Finneran
- Division of Cardiology, Department of Medicine, Center for Genomic MedicineMassachusetts General HospitalBostonMAUSA
| | - Whitney Hornsby
- Division of Cardiology, Department of Medicine, Center for Genomic MedicineMassachusetts General HospitalBostonMAUSA
| | - Kathleen V. Fitch
- Harvard Medical SchoolBostonMAUSA
- Metabolism UnitMassachusetts General HospitalBostonMSUSA
| | - Heather J. Ribaudo
- Department of Biostatistics, Center for Biostatistics in AIDS ResearchHarvard TH Chan School of Public HealthBostonMAUSA
| | - Markella V. Zanni
- Harvard Medical SchoolBostonMAUSA
- Metabolism UnitMassachusetts General HospitalBostonMSUSA
| | - Pamela S. Douglas
- Duke Clinical Research Institute, Duke University School of MedicineDurhamNCUSA
| | - Steven K. Grinspoon
- Harvard Medical SchoolBostonMAUSA
- Metabolism UnitMassachusetts General HospitalBostonMSUSA
| | - Aniruddh P. Patel
- Cardiovascular Disease InitiativeBroad Institute of MIT and HarvardCambridgeMAUSA
- Harvard Medical SchoolBostonMAUSA
- Division of Cardiology, Department of Medicine, Center for Genomic MedicineMassachusetts General HospitalBostonMAUSA
| | - Pradeep Natarajan
- Cardiovascular Disease InitiativeBroad Institute of MIT and HarvardCambridgeMAUSA
- Harvard Medical SchoolBostonMAUSA
- Division of Cardiology, Department of Medicine, Center for Genomic MedicineMassachusetts General HospitalBostonMAUSA
| |
Collapse
|
20
|
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
|
21
|
Møller PL, Rohde PD, Dahl JN, Rasmussen LD, Nissen L, Schmidt SE, McGilligan V, Gudbjartsson DF, Stefansson K, Holm H, Bentzon JF, Bøttcher M, Winther S, Nyegaard M. Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease. Genome Med 2024; 16:40. [PMID: 38509622 PMCID: PMC10953133 DOI: 10.1186/s13073-024-01313-8] [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: 08/29/2023] [Accepted: 03/12/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. METHODS Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. RESULTS HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. CONCLUSIONS In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. TRIAL REGISTRATION https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).
Collapse
Affiliation(s)
- Peter Loof Møller
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Palle Duun Rohde
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Jonathan Nørtoft Dahl
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Laust Dupont Rasmussen
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Louise Nissen
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Samuel Emil Schmidt
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Victoria McGilligan
- Personalized Medicine Centre, School of Medicine, Ulster University, Derry, Northern Ireland
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE Genetics/Amgen, Inc, Reykjavik, Iceland
| | - Jacob Fog Bentzon
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Morten Bøttcher
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Simon Winther
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
| |
Collapse
|
22
|
Andreoli L, Peeters H, Van Steen K, Dierickx K. Taking the risk. A systematic review of ethical reasons and moral arguments in the clinical use of polygenic risk scores. Am J Med Genet A 2024:e63584. [PMID: 38450933 DOI: 10.1002/ajmg.a.63584] [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: 01/23/2024] [Revised: 02/08/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
Debates about the prospective clinical use of polygenic risk scores (PRS) have grown considerably in the last years. The potential benefits of PRS to improve patient care at individual and population levels have been extensively underlined. Nonetheless, the use of PRS in clinical contexts presents a number of unresolved ethical challenges and consequent normative gaps that hinder their optimal implementation. Here, we conducted a systematic review of reasons of the normative literature discussing ethical issues and moral arguments related to the use of PRS for the prevention and treatment of common complex diseases. In total, we have included and analyzed 34 records, spanning from 2013 to 2023. The findings have been organized in three major themes: in the first theme, we consider the potential harms of PRS to individuals and their kin. In the theme "Threats to health equity," we consider ethical concerns of social relevance, with a focus on justice issues. Finally, the theme "Towards best practices" collects a series of research priorities and provisional recommendations to be considered for an optimal clinical translation of PRS. We conclude that the use of PRS in clinical care reinvigorates old debates in matters of health justice; however, open questions, regarding best practices in clinical counseling, suggest that the ethical considerations applicable in monogenic settings will not be sufficient to face PRS emerging challenges.
Collapse
Affiliation(s)
- Lara Andreoli
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Kris Dierickx
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
| |
Collapse
|
23
|
Taha M, Ibrahim MMM, Sedrak H. Association of epistatic effects of MTHFR, ACE, APOB, and APOE gene polymorphisms with the risk of myocardial infarction and unstable angina in Egyptian patients. Gene 2024; 895:147976. [PMID: 37952748 DOI: 10.1016/j.gene.2023.147976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/29/2023] [Accepted: 11/08/2023] [Indexed: 11/14/2023]
Abstract
Despite remarkable discoveries in the genetic susceptibility of coronary artery disease (CAD), a large part of heritability awaits identification. Epistasis or gene-gene interaction has a profound influence on CAD and might contribute to its missed genetic variability; however, this impact was largely unexplored. Here, we appraised the associations of gene-gene interactions and haplotypes of five polymorphisms, namely methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C, angiotensin converting enzyme (ACE) insertion/deletion (I/D), apolipoprotein B (APOB) R3500Q, and apolipoprotein E (APOE) ε4 with the risk of myocardial infarction (MI) and unstable angina (UA). Gene-environment interactions with traditional risk factors and clinical data were also scrutinized. This study recruited 100 MI, 50 UA patients, and 100 apparently healthy controls. Logistic regression models were employed in association analyses. We remarked that the single locus effect of individual polymorphisms was relatively weak; however, a magnified effect of their combination via gene-gene interaction may predict MI risk after adjustment for multiple comparisons. Only MTHFR C677T, ACE I/D, and APOB R5300Q were associated with the risk of UA, and the ACE I/D-R3500Q interaction posed a decreased UA risk. APOB R3500Q was in strong linkage disequilibrium with MTHFR C677T, ACE I/D, and APE ε4 polymorphisms. The TCDGε3, CADGε4, and TADGε4-C677T-A1298C-ACE I/D-R3500Q-APOE haplotypes were associated with escalating MI risk, while the CDG or CIG-C677T-ACE I/D-R3500Q haplotype was highly protective against UA risk compared to controls. Interestingly, the CADGε4 and CAIGε3 haplotypes were strongly associated with the presence of diabetes and hypertension, respectively in MI patients; both haplotypes stratified patients according to the ECHO results. In UA, the CDG haplotype was negatively associated with the presence of diabetes or dilated heart. Conclusively, our results advocate that a stronger combined effect of polymorphisms in MTHFR, ACE, APOB, and APOE genes via gene-gene and gene-environment interactions might help in risk stratification of MI and UA patients.
Collapse
Affiliation(s)
- Mohamed Taha
- Department of Biochemistry, Faculty of Pharmacy, Cairo University, Cairo 11562, Egypt.
| | | | - Heba Sedrak
- Department of Internal Medicine, Faculty of Medicine, Cairo University, Egypt
| |
Collapse
|
24
|
Pan C, Cheng B, Qin X, Cheng S, Liu L, Yang X, Meng P, Zhang N, He D, Cai Q, Wei W, Hui J, Wen Y, Jia Y, Liu H, Zhang F. Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function. Brief Bioinform 2024; 25:bbae070. [PMID: 38436562 DOI: 10.1093/bib/bbae070] [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: 10/19/2023] [Revised: 01/18/2024] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions. METHODS We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42 886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking. RESULTS We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (β = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (β = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (β = 0.088, FDR = 0.010), whereas no such association was found with PRS (β = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (β = -0.042, FDR = 6.30 × 10-9), but not with PRS (β = -0.003, FDR = 0.857). CONCLUSIONS Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.
Collapse
Affiliation(s)
- Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Xiaoyue Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Jingni Hui
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| |
Collapse
|
25
|
Uffelmann E, Price AL, Posthuma D, Peyrot WJ. Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301157. [PMID: 38260678 PMCID: PMC10802765 DOI: 10.1101/2024.01.12.24301157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability. The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning dataset with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.
Collapse
Affiliation(s)
- Emil Uffelmann
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
| | | | | | - Alkes L. Price
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex, Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Wouter J. Peyrot
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
- Department of Psychiatry, Amsterdam UMC, The Netherlands
| |
Collapse
|
26
|
Non AL, Cerdeña JP. Considerations, Caveats, and Suggestions for the Use of Polygenic Scores for Social and Behavioral Traits. Behav Genet 2024; 54:34-41. [PMID: 37801150 PMCID: PMC10822803 DOI: 10.1007/s10519-023-10162-x] [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: 04/04/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
Polygenic scores (PGS) are increasingly being used for prediction of social and behavioral traits, but suffer from many methodological, theoretical, and ethical concerns that profoundly limit their value. Primarily, these scores are derived from statistical correlations, carrying no inherent biological meaning, and thus may capture indirect effects. Further, the performance of these scores depends upon the diversity of the reference populations and the genomic panels from which they were derived, which consistently underrepresent minoritized populations, leading to poor fit when applied to diverse groups. There is also inherent danger of eugenic applications for the information gained from these scores, and general risk of misunderstandings that could lead to stigmatization for underrepresented groups. We urge extreme caution in use of PGS particularly for social/behavioral outcomes fraught for misinterpretation, with potential harm for the minoritized groups least likely to benefit from their use.
Collapse
Affiliation(s)
- Amy L Non
- Department of Anthropology, University of California San Diego, La Jolla, CA, USA.
| | - Jessica P Cerdeña
- Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT, USA
- Department of Anthropology, University of Connecticut, Storrs, CT, USA
- Department of Family Medicine, Middlesex Health, Middletown, CT, USA
| |
Collapse
|
27
|
Carrascosa-Carrillo JM, Aterido A, Li T, Guillén Y, Martinez S, Marsal S, Julià A. Toward Precision Medicine in Atopic Dermatitis Using Molecular-Based Approaches. ACTAS DERMO-SIFILIOGRAFICAS 2024; 115:66-75. [PMID: 37652096 DOI: 10.1016/j.ad.2023.08.003] [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: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023] Open
Abstract
Atopic dermatitis is the most common chronic inflammatory skin disorder, affecting up to 20% of children and 10% of adults in developed countries. The pathophysiology of atopic dermatitis is complex and involves a strong genetic predisposition and T-cell driven inflammation. Although our understanding of the pathology and drivers of this disease has improved in recent years, there are still knowledge gaps in the immune pathways involved. Therefore, advances in new omics technologies in atopic dermatitis will play a key role in understanding the pathogenesis of this burden disease and could develop preventive strategies and personalized treatment strategies. In this review, we discuss the latest developments in genetics, transcriptomics, epigenomics, proteomics, and metagenomics and understand how integrating multiple omics datasets will identify potential biomarkers and uncover nets of associations between several molecular levels.
Collapse
Affiliation(s)
- J M Carrascosa-Carrillo
- Dermatology Department, Hospital Germans Trias i Pujol, UAB, IGTP, Badalona, Barcelona, Spain
| | - A Aterido
- IMIDomics, Inc., Barcelona, Spain; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, Spain
| | - T Li
- IMIDomics, Inc., Barcelona, Spain
| | | | | | - S Marsal
- IMIDomics, Inc., Barcelona, Spain; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, Spain.
| | - A Julià
- IMIDomics, Inc., Barcelona, Spain; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, Spain
| |
Collapse
|
28
|
Carrascosa-Carrillo JM, Aterido A, Li T, Guillén Y, Martinez S, Marsal S, Julià A. Toward Precision Medicine in Atopic Dermatitis Using Molecular-Based Approaches. ACTAS DERMO-SIFILIOGRAFICAS 2024; 115:T66-T75. [PMID: 37923065 DOI: 10.1016/j.ad.2023.10.032] [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: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 11/07/2023] Open
Abstract
Atopic dermatitis is the most common chronic inflammatory skin disorder, affecting up to 20% of children and 10% of adults in developed countries. The pathophysiology of atopic dermatitis is complex and involves a strong genetic predisposition and T-cell driven inflammation. Although our understanding of the pathology and drivers of this disease has improved in recent years, there are still knowledge gaps in the immune pathways involved. Therefore, advances in new omics technologies in atopic dermatitis will play a key role in understanding the pathogenesis of this burden disease and could develop preventive strategies and personalized treatment strategies. In this review, we discuss the latest developments in genetics, transcriptomics, epigenomics, proteomics, and metagenomics and understand how integrating multiple omics datasets will identify potential biomarkers and uncover nets of associations between several molecular levels.
Collapse
Affiliation(s)
- J M Carrascosa-Carrillo
- Dermatology Department, Hospital Germans Trias i Pujol, UAB, IGTP, Badalona, Barcelona, España
| | - A Aterido
- IMIDomics, Inc., Barcelona, España; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, España
| | - T Li
- IMIDomics, Inc., Barcelona, España
| | | | | | - S Marsal
- IMIDomics, Inc., Barcelona, España; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, España.
| | - A Julià
- IMIDomics, Inc., Barcelona, España; Rheumatology Research Group, Vall Hebron Research Institute, Barcelona, España
| |
Collapse
|
29
|
Dennison CA, Martin J, Shakeshaft A, Riglin L, Rice F, Lewis CM, O'Donovan MC, Thapar A. Stratifying early-onset emotional disorders: using genetics to assess persistence in young people of European and South Asian ancestry. J Child Psychol Psychiatry 2024; 65:42-51. [PMID: 37469035 PMCID: PMC10807819 DOI: 10.1111/jcpp.13862] [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] [Accepted: 05/26/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Depression and anxiety are the most common mental health problems in young people. Currently, clinicians are advised to wait before initiating treatment for young people with these disorders as many spontaneously remit. However, others develop recurrent disorder but this subgroup cannot be identified at the outset. We examined whether psychiatric polygenic scores (PGS) could help inform stratification efforts to predict those at higher risk of recurrence. METHODS Probable emotional disorder was examined in two UK population cohorts using the emotional symptoms subscale of the Strengths and Difficulties Questionnaire (SDQ). Those with emotional disorder at two or more time points between ages 5 and 25 years were classed as 'recurrent emotional disorder' (n = 1,643) and those with emotional disorder at one time point as having 'single episode emotional disorder' (n = 1,435, controls n = 8,715). We first examined the relationship between psychiatric PGS and emotional disorders in childhood and adolescence. Second, we tested whether psychiatric PGS added to predictor variables of known association with emotional disorder (neurodevelopmental comorbidity, special educational needs, family history of depression and socioeconomic status) when discriminating between single-episode and recurrent emotional disorder. Analyses were conducted separately in individuals of European and South Asian ancestry. RESULTS Probable emotional disorder was associated with higher PGS for major depressive disorder (MDD), anxiety, broad depression, ADHD and autism spectrum disorder (ASD) in those of European ancestry. Higher MDD and broad depression PGS were associated with emotional disorder in people of South Asian ancestry. Recurrent, compared to single-episode, emotional disorder was associated with ASD and parental psychiatric history. PGS were not associated with episode recurrence, and PGS did not improve discrimination of recurrence when combined with clinical predictors. CONCLUSIONS Our findings do not support the use of PGS as a tool to assess the likelihood of recurrence in young people experiencing their first episode of emotional disorder.
Collapse
Affiliation(s)
- Charlotte A. Dennison
- Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
- Centre for Neuropsychiatric Genetics and Genomics, School of MedicineCardiff UniversityCardiffUK
| | - Joanna Martin
- Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
- Centre for Neuropsychiatric Genetics and Genomics, School of MedicineCardiff UniversityCardiffUK
| | - Amy Shakeshaft
- Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
- Centre for Neuropsychiatric Genetics and Genomics, School of MedicineCardiff UniversityCardiffUK
| | - Lucy Riglin
- Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
- Centre for Neuropsychiatric Genetics and Genomics, School of MedicineCardiff UniversityCardiffUK
| | - Frances Rice
- Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
- Centre for Neuropsychiatric Genetics and Genomics, School of MedicineCardiff UniversityCardiffUK
| | - Cathryn M. Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Michael C. O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, School of MedicineCardiff UniversityCardiffUK
| | - Anita Thapar
- Wolfson Centre for Young People's Mental HealthCardiff UniversityCardiffUK
- Centre for Neuropsychiatric Genetics and Genomics, School of MedicineCardiff UniversityCardiffUK
| |
Collapse
|
30
|
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
|
31
|
Fahed AC, Natarajan P. Clinical applications of polygenic risk score for coronary artery disease through the life course. Atherosclerosis 2023; 386:117356. [PMID: 37931336 PMCID: PMC10842813 DOI: 10.1016/j.atherosclerosis.2023.117356] [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: 05/26/2023] [Revised: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, highlighting the limitations of current primary and secondary prevention frameworks. In this review, we detail how the polygenic risk score for CAD can improve our current preventive and treatment frameworks across three clinical applications that span the life course: (i) identification and treatment of people at increased risk early in the life course prior to the onset of clinical risk factors, (ii) improving the precision around risk estimation in middle age, and (ii) guiding treatment decisions and enabling more efficient clinical trials even after the onset of CAD. We end by summarizing the efforts needed as we head towards more widespread use of polygenic risk score for CAD in clinical practice.
Collapse
Affiliation(s)
- Akl C Fahed
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
32
|
Urbut SM, Cho SMJ, Paruchuri K, Truong B, Haidermota S, Peloso G, Hornsby W, Philippakis A, Fahed AC, Natarajan P. Dynamic Importance of Genomic and Clinical Risk for Coronary Artery Disease Over the Life Course. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.03.23298055. [PMID: 37961553 PMCID: PMC10635271 DOI: 10.1101/2023.11.03.23298055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Importance Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. Understanding the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction. Objective To assess the time-varying significance of genomic and clinical risk factors in CAD risk estimation across various age groups. Design Setting and Participants A longitudinal study was performed using data from two cohort studies: the Framingham Offspring Study (FOS) with 3,588 participants aged 19-57 years and the UK Biobank (UKB) with 327,837 participants aged 40-70 years. A total of 134,765 and 3,831,734 person-time years were observed in FOS and UKB, respectively. Main Outcomes and Measures Hazard ratios (HR) for CAD were calculated for polygenic risk scores (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and Pooled Cohort Equations (PCE) in predicting CAD events was also evaluated by age groups. Results The importance of CAD PRS diminished over the life course, with an HR of 3.58 (95% CI 1.39-9.19) at age 19 in FOS and an HR of 1.51 (95% CI 1.48-1.54) by age 70 in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed PCE in identifying subsequent CAD events in the 40-45-year age group, with 3.2-fold more appropriately identified events. The mean age of CAD events occurred 1.8 years earlier for those at high genomic risk but 9.6 years later for those at high clinical risk (p<0.001). Overall, adding PRS improved the area under the receiving operating curve of the PCE by an average of +5.1% (95% CI 4.9-5.2%) across all age groups; among individuals <55 years, PRS augmented the AUC-ROC of the PCE by 6.5% (95% CI 5.5-7.5%, p<0.001). Conclusions and Relevance Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies.
Collapse
Affiliation(s)
- Sarah M. Urbut
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospita: l, Harvard Medical School, Boston, Massachusetts
| | - So Mi Jemma Cho
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospita: l, Harvard Medical School, Boston, Massachusetts
- Integrative Research Center for Cerebrovascular and Cardiovascular diseases, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kaavya Paruchuri
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospita: l, Harvard Medical School, Boston, Massachusetts
| | - Buu Truong
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospita: l, Harvard Medical School, Boston, Massachusetts
| | - Sara Haidermota
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospita: l, Harvard Medical School, Boston, Massachusetts
| | - Gina Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Whitney Hornsby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospita: l, Harvard Medical School, Boston, Massachusetts
| | - Anthony Philippakis
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Akl C. Fahed
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospita: l, Harvard Medical School, Boston, Massachusetts
| | - Pradeep Natarajan
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospita: l, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
33
|
Sousa Paiva M, Aguiar C. Coronary artery disease and genetics: Steps toward a tailored approach. Rev Port Cardiol 2023; 42:915-916. [PMID: 37451540 DOI: 10.1016/j.repc.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023] Open
Affiliation(s)
- Mariana Sousa Paiva
- Serviço de Cardiologia, Hospital de Santa Cruz, Centro Hospitalar de Lisboa Ocidental EPE, Carnaxide, Portugal.
| | - Carlos Aguiar
- Serviço de Cardiologia, Hospital de Santa Cruz, Centro Hospitalar de Lisboa Ocidental EPE, Carnaxide, Portugal
| |
Collapse
|
34
|
Girdhar K, Bendl J, Baumgartner A, Therrien K, Venkatesh S, Mathur D, Dong P, Rahman S, Kleopoulos SP, Misir R, Reach SM, Auluck PK, Marenco S, Lewis DA, Haroutunian V, Funk C, Voloudakis G, Hoffman GE, Fullard JF, Roussos P. The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.02.23296067. [PMID: 37873320 PMCID: PMC10593028 DOI: 10.1101/2023.10.02.23296067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.
Collapse
Affiliation(s)
- Kiran Girdhar
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Sanan Venkatesh
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Deepika Mathur
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Pengfei Dong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ruth Misir
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah M Reach
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pavan K Auluck
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| |
Collapse
|
35
|
Girdhar K, Bendl J, Baumgartner A, Therrien K, Venkatesh S, Mathur D, Dong P, Rahman S, Kleopoulos SP, Misir R, Reach SM, Auluck PK, Marenco S, Lewis DA, Haroutunian V, Funk C, Voloudakis G, Hoffman GE, Fullard JF, Roussos P. The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development. RESEARCH SQUARE 2023:rs.3.rs-3393581. [PMID: 37886514 PMCID: PMC10602154 DOI: 10.21203/rs.3.rs-3393581/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.
Collapse
Affiliation(s)
- Kiran Girdhar
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Sanan Venkatesh
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Deepika Mathur
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Pengfei Dong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ruth Misir
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah M Reach
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pavan K Auluck
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| |
Collapse
|
36
|
Chapman CR. Ethical, legal, and social implications of genetic risk prediction for multifactorial disease: a narrative review identifying concerns about interpretation and use of polygenic scores. J Community Genet 2023; 14:441-452. [PMID: 36529843 PMCID: PMC10576696 DOI: 10.1007/s12687-022-00625-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/04/2022] [Indexed: 12/23/2022] Open
Abstract
Advances in genomics have enabled the development of polygenic scores (PGS), sometimes called polygenic risk scores, in the context of multifactorial diseases and disorders such as cancer, cardiovascular disease, and schizophrenia. PGS estimate an individual's genetic predisposition, as compared to other members of a population, for conditions which are influenced by both genetic and environmental factors. There is significant interest in using genetic risk prediction afforded through PGS in public health, clinical care, and research settings, yet many acknowledge the need to thoughtfully consider and address ethical, legal, and social implications (ELSI). To contribute to this effort, this paper reports on a narrative review of the literature, with the aim of identifying and categorizing ELSI relating to genetic risk prediction in the context of multifactorial disease, which have been raised by scholars in the field. Ninety-two articles, spanning from 1977 to 2021, met the inclusion criteria for this study. Identified ELSI included potential benefits, challenges and risks that focused on concerns about interpretation and use, and ethical obligations to maximize benefits, minimize risks, promote justice, and support autonomy. This research will support geneticists, clinicians, genetic counselors, patients, patient advocates, and policymakers in recognizing and addressing ethical concerns associated with PGS; it will also guide future empirical and normative research.
Collapse
Affiliation(s)
- Carolyn Riley Chapman
- Department of Population Health (Division of Medical Ethics), NYU Grossman School of Medicine, New York, NY, USA.
- Center for Human Genetics and Genomics, NYU Grossman School of Medicine, Science Building, 435 E. 30th St, 8th Floor, New York, NY, 10016, USA.
| |
Collapse
|
37
|
Hou XG, Wu TT, Zheng YY, Yang HT, Yang Y, Ma YT, Xie X. The Fibrinogen-to-Albumin Ratio Is Associated with Poor Prognosis in Patients with Coronary Artery Disease: Findings from a Large Cohort. J Cardiovasc Transl Res 2023; 16:1177-1183. [PMID: 37349658 DOI: 10.1007/s12265-023-10402-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/01/2023] [Indexed: 06/24/2023]
Abstract
We aimed to evaluate the association of the fibrinogen-to-albumin ratio (FAR) with the clinical outcomes of coronary artery disease (CAD). All 14,944 patients with CAD evaluated in the present study were from a prospective cohort that recruited 15,250 patients admitted in the First Affiliated Hospital of Xinjiang Medical University between December 2016 and October 2021. The all-cause mortality (ACM) and cardiac mortality (CM) were selected as the primary endpoints. The secondary endpoints were major adverse cardiovascular events (MACEs), major adverse cardiac and cerebrovascular events (MACCEs), and non-fatal myocardial infarction (NFMI). The optimal FAR cutoff value was determined by using a receiver operating characteristic (ROC) curve analysis. Using 0.1 as the cutoff value, all the patients were divided into two groups: a low-FAR group (FAR < 0.1, n = 10,076) and a high-FAR group (FAR ≥ 0.1, n = 4918). The incidence of outcomes between the two groups was compared. The high-FAR group exhibited a higher incidence of ACM (5.3% vs. 1.9%), CM (3.9% vs. 1.4%), MACEs (9.8% vs. 6.7%), MACCEs (10.4% vs. 7.6%), and NFMI (2.3% vs. 1.3%) than the low-FAR group. To adjust the confounders, multivariate Cox regression analyses showed that the risk in the high-FAR group was increased 2.182 fold in ACM (HR = 2.182, 95% CI: 1.761 ~ 2.704, P < 0.001), 2.116 fold in CM (HR = 2.116, 95% CI: 1.761 ~ 2.704, P < 0.001), 1.327 fold in MACEs (HR = 1.327, 95% CI: 1.166 ~ 1.510, P < 0.001), 1.280 fold in MACCEs (HR = 1.280, 95% CI: 1.131 ~ 1.448, P < 0.001), and 1.791 fold in NFMI (HR = 1.791, 95% CI:1.331 ~ 2.411, P < 0.001), compared to the low-FAR group. The present study suggested that the high-FAR group was an independent and powerful predictor of adverse outcomes in CAD patients.
Collapse
Affiliation(s)
- Xian-Geng Hou
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Ting-Ting Wu
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Ying-Ying Zheng
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Hai-Tao Yang
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Yi Yang
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Yi-Tong Ma
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Xiang Xie
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People's Republic of China.
| |
Collapse
|
38
|
Figtree GA, Vernon ST, Harmer JA, Gray MP, Arnott C, Bachour E, Barsha G, Brieger D, Brown A, Celermajer DS, Channon KM, Chew NWS, Chong JJH, Chow CK, Cistulli PA, Ellinor PT, Grieve SM, Guzik TJ, Hagström E, Jenkins A, Jennings G, Keech AC, Kott KA, Kritharides L, Mamas MA, Mehran R, Meikle PJ, Natarajan P, Negishi K, O'Sullivan J, Patel S, Psaltis PJ, Redfern J, Steg PG, Sullivan DR, Sundström J, Vogel B, Wilson A, Wong D, Bhatt DL, Kovacic JC, Nicholls SJ. Clinical Pathway for Coronary Atherosclerosis in Patients Without Conventional Modifiable Risk Factors: JACC State-of-the-Art Review. J Am Coll Cardiol 2023; 82:1343-1359. [PMID: 37730292 PMCID: PMC10522922 DOI: 10.1016/j.jacc.2023.06.045] [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: 06/06/2023] [Accepted: 06/28/2023] [Indexed: 09/22/2023]
Abstract
Reducing the incidence and prevalence of standard modifiable cardiovascular risk factors (SMuRFs) is critical to tackling the global burden of coronary artery disease (CAD). However, a substantial number of individuals develop coronary atherosclerosis despite no SMuRFs. SMuRFless patients presenting with myocardial infarction have been observed to have an unexpected higher early mortality compared to their counterparts with at least 1 SMuRF. Evidence for optimal management of these patients is lacking. We assembled an international, multidisciplinary team to develop an evidence-based clinical pathway for SMuRFless CAD patients. A modified Delphi method was applied. The resulting pathway confirms underlying atherosclerosis and true SMuRFless status, ensures evidence-based secondary prevention, and considers additional tests and interventions for less typical contributors. This dedicated pathway for a previously overlooked CAD population, with an accompanying registry, aims to improve outcomes through enhanced adherence to evidence-based secondary prevention and additional diagnosis of modifiable risk factors observed.
Collapse
Affiliation(s)
- Gemma A Figtree
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Cardiovascular Discovery Group, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia; Department of Cardiology, Royal North Shore Hospital, St Leonards, New South Wales, Australia.
| | - Stephen T Vernon
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Cardiovascular Discovery Group, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia; Department of Cardiology, Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Jason A Harmer
- Department of Cardiology, Royal North Shore Hospital, St Leonards, New South Wales, Australia; The George Institute for Global Health, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia
| | - Michael P Gray
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Cardiovascular Discovery Group, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia
| | - Clare Arnott
- The George Institute for Global Health, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Eric Bachour
- Consumer Representative, Agile Group Switzerland AG, Zug, Switzerland
| | - Giannie Barsha
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Cardiovascular Discovery Group, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia
| | - David Brieger
- Department of Cardiology, Concord Repatriation General Hospital, Concord, New South Wales, Australia
| | - Alex Brown
- National Centre for Indigenous Genomics, Australian National University, Canberra, Australian Capitol Territory, Australia; Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - David S Celermajer
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Keith M Channon
- British Heart Foundation Centre of Research Excellence, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicholas W S Chew
- Department of Cardiology, National University Heart Centre, National University Health System, Singapore
| | - James J H Chong
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Westmead, New South Wales, Australia; Westmead Institute for Medical Research, University of Sydney, Westmead, New South Wales, Australia; Department of Cardiology, Westmead Hospital, Westmead, New South Wales, Australia
| | - Clara K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Westmead, New South Wales, Australia; Department of Cardiology, Westmead Hospital, Westmead, New South Wales, Australia
| | - Peter A Cistulli
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia; Department of Respiratory & Sleep Medicine, Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Stuart M Grieve
- Department of Radiology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia; Imaging and Phenotyping Laboratory, Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Tomasz J Guzik
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom; Department of Internal Medicine and Omicron Medical Genomics Laboratory, Jagiellonian University Medical College, Krakow, Poland
| | - Emil Hagström
- Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden
| | - Alicia Jenkins
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia; Diabetes and Vascular Medicine, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Garry Jennings
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Anthony C Keech
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Katharine A Kott
- Cardiovascular Discovery Group, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia; Department of Cardiology, Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Leonard Kritharides
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Department of Cardiology, Concord Repatriation General Hospital, Concord, New South Wales, Australia; The ANZAC Research Institute, Concord Repatriation General Hospital, Concord, New South Wales, Australia
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Centre for Prognostic Research, Keele University, Keele, United Kingdom; Department of Cardiology, Royal Stoke University Hospital, Stoke-on-Trent, United Kingdom
| | - Roxana Mehran
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Vicotria, Australia
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA; Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kazuaki Negishi
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia; Department of Cardiology, Nepean Hospital, Kingswood, New South Wales, Australia
| | - John O'Sullivan
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Department of Cardiology, Royal North Shore Hospital, St Leonards, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia; Precision Cardiovascular Laboratory, University of Sydney, Camperdown, New South Wales, Australia; Heart Research Institute, University of Sydney, Camperdown, New South Wales, Australia
| | - Sanjay Patel
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia; Heart Research Institute, University of Sydney, Camperdown, New South Wales, Australia
| | - Peter J Psaltis
- Vascular Research Centre, Heart and Vascular Program, Lifelong Health Theme, SAHMRI, Adelaide, South Australia, Australia; Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; Department of Cardiology, Royal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, South Australia, Australia
| | - Julie Redfern
- The George Institute for Global Health, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia; Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
| | - Philippe G Steg
- Université de Paris, Assistance Publique-Hôpitaux de Paris, French Alliance for Cardiovascular Trials and INSERM Unité 1148, Paris, France
| | - David R Sullivan
- Department of Chemical Pathology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Johan Sundström
- The George Institute for Global Health, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia; Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Birgit Vogel
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Andrew Wilson
- Menzies Centre for Health Policy and Economics, Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
| | - Dennis Wong
- Monash Cardiovascular Research Centre, Monash University, Clayton, Victoria, Australia; MonashHeart, Monash Health, Clayton, Victoria, Australia
| | - Deepak L Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, New York, USA
| | - Jason C Kovacic
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Stephen J Nicholls
- Victorian Heart Institute, Monash University, Clayton, Victoria, Australia
| |
Collapse
|
39
|
Cho SMJ, Koyama S, Honigberg MC, Surakka I, Haidermota S, Ganesh S, Patel AP, Bhattacharya R, Lee H, Kim HC, Natarajan P. Genetic, sociodemographic, lifestyle, and clinical risk factors of recurrent coronary artery disease events: a population-based cohort study. Eur Heart J 2023; 44:3456-3465. [PMID: 37350734 PMCID: PMC10516626 DOI: 10.1093/eurheartj/ehad380] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 05/07/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023] Open
Abstract
AIMS Complications of coronary artery disease (CAD) represent the leading cause of death among adults globally. This study examined the associations and clinical utilities of genetic, sociodemographic, lifestyle, and clinical risk factors on CAD recurrence. METHODS AND RESULTS Data were from 7024 UK Biobank middle-aged adults with established CAD at enrolment. Cox proportional hazards regressions modelled associations of age at enrolment, age at first CAD diagnosis, sex, cigarette smoking, physical activity, diet, sleep, Townsend Deprivation Index, body mass index, blood pressure, blood lipids, glucose, lipoprotein(a), C reactive protein, estimated glomerular filtration rate (eGFR), statin prescription, and CAD polygenic risk score (PRS) with first post-enrolment CAD recurrence. Over a median [interquartile range] follow-up of 11.6 [7.2-12.7] years, 2003 (28.5%) recurrent CAD events occurred. The hazard ratio (95% confidence interval [CI]) for CAD recurrence was the most pronounced with current smoking (1.35, 1.13-1.61) and per standard deviation increase in age at first CAD (0.74, 0.67-0.82). Additionally, age at enrolment, CAD PRS, C-reactive protein, lipoprotein(a), glucose, low-density lipoprotein cholesterol, deprivation, sleep quality, eGFR, and high-density lipoprotein (HDL) cholesterol also significantly associated with recurrence risk. Based on C indices (95% CI), the strongest predictors were CAD PRS (0.58, 0.57-0.59), HDL cholesterol (0.57, 0.57-0.58), and age at initial CAD event (0.57, 0.56-0.57). In addition to traditional risk factors, a comprehensive model improved the C index from 0.644 (0.632-0.654) to 0.676 (0.667-0.686). CONCLUSION Sociodemographic, clinical, and laboratory factors are each associated with CAD recurrence with genetic risk, age at first CAD event, and HDL cholesterol concentration explaining the most.
Collapse
Affiliation(s)
- So Mi Jemma Cho
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Satoshi Koyama
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Michael C Honigberg
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02114, USA
| | - Ida Surakka
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Division of Cardiology, Department of Internal Medicine, University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA
| | - Sara Haidermota
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Shriienidhie Ganesh
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Aniruddh P Patel
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02114, USA
| | - Romit Bhattacharya
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02114, USA
| | - Hokyou Lee
- Department of Preventive Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Hyeon Chang Kim
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
- Department of Preventive Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
- Institute for Innovation in Digital Healthcare, Yonsei University Health System, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02114, USA
| |
Collapse
|
40
|
Honigberg MC, Faaborg-Andersen CC. Integrating Indices of Genetic Risk for Cardiovascular Disease. JACC. ADVANCES 2023; 2:100568. [PMID: 38939494 PMCID: PMC11198607 DOI: 10.1016/j.jacadv.2023.100568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Affiliation(s)
- Michael C. Honigberg
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | |
Collapse
|
41
|
Xie J, Feng Y, Newby D, Zheng B, Feng Q, Prats-Uribe A, Li C, Wareham NJ, Paredes R, Prieto-Alhambra D. Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection. Nat Commun 2023; 14:4659. [PMID: 37537214 PMCID: PMC10400557 DOI: 10.1038/s41467-023-40310-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual's hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.
Collapse
Affiliation(s)
- Junqing Xie
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Yuliang Feng
- Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Danielle Newby
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Bang Zheng
- Department Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Qi Feng
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Chunxiao Li
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - R Paredes
- Department of Infectious Diseases Department & irsiCaixa AIDS Research Institute, Hospital Universitari Germans 13 Trias i Pujol, Catalonia, Spain
- Center for Global Health and Diseases, Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH, US
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK.
- Department of Medical Informatics, Erasmus Medical Center University, Rotterdam, Netherlands.
| |
Collapse
|
42
|
Aday AW, Bagheri M, Vaitinadin NS, Mosley JD, Wang TJ. Polygenic risk score in comparison with C-reactive protein for predicting incident coronary heart disease. Atherosclerosis 2023; 379:117194. [PMID: 37536150 PMCID: PMC10529589 DOI: 10.1016/j.atherosclerosis.2023.117194] [Citation(s) in RCA: 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: 03/30/2023] [Revised: 07/11/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND AND AIMS Despite interest in the use of polygenic risk scores (PRS) for predicting coronary heart disease (CHD) risk, the clinical utility of PRS compared to conventional risk factors has not been demonstrated. We compared the performance of PRS with that of high-sensitivity C-reactive protein (hsCRP) in two well-established cohorts. METHODS The study population included individuals of European ancestry free of baseline CHD from ARIC (N = 13,113) and the Framingham Offspring Study (FHS) (N = 2,696). The primary predictors included a validated PRS consisting of >6.6 million single nucleotide polymorphisms and hsCRP. The outcome was incident CHD, defined as non-fatal or fatal myocardial infarction. We compared the performance of both predictors after adjusting for the Pooled Cohort Equations in multivariable-adjusted Cox regression models. We assessed discrimination and reclassification using c-statistics and net reclassification improvement. RESULTS Incident CHD occurred in 565 ARIC and 153 FHS participants. In multivariable-adjusted models, both PRS and hsCRP were associated with incident CHD (p < 0.05 in both cohorts). In models incorporating both predictors, strengths of association were similar. For instance, in ARIC, the hazard ratio per SD increment was 1.38 (95% CI, 1.27-1.50, p = 2.94 × 10-14) for PRS and 1.41 (1.30-1.55, p = 3.10 × 10-15) for hsCRP. Neither predictor significantly increased model discrimination or net reclassification when compared with models containing the Pooled Cohort Equations alone. CONCLUSIONS In two independent cohorts, PRS performed similarly to hsCRP for the prediction of CHD risk. These findings suggest PRS does not have unique clinical utility beyond this widely-available, inexpensive measure of risk in unselected middle-aged populations.
Collapse
Affiliation(s)
- Aaron W Aday
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Minoo Bagheri
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nataraja Sarma Vaitinadin
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan D Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Thomas J Wang
- Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| |
Collapse
|
43
|
Liu J, Zhang C, Song J, Zhang Q, Zhang R, Zhang M, Han D, Tan W. Unlocking Genetic Profiles with a Programmable DNA-Powered Decoding Circuit. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206343. [PMID: 37116171 PMCID: PMC10369254 DOI: 10.1002/advs.202206343] [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: 10/30/2022] [Revised: 04/12/2023] [Indexed: 06/19/2023]
Abstract
Human genetic architecture provides remarkable insights into disease risk prediction and personalized medication. Advances in genomics have boosted the fine-mapping of disease-associated genetic variants across human genome. In healthcare practice, interpreting intricate genetic profiles into actionable medical decisions can improve health outcomes but remains challenging. Here an intelligent genetic decoder is engineered with programmable DNA computation to automate clinical analyses and interpretations. The DNA-based decoder recognizes multiplex genetic information by one-pot ligase-dependent reactions and interprets implicit genetic profiles into explicit decision reports. It is shown that the DNA decoder implements intended computation on genetic profiles and outputs a corresponding answer within hours. Effectiveness in 30 human genomic samples is validated and it is shown that it achieves desirable performance on the interpretation of CYP2C19 genetic profiles into drug responses, with accuracy equivalent to that of Sanger sequencing. Circuit modules of the DNA decoder can also be readily reprogrammed to interpret another pharmacogenetics genes, provide drug dosing recommendations, and implement reliable molecular calculation of polygenic risk score (PRS) and PRS-informed cancer risk assessment. The DNA-powered intelligent decoder provides a general solution to the translation of complex genetic profiles into actionable healthcare decisions and will facilitate personalized healthcare in primary care.
Collapse
Affiliation(s)
- Junlan Liu
- Institute of Molecular Medicine (IMM)Renji HospitalSchool of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Chao Zhang
- Institute of Molecular Medicine (IMM)Renji HospitalSchool of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Jinxing Song
- Institute of Molecular Medicine (IMM)Renji HospitalSchool of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Qing Zhang
- Institute of Molecular Medicine (IMM)Renji HospitalSchool of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Rongjun Zhang
- Institute of Molecular Medicine (IMM)Renji HospitalSchool of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Mingzhi Zhang
- Institute of Molecular Medicine (IMM)Renji HospitalSchool of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Da Han
- Institute of Molecular Medicine (IMM)Renji HospitalSchool of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai200240China
- The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Zhejiang Cancer HospitalHangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouZhejiang310022China
| | - Weihong Tan
- Institute of Molecular Medicine (IMM)Renji HospitalSchool of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai200240China
- The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Zhejiang Cancer HospitalHangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouZhejiang310022China
- Molecular Science and Biomedicine Laboratory (MBL)State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringCollege of BiologyAptamer Engineering Center of Hunan ProvinceHunan UniversityChangshaHunan410082China
| |
Collapse
|
44
|
Papadopoulou E, Bouzarelou D, Tsaousis G, Papathanasiou A, Vogiatzi G, Vlachopoulos C, Miliou A, Papachristou P, Prappa E, Servos G, Ritsatos K, Seretis A, Frogoudaki A, Nasioulas G. Application of next generation sequencing in cardiology: current and future precision medicine implications. Front Cardiovasc Med 2023; 10:1202381. [PMID: 37424920 PMCID: PMC10327645 DOI: 10.3389/fcvm.2023.1202381] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023] Open
Abstract
Inherited cardiovascular diseases are highly heterogeneous conditions with multiple genetic loci involved. The application of advanced molecular tools, such as Next Generation Sequencing, has facilitated the genetic analysis of these disorders. Accurate analysis and variant identification are required to maximize the quality of the sequencing data. Therefore, the application of NGS for clinical purposes should be limited to laboratories with a high level of technological expertise and resources. In addition, appropriate gene selection and variant interpretation can result in the highest possible diagnostic yield. Implementation of genetics in cardiology is imperative for the accurate diagnosis, prognosis and management of several inherited disorders and could eventually lead to the realization of precision medicine in this field. However, genetic testing should also be accompanied by an appropriate genetic counseling procedure that clarifies the significance of the genetic analysis results for the proband and his family. In this regard, a multidisciplinary collaboration among physicians, geneticists, and bioinformaticians is imperative. In the present review, we address the current state of knowledge regarding genetic analysis strategies employed in the field of cardiogenetics. Variant interpretation and reporting guidelines are explored. Additionally, gene selection procedures are accessed, with a particular emphasis on information concerning gene-disease associations collected from international alliances such as the Gene Curation Coalition (GenCC). In this context, a novel approach to gene categorization is proposed. Moreover, a sub-analysis is conducted on the 1,502,769 variation records with submitted interpretations in the Clinical Variation (ClinVar) database, focusing on cardiology-related genes. Finally, the most recent information on genetic analysis's clinical utility is reviewed.
Collapse
Affiliation(s)
| | | | | | | | - Georgia Vogiatzi
- Third Department of Cardiology, Sotiria Hospital, Athens, Greece
| | - Charalambos Vlachopoulos
- Unit of Inherited Cardiac Conditions and Sports Cardiology, First Department of Cardiology, National and Kapodistrian University of Athens, Athens, Greece
| | - Antigoni Miliou
- Unit of Inherited Cardiac Conditions and Sports Cardiology, First Department of Cardiology, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Efstathia Prappa
- Second Department of Cardiology, Arrhythmia Unit, Evangelismos General Hospital of Athens, Athens, Greece
| | - Georgios Servos
- Pediatric Cardiology Unit, “P. & A. Kyriakou” Children’s Hospital, Athens, Greece
| | - Konstantinos Ritsatos
- Unit of Inherited and Rare Cardiovascular Diseases, Onassis Cardiac Surgery Center, Athens, Greece
| | - Aristeidis Seretis
- Unit of Inherited and Rare Cardiovascular Diseases, Onassis Cardiac Surgery Center, Athens, Greece
| | - Alexandra Frogoudaki
- Second Department of Cardiology, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | | |
Collapse
|
45
|
Li C, Pan Y, Zhang R, Huang Z, Li D, Han Y, Larkin C, Rao V, Sun X, Kelly TN. Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment. Circ Res 2023; 132:1628-1647. [PMID: 37289909 PMCID: PMC10328558 DOI: 10.1161/circresaha.123.321999] [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] [Indexed: 06/10/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.
Collapse
Affiliation(s)
- Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Davey Li
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Yunan Han
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Claire Larkin
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Varun Rao
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| |
Collapse
|
46
|
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
|
47
|
Ahern J, Thompson W, Fan CC, Loughnan R. Comparing Pruning and Thresholding with Continuous Shrinkage Polygenic Score Methods in a Large Sample of Ancestrally Diverse Adolescents from the ABCD Study ®. Behav Genet 2023; 53:292-309. [PMID: 37017779 PMCID: PMC10655749 DOI: 10.1007/s10519-023-10139-w] [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: 12/01/2022] [Accepted: 02/28/2023] [Indexed: 04/06/2023]
Abstract
Using individuals' genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and Bayesian continuous shrinkage models, to determine which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWAS summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n = 811), European ancestry Individuals (n = 6703), and admixed ancestry individuals (n = 3664). We find that the single ancestry continuous shrinkage method, PRScs (CS), and the multi ancestry meta method, PRScsx Meta (CSx Meta), show the best performance across ancestries and phenotypes.
Collapse
Affiliation(s)
- Jonathan Ahern
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92161, USA.
| | - Wesley Thompson
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Drive, La Jolla, San Diego, CA, 92161, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74103, USA
| | - Chun Chieh Fan
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74103, USA
- Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA, 92037, USA
| | - Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92161, USA
| |
Collapse
|
48
|
Örd T, Lönnberg T, Nurminen V, Ravindran A, Niskanen H, Kiema M, Õunap K, Maria M, Moreau PR, Mishra PP, Palani S, Virta J, Liljenbäck H, Aavik E, Roivainen A, Ylä-Herttuala S, Laakkonen JP, Lehtimäki T, Kaikkonen MU. Dissecting the polygenic basis of atherosclerosis via disease-associated cell state signatures. Am J Hum Genet 2023; 110:722-740. [PMID: 37060905 PMCID: PMC10183377 DOI: 10.1016/j.ajhg.2023.03.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/21/2023] [Indexed: 04/17/2023] Open
Abstract
Coronary artery disease (CAD) is a pandemic disease where up to half of the risk is explained by genetic factors. Advanced insights into the genetic basis of CAD require deeper understanding of the contributions of different cell types, molecular pathways, and genes to disease heritability. Here, we investigate the biological diversity of atherosclerosis-associated cell states and interrogate their contribution to the genetic risk of CAD by using single-cell and bulk RNA sequencing (RNA-seq) of mouse and human lesions. We identified 12 disease-associated cell states that we characterized further by gene set functional profiling, ligand-receptor prediction, and transcription factor inference. Importantly, Vcam1+ smooth muscle cell state genes contributed most to SNP-based heritability of CAD. In line with this, genetic variants near smooth muscle cell state genes and regulatory elements explained the largest fraction of CAD-risk variance between individuals. Using this information for variant prioritization, we derived a hybrid polygenic risk score (PRS) that demonstrated improved performance over a classical PRS. Our results provide insights into the biological mechanisms associated with CAD risk, which could make a promising contribution to precision medicine and tailored therapeutic interventions in the future.
Collapse
Affiliation(s)
- Tiit Örd
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Tapio Lönnberg
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku
| | - Valtteri Nurminen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Aarthi Ravindran
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Henri Niskanen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Miika Kiema
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Kadri Õunap
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Maleeha Maria
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Pierre R Moreau
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
| | - Senthil Palani
- Turku PET Centre, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
| | - Jenni Virta
- Turku PET Centre, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
| | - Heidi Liljenbäck
- Turku PET Centre, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland; Turku Center for Disease Modeling, University of Turku, 20520 Turku, Finland
| | - Einari Aavik
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Anne Roivainen
- Turku PET Centre, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland; Turku Center for Disease Modeling, University of Turku, 20520 Turku, Finland; Turku PET Centre, Turku University Hospital, 20520 Turku, Finland
| | - Seppo Ylä-Herttuala
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Johanna P Laakkonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
| | - Minna U Kaikkonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland.
| |
Collapse
|
49
|
Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.21.23286110. [PMID: 36865265 PMCID: PMC9980241 DOI: 10.1101/2023.02.21.23286110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
Collapse
Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Leland E. Hull
- Division of General Internal Medicine, 100 Cambridge Street,
Massachusetts General Hospital, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - David A. van Heel
- Blizard Institute, Barts and the London School of Medicine and
Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - Alicia R. Martin
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - S. Hong Lee
- Australian Centre for Precision Health, University of South
Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000,
Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
| |
Collapse
|
50
|
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
|