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Singh M, Kumar A, Khanna NN, Laird JR, Nicolaides A, Faa G, Johri AM, Mantella LE, Fernandes JFE, Teji JS, Singh N, Fouda MM, Singh R, Sharma A, Kitas G, Rathore V, Singh IM, Tadepalli K, Al-Maini M, Isenovic ER, Chaturvedi S, Garg D, Paraskevas KI, Mikhailidis DP, Viswanathan V, Kalra MK, Ruzsa Z, Saba L, Laine AF, Bhatt DL, Suri JS. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review. EClinicalMedicine 2024; 73:102660. [PMID: 38846068 PMCID: PMC11154124 DOI: 10.1016/j.eclinm.2024.102660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Background The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding No funding received.
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Affiliation(s)
- Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Bennett University, 201310, Greater Noida, India
| | - Ashish Kumar
- Bennett University, 201310, Greater Noida, India
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Gavino Faa
- Department of Pathology, University of Cagliari, Cagliari, Italy
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura E. Mantella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | | | - Jagjit S. Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, 22901, VA, USA
| | - George Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, DY1, Dudley, UK
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | | | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Esma R. Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 110010, Serbia
| | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | | | | | - Dimitri P. Mikhailidis
- Department of Clinical Biochemistry, Royal Free Hospital Campus, University College London Medical School, University College London (UCL), London, UK
| | | | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Andrew F. Laine
- Departments of Biomedical and Radiology, Columbia University, New York, NY, USA
| | | | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
- Department of Computer Science, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
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2
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Steinfeldt J, Wild B, Buergel T, Pietzner M, Upmeier Zu Belzen J, Vauvelle A, Hegselmann S, Denaxas S, Hemingway H, Langenberg C, Landmesser U, Deanfield J, Eils R. Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats. Nat Commun 2024; 15:4257. [PMID: 38763986 PMCID: PMC11102902 DOI: 10.1038/s41467-024-48568-8] [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: 11/17/2023] [Accepted: 05/03/2024] [Indexed: 05/21/2024] Open
Abstract
The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.
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Affiliation(s)
- Jakob Steinfeldt
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Klinik/Centrum, Charitéplatz 1, 10117, Berlin, Germany
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- Friede Springer Cardiovascular Prevention Center@Charite, Charite - University Medicine Berlin, Berlin, Germany
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Benjamin Wild
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Thore Buergel
- Institute of Cardiovascular Sciences, University College London, London, UK
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Maik Pietzner
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Health University Research Institute, Queen Mary University of London and Barts NHS Trust, London, UK
| | - Julius Upmeier Zu Belzen
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Andre Vauvelle
- Institute of Health Informatics, University College London, London, UK
| | - Stefan Hegselmann
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Massachusetts, USA
- Pattern Recognition and Image Analysis Lab, University of Münster, Münster, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
- Health Data Research UK, London, UK
- National Institute for Health Research, Biomedical Research Centre at University College London Hospitals National Institute for Health Research, Biomedical Research Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- National Institute for Health Research, Biomedical Research Centre at University College London Hospitals National Institute for Health Research, Biomedical Research Centre, London, UK
| | - Claudia Langenberg
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Health University Research Institute, Queen Mary University of London and Barts NHS Trust, London, UK
| | - Ulf Landmesser
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Klinik/Centrum, Charitéplatz 1, 10117, Berlin, Germany
- Friede Springer Cardiovascular Prevention Center@Charite, Charite - University Medicine Berlin, Berlin, Germany
- Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Berlin, Germany
| | - John Deanfield
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany.
- Health Data Science Unit, Heidelberg University Hospital and BioQuant, Heidelberg, Germany.
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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Han C, Kim DW, Kim S, Chan You S, Park JY, Bae S, Yoon D. Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data. iScience 2024; 27:109022. [PMID: 38357664 PMCID: PMC10865411 DOI: 10.1016/j.isci.2024.109022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/28/2023] [Accepted: 01/22/2024] [Indexed: 02/16/2024] Open
Abstract
Cardiovascular disease (CVD) remains a pressing global health concern. While traditional risk prediction methods such as the Framingham and American College of Cardiology/American Heart Association (ACC/AHA) risk scores have been widely used in the practice, artificial intelligence (AI), especially GPT-4, offers new opportunities. Utilizing large scale of multi-center data from 47,468 UK Biobank participants and 5,718 KoGES participants, this study quantitatively evaluated the predictive capabilities of GPT-4 in comparison with traditional models. Our results suggest that the GPT-based score showed commendably comparable performance in CVD prediction when compared to traditional models (AUROC on UKB: 0.725 for GPT-4, 0.733 for ACC/AHA, 0.728 for Framingham; KoGES: 0.664 for GPT-4, 0.674 for ACC/AHA, 0.675 for Framingham). Even with omission of certain variables, GPT-4's performance was robust, demonstrating its adaptability to data-scarce situations. In conclusion, this study emphasizes the promising role of GPT-4 in predicting CVD risks across varied ethnic datasets, pointing toward its expansive future applications in the medical practice.
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Affiliation(s)
- Changho Han
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Dong Won Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Songsoo Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
- Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea
| | - Jin Young Park
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
- Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - SungA Bae
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
- Department of Cardiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Dukyong Yoon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
- Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
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5
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You J, Guo Y, Kang JJ, Wang HF, Yang M, Feng JF, Yu JT, Cheng W. Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study. Stroke Vasc Neurol 2023; 8:475-485. [PMID: 37105576 PMCID: PMC10800279 DOI: 10.1136/svn-2023-002332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Previous prediction algorithms for cardiovascular diseases (CVD) were established using risk factors retrieved largely based on empirical clinical knowledge. This study sought to identify predictors among a comprehensive variable space, and then employ machine learning (ML) algorithms to develop a novel CVD risk prediction model. METHODS From a longitudinal population-based cohort of UK Biobank, this study included 473 611 CVD-free participants aged between 37 and 73 years old. We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD. The model was validated through a leave-one-center-out cross-validation. RESULTS During a median follow-up of 12.2 years, 31 466 participants developed CVD within 10 years after baseline visits. A novel UK Biobank CVD risk prediction (UKCRP) model was established that comprised 10 predictors including age, sex, medication of cholesterol and blood pressure, cholesterol ratio (total/high-density lipoprotein), systolic blood pressure, previous angina or heart disease, number of medications taken, cystatin C, chest pain and pack-years of smoking. Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.762±0.010 that outperformed multiple existing clinical models, and it was well-calibrated with a Brier Score of 0.057±0.006. Further, the UKCRP can obtain comparable performance for myocardial infarction (AUC 0.774±0.011) and ischaemic stroke (AUC 0.730±0.020), but inferior performance for haemorrhagic stroke (AUC 0.644±0.026). CONCLUSION ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.
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Affiliation(s)
- Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yu Guo
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Hui-Fu Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ming Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China
- School of Data Science, Fudan University, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
| | - Jin-Tai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Fudan University, Shanghai, China
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [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: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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Liu X, Hu P, Yeung W, Zhang Z, Ho V, Liu C, Dumontier C, Thoral PJ, Mao Z, Cao D, Mark RG, Zhang Z, Feng M, Li D, Celi LA. Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation. Lancet Digit Health 2023; 5:e657-e667. [PMID: 37599147 DOI: 10.1016/s2589-7500(23)00128-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 05/31/2023] [Accepted: 06/22/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND Comorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias. METHODS In this retrospective, international multicentre study, the ELDER-ICU model was developed using data from 14 US hospitals, and validated in 171 hospitals from the USA and Netherlands. Data were extracted from the Medical Information Mart for Intensive Care database, electronic ICU Collaborative Research Database, and Amsterdam University Medical Centers Database. We used six categories of data as predictors, including demographics and comorbidities, physical frailty, laboratory tests, vital signs, treatments, and urine output. Patient data from the first day of ICU stay were used to predict in-hospital mortality. We used the eXtreme Gradient Boosting algorithm (XGBoost) to develop models and the SHapley Additive exPlanations method to explain model prediction. The trained model was calibrated before internal, external, and temporal validation. The final XGBoost model was compared against three other machine learning algorithms and five clinical scores. We performed subgroup analysis based on age, sex, and race. We assessed the discrimination and calibration of models using the area under receiver operating characteristic (AUROC) and standardised mortality ratio (SMR) with 95% CIs. FINDINGS Using the development dataset (n=50 366) and predictive model building process, the XGBoost algorithm performed the best in all types of validations compared with other machine learning algorithms and clinical scores (internal validation with 5037 patients from 14 US hospitals, AUROC=0·866 [95% CI 0·851-0·880]; external validation in the US population with 20 541 patients from 169 hospitals, AUROC=0·838 [0·829-0·847]; external validation in European population with 2411 patients from one hospital, AUROC=0·833 [0·812-0·853]; temporal validation with 4311 patients from one hospital, AUROC=0·884 [0·869-0·897]). In the external validation set (US population), the median AUROCs of bias evaluations covering eight subgroups were above 0·81, and the overall SMR was 0·99 (0·96-1·03). The top ten risk predictors were the minimum Glasgow Coma Scale score, total urine output, average respiratory rate, mechanical ventilation use, best state of activity, Charlson Comorbidity Index score, geriatric nutritional risk index, code status, age, and maximum blood urea nitrogen. A simplified model containing only the top 20 features (ELDER-ICU-20) had similar predictive performance to the full model. INTERPRETATION The ELDER-ICU model reliably predicts the risk of in-hospital mortality using routinely collected clinical features. The predictions could inform clinicians about patients who are at elevated risk of deterioration. Prospective validation of this model in clinical practice and a process for continuous performance monitoring and model recalibration are needed. FUNDING National Institutes of Health, National Natural Science Foundation of China, National Special Health Science Program, Health Science and Technology Plan of Zhejiang Province, Fundamental Research Funds for the Central Universities, Drug Clinical Evaluate Research of Chinese Pharmaceutical Association, and National Key R&D Program of China.
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Affiliation(s)
- Xiaoli Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing, China; Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pan Hu
- Department of Anesthesiology, The 920 Hospital of Joint Logistic Support Force of Chinese PLA, Kunming Yunnan, China; Department of Critical Care Medicine, The First Medical Center of PLA General Hospital, Beijing, China
| | - Wesley Yeung
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Cardiology, National University Heart Centre, Singapore
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Vanda Ho
- Division of Geriatric Medicine, Department of Medicine, National University Hospital, Singapore
| | - Chao Liu
- Department of Critical Care Medicine, The First Medical Center of PLA General Hospital, Beijing, China
| | - Clark Dumontier
- New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA, USA; Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
| | - Patrick J Thoral
- Center for Critical Care Computational Intelligence, Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center of PLA General Hospital, Beijing, China
| | - Desen Cao
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, China
| | - Roger G Mark
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing, China
| | - Mengling Feng
- Saw Swee Hock School of Public Health and the Institute of Data Science, National University of Singapore, Singapore
| | - Deyu Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; National Key Lab for Virtual Reality Technology and Systems, Beihang University, Beijing, China.
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
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8
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Aradhya S, Facio FM, Metz H, Manders T, Colavin A, Kobayashi Y, Nykamp K, Johnson B, Nussbaum RL. Applications of artificial intelligence in clinical laboratory genomics. AMERICAN JOURNAL OF MEDICAL GENETICS. PART C, SEMINARS IN MEDICAL GENETICS 2023; 193:e32057. [PMID: 37507620 DOI: 10.1002/ajmg.c.32057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.
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Affiliation(s)
- Swaroop Aradhya
- Invitae Corporation, San Francisco, California, USA
- Adjunct Clinical Faculty, Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
| | | | - Hillery Metz
- Invitae Corporation, San Francisco, California, USA
| | - Toby Manders
- Invitae Corporation, San Francisco, California, USA
| | | | | | - Keith Nykamp
- Invitae Corporation, San Francisco, California, USA
| | | | - Robert L Nussbaum
- Invitae Corporation, San Francisco, California, USA
- Volunteer Faculty, School of Medicine, University of California San Francisco, San Francisco, California, USA
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Long E, Wan P, Chen Q, Lu Z, Choi J. From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence. CELL GENOMICS 2023; 3:100320. [PMID: 37388909 PMCID: PMC10300605 DOI: 10.1016/j.xgen.2023.100320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
While genome-wide association studies (GWAS) have discovered thousands of disease-associated loci, molecular mechanisms for a considerable fraction of the loci remain to be explored. The logical next steps for post-GWAS are interpreting these genetic associations to understand disease etiology (GWAS functional studies) and translating this knowledge into clinical benefits for the patients (GWAS translational studies). Although various datasets and approaches using functional genomics have been developed to facilitate these studies, significant challenges remain due to data heterogeneity, multiplicity, and high dimensionality. To address these challenges, artificial intelligence (AI) technology has demonstrated considerable promise in decoding complex functional datasets and providing novel biological insights into GWAS findings. This perspective first describes the landmark progress driven by AI in interpreting and translating GWAS findings and then outlines specific challenges followed by actionable recommendations related to data availability, model optimization, and interpretation, as well as ethical concerns.
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Affiliation(s)
- Erping Long
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peixing Wan
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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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.
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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.)
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Kwak S, Lee HJ, Kim S, Park JB, Lee SP, Kim HK, Kim YJ. Machine learning reveals sex-specific associations between cardiovascular risk factors and incident atherosclerotic cardiovascular disease. Sci Rep 2023; 13:9364. [PMID: 37291421 PMCID: PMC10250402 DOI: 10.1038/s41598-023-36450-4] [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: 02/08/2023] [Accepted: 06/03/2023] [Indexed: 06/10/2023] Open
Abstract
We aimed to investigate sex-specific associations between cardiovascular risk factors and atherosclerotic cardiovascular disease (ASCVD) risk using machine learning. We studied 258,279 individuals (132,505 [51.3%] men and 125,774 [48.7%] women) without documented ASCVD who underwent national health screening. A random forest model was developed using 16 variables to predict the 10-year ASCVD in each sex. The association between cardiovascular risk factors and 10-year ASCVD probabilities was examined using partial dependency plots. During the 10-year follow-up, 12,319 (4.8%) individuals developed ASCVD, with a higher incidence in men than in women (5.3% vs. 4.2%, P < 0.001). The performance of the random forest model was similar to that of the pooled cohort equations (area under the receiver operating characteristic curve, men: 0.733 vs. 0.727; women: 0.769 vs. 0.762). Age and body mass index were the two most important predictors in the random forest model for both sexes. In partial dependency plots, advanced age and increased waist circumference were more strongly associated with higher probabilities of ASCVD in women. In contrast, ASCVD probabilities increased more steeply with higher total cholesterol and low-density lipoprotein (LDL) cholesterol levels in men. These sex-specific associations were verified in the conventional Cox analyses. In conclusion, there were significant sex differences in the association between cardiovascular risk factors and ASCVD events. While higher total cholesterol or LDL cholesterol levels were more strongly associated with the risk of ASCVD in men, older age and increased waist circumference were more strongly associated with the risk of ASCVD in women.
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Affiliation(s)
- Soongu Kwak
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Hyun-Jung Lee
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea.
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| | - Seungyeon Kim
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea.
- College of Pharmacy, Dankook University, Dandae-ro 119, Dongnam-gu, Cheonan-si, Chungcheongnam-do, 31116, Republic of Korea.
| | - Jun-Bean Park
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Seung-Pyo Lee
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Hyung-Kwan Kim
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Yong-Jin Kim
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea
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Vassy JL, Posner DC, Ho YL, Gagnon DR, Galloway A, Tanukonda V, Houghton SC, Madduri RK, McMahon BH, Tsao PS, Damrauer SM, O’Donnell CJ, Assimes TL, Casas JP, Gaziano JM, Pencina MJ, Sun YV, Cho K, Wilson PW. Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program. JAMA Cardiol 2023; 8:564-574. [PMID: 37133828 PMCID: PMC10157509 DOI: 10.1001/jamacardio.2023.0857] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/09/2023] [Indexed: 05/04/2023]
Abstract
Importance Primary prevention of atherosclerotic cardiovascular disease (ASCVD) relies on risk stratification. Genome-wide polygenic risk scores (PRSs) are proposed to improve ASCVD risk estimation. Objective To determine whether genome-wide PRSs for coronary artery disease (CAD) and acute ischemic stroke improve ASCVD risk estimation with traditional clinical risk factors in an ancestrally diverse midlife population. Design, Setting, and Participants This was a prognostic analysis of incident events in a retrospectively defined longitudinal cohort conducted from January 1, 2011, to December 31, 2018. Included in the study were adults free of ASCVD and statin naive at baseline from the Million Veteran Program (MVP), a mega biobank with genetic, survey, and electronic health record data from a large US health care system. Data were analyzed from March 15, 2021, to January 5, 2023. Exposures PRSs for CAD and ischemic stroke derived from cohorts of largely European descent and risk factors, including age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, smoking, and diabetes status. Main Outcomes and Measures Incident nonfatal myocardial infarction (MI), ischemic stroke, ASCVD death, and composite ASCVD events. Results A total of 79 151 participants (mean [SD] age, 57.8 [13.7] years; 68 503 male [86.5%]) were included in the study. The cohort included participants from the following harmonized genetic ancestry and race and ethnicity categories: 18 505 non-Hispanic Black (23.4%), 6785 Hispanic (8.6%), and 53 861 non-Hispanic White (68.0%) with a median (5th-95th percentile) follow-up of 4.3 (0.7-6.9) years. From 2011 to 2018, 3186 MIs (4.0%), 1933 ischemic strokes (2.4%), 867 ASCVD deaths (1.1%), and 5485 composite ASCVD events (6.9%) were observed. CAD PRS was associated with incident MI in non-Hispanic Black (hazard ratio [HR], 1.10; 95% CI, 1.02-1.19), Hispanic (HR, 1.26; 95% CI, 1.09-1.46), and non-Hispanic White (HR, 1.23; 95% CI, 1.18-1.29) participants. Stroke PRS was associated with incident stroke in non-Hispanic White participants (HR, 1.15; 95% CI, 1.08-1.21). A combined CAD plus stroke PRS was associated with ASCVD deaths among non-Hispanic Black (HR, 1.19; 95% CI, 1.03-1.17) and non-Hispanic (HR, 1.11; 95% CI, 1.03-1.21) participants. The combined PRS was also associated with composite ASCVD across all ancestry groups but greater among non-Hispanic White (HR, 1.20; 95% CI, 1.16-1.24) than non-Hispanic Black (HR, 1.11; 95% CI, 1.05-1.17) and Hispanic (HR, 1.12; 95% CI, 1.00-1.25) participants. Net reclassification improvement from adding PRS to a traditional risk model was modest for the intermediate risk group for composite CVD among men (5-year risk >3.75%, 0.38%; 95% CI, 0.07%-0.68%), among women, (6.79%; 95% CI, 3.01%-10.58%), for age older than 55 years (0.25%; 95% CI, 0.03%-0.47%), and for ages 40 to 55 years (1.61%; 95% CI, -0.07% to 3.30%). Conclusions and Relevance Study results suggest that PRSs derived predominantly in European samples were statistically significantly associated with ASCVD in the multiancestry midlife and older-age MVP cohort. Overall, modest improvement in discrimination metrics were observed with addition of PRSs to traditional risk factors with greater magnitude in women and younger age groups.
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Affiliation(s)
- Jason L. Vassy
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Daniel C. Posner
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - Yuk-Lam Ho
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - David R. Gagnon
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Ashley Galloway
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | | | | | - Ravi K. Madduri
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois
- University of Chicago Consortium for Advanced Science and Engineering, The University of Chicago, Chicago, Illinois
| | - Benjamin H. McMahon
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Philip S. Tsao
- Palo Alto VA Healthcare System, Palo Alto, California
- Stanford Cardiovascular Institute, Stanford University, Stanford, California
| | - Scott M. Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | | | - Themistocles L. Assimes
- Palo Alto VA Healthcare System, Palo Alto, California
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Stanford Cardiovascular Institute, Stanford University, Stanford, California
| | - Juan P. Casas
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - J. Michael Gaziano
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Michael J. Pencina
- Department of Biostatistics, Duke University Medical Center, Durham, North Carolina
| | - Yan V. Sun
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Kelly Cho
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Peter W.F. Wilson
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
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Clift AK, Dodwell D, Lord S, Petrou S, Brady M, Collins GS, Hippisley-Cox J. Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study. BMJ 2023; 381:e073800. [PMID: 37164379 PMCID: PMC10170264 DOI: 10.1136/bmj-2022-073800] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 05/12/2023]
Abstract
OBJECTIVE To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches. DESIGN Population based cohort study. SETTING QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers. PARTICIPANTS 141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020. MAIN OUTCOME MEASURES Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility. RESULTS During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model's random effects meta-analysis pooled estimate for Harrell's C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell's C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell's C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches. CONCLUSION In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.
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Affiliation(s)
- Ash Kieran Clift
- Cancer Research UK Oxford Centre, Oxford, UK
- Nuffield Department of Primary Care Health Sciences, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, University of Oxford, Oxford OX2 6GG, UK
| | - David Dodwell
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Simon Lord
- Department of Oncology, University of Oxford, Oxford, UK
| | - Stavros Petrou
- Nuffield Department of Primary Care Health Sciences, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, University of Oxford, Oxford OX2 6GG, UK
| | - Michael Brady
- Department of Oncology, University of Oxford, Oxford, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, University of Oxford, Oxford OX2 6GG, UK
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Kurant DE. Opportunities and Challenges with Artificial Intelligence in Genomics. Clin Lab Med 2023; 43:87-97. [PMID: 36764810 DOI: 10.1016/j.cll.2022.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The development of artificial intelligence and machine learning algorithms may allow for advances in patient care. There are existing and potential applications in cancer diagnosis and monitoring, identification of at-risk groups of individuals, classification of genetic variants, and even prediction of patient ancestry. This article provides an overview of some current and future applications of artificial intelligence in genomic medicine, in addition to discussing challenges and considerations when bringing these tools into clinical practice.
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Affiliation(s)
- Danielle E Kurant
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
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Sutton NR, Malhotra R, Hilaire C, Aikawa E, Blumenthal RS, Gackenbach G, Goyal P, Johnson A, Nigwekar SU, Shanahan CM, Towler DA, Wolford BN, Chen Y. Molecular Mechanisms of Vascular Health: Insights From Vascular Aging and Calcification. Arterioscler Thromb Vasc Biol 2023; 43:15-29. [PMID: 36412195 PMCID: PMC9793888 DOI: 10.1161/atvbaha.122.317332] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022]
Abstract
Cardiovascular disease is the most common cause of death worldwide, especially beyond the age of 65 years, with the vast majority of morbidity and mortality due to myocardial infarction and stroke. Vascular pathology stems from a combination of genetic risk, environmental factors, and the biologic changes associated with aging. The pathogenesis underlying the development of vascular aging, and vascular calcification with aging, in particular, is still not fully understood. Accumulating data suggests that genetic risk, likely compounded by epigenetic modifications, environmental factors, including diabetes and chronic kidney disease, and the plasticity of vascular smooth muscle cells to acquire an osteogenic phenotype are major determinants of age-associated vascular calcification. Understanding the molecular mechanisms underlying genetic and modifiable risk factors in regulating age-associated vascular pathology may inspire strategies to promote healthy vascular aging. This article summarizes current knowledge of concepts and mechanisms of age-associated vascular disease, with an emphasis on vascular calcification.
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Affiliation(s)
- Nadia R. Sutton
- Division of Cardiovascular Medicine, Michigan Medicine, Ann Arbor, Michigan, USA
| | - Rajeev Malhotra
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
| | - Cynthia Hilaire
- Division of Cardiology, Departments of Medicine and Bioengineering, Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh, 1744 BSTWR, 200 Lothrop St, Pittsburgh, PA, 15260 USA
| | - Elena Aikawa
- Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Roger S. Blumenthal
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease; Baltimore, MD
| | - Grace Gackenbach
- Division of Cardiovascular Medicine, Michigan Medicine, Ann Arbor, Michigan, USA
| | - Parag Goyal
- Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Adam Johnson
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
| | - Sagar U. Nigwekar
- Division of Nephrology, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
| | - Catherine M. Shanahan
- School of Cardiovascular and Metabolic Medicine and Sciences, King’s College London, London, UK
| | - Dwight A. Towler
- Department of Medicine | Endocrine Division and Pak Center for Mineral Metabolism Research, UT Southwestern Medical Center, Dallas, TX USA
| | - Brooke N. Wolford
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yabing Chen
- Department of Pathology, University of Alabama at Birmingham and Research Department, Veterans Affairs Birmingham Medical Center, Birmingham, AL, USA
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Metabolomic profiles predict individual multidisease outcomes. Nat Med 2022; 28:2309-2320. [PMID: 36138150 PMCID: PMC9671812 DOI: 10.1038/s41591-022-01980-3] [Citation(s) in RCA: 102] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 07/28/2022] [Indexed: 02/02/2023]
Abstract
Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.
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Zhou X, Chen L, Liu HX. Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review. Front Nutr 2022; 9:933130. [PMID: 35866076 PMCID: PMC9294383 DOI: 10.3389/fnut.2022.933130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 05/19/2022] [Indexed: 11/28/2022] Open
Abstract
Research on obesity and related diseases has received attention from government policymakers; interventions targeting nutrient intake, dietary patterns, and physical activity are deployed globally. An urgent issue now is how can we improve the efficiency of obesity research or obesity interventions. Currently, machine learning (ML) methods have been widely applied in obesity-related studies to detect obesity disease biomarkers or discover intervention strategies to optimize weight loss results. In addition, an open source of these algorithms is necessary to check the reproducibility of the research results. Furthermore, appropriate applications of these algorithms could greatly improve the efficiency of similar studies by other researchers. Here, we proposed a mini-review of several open-source ML algorithms, platforms, or related databases that are of particular interest or can be applied in the field of obesity research. We focus our topic on nutrition, environment and social factor, genetics or genomics, and microbiome-adopting ML algorithms.
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Affiliation(s)
- Xiaobei Zhou
- Health Sciences Institute, China Medical University, Shenyang, China
- Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, China
| | - Lei Chen
- Health Sciences Institute, China Medical University, Shenyang, China
- Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, China
- Institute of Life Sciences, China Medical University, Shenyang, China
| | - Hui-Xin Liu
- Health Sciences Institute, China Medical University, Shenyang, China
- Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, China
- Institute of Life Sciences, China Medical University, Shenyang, China
- *Correspondence: Hui-Xin Liu
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