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Perry AS, Amancherla K, Huang X, Lance ML, Farber-Eger E, Gajjar P, Amrute J, Stolze L, Zhao S, Sheng Q, Joynes CM, Peng Z, Tanaka T, Drakos SG, Lavine KJ, Selzman C, Visker JR, Shankar TS, Ferrucci L, Das S, Wilcox J, Patel RB, Kalhan R, Shah SJ, Walker KA, Wells Q, Tucker N, Nayor M, Shah RV, Khan SS. Clinical-transcriptional prioritization of the circulating proteome in human heart failure. Cell Rep Med 2024; 5:101704. [PMID: 39226894 DOI: 10.1016/j.xcrm.2024.101704] [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: 12/15/2023] [Revised: 06/15/2024] [Accepted: 08/07/2024] [Indexed: 09/05/2024]
Abstract
Given expanding studies in epidemiology and disease-oriented human studies offering hundreds of associations between the human "ome" and disease, prioritizing molecules relevant to disease mechanisms among this growing breadth is important. Here, we link the circulating proteome to human heart failure (HF) propensity (via echocardiographic phenotyping and clinical outcomes) across the lifespan, demonstrating key pathways of fibrosis, inflammation, metabolism, and hypertrophy. We observe a broad array of genes encoding proteins linked to HF phenotypes and outcomes in clinical populations dynamically expressed at a transcriptional level in human myocardium during HF and cardiac recovery (several in a cell-specific fashion). Many identified targets do not have wide precedent in large-scale genomic discovery or human studies, highlighting the complementary roles for proteomic and tissue transcriptomic discovery to focus epidemiological targets to those relevant in human myocardium for further interrogation.
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Affiliation(s)
- Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kaushik Amancherla
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Xiaoning Huang
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Eric Farber-Eger
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Priya Gajjar
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Junedh Amrute
- Cardiology Division, Washington University School of Medicine, St. Louis, MO, USA
| | - Lindsey Stolze
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shilin Zhao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quanhu Sheng
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cassandra M Joynes
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Zhongsheng Peng
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Stavros G Drakos
- Division of Cardiovascular Medicine, University of Utah and Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI), Salt Lake City, UT, USA
| | - Kory J Lavine
- Cardiology Division, Washington University School of Medicine, St. Louis, MO, USA
| | - Craig Selzman
- Department of Cardiac Surgery, University of Utah School of Medicine, Division of Cardiothoracic Surgery, University of Utah and Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI), Salt Lake City, UT, USA
| | - Joseph R Visker
- Division of Cardiovascular Medicine, University of Utah and Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI), Salt Lake City, UT, USA
| | - Thirupura S Shankar
- Division of Cardiovascular Medicine, University of Utah and Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI), Salt Lake City, UT, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Saumya Das
- Cardiovascular Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jane Wilcox
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ravi B Patel
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ravi Kalhan
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Sanjiv J Shah
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Quinn Wells
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - Matthew Nayor
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
| | - Sadiya S Khan
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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Miller PE, Gajjar P, Mitchell GF, Khan SS, Vasan RS, Larson MG, Lewis GD, Shah RV, Nayor M. Clusters of multidimensional exercise response patterns and estimated heart failure risk in the Framingham Heart Study. ESC Heart Fail 2024. [PMID: 38943268 DOI: 10.1002/ehf2.14797] [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: 10/27/2023] [Revised: 02/28/2024] [Accepted: 03/21/2024] [Indexed: 07/01/2024] Open
Abstract
AIMS New tools are needed to identify heart failure (HF) risk earlier in its course. We evaluated the association of multidimensional cardiopulmonary exercise testing (CPET) phenotypes with subclinical risk markers and predicted long-term HF risk in a large community-based cohort. METHODS AND RESULTS We studied 2532 Framingham Heart Study participants [age 53 ± 9 years, 52% women, body mass index (BMI) 28.0 ± 5.3 kg/m2, peak oxygen uptake (VO2) 21.1 ± 5.9 kg/m2 in women, 26.4 ± 6.7 kg/m2 in men] who underwent maximum effort CPET and were not taking atrioventricular nodal blocking agents. Higher peak VO2 was associated with a lower estimated HF risk score (Spearman correlation r: -0.60 in men and -0.55 in women, P < 0.0001), with an observed overlap of estimated risk across peak VO2 categories. Hierarchical clustering of 26 separate CPET phenotypes (values residualized on age, sex, and BMI to provide uniformity across these variables) identified three clusters with distinct exercise physiologies: Cluster 1-impaired oxygen kinetics; Cluster 2-impaired vascular; and Cluster 3-favourable exercise response. These clusters were similar in age, sex distribution, and BMI but displayed distinct associations with relevant subclinical phenotypes [Cluster 1-higher subcutaneous and visceral fat and lower pulmonary function; Cluster 2-higher carotid-femoral pulse wave velocity (CFPWV); and Cluster 3-lower CFPWV, C-reactive protein, fat volumes, and higher lung function; all false discovery rate < 5%]. Cluster membership provided incremental variance explained (adjusted R2 increment of 0.10 in women and men, P < 0.0001 for both) when compared with peak VO2 alone in association with predicted HF risk. CONCLUSIONS Integrated CPET response patterns identify physiologically relevant profiles with distinct associations to subclinical phenotypes that are largely independent of standard risk factor-based assessment, which may suggest alternate pathways for prevention.
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Affiliation(s)
- Patricia E Miller
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Priya Gajjar
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | | | - Sadiya S Khan
- Division of Cardiology, Department of Medicine and Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ramachandran S Vasan
- Boston University's and NHLBI's Framingham Heart Study, Framingham, MA, USA
- University of Texas School of Public Health San Antonio, San Antonio, TX, USA
- Department of Medicine, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Boston University's and NHLBI's Framingham Heart Study, Framingham, MA, USA
| | - Gregory D Lewis
- Division of Cardiology, Cardiovascular Research Center, and Pulmonary Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ravi V Shah
- Division of Cardiology, Vanderbilt Translational and Clinical Research Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew Nayor
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Boston University's and NHLBI's Framingham Heart Study, Framingham, MA, USA
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA
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Perry AS, Zhang K, Murthy VL, Choi B, Zhao S, Gajjar P, Colangelo LA, Hou L, Rice MB, Carr JJ, Carson AP, Nigra AE, Vasan RS, Gerszten RE, Khan SS, Kalhan R, Nayor M, Shah RV. Proteomics, Human Environmental Exposure, and Cardiometabolic Risk. Circ Res 2024; 135:138-154. [PMID: 38662804 PMCID: PMC11189739 DOI: 10.1161/circresaha.124.324559] [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: 03/05/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND The biological mechanisms linking environmental exposures with cardiovascular disease pathobiology are incompletely understood. We sought to identify circulating proteomic signatures of environmental exposures and examine their associations with cardiometabolic and respiratory disease in observational cohort studies. METHODS We tested the relations of >6500 circulating proteins with 29 environmental exposures across the built environment, green space, air pollution, temperature, and social vulnerability indicators in ≈3000 participants of the CARDIA study (Coronary Artery Risk Development in Young Adults) across 4 centers using penalized and ordinary linear regression. In >3500 participants from FHS (Framingham Heart Study) and JHS (Jackson Heart Study), we evaluated the prospective relations of proteomic signatures of the envirome with cardiovascular disease and mortality using Cox models. RESULTS Proteomic signatures of the envirome identified novel/established cardiovascular disease-relevant pathways including DNA damage, fibrosis, inflammation, and mitochondrial function. The proteomic signatures of the envirome were broadly related to cardiometabolic disease and respiratory phenotypes (eg, body mass index, lipids, and left ventricular mass) in CARDIA, with replication in FHS/JHS. A proteomic signature of social vulnerability was associated with a composite of cardiovascular disease/mortality (1428 events; FHS: hazard ratio, 1.16 [95% CI, 1.08-1.24]; P=1.77×10-5; JHS: hazard ratio, 1.25 [95% CI, 1.14-1.38]; P=6.38×10-6; hazard ratio expressed as per 1 SD increase in proteomic signature), robust to adjustment for known clinical risk factors. CONCLUSIONS Environmental exposures are related to an inflammatory-metabolic proteome, which identifies individuals with cardiometabolic disease and respiratory phenotypes and outcomes. Future work examining the dynamic impact of the environment on human cardiometabolic health is warranted.
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Affiliation(s)
- Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN (A.S.P., S.Z., J.J.C., R.V.S.)
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, (K.Z.)
| | | | - Bina Choi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA (B.C.)
| | - Shilin Zhao
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN (A.S.P., S.Z., J.J.C., R.V.S.)
| | - Priya Gajjar
- Cardiovascular Medicine Section, Department of Medicine (P.G.), Boston University School of Medicine, MA
| | - Laura A Colangelo
- Department of Preventive Medicine (L.A.C., L.H.), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Lifang Hou
- Department of Preventive Medicine (L.A.C., L.H.), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Mary B Rice
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA (M.B.R.)
| | - J Jeffrey Carr
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN (A.S.P., S.Z., J.J.C., R.V.S.)
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson (A.P.C.)
| | - Anne E Nigra
- Department of Environmental Health Science, Columbia University Mailman School of Public Health, New York, NY (A.E.N.)
| | - Ramachandran S Vasan
- School of Public Health, School of Medicine, University of Texas San Antonio (R.S.V.)
| | - Robert E Gerszten
- Broad Institute of Harvard and MIT, Cambridge, MA (R.E.G.)
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (R.E.G.)
| | - Sadiya S Khan
- Division of Cardiology, Department of Medicine (S.S.K.), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Ravi Kalhan
- Division of Pulmonary and Critical Care Medicine, Department of Medicine (R.K.), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Matthew Nayor
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of Medicine (M.N.), Boston University School of Medicine, MA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN (A.S.P., S.Z., J.J.C., R.V.S.)
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Xu Y, Zheng X, Li Y, Ye X, Cheng H, Wang H, Lyu J. Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. J Evid Based Med 2023; 16:342-375. [PMID: 37718729 DOI: 10.1111/jebm.12548] [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: 07/04/2023] [Accepted: 08/30/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery. METHODS We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications. RESULTS Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality. CONCLUSION Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.
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Affiliation(s)
- Yixian Xu
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinkai Zheng
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuanjie Li
- Planning & Discipline Construction Office, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinmiao Ye
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongtao Cheng
- School of Nursing, Jinan University, Guangzhou, China
| | - Hao Wang
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China
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Lu C, Wang YG, Zaman F, Wu X, Adhaduk M, Chang A, Ji J, Wei T, Suksaranjit P, Christodoulidis G, Scalzetti E, Han Y, Feiglin D, Liu K. Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2022; 38:1825-1836. [PMID: 35194707 DOI: 10.1007/s10554-022-02564-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 02/11/2022] [Indexed: 12/11/2022]
Abstract
Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial pathology occurs. We aimed to characterize regional CS myocardial remodeling features correlating with future adverse cardiac events by coupling automated image processing and data analysis on cardiac magnetic resonance (CMR) imaging datasets. A deep convolutional neural network (DCNN) was used to process a CMR database of a 10-year cohort of 117 consecutive biopsy-proven sarcoidosis patients. The maximum relevance - minimum redundancy method was used to select the best subset of all the features-24 (from manual processing) and 232 (from automated processing) left ventricular (LV) structural/functional features. Three machine learning (ML) algorithms, logistic regression (LogR), support vector machine (SVM) and multi-layer neural networks (MLP), were used to build classifiers to categorize endpoints. Over a median follow-up of 41.8 (inter-quartile range 20.4-60.5) months, 35 sarcoidosis patients experienced a total of 43 cardiac events. After manual processing, LV ejection fraction (LVEF), late gadolinium enhancement, abnormal segmental wall motion, LV mass (LVM), LVMI index (LVMI), septal wall thickness, lateral wall thickness, relative wall thickness, and wall thickness of 9 (out of 17) individual LV segments were significantly different between patients with and without endpoints. After automated processing, LVEF, end-diastolic volume, end-systolic volume, LV mass and wall thickness of 92 (out of 216) individual LV segments were significantly different between patients with and without endpoints. To achieve the best predictive performance, ML algorithms selected lateral wall thickness, abnormal segmental wall motion, septal wall thickness, and increased wall thickness of 3 individual segments after manual image processing, and selected end-diastolic volume and 7 individual segments after automated image processing. LogR, SVM and MLP based on automated image processing consistently showed better predictive accuracies than those based on manual image processing. Automated image processing with a DCNN improves data resolution and regional CS myocardial remodeling pattern recognition, suggesting that a framework coupling automated image processing with data analysis can help clinical risk stratification.
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Affiliation(s)
- Chenying Lu
- Departments of Medicine and Radiology, State University of New York, Upstate Medical University Hospital, Syracuse, USA
- Zhejiang Provincial Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Yi Grace Wang
- Department of Mathematics, California State University Dominguez Hills, Carson, USA
| | - Fahim Zaman
- Department of Electrical and Electronic Engineering, University of Iowa, Iowa City, USA
| | - Xiaodong Wu
- Department of Electrical and Electronic Engineering, University of Iowa, Iowa City, USA
| | - Mehul Adhaduk
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, USA
| | - Amanda Chang
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, USA
| | - Jiansong Ji
- Zhejiang Provincial Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Tiemin Wei
- Zhejiang Provincial Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Promporn Suksaranjit
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, USA
| | | | - Ernest Scalzetti
- Departments of Medicine and Radiology, State University of New York, Upstate Medical University Hospital, Syracuse, USA
| | - Yuchi Han
- Cardiovascular Division, University of Pennsylvania, Philadelphia, USA
| | - David Feiglin
- Departments of Medicine and Radiology, State University of New York, Upstate Medical University Hospital, Syracuse, USA
| | - Kan Liu
- Departments of Medicine and Radiology, State University of New York, Upstate Medical University Hospital, Syracuse, USA.
- Division of Cardiology and Heart Vascular Center, University of Iowa, Iowa City, IA, 52242, USA.
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Flores AM, Schuler A, Eberhard AV, Olin JW, Cooke JP, Leeper NJ, Shah NH, Ross EG. Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups. J Am Heart Assoc 2021; 10:e021976. [PMID: 34845917 PMCID: PMC9075403 DOI: 10.1161/jaha.121.021976] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
Background The promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that account for clinical, genetic, and environmental variability. This study sought to evaluate whether unsupervised machine learning approaches could interpret heterogeneous and missing clinical data to discover clinically important coronary artery disease subgroups. Methods and Results The Genetic Determinants of Peripheral Arterial Disease study is a prospective cohort that includes individuals with newly diagnosed and/or symptomatic coronary artery disease. We applied generalized low rank modeling and K-means cluster analysis using 155 phenotypic and genetic variables from 1329 participants. Cox proportional hazard models were used to examine associations between clusters and major adverse cardiovascular and cerebrovascular events and all-cause mortality. We then compared performance of risk stratification based on clusters and the American College of Cardiology/American Heart Association pooled cohort equations. Unsupervised analysis identified 4 phenotypically and prognostically distinct clusters. All-cause mortality was highest in cluster 1 (oldest/most comorbid; 26%), whereas major adverse cardiovascular and cerebrovascular event rates were highest in cluster 2 (youngest/multiethnic; 41%). Cluster 4 (middle-aged/healthiest behaviors) experienced more incident major adverse cardiovascular and cerebrovascular events (30%) than cluster 3 (middle-aged/lowest medication adherence; 23%), despite apparently similar risk factor and lifestyle profiles. In comparison with the pooled cohort equations, cluster membership was more informative for risk assessment of myocardial infarction, stroke, and mortality. Conclusions Unsupervised clustering identified 4 unique coronary artery disease subgroups with distinct clinical trajectories. Flexible unsupervised machine learning algorithms offer the ability to meaningfully process heterogeneous patient data and provide sharper insights into disease characterization and risk assessment. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00380185.
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Affiliation(s)
- Alyssa M. Flores
- Division of Vascular SurgeryDepartment of SurgeryStanford University School of MedicineStanfordCA
| | - Alejandro Schuler
- Center for Biomedical Informatics ResearchStanford UniversityStanfordCA
| | - Anne Verena Eberhard
- Division of Vascular SurgeryDepartment of SurgeryStanford University School of MedicineStanfordCA
| | - Jeffrey W. Olin
- Zena and Michael A. Wiener Cardiovascular InstituteMarie‐Josée and Henry R. Kravis Center for Cardiovascular HealthIcahn School of Medicine at Mount SinaiNew YorkNY
| | - John P. Cooke
- Department of Cardiovascular SciencesHouston Methodist Research InstituteHoustonTX
| | - Nicholas J. Leeper
- Division of Vascular SurgeryDepartment of SurgeryStanford University School of MedicineStanfordCA
- Division of Cardiovascular MedicineDepartment of MedicineStanford University School of MedicineStanfordCA
- Stanford Cardiovascular InstituteStanfordCA
| | - Nigam H. Shah
- Center for Biomedical Informatics ResearchStanford UniversityStanfordCA
| | - Elsie G. Ross
- Division of Vascular SurgeryDepartment of SurgeryStanford University School of MedicineStanfordCA
- Center for Biomedical Informatics ResearchStanford UniversityStanfordCA
- Stanford Cardiovascular InstituteStanfordCA
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Antonopoulos AS, Tsioufis K. Cardiometabolic risk assessment by imaging: current status and future perspectives. Eur J Prev Cardiol 2021; 28:2056-2058. [PMID: 34463722 DOI: 10.1093/eurjpc/zwab139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 08/05/2021] [Indexed: 11/14/2022]
Affiliation(s)
- Alexios S Antonopoulos
- 1st Cardiology Department, National and Kapodistrian University of Athens, Vas. Sofias Ave 114, Athens, Greece
| | - Konstantinos Tsioufis
- 1st Cardiology Department, National and Kapodistrian University of Athens, Vas. Sofias Ave 114, Athens, Greece
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Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res 2021; 116:2040-2054. [PMID: 32090243 DOI: 10.1093/cvr/cvaa021] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/29/2019] [Accepted: 01/23/2020] [Indexed: 12/23/2022] Open
Abstract
ABSTRACT Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.
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Affiliation(s)
- Evangelos K Oikonomou
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Department of Internal Medicine, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, USA
| | - Musib Siddique
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Caristo Diagnostics Ltd., Oxford, UK
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Oxford Centre of Research Excellence, British Heart Foundation, Oxford, UK.,Oxford Biomedical Research Centre, National Institute of Health Research, Oxford, UK
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Reid S, Schousboe JT, Kimelman D, Monchka BA, Jafari Jozani M, Leslie WD. Machine learning for automated abdominal aortic calcification scoring of DXA vertebral fracture assessment images: A pilot study. Bone 2021; 148:115943. [PMID: 33836309 DOI: 10.1016/j.bone.2021.115943] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/28/2021] [Accepted: 03/30/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND Abdominal aortic calcification (AAC) identified on dual-energy x-ray absorptiometry (DXA) vertebral fracture assessment (VFA) lateral spine images is predictive of cardiovascular outcomes, but is time-consuming to perform manually. Whether this procedure can be automated using convolutional neural networks (CNNs), a class of machine learning algorithms used for image processing, has not been widely investigated. METHODS Using the Province of Manitoba Bone Density Program DXA database, we selected a random sample of 1100 VFA images from individuals qualifying for VFA as part of their osteoporosis assessment. For each scan, AAC was manually scored using the 24-point semi-quantitative scale and categorized as low (score < 2), moderate (score 2 to <6), or high (score ≥ 6). An ensemble consisting of two CNNs was developed, by training and evaluating separately on single-energy and dual-energy images. AAC prediction was performed using the mean AAC score of the two models. RESULTS Mean (SD) age of the cohort was 75.5 (6.7) years, 95.5% were female. Training (N = 770, 70%), validation (N = 110, 10%) and test sets (N = 220, 20%) were well-balanced with respect to baseline characteristics and AAC scores. For the test set, the Pearson correlation between the CNN-predicted and human-labelled scores was 0.93 with intraclass correlation coefficient for absolute agreement 0.91 (95% CI 0.89-0.93). Kappa for AAC category agreement (prevalence- and bias-adjusted, ordinal scale) was 0.71 (95% CI 0.65-0.78). There was complete separation of the low and high categories, without any low AAC score scans predicted to be high and vice versa. CONCLUSIONS CNNs are capable of detecting AAC in VFA images, with high correlation between the human and predicted scores. These preliminary results suggest CNNs are a promising method for automatically detecting and quantifying AAC.
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Affiliation(s)
| | - John T Schousboe
- Park Nicollet Clinic and HealthPartners Institute, Bloomington, MN, USA; University of Minnesota, Minneapolis, MN, USA.
| | - Douglas Kimelman
- University of Manitoba, Winnipeg, Canada; St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, Manitoba, Canada
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10
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Abdominal periaortic and renal sinus fat attenuation indices measured on computed tomography are associated with metabolic syndrome. Eur Radiol 2021; 32:395-404. [PMID: 34156551 DOI: 10.1007/s00330-021-08090-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/03/2021] [Accepted: 05/21/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES To investigate the association between abdominal periaortic (APA) and renal sinus (RS) fat attenuation index (FAI) measured on MDCT and metabolic syndrome in non-obese and obese individuals. METHODS Visceral, subcutaneous, RS, and APA adipose tissue were measured in preoperative abdominal CT scans of individuals who underwent donor nephrectomy (n = 84) or bariatric surgery (n = 155). FAI was defined as the mean attenuation of measured fat volume. Participants were categorized into four groups: non-obese without metabolic syndrome (n = 64), non-obese with metabolic syndrome (n = 25), obese without metabolic syndrome (n = 21), and obese with metabolic syndrome (n = 129). The volume and FAI of each fat segment were compared among the groups. Receiver operator characteristics curve analysis was used to assess the association between the FAIs and metabolic syndrome. RESULTS FAIs of all abdominal fat segments were significantly lower in the obese group than in the non-obese group (p < 0.001). RS, APA, and the visceral adipose tissue FAIs were significantly lower in participants with metabolic syndrome than in those without metabolic syndrome in the non-obese group (p < 0.001, p = 0.006, and p < 0.001, respectively). The area under the curve for predicting metabolic syndrome was significantly higher for APA FAI (0.790) than subcutaneous, visceral, and RS FAI in all groups (0.649, 0.647, and 0.655, respectively). CONCLUSION Both metabolic syndrome and obesity were associated with lower RS and APA adipose tissue FAI, and APA FAI performed best for predicting metabolic syndrome. KEY POINTS • The volume and FAI of RS, APA, and visceral adipose tissue showed opposite trends with regard to metabolic syndrome or obesity. • Both metabolic syndrome and obesity were associated with lower RS FAI and APA FAI. • APA FAI performed best for predicting metabolic syndrome among FAIs of abdominal fat segments.
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11
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Lenchik L, Barnard R, Boutin RD, Kritchevsky SB, Chen H, Tan J, Cawthon PM, Weaver AA, Hsu FC. Automated Muscle Measurement on Chest CT Predicts All-Cause Mortality in Older Adults From the National Lung Screening Trial. J Gerontol A Biol Sci Med Sci 2021; 76:277-285. [PMID: 32504466 PMCID: PMC7812435 DOI: 10.1093/gerona/glaa141] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Muscle metrics derived from computed tomography (CT) are associated with adverse health events in older persons, but obtaining these metrics using current methods is not practical for large datasets. We developed a fully automated method for muscle measurement on CT images. This study aimed to determine the relationship between muscle measurements on CT with survival in a large multicenter trial of older adults. METHOD The relationship between baseline paraspinous skeletal muscle area (SMA) and skeletal muscle density (SMD) and survival over 6 years was determined in 6,803 men and 4,558 women (baseline age: 60-69 years) in the National Lung Screening Trial (NLST). The automated machine learning pipeline selected appropriate CT series, chose a single image at T12, and segmented left paraspinous muscle, recording cross-sectional area and density. Associations between SMA and SMD with all-cause mortality were determined using sex-stratified Cox proportional hazards models, adjusted for age, race, height, weight, pack-years of smoking, and presence of diabetes, chronic lung disease, cardiovascular disease, and cancer at enrollment. RESULTS After a mean 6.44 ± 1.06 years of follow-up, 635 (9.33%) men and 265 (5.81%) women died. In men, higher SMA and SMD were associated with a lower risk of all-cause mortality, in fully adjusted models. A one-unit standard deviation increase was associated with a hazard ratio (HR) = 0.85 (95% confidence interval [CI] = 0.79, 0.91; p < .001) for SMA and HR = 0.91 (95% CI = 0.84, 0.98; p = .012) for SMD. In women, the associations did not reach significance. CONCLUSION Higher paraspinous SMA and SMD, automatically derived from CT exams, were associated with better survival in a large multicenter cohort of community-dwelling older men.
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Affiliation(s)
- Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Ryan Barnard
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Robert D Boutin
- Department of Radiology, Stanford University Medical Center, California
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Haiying Chen
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Josh Tan
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Peggy M Cawthon
- California Pacific Medical Center Research Institute, San Francisco
| | - Ashley A Weaver
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
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Murthy VL, Reis JP, Pico AR, Kitchen R, Lima JAC, Lloyd-Jones D, Allen NB, Carnethon M, Lewis GD, Nayor M, Vasan RS, Freedman JE, Clish CB, Shah RV. Comprehensive Metabolic Phenotyping Refines Cardiovascular Risk in Young Adults. Circulation 2020; 142:2110-2127. [PMID: 33073606 PMCID: PMC7880553 DOI: 10.1161/circulationaha.120.047689] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 09/17/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Whereas cardiovascular disease (CVD) metrics define risk in individuals >40 years of age, the earliest lesions of CVD appear well before this age. Despite the role of metabolism in CVD antecedents, studies in younger, biracial populations to define precise metabolic risk phenotypes are lacking. METHODS We studied 2330 White and Black young adults (mean age, 32 years; 45% Black) in the CARDIA study (Coronary Artery Risk Development in Young Adults) to identify metabolite profiles associated with an adverse CVD phenome (myocardial structure/function, fitness, vascular calcification), mechanisms, and outcomes over 2 decades. Statistical learning methods (elastic nets/principal components analysis) and Cox regression generated parsimonious, metabolite-based risk scores validated in >1800 individuals in the Framingham Heart Study. RESULTS In the CARDIA study, metabolite profiles quantified in early adulthood were associated with subclinical CVD development over 20 years, specifying known and novel pathways of CVD (eg, transcriptional regulation, brain-derived neurotrophic factor, nitric oxide, renin-angiotensin). We found 2 multiparametric, metabolite-based scores linked independently to vascular and myocardial health, with metabolites included in each score specifying microbial metabolism, hepatic steatosis, oxidative stress, nitric oxide modulation, and collagen metabolism. The metabolite-based vascular scores were lower in men, and myocardial scores were lower in Black participants. Over a nearly 25-year median follow-up in CARDIA, the metabolite-based vascular score (hazard ratio, 0.68 per SD [95% CI, 0.50-0.92]; P=0.01) and myocardial score (hazard ratio, 0.60 per SD [95% CI, 0.45-0.80]; P=0.0005) in the third and fourth decades of life were associated with clinical CVD with a synergistic association with outcome (Pinteraction=0.009). We replicated these findings in 1898 individuals in the Framingham Heart Study over 2 decades, with a similar association with outcome (including interaction), reclassification, and discrimination. In the Framingham Heart Study, the metabolite scores exhibited an age interaction (P=0.0004 for a combined myocardial-vascular score with incident CVD), such that young adults with poorer metabolite-based health scores had highest hazard of future CVD. CONCLUSIONS Metabolic signatures of myocardial and vascular health in young adulthood specify known/novel pathways of metabolic dysfunction relevant to CVD, associated with outcome in 2 independent cohorts. Efforts to include precision measures of metabolic health in risk stratification to interrupt CVD at its earliest stage are warranted.
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Affiliation(s)
| | - Jared P. Reis
- National Heart, Lung, and Blood Institute, Bethesda, MD
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, University of California at San Francisco, San Francisco, CA
| | - Robert Kitchen
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Joao A. C. Lima
- Cardiology Division, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD
| | | | | | | | - Gregory D. Lewis
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Matthew Nayor
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Ramachandran S. Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, and Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, and the Framingham Heart Study, Framingham, MA
| | - Jane E. Freedman
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA
| | | | - Ravi V. Shah
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
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Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms. AJR Am J Roentgenol 2020; 216:85-92. [PMID: 32603223 DOI: 10.2214/ajr.20.23049] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Metabolic syndrome describes a constellation of reversible cardiometabolic abnormalities associated with cardiovascular risk and diabetes. The present study investigates the use of fully automated abdominal CT-based biometric measures for opportunistic identification of metabolic syndrome in adults without symptoms. MATERIALS AND METHODS International Diabetes Federation criteria were applied to a cohort of 9223 adults without symptoms who underwent unenhanced abdominal CT. After patients with insufficient clinical data for diagnosis were excluded, the final cohort consisted of 7785 adults (mean age, 57.0 years; 4361 women and 3424 men). Previously validated and fully automated CT-based algorithms for quantifying muscle, visceral and subcutaneous fat, liver fat, and abdominal aortic calcification were applied to this final cohort. RESULTS A total of 738 subjects (9.5% of all subjects; mean age, 56.7 years; 372 women and 366 men) met the clinical criteria for metabolic syndrome. Subsequent major cardiovascular events occurred more frequently in the cohort with metabolic syndrome (p < 0.001). Significant differences were observed between the two groups for all CT-based biomarkers (p < 0.001). Univariate L1-level total abdominal fat (area under the ROC curve [AUROC] = 0.909; odds ratio [OR] = 27.2), L3-level skeletal muscle index (AUROC = 0.776; OR = 5.8), and volumetric liver attenuation (AUROC = 0.738; OR = 5.1) performed well when compared with abdominal aortic calcification scoring (AUROC = 0.578; OR = 1.6). An L1-level total abdominal fat threshold of 460.6 cm2 was 80.1% sensitive and 85.4% specific for metabolic syndrome. For women, the AUROC was 0.930 when fat and muscle measures were combined. CONCLUSION Fully automated quantitative tissue measures of fat, muscle, and liver derived from abdominal CT scans can help identify individuals who are at risk for metabolic syndrome. These visceral measures can be opportunistically applied to CT scans obtained for other clinical indications, and they may ultimately provide a more direct and useful definition of metabolic syndrome.
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14
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Chen SI, Chiang CL, Chao CT, Chiang CK, Huang JW. Gustatory Function and the Uremic Toxin, Phosphate, Are Modulators of the Risk of Vascular Calcification among Patients with Chronic Kidney Disease: A Pilot Study. Toxins (Basel) 2020; 12:toxins12060420. [PMID: 32630499 PMCID: PMC7354456 DOI: 10.3390/toxins12060420] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 06/18/2020] [Accepted: 06/23/2020] [Indexed: 12/25/2022] Open
Abstract
Patients with chronic kidney disease (CKD) have an increased risk of vascular calcification (VC), including aortic arch calcification (AAC). Few investigated the influence of gustatory function on the probability of having VC. We examined whether gustatory function results modulated the probability of having VC in patients with CKD. We prospectively enrolled adults with CKD (estimated glomerular filtration rate <60 mL/min/1.73 m2), with their AAC rated semi-quantitatively and gustatory function assessed by objective and subjective approaches. Multiple logistic regression was used to analyze the relationship between gustatory function results and AAC. Those with AAC had significantly better objective gustatory function in aggregate scores (p = 0.039) and categories (p = 0.022) and less defective bitter taste (p = 0.045) and scores (p = 0.037) than those without. Multiple regression analyses showed that higher aggregate scores (odds ratio (OR) 1.288, p = 0.032), or better gustatory function, and higher bitter taste scores (OR 2.558, p = 0.019) were each associated with a higher probability of having AAC among CKD patients; such an association was modulated by serum phosphate levels. In conclusion, better gustatory function was independently correlated with having AAC among CKD patients. A follow-up of VC severity may be an underrecognized component of care for CKD patients with a preserved gustatory function.
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Affiliation(s)
- Shih-I Chen
- Nephrology Division, Department of Internal Medicine, National Taiwan University Hospital Beihu Branch, Taipei 108, Taiwan;
- Geriatric and Community Medicine Research Center, National Taiwan University Hospital BeiHu Branch, Taipei 108, Taiwan
| | - Chin-Ling Chiang
- Department of Nursing, National Taiwan University Hospital Beihu Branch, Taipei 108, Taiwan;
| | - Chia-Ter Chao
- Nephrology Division, Department of Internal Medicine, National Taiwan University Hospital Beihu Branch, Taipei 108, Taiwan;
- Geriatric and Community Medicine Research Center, National Taiwan University Hospital BeiHu Branch, Taipei 108, Taiwan
- Graduate Institute of Toxicology, National Taiwan University, Taipei 10617, Taiwan;
- Correspondence: Chia-Ter Chao, ; Tel.: +886-2-23717101-5307; Fax: +886-2-23123456
| | - Chih-Kang Chiang
- Graduate Institute of Toxicology, National Taiwan University, Taipei 10617, Taiwan;
- Department of Integrative Diagnostics and Therapeutics, National Taiwan University Hospital, Taipei 100, Taiwan
| | - Jenq-Wen Huang
- Nephrology Division, Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Yunlin County 260, Taiwan;
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Kwak S, Lee Y, Ko T, Yang S, Hwang IC, Park JB, Yoon YE, Kim HL, Kim HK, Kim YJ, Cho GY, Sohn DW, Won S, Lee SP. Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes. Circ Cardiovasc Imaging 2020; 13:e009707. [PMID: 32418453 DOI: 10.1161/circimaging.119.009707] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS. METHODS Newly diagnosed patients with moderate or severe AS were prospectively enrolled between 2013 and 2016 (n=398, mean 71 years, 55% male). Among demographics, laboratory, and echocardiography parameters (n=32), 11 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and causes of mortality were compared between the clusters. RESULTS Three clusters with markedly different features were identified. Cluster 1 (n=60) was predominantly associated with cardiac dysfunction, cluster 2 (n=86) consisted of elderly with comorbidities, especially end-stage renal disease, whereas cluster 3 (n=252) demonstrated neither cardiac dysfunction nor comorbidities. Although AS severity did not differ, there was a significant difference in adverse outcomes between the clusters during a median 2.4 years follow-up (mortality rate, 13.3% versus 19.8% versus 6.0% for cluster 1, 2, and 3, P<0.001). Particularly, compared with cluster 3, cluster 1 was associated with only cardiac mortality (adjusted hazard ratio, 7.37 [95% CI, 2.00-27.13]; P=0.003), whereas cluster 2 was associated with higher noncardiac mortality (adjusted hazard ratio, 3.35 [95% CI, 1.26-8.90]; P=0.015). Phenotypes and association of clusters with specific outcomes were reproduced in an independent validation cohort (n=262). CONCLUSIONS Unsupervised cluster analysis of patients with AS revealed 3 distinct groups with different causes of death. This provides a new perspective in the categorization of patients with AS that takes into account comorbidities and extravalvular cardiac dysfunction.
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Affiliation(s)
- Soongu Kwak
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Yunhwan Lee
- Department of Public Health Sciences, Seoul National University (Y.L., S.W.)
| | - Taehoon Ko
- Office of Hospital Information (T.K.), Seoul National University Hospital
| | - Seokhun Yang
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - In-Chang Hwang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.)
| | - Jun-Bean Park
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Yeonyee E Yoon
- Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.)
| | - Hack-Lyoung Kim
- Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, South Korea (H.-L.K.)
| | - Hyung-Kwan Kim
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Yong-Jin Kim
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Goo-Yeong Cho
- Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.)
| | - Dae-Won Sohn
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
| | - Sungho Won
- Department of Public Health Sciences, Seoul National University (Y.L., S.W.)
| | - Seung-Pyo Lee
- Department of Internal Medicine (S.K., S.Y., J.-B.P., H.-K.K., Y.-J.K., D.-W.S., S.-P.L.), Seoul National University Hospital
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Pickhardt PJ, Graffy PM, Zea R, Lee SJ, Liu J, Sandfort V, Summers RM. Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. LANCET DIGITAL HEALTH 2020; 2:e192-e200. [PMID: 32864598 DOI: 10.1016/s2589-7500(20)30025-x] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Background Body CT scans are frequently performed for a wide variety of clinical indications, but potentially valuable biometric information typically goes unused. We investigated the prognostic ability of automated CT-based body composition biomarkers derived from previously-developed deep-learning and feature-based algorithms for predicting major cardiovascular events and overall survival in an adult screening cohort, compared with clinical parameters. Methods Mature and fully-automated CT-based algorithms with pre-defined metrics for quantifying aortic calcification, muscle density, visceral/subcutaneous fat, liver fat, and bone mineral density (BMD) were applied to a generally-healthy asymptomatic outpatient cohort of 9223 adults (mean age, 57.1 years; 5152 women) undergoing abdominal CT for routine colorectal cancer screening. Longitudinal clinical follow-up (median, 8.8 years; IQR, 5.1-11.6 years) documented subsequent major cardiovascular events or death in 19.7% (n=1831). Predictive ability of CT-based biomarkers was compared against the Framingham Risk Score (FRS) and body mass index (BMI). Findings Significant differences were observed for all five automated CT-based body composition measures according to adverse events (p<0.001). Univariate 5-year AUROC (with 95% CI) for automated CT-based aortic calcification, muscle density, visceral/subcutaneous fat ratio, liver density, and vertebral density for predicting death were 0.743(0.705-0.780)/0.721(0.683-0.759)/0.661(0.625-0.697)/0.619 (0.582-0.656)/0.646(0.603-0.688), respectively, compared with 0.499(0.454-0.544) for BMI and 0.688(0.650-0.727) for FRS (p<0.05 for aortic calcification vs. FRS and BMI); all trends were similar for 2-year and 10-year ROC analyses. Univariate hazard ratios (with 95% CIs) for highest-risk quartile versus others for these same CT measures were 4.53(3.82-5.37) /3.58(3.02-4.23)/2.28(1.92-2.71)/1.82(1.52-2.17)/2.73(2.31-3.23), compared with 1.36(1.13-1.64) and 2.82(2.36-3.37) for BMI and FRS, respectively. Similar significant trends were observed for cardiovascular events. Multivariate combinations of CT biomarkers further improved prediction over clinical parameters (p<0.05 for AUROCs). For example, by combining aortic calcification, muscle density, and liver density, the 2-year AUROC for predicting overall survival was 0.811 (0.761-0.860). Interpretation Fully-automated quantitative tissue biomarkers derived from CT scans can outperform established clinical parameters for pre-symptomatic risk stratification for future serious adverse events, and add opportunistic value to CT scans performed for other indications.
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Affiliation(s)
- Perry J Pickhardt
- The University of Wisconsin School of Medicine & Public Health, Madison, WI
| | - Peter M Graffy
- The University of Wisconsin School of Medicine & Public Health, Madison, WI
| | - Ryan Zea
- The University of Wisconsin School of Medicine & Public Health, Madison, WI
| | - Scott J Lee
- The University of Wisconsin School of Medicine & Public Health, Madison, WI
| | - Jiamin Liu
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
| | - Veit Sandfort
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
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2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol 2019; 70:2696-2718. [PMID: 29169478 DOI: 10.1016/j.jacc.2017.10.018] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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