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Bonazzola R, Ferrante E, Ravikumar N, Xia Y, Keavney B, Plein S, Syeda-Mahmood T, Frangi AF. Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology. NAT MACH INTELL 2024; 6:291-306. [PMID: 38523678 PMCID: PMC10957472 DOI: 10.1038/s42256-024-00801-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 01/25/2024] [Indexed: 03/26/2024]
Abstract
Recent genome-wide association studies have successfully identified associations between genetic variants and simple cardiac morphological parameters derived from cardiac magnetic resonance images. However, the emergence of large databases, including genetic data linked to cardiac magnetic resonance facilitates the investigation of more nuanced patterns of cardiac shape variability than those studied so far. Here we propose a framework for gene discovery coined unsupervised phenotype ensembles. The unsupervised phenotype ensemble builds a redundant yet highly expressive representation by pooling a set of phenotypes learnt in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analysed via genome-wide association studies, retaining only highly confident and stable associations across the ensemble. We applied our approach to the UK Biobank database to extract geometric features of the left ventricle from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes that influence left ventricle shape, identifying 49 loci with study-wide significance and 25 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.
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Affiliation(s)
- Rodrigo Bonazzola
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing and School of Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK
| | - Enzo Ferrante
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing and School of Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK
| | - Yan Xia
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing and School of Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, UK
| | - Sven Plein
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK
| | | | - Alejandro F. Frangi
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, UK
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Department of Computer Science, School of Engineering, Faculty of Science and Engineering, University of Manchester, Manchester, UK
- Medical Imaging Research Center (MIRC), University Hospital Gasthuisberg. Cardiovascular Sciences and Electrical Engineering Departments, KU Leuven, Leuven, Belgium
- Alan Turing Institute, London, UK
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Bell JD, Thomas EL. Liver shape analysis using statistical parametric maps at population scale. BMC Med Imaging 2024; 24:15. [PMID: 38195400 PMCID: PMC10775563 DOI: 10.1186/s12880-023-01149-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 10/31/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. METHODS Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes. RESULTS We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease. CONCLUSIONS The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Srinivasan R, Lennon R, Bell JD, Thomas EL. Kidney shape statistical analysis: associations with disease and anthropometric factors. BMC Nephrol 2023; 24:362. [PMID: 38057740 PMCID: PMC10698953 DOI: 10.1186/s12882-023-03407-8] [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: 07/04/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions. METHODS We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events. RESULTS We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney). CONCLUSIONS The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Curran L, de Marvao A, Inglese P, McGurk KA, Schiratti PR, Clement A, Zheng SL, Li S, Pua CJ, Shah M, Jafari M, Theotokis P, Buchan RJ, Jurgens SJ, Raphael CE, Baksi AJ, Pantazis A, Halliday BP, Pennell DJ, Bai W, Chin CW, Tadros R, Bezzina CR, Watkins H, Cook SA, Prasad SK, Ware JS, O’Regan DP. Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e004200. [PMID: 38014537 PMCID: PMC10729901 DOI: 10.1161/circgen.123.004200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/25/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes, but there is no systematic framework for classifying morphology or assessing associated risks. Here, we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression. METHODS We enrolled 436 patients with HCM (median age, 60 years; 28.8% women) with clinical, genetic, and imaging data. An independent cohort of 60 patients with HCM from Singapore (median age, 59 years; 11% women) and a reference population from the UK Biobank (n=16 691; mean age, 55 years; 52.5% women) were also recruited. We used machine learning to analyze the 3-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree. RESULTS Carriers of pathogenic or likely pathogenic variants for HCM had lower left ventricular mass, but greater basal septal hypertrophy, with reduced life span (mean follow-up, 9.9 years) compared with genotype negative individuals (hazard ratio, 2.66 [95% CI, 1.42-4.96]; P<0.002). Four main phenotypic branches were identified using unsupervised learning of 3-dimensional shape: (1) nonsarcomeric hypertrophy with coexisting hypertension; (2) diffuse and basal asymmetrical hypertrophy associated with outflow tract obstruction; (3) isolated basal hypertrophy; and (4) milder nonobstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for pathogenic or likely pathogenic variants, 2.18 [95% CI, 1.93-2.28]; P=0.0001). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalizable to an independent cohort (trustworthiness, M1: 0.86-0.88). CONCLUSIONS We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk, and outcomes. This approach will be of value in understanding the causes and consequences of disease diversity.
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Affiliation(s)
- Lara Curran
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
| | - Antonio de Marvao
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
- Department of Women and Children’s Health (A.d.M.)
- British Heart Foundation Centre of Research Excellence, School of Cardiovascular & Metabolic Medicine and Sciences, King’s College London, United Kingdom (A.d.M.)
| | - Paolo Inglese
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Kathryn A. McGurk
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Pierre-Raphaël Schiratti
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Adam Clement
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Sean L. Zheng
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Surui Li
- Biomedical Image Analysis Group, Department of Computing (S.L., W.B.)
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Chee Jian Pua
- National Heart Research Institute Singapore, Singapore, PRC (C.J.P., C.W.L.C., S.A.C.)
| | - Mit Shah
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Mina Jafari
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Biomedical Image Analysis Group, Department of Computing (S.L., W.B.)
- Department of Brain Sciences, Imperial College London, London, United Kingdom (W.B.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
- Department of Women and Children’s Health (A.d.M.)
- British Heart Foundation Centre of Research Excellence, School of Cardiovascular & Metabolic Medicine and Sciences, King’s College London, United Kingdom (A.d.M.)
- National Heart Research Institute Singapore, Singapore, PRC (C.J.P., C.W.L.C., S.A.C.)
- Department of Cardiology, National Heart Center Singapore, Singapore, PRC (C.W.L.C.)
- Cardiovascular Sciences ACP, Duke NUS Medical School, Singapore (C.W.L.C.)
- Mayo Clinic Rochester, MN (C.E.R.)
- Department of Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, University of Amsterdam, the Netherlands (S.J.J., C.R.B.)
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.J.J.)
- Cardiovascular Genetics Centre, Montreal Heart Institute (R.T.)
- Faculty of Medicine, Université de Montréal, QC, Canada (R.T.)
- Radcliffe Department of Medicine, University of Oxford, United Kingdom (H.W.)
| | - Pantazis Theotokis
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Rachel J. Buchan
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Sean J. Jurgens
- Department of Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, University of Amsterdam, the Netherlands (S.J.J., C.R.B.)
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.J.J.)
| | - Claire E. Raphael
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Mayo Clinic Rochester, MN (C.E.R.)
| | - Arun John Baksi
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
| | - Antonis Pantazis
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
| | - Brian P. Halliday
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
| | - Dudley J. Pennell
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing (S.L., W.B.)
- Department of Brain Sciences, Imperial College London, London, United Kingdom (W.B.)
| | - Calvin W.L. Chin
- National Heart Research Institute Singapore, Singapore, PRC (C.J.P., C.W.L.C., S.A.C.)
- Department of Cardiology, National Heart Center Singapore, Singapore, PRC (C.W.L.C.)
- Cardiovascular Sciences ACP, Duke NUS Medical School, Singapore (C.W.L.C.)
| | - Rafik Tadros
- Cardiovascular Genetics Centre, Montreal Heart Institute (R.T.)
- Faculty of Medicine, Université de Montréal, QC, Canada (R.T.)
| | - Connie R. Bezzina
- Department of Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, University of Amsterdam, the Netherlands (S.J.J., C.R.B.)
| | - Hugh Watkins
- Radcliffe Department of Medicine, University of Oxford, United Kingdom (H.W.)
| | - Stuart A. Cook
- Department of Women and Children’s Health (A.d.M.)
- National Heart Research Institute Singapore, Singapore, PRC (C.J.P., C.W.L.C., S.A.C.)
| | - Sanjay K. Prasad
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
| | - James S. Ware
- National Heart and Lung Institute (L.C., K.A.M., S.L.Z., P.T., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Royal Brompton and Harefield Hospitals, Guy’s and St. Thomas’ NHS Foundation Trust (L.C., R.J.B., C.E.R., A.J.B., A.P., B.P.H., D.J.P., S.K.P., J.S.W.)
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
| | - Declan P. O’Regan
- Medical Research Council Laboratory of Medical Sciences, Imperial College London, United Kingdom (A.d.M., P.I., K.A.M., P.-R.S., A.C., S.L.Z., S.L., M.S., M.J., P.T., R.J.B., S.A.C., J.S.W., D.P.O.)
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5
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Shah M, de A Inácio MH, Lu C, Schiratti PR, Zheng SL, Clement A, de Marvao A, Bai W, King AP, Ware JS, Wilkins MR, Mielke J, Elci E, Kryukov I, McGurk KA, Bender C, Freitag DF, O'Regan DP. Environmental and genetic predictors of human cardiovascular ageing. Nat Commun 2023; 14:4941. [PMID: 37604819 PMCID: PMC10442405 DOI: 10.1038/s41467-023-40566-6] [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] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we use machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.
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Affiliation(s)
- Mit Shah
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Marco H de A Inácio
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | | | - Sean L Zheng
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Adam Clement
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Wenjia Bai
- Department of Computing, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - James S Ware
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Martin R Wilkins
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Johanna Mielke
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Eren Elci
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Ivan Kryukov
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Kathryn A McGurk
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Christian Bender
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Daniel F Freitag
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK.
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Quintero Santofimio V, Clement A, O'Regan DP, Ware JS, McGurk KA. Identification of an increased lifetime risk of major adverse cardiovascular events in UK Biobank participants with scoliosis. Open Heart 2023; 10:e002224. [PMID: 37137668 PMCID: PMC10163590 DOI: 10.1136/openhrt-2022-002224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/11/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND Structural changes caused by spinal curvature may impact the organs within the thoracic cage, including the heart. Cardiac abnormalities in patients with idiopathic scoliosis are often studied post-corrective surgery or secondary to diseases. To investigate cardiac structure, function and outcomes in participants with scoliosis, phenotype and imaging data of the UK Biobank (UKB) adult population cohort were analysed. METHODS Hospital episode statistics of 502 324 adults were analysed to identify participants with scoliosis. Summary 2D cardiac phenotypes from 39 559 cardiac MRI (CMR) scans were analysed alongside a 3D surface-to-surface (S2S) analysis. RESULTS A total of 4095 (0.8%, 1 in 120) UKB participants were identified to have all-cause scoliosis. These participants had an increased lifetime risk of major adverse cardiovascular events (MACEs) (HR=1.45, p<0.001), driven by heart failure (HR=1.58, p<0.001) and atrial fibrillation (HR=1.54, p<0.001). Increased radial and decreased longitudinal peak diastolic strain rates were identified in participants with scoliosis (+0.29, Padj <0.05; -0.25, Padj <0.05; respectively). Cardiac compression of the top and bottom of the heart and decompression of the sides was observed through S2S analysis. Additionally, associations between scoliosis and older age, female sex, heart failure, valve disease, hypercholesterolemia, hypertension and decreased enrolment for CMR were identified. CONCLUSION The spinal curvature observed in participants with scoliosis alters the movement of the heart. The association with increased MACE may have clinical implications for whether to undertake surgical correction. This work identifies, in an adult population, evidence for altered cardiac function and an increased lifetime risk of MACE in participants with scoliosis.
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Affiliation(s)
| | - Adam Clement
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Kathryn A McGurk
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
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7
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Artificial Intelligence and Cardiovascular Genetics. Life (Basel) 2022; 12:life12020279. [PMID: 35207566 PMCID: PMC8875522 DOI: 10.3390/life12020279] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/26/2022] [Accepted: 02/09/2022] [Indexed: 12/13/2022] Open
Abstract
Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.
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8
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de Marvao A, McGurk KA, Zheng SL, Thanaj M, Bai W, Duan J, Biffi C, Mazzarotto F, Statton B, Dawes TJW, Savioli N, Halliday BP, Xu X, Buchan RJ, Baksi AJ, Quinlan M, Tokarczuk P, Tayal U, Francis C, Whiffin N, Theotokis PI, Zhang X, Jang M, Berry A, Pantazis A, Barton PJR, Rueckert D, Prasad SK, Walsh R, Ho CY, Cook SA, Ware JS, O'Regan DP. Phenotypic Expression and Outcomes in Individuals With Rare Genetic Variants of Hypertrophic Cardiomyopathy. J Am Coll Cardiol 2021; 78:1097-1110. [PMID: 34503678 PMCID: PMC8434420 DOI: 10.1016/j.jacc.2021.07.017] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/01/2021] [Accepted: 07/06/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population. OBJECTIVES The goal of this study was to compare lifetime outcomes and cardiovascular phenotypes according to the presence of rare variants in sarcomere-encoding genes among middle-aged adults. METHODS This study analyzed whole exome sequencing and cardiac magnetic resonance imaging in UK Biobank participants stratified according to sarcomere-encoding variant status. RESULTS The prevalence of rare variants (allele frequency <0.00004) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n = 5,712; 1 in 35), and the prevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was 0.25% (n = 493; 1 in 407). SARC-HCM-P/LP variants were associated with an increased risk of death or major adverse cardiac events compared with controls (hazard ratio: 1.69; 95% confidence interval [CI]: 1.38-2.07; P < 0.001), mainly due to heart failure endpoints (hazard ratio: 4.23; 95% CI: 3.07-5.83; P < 0.001). In 21,322 participants with both cardiac magnetic resonance imaging and whole exome sequencing, SARC-HCM-P/LP variants were associated with an asymmetric increase in left ventricular maximum wall thickness (10.9 ± 2.7 mm vs 9.4 ± 1.6 mm; P < 0.001), but hypertrophy (≥13 mm) was only present in 18.4% (n = 9 of 49; 95% CI: 9%-32%). SARC-HCM-P/LP variants were still associated with heart failure after adjustment for wall thickness (hazard ratio: 6.74; 95% CI: 2.43-18.7; P < 0.001). CONCLUSIONS In this population of middle-aged adults, SARC-HCM-P/LP variants have low aggregate penetrance for overt HCM but are associated with an increased risk of adverse cardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absolute event rates are low, identification of these variants may enhance risk stratification beyond familial disease.
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Affiliation(s)
- Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Kathryn A McGurk
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Sean L Zheng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Marjola Thanaj
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Jinming Duan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Carlo Biffi
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Francesco Mazzarotto
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy; Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
| | - Ben Statton
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Timothy J W Dawes
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Nicolò Savioli
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Brian P Halliday
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Xiao Xu
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Rachel J Buchan
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - A John Baksi
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Marina Quinlan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Paweł Tokarczuk
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Upasana Tayal
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Catherine Francis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Nicola Whiffin
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom; Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Pantazis I Theotokis
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Xiaolei Zhang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Mikyung Jang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Alaine Berry
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Antonis Pantazis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Paul J R Barton
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Faculty of Informatics and Medicine, Klinikum Rechts der Isar, TU Munich, Munich, Germany
| | - Sanjay K Prasad
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Roddy Walsh
- Department of Experimental Cardiology, Amsterdam UMC, AMC Heart Centre, Amsterdam, the Netherlands
| | - Carolyn Y Ho
- Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Stuart A Cook
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom; National Heart Centre Singapore, Singapore; Duke-NUS Graduate Medical School, Singapore
| | - James S Ware
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre at Royal Brompton and Harefield Hospitals, London, United Kingdom.
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom.
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9
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Zhou J, Hu L, Jiang Y, Liu L. A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning. BIOMED RESEARCH INTERNATIONAL 2021; 2021:8890513. [PMID: 33628827 PMCID: PMC7886593 DOI: 10.1155/2021/8890513] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 12/23/2020] [Accepted: 01/27/2021] [Indexed: 12/31/2022]
Abstract
Motivation. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature selection aiming at eliminating redundant signals and obtain representative features; and the third step is to build a learning model and predict the unknown data with regression or bivariate correlation analysis. This type of method requires manual extraction of feature single-nucleotide polymorphisms (SNPs), and the extraction process relies on empirical knowledge to a certain extent, such as linkage imbalance and gene function information in a group sparse model, which puts forward certain requirements for applicable scenarios and application personnel. To solve the problems of insufficient biological significance and large errors in the previous methods of association analysis and disease diagnosis, this paper presents a method of correlation analysis and disease diagnosis between SNP and region of interest (ROI) based on a deep learning model. It is a data-driven method, which has no obvious feature selection process. Results. The deep learning method adopted in this paper has no obvious feature extraction process relying on prior knowledge and model assumptions. From the results of correlation analysis between SNP and ROI, this method is complementary to other regression model methods in application scenarios. In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics. The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique. The experimental results show that the SNP-ROI joint feature describes the information of the samples from different angles, which makes the diagnosis accuracy higher.
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Affiliation(s)
- Juan Zhou
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Linfeng Hu
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Yu Jiang
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Liyue Liu
- School of Software, East China Jiaotong University, Nanchang 330013, China
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10
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Attard MI, Dawes TJW, de Marvao A, Biffi C, Shi W, Wharton J, Rhodes CJ, Ghataorhe P, Gibbs JSR, Howard LSGE, Rueckert D, Wilkins MR, O'Regan DP. Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging. Eur Heart J Cardiovasc Imaging 2020; 20:668-676. [PMID: 30535300 PMCID: PMC6529902 DOI: 10.1093/ehjci/jey175] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 10/27/2018] [Indexed: 12/14/2022] Open
Abstract
Aims We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress. Methods and results In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function—including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = −0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28–0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = −0.40) of sulfated androgen. Conclusion Using computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.
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Affiliation(s)
- Mark I Attard
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK.,Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - Timothy J W Dawes
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK.,Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK.,Royal Brompton Cardiovascular Research Centre, National Heart & Lung Institute, Imperial College London, Dovehouse Street, London, UK
| | | | - Carlo Biffi
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK.,Department of Computing, Imperial College London, South Kensington Campus, Queen's Gate, London, UK
| | - Wenzhe Shi
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK.,Department of Computing, Imperial College London, South Kensington Campus, Queen's Gate, London, UK
| | - John Wharton
- Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - Christopher J Rhodes
- Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - Pavandeep Ghataorhe
- Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - J Simon R Gibbs
- Royal Brompton Cardiovascular Research Centre, National Heart & Lung Institute, Imperial College London, Dovehouse Street, London, UK
| | | | - Daniel Rueckert
- Department of Computing, Imperial College London, South Kensington Campus, Queen's Gate, London, UK
| | - Martin R Wilkins
- Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK
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11
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Gilbert K, Mauger C, Young AA, Suinesiaputra A. Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy. Front Cardiovasc Med 2020; 7:102. [PMID: 32695795 PMCID: PMC7338378 DOI: 10.3389/fcvm.2020.00102] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/14/2020] [Indexed: 12/14/2022] Open
Abstract
In many cardiovascular pathologies, the shape and motion of the heart provide important clues to understanding the mechanisms of the disease and how it progresses over time. With the advent of large-scale cardiac data, statistical modeling of cardiac anatomy has become a powerful tool to provide automated, precise quantification of the status of patient-specific heart geometry with respect to reference populations. Powered by supervised or unsupervised machine learning algorithms, statistical cardiac shape analysis can be used to automatically identify and quantify the severity of heart diseases, to provide morphometric indices that are optimally associated with clinical factors, and to evaluate the likelihood of adverse outcomes. Recently, statistical cardiac atlases have been integrated with deep neural networks to enable anatomical consistency of cardiac segmentation, registration, and automated quality control. These combinations have already shown significant improvements in performance and avoid gross anatomical errors that could make the results unusable. This current trend is expected to grow in the near future. Here, we aim to provide a mini review highlighting recent advances in statistical atlasing of cardiac function in the context of artificial intelligence in cardiac imaging.
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Affiliation(s)
- Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Charlène Mauger
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.,Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.,Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.,Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, United Kingdom.,School of Medicine, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
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12
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Bhuva AN, Treibel TA, De Marvao A, Biffi C, Dawes TJW, Doumou G, Bai W, Patel K, Boubertakh R, Rueckert D, O'Regan DP, Hughes AD, Moon JC, Manisty CH. Sex and regional differences in myocardial plasticity in aortic stenosis are revealed by 3D model machine learning. Eur Heart J Cardiovasc Imaging 2020; 21:417-427. [PMID: 31280289 PMCID: PMC7100908 DOI: 10.1093/ehjci/jez166] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/22/2019] [Indexed: 12/12/2022] Open
Abstract
AIMS Left ventricular hypertrophy (LVH) in aortic stenosis (AS) varies widely before and after aortic valve replacement (AVR), and deeper phenotyping beyond traditional global measures may improve risk stratification. We hypothesized that machine learning derived 3D LV models may provide a more sensitive assessment of remodelling and sex-related differences in AS than conventional measurements. METHODS AND RESULTS One hundred and sixteen patients with severe, symptomatic AS (54% male, 70 ± 10 years) underwent cardiovascular magnetic resonance pre-AVR and 1 year post-AVR. Computational analysis produced co-registered 3D models of wall thickness, which were compared with 40 propensity-matched healthy controls. Preoperative regional wall thickness and post-operative percentage wall thickness regression were analysed, stratified by sex. AS hypertrophy and regression post-AVR was non-uniform-greatest in the septum with more pronounced changes in males than females (wall thickness regression: -13 ± 3.6 vs. -6 ± 1.9%, respectively, P < 0.05). Even patients without LVH (16% with normal indexed LV mass, 79% female) had greater septal and inferior wall thickness compared with controls (8.8 ± 1.6 vs. 6.6 ± 1.2 mm, P < 0.05), which regressed post-AVR. These differences were not detectable by global measures of remodelling. Changes to clinical parameters post-AVR were also greater in males: N-terminal pro-brain natriuretic peptide (NT-proBNP) [-37 (interquartile range -88 to -2) vs. -1 (-24 to 11) ng/L, P = 0.008], and systolic blood pressure (12.9 ± 23 vs. 2.1 ± 17 mmHg, P = 0.009), with changes in NT-proBNP correlating with percentage LV mass regression in males only (ß 0.32, P = 0.02). CONCLUSION In patients with severe AS, including those without overt LVH, LV remodelling is most plastic in the septum, and greater in males, both pre-AVR and post-AVR. Three-dimensional machine learning is more sensitive than conventional analysis to these changes, potentially enhancing risk stratification. CLINICAL TRIAL REGISTRATION Regression of myocardial fibrosis after aortic valve replacement (RELIEF-AS); NCT02174471. https://clinicaltrials.gov/ct2/show/NCT02174471.
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Affiliation(s)
- Anish N Bhuva
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Thomas A Treibel
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Antonio De Marvao
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
| | - Carlo Biffi
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
- Department of Computing, Imperial College London, South Kensington Campus, 180 Queen's Gate, London SW72RH, UK
| | - Timothy J W Dawes
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
- National Heart and Lung Institute, Imperial College London, Du Cane Road, London W120NN, UK
| | - Georgia Doumou
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
| | - Wenjia Bai
- Department of Computing, Imperial College London, South Kensington Campus, 180 Queen's Gate, London SW72RH, UK
| | - Kush Patel
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Redha Boubertakh
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, South Kensington Campus, 180 Queen's Gate, London SW72RH, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
| | - Alun D Hughes
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
| | - James C Moon
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Charlotte H Manisty
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
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13
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Phua AIH, Le TT, Tara SW, De Marvao A, Duan J, Toh DF, Ang B, Bryant JA, O'Regan DP, Cook SA, Chin CWL. Paradoxical Higher Myocardial Wall Stress and Increased Cardiac Remodeling Despite Lower Mass in Females. J Am Heart Assoc 2020; 9:e014781. [PMID: 32067597 PMCID: PMC7070215 DOI: 10.1161/jaha.119.014781] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background Increased left ventricular (LV) mass is characterized by increased myocardial wall thickness and/or ventricular dilatation that is associated with worse outcomes. We aim to comprehensively compare sex‐stratified associations between measures of LV remodeling and increasing LV mass in the general population. Methods and Results Participants were prospectively recruited in the National Heart Center Singapore Biobank to examine health and cardiovascular risk factors in the general population. Cardiovascular magnetic resonance was performed in all individuals. Participants with established cardiovascular diseases and abnormal cardiovascular magnetic resonance scan results were excluded. Global and regional measures of LV remodeling (geometry, function, interstitial volumes, and wall stress) were performed using conventional image analysis and novel 3‐dimensional machine learning phenotyping. Sex‐stratified analyses were performed in 1005 participants (57% males; 53±13 years). Age and prevalence of cardiovascular risk factors were well‐matched in both sexes (P>0.05 for all). Progressive increase in LV mass was associated with increased concentricity in either sex, but to a greater extent in females. Compared with males, females had higher wall stress (mean difference: 170 mm Hg, P<0.0001) despite smaller LV mass (42.4±8.2 versus 55.6±14.2 g/m2, P<0.0001), lower blood pressures (P<0.0001), and higher LV ejection fraction (61.9±5.9% versus 58.6±6.4%, P<0.0001). The regions of increased concentric remodeling corresponded to regions of increased wall stress. Compared with males, females had increased extracellular volume fraction (27.1±2.4% versus 25.1±2.9%, P<0.0001). Conclusions Compared with males, females have lower LV mass, increased wall stress, and concentric remodeling. These findings provide mechanistic insights that females are susceptible to particular cardiovascular complications.
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Affiliation(s)
- Andrew I H Phua
- College of Medicine and Public Health Flinders University Adelaide South Australia
| | - Thu-Thao Le
- National Heart Research Institute Singapore National Heart Center Singapore Singapore
| | - Su W Tara
- College of Medicine and Public Health Flinders University Adelaide South Australia
| | - Antonio De Marvao
- MRC London Institute of Medical Sciences Imperial College London London United Kingdom
| | - Jinming Duan
- MRC London Institute of Medical Sciences Imperial College London London United Kingdom
| | - Desiree-Faye Toh
- National Heart Research Institute Singapore National Heart Center Singapore Singapore
| | - Briana Ang
- National Heart Research Institute Singapore National Heart Center Singapore Singapore
| | - Jennifer A Bryant
- National Heart Research Institute Singapore National Heart Center Singapore Singapore
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences Imperial College London London United Kingdom
| | - Stuart A Cook
- National Heart Research Institute Singapore National Heart Center Singapore Singapore.,Department of Cardiology National Heart Center Singapore Singapore.,Cardiovascular Sciences ACP Duke NUS Medical School Singapore
| | - Calvin W L Chin
- National Heart Research Institute Singapore National Heart Center Singapore Singapore.,Department of Cardiology National Heart Center Singapore Singapore.,Cardiovascular Sciences ACP Duke NUS Medical School Singapore
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14
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de Marvao A, Dawes TJW, O'Regan DP. Artificial Intelligence for Cardiac Imaging-Genetics Research. Front Cardiovasc Med 2020; 6:195. [PMID: 32039240 PMCID: PMC6985036 DOI: 10.3389/fcvm.2019.00195] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/27/2019] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
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Affiliation(s)
| | | | - Declan P. O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
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15
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Zuber V, Colijn JM, Klaver C, Burgess S. Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization. Nat Commun 2020; 11:29. [PMID: 31911605 DOI: 10.1101/396333] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/26/2019] [Indexed: 05/24/2023] Open
Abstract
Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration.
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Affiliation(s)
- Verena Zuber
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Johanna Maria Colijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Caroline Klaver
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- MRC/BHF Cardiovascular Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
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16
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Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization. Nat Commun 2020; 11:29. [PMID: 31911605 PMCID: PMC6946691 DOI: 10.1038/s41467-019-13870-3] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/26/2019] [Indexed: 02/08/2023] Open
Abstract
Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration. Multivariable Mendelian randomization (MR) extends the standard MR framework to consider multiple risk factors in a single model. Here, Zuber et al. propose MR-BMA, a Bayesian variable selection approach to identify the likely causal determinants of a disease from many candidate risk factors as for example high-throughput data sets.
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17
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Abstract
Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.
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18
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Leiner T, Rueckert D, Suinesiaputra A, Baeßler B, Nezafat R, Išgum I, Young AA. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson 2019; 21:61. [PMID: 31590664 PMCID: PMC6778980 DOI: 10.1186/s12968-019-0575-y] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 09/02/2019] [Indexed: 12/18/2022] Open
Abstract
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.
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Affiliation(s)
- Tim Leiner
- Department of Radiology | E.01.132, Utrecht University Medical Center, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College, London, UK
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Bettina Baeßler
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA USA
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Alistair A. Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King’s College London, London, UK
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19
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O'Regan DP. Putting machine learning into motion: applications in cardiovascular imaging. Clin Radiol 2019; 75:33-37. [PMID: 31079952 DOI: 10.1016/j.crad.2019.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 04/04/2019] [Indexed: 12/24/2022]
Abstract
Heart and circulatory diseases cause a quarter of all deaths in the UK and cardiac imaging offers an effective tool for early diagnosis and risk-stratification to improve premature death and disability. This domain of radiology is unique in that assessing flow and motion is essential for understanding and quantifying normal physiology and disease processes. Conventional image interpretation relies on manual analysis but this often fails to capture important prognostic features in the complex disturbances of cardiovascular physiology. Machine learning (ML) in cardiovascular imaging promises to be a transformative tool and addresses an unmet need for patient-specific management, accurate prediction of future events, and the discovery of tractable molecular mechanisms of disease. This review discusses the potential of ML across every aspect of image analysis including efficient acquisition, segmentation and motion tracking, disease classification, prediction tasks and modelling of genotype-phenotype interactions; however, significant challenges remain in access to high-quality data at scale, robust validation, and clinical interpretability.
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Affiliation(s)
- D P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK.
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20
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Gilbert K, Bai W, Mauger C, Medrano-Gracia P, Suinesiaputra A, Lee AM, Sanghvi MM, Aung N, Piechnik SK, Neubauer S, Petersen SE, Rueckert D, Young AA. Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study. Sci Rep 2019; 9:1130. [PMID: 30718635 PMCID: PMC6362245 DOI: 10.1038/s41598-018-37916-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/17/2018] [Indexed: 01/08/2023] Open
Abstract
Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.
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Affiliation(s)
- Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Charlene Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Pau Medrano-Gracia
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Aaron M Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Mihir M Sanghvi
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Stefan K Piechnik
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Oxford NIHR Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
- Department of Biomedical Engineering, King's College London, London, UK.
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21
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22
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Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease. J Cardiovasc Transl Res 2018; 11:123-132. [PMID: 29294215 DOI: 10.1007/s12265-017-9778-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/18/2017] [Indexed: 12/18/2022]
Abstract
Approximately 1% of all babies are born with some form of congenital heart defect. Many serious forms of CHD can now be surgically corrected after birth, which has led to improved survival into adulthood. However, many patients require serial monitoring to evaluate progression of heart failure and determine timing of interventions. Accurate multidimensional quantification of regional heart shape and function is required for characterizing these patients. A computational atlas of single ventricle and biventricular heart shape and function enables quantification of remodeling in terms of z scores in relation to specific reference populations. Progression of disease can then be monitored effectively by longitudinal evaluation of z scores. A biomechanical analysis of cardiac function in relation to population variation enables investigation of the underlying mechanisms for developing pathology. Here, we summarize recent progress in this field, with examples in single ventricle and biventricular congenital pathologies.
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