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Lip G, O'Regan DP. Can machine learning predict cardiac risk using mammography? Eur Heart J Cardiovasc Imaging 2024; 25:467-468. [PMID: 38262145 DOI: 10.1093/ehjci/jeae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 01/13/2024] [Accepted: 01/14/2024] [Indexed: 01/25/2024] Open
Affiliation(s)
- Gerald Lip
- North East of Scotland Breast Screening Program, Foresterhill Road, Aberdeen, UK
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, Du Cane Road, London, W12 0HS, UK
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Topriceanu CC, Shah M, Webber M, Chan F, Shiwani H, Richards M, Schott J, Chaturvedi N, Moon JC, Hughes AD, Hingorani AD, O'Regan DP, Captur G. APOE ε4 carriage associates with improved myocardial performance from adolescence to older age. BMC Cardiovasc Disord 2024; 24:172. [PMID: 38509472 PMCID: PMC10956279 DOI: 10.1186/s12872-024-03808-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 02/21/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Although APOE ε4 allele carriage confers a risk for coronary artery disease, its persistence in humans might be explained by certain survival advantages (antagonistic pleiotropy). METHODS Combining data from ~ 37,000 persons from three older age British cohorts (1946 National Survey of Health and Development [NSHD], Southall and Brent Revised [SABRE], and UK Biobank) and one younger age cohort (Avon Longitudinal Study of Parents and Children [ALSPAC]), we explored whether APOE ε4 carriage associates with beneficial or unfavorable left ventricular (LV) structural and functional metrics by echocardiography and cardiovascular magnetic resonance (CMR). RESULTS Compared to the non-APOE ε4 group, APOE ε4 carriers had similar cardiac phenotypes in terms of LV ejection fraction, E/e', posterior wall and interventricular septal thickness, and LV mass. However, they had improved myocardial performance resulting in greater LV stroke volume generation per 1 mL of myocardium (higher myocardial contraction fraction). In NSHD (n = 1467) and SABRE (n = 1187), ε4 carriers had a 4% higher MCF (95% CI 1-7%, p = 0.016) using echocardiography. Using CMR data, in UK Biobank (n = 32,972), ε4 carriers had a 1% higher MCF 95% (CI 0-1%, p = 0.020) with a dose-response relationship based on the number of ε4 alleles. In addition, UK Biobank ε4 carriers also had more favorable radial and longitudinal strain rates compared to non APOE ε4 carriers. In ALSPAC (n = 1397), APOE ε4 carriers aged < 24 years had a 2% higher MCF (95% CI 0-5%, p = 0.059). CONCLUSIONS By triangulating results in four independent cohorts, across imaging modalities (echocardiography and CMR), and in ~ 37,000 individuals, our results point towards an association between ε4 carriage and improved cardiac performance in terms of LV MCF. This potentially favorable cardiac phenotype adds to the growing number of reported survival advantages attributed to the pleiotropic effects APOE ε4 carriage that might collectively explain its persistence in human populations.
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Affiliation(s)
- Constantin-Cristian Topriceanu
- UCL MRC Unit for Lifelong Health and Ageing, University College London, London, UK
- UCL Institute of Cardiovascular Science, University College London, London, UK
- Cardiac MRI Unit, Barts Heart Centre, London, UK
- Cardiology Department, Centre for Inherited Heart Muscle Conditions, The Royal Free Hospital, Pond Street, Hampstead, London, UK
| | - Mit Shah
- Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Science, Imperial College London, London, UK
| | - Matthew Webber
- UCL MRC Unit for Lifelong Health and Ageing, University College London, London, UK
- UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Fiona Chan
- UCL MRC Unit for Lifelong Health and Ageing, University College London, London, UK
- UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Hunain Shiwani
- UCL Institute of Cardiovascular Science, University College London, London, UK
- Cardiac MRI Unit, Barts Heart Centre, London, UK
| | - Marcus Richards
- UCL MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Jonathan Schott
- UCL MRC Unit for Lifelong Health and Ageing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Nishi Chaturvedi
- UCL MRC Unit for Lifelong Health and Ageing, University College London, London, UK
- UCL Institute of Cardiovascular Science, University College London, London, UK
| | - James C Moon
- UCL Institute of Cardiovascular Science, University College London, London, UK
- Cardiac MRI Unit, Barts Heart Centre, London, UK
| | - Alun D Hughes
- UCL MRC Unit for Lifelong Health and Ageing, University College London, London, UK
- UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Aroon D Hingorani
- UCL Institute of Cardiovascular Science, University College London, London, UK
- BHF Research Accelerator, University College London, London, UK
- Health Data Research, University College London, London, UK
| | - Declan P O'Regan
- Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Science, Imperial College London, London, UK
| | - Gabriella Captur
- UCL MRC Unit for Lifelong Health and Ageing, University College London, London, UK.
- UCL Institute of Cardiovascular Science, University College London, London, UK.
- Cardiac MRI Unit, Barts Heart Centre, London, UK.
- Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute, Imperial College London, London, UK.
- Cardiology Department, Centre for Inherited Heart Muscle Conditions, The Royal Free Hospital, Pond Street, Hampstead, London, UK.
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Kasapi M, Xu K, Ebbels TMD, O'Regan DP, Ware JS, Posma JM. LAVASET: Latent Variable Stochastic Ensemble of Trees. An ensemble method for correlated datasets with spatial, spectral, and temporal dependencies. Bioinformatics 2024; 40:btae101. [PMID: 38383048 DOI: 10.1093/bioinformatics/btae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/30/2024] [Accepted: 02/20/2024] [Indexed: 02/23/2024] Open
Abstract
MOTIVATION Random forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction. However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high-dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the dimension reduction most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step. RESULTS Without compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single 'important' noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the model's predictive ability. AVAILABILITY AND IMPLEMENTATION LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.
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Affiliation(s)
- Melpomeni Kasapi
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London W12 0NN, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, United Kingdom
| | - Kexin Xu
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
| | - Timothy M D Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
| | - Declan P O'Regan
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London W12 0NN, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, United Kingdom
| | - James S Ware
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London W12 0NN, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, United Kingdom
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London SW3 6NP, United Kingdom
- Program in Medical & Population Genetics, Broad Institute of MIT & Harvard, Cambridge, MA 02142, United States
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
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Jones RE, Hammersley DJ, Zheng S, McGurk KA, de Marvao A, Theotokis PI, Owen R, Tayal U, Rea G, Hatipoglu S, Buchan RJ, Mach L, Curran L, Lota AS, Simard F, Reddy RK, Talukder S, Yoon WY, Vazir A, Pennell DJ, O'Regan DP, Baksi AJ, Halliday BP, Ware JS, Prasad SK. Assessing the association between genetic and phenotypic features of dilated cardiomyopathy and outcome in patients with coronary artery disease. Eur J Heart Fail 2024; 26:46-55. [PMID: 37702310 DOI: 10.1002/ejhf.3033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 08/17/2023] [Accepted: 09/11/2023] [Indexed: 09/14/2023] Open
Abstract
AIMS To examine the relevance of genetic and cardiovascular magnetic resonance (CMR) features of dilated cardiomyopathy (DCM) in individuals with coronary artery disease (CAD). METHODS AND RESULTS This study includes two cohorts. First, individuals with CAD recruited into the UK Biobank (UKB) were evaluated. Second, patients with CAD referred to a tertiary centre for evaluation with late gadolinium enhancement (LGE)-CMR were recruited (London cohort); patients underwent genetic sequencing as part of the research protocol and long-term follow-up. From 31 154 individuals with CAD recruited to UKB, rare pathogenic variants in DCM genes were associated with increased risk of death or major adverse cardiac events (hazard ratio 1.57, 95% confidence interval [CI] 1.22-2.01, p < 0.001). Of 1619 individuals with CAD included from the UKB CMR substudy, participants with a rare variant in a DCM-associated gene had lower left ventricular ejection fraction (LVEF) compared to genotype negative individuals (mean 47 ± 10% vs. 57 ± 8%, p < 0.001). Of 453 patients in the London cohort, 63 (14%) had non-infarct pattern LGE (NI-LGE) on CMR. Patients with NI-LGE had lower LVEF (mean 38 ± 18% vs. 48 ± 16%, p < 0.001) compared to patients without NI-LGE, with no significant difference in the burden of rare protein altering variants in DCM-associated genes between groups (9.5% vs. 6.7%, odds ratio 1.5, 95% CI 0.4-4.3, p = 0.4). NI-LGE was not independently associated with adverse clinical outcomes. CONCLUSION Rare pathogenic variants in DCM-associated genes impact left ventricular remodelling and outcomes in stable CAD. NI-LGE is associated with adverse remodelling but is not an independent predictor of outcome and had no rare genetic basis in our study.
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Affiliation(s)
- Richard E Jones
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
- Anglia Ruskin University, Chelmsford, UK
- Essex Cardiothoracic Centre, Basildon, UK
| | - Daniel J Hammersley
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sean Zheng
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, 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
| | - Antonio de Marvao
- Department of Women and Children's Health, King's College London, London, UK
- British Heart Foundation Centre of Research Excellence, School of Cardiovascular Medicine and Sciences, King's College London, London, UK
| | - Pantazis I Theotokis
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Ruth Owen
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Upasana Tayal
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Gillian Rea
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Suzan Hatipoglu
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Rachel J Buchan
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Lukas Mach
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Lara Curran
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Amrit S Lota
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - François Simard
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Rohin K Reddy
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Suprateeka Talukder
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Won Young Yoon
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Ali Vazir
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Dudley J Pennell
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - A John Baksi
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Brian P Halliday
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Sanjay K Prasad
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
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Raman B, McCracken C, Cassar MP, Moss AJ, Finnigan L, Samat AHA, Ogbole G, Tunnicliffe EM, Alfaro-Almagro F, Menke R, Xie C, Gleeson F, Lukaschuk E, Lamlum H, McGlynn K, Popescu IA, Sanders ZB, Saunders LC, Piechnik SK, Ferreira VM, Nikolaidou C, Rahman NM, Ho LP, Harris VC, Shikotra A, Singapuri A, Pfeffer P, Manisty C, Kon OM, Beggs M, O'Regan DP, Fuld J, Weir-McCall JR, Parekh D, Steeds R, Poinasamy K, Cuthbertson DJ, Kemp GJ, Semple MG, Horsley A, Miller CA, O'Brien C, Shah AM, Chiribiri A, Leavy OC, Richardson M, Elneima O, McAuley HJC, Sereno M, Saunders RM, Houchen-Wolloff L, Greening NJ, Bolton CE, Brown JS, Choudhury G, Diar Bakerly N, Easom N, Echevarria C, Marks M, Hurst JR, Jones MG, Wootton DG, Chalder T, Davies MJ, De Soyza A, Geddes JR, Greenhalf W, Howard LS, Jacob J, Man WDC, Openshaw PJM, Porter JC, Rowland MJ, Scott JT, Singh SJ, Thomas DC, Toshner M, Lewis KE, Heaney LG, Harrison EM, Kerr S, Docherty AB, Lone NI, Quint J, Sheikh A, Zheng B, Jenkins RG, Cox E, Francis S, Halling-Brown M, Chalmers JD, Greenwood JP, Plein S, Hughes PJC, Thompson AAR, Rowland-Jones SL, Wild JM, Kelly M, Treibel TA, Bandula S, Aul R, Miller K, Jezzard P, Smith S, Nichols TE, McCann GP, Evans RA, Wain LV, Brightling CE, Neubauer S, Baillie JK, Shaw A, Hairsine B, Kurasz C, Henson H, Armstrong L, Shenton L, Dobson H, Dell A, Lucey A, Price A, Storrie A, Pennington C, Price C, Mallison G, Willis G, Nassa H, Haworth J, Hoare M, Hawkings N, Fairbairn S, Young S, Walker S, Jarrold I, Sanderson A, David C, Chong-James K, Zongo O, James WY, Martineau A, King B, Armour C, McAulay D, Major E, McGinness J, McGarvey L, Magee N, Stone R, Drain S, Craig T, Bolger A, Haggar A, Lloyd A, Subbe C, Menzies D, Southern D, McIvor E, Roberts K, Manley R, Whitehead V, Saxon W, Bularga A, Mills NL, El-Taweel H, Dawson J, Robinson L, Saralaya D, Regan K, Storton K, Brear L, Amoils S, Bermperi A, Elmer A, Ribeiro C, Cruz I, Taylor J, Worsley J, Dempsey K, Watson L, Jose S, Marciniak S, Parkes M, McQueen A, Oliver C, Williams J, Paradowski K, Broad L, Knibbs L, Haynes M, Sabit R, Milligan L, Sampson C, Hancock A, Evenden C, Lynch C, Hancock K, Roche L, Rees M, Stroud N, Thomas-Woods T, Heller S, Robertson E, Young B, Wassall H, Babores M, Holland M, Keenan N, Shashaa S, Price C, Beranova E, Ramos H, Weston H, Deery J, Austin L, Solly R, Turney S, Cosier T, Hazelton T, Ralser M, Wilson A, Pearce L, Pugmire S, Stoker W, McCormick W, Dewar A, Arbane G, Kaltsakas G, Kerslake H, Rossdale J, Bisnauthsing K, Aguilar Jimenez LA, Martinez LM, Ostermann M, Magtoto MM, Hart N, Marino P, Betts S, Solano TS, Arias AM, Prabhu A, Reed A, Wrey Brown C, Griffin D, Bevan E, Martin J, Owen J, Alvarez Corral M, Williams N, Payne S, Storrar W, Layton A, Lawson C, Mills C, Featherstone J, Stephenson L, Burdett T, Ellis Y, Richards A, Wright C, Sykes DL, Brindle K, Drury K, Holdsworth L, Crooks MG, Atkin P, Flockton R, Thackray-Nocera S, Mohamed A, Taylor A, Perkins E, Ross G, McGuinness H, Tench H, Phipps J, Loosley R, Wolf-Roberts R, Coetzee S, Omar Z, Ross A, Card B, Carr C, King C, Wood C, Copeland D, Calvelo E, Chilvers ER, Russell E, Gordon H, Nunag JL, Schronce J, March K, Samuel K, Burden L, Evison L, McLeavey L, Orriss-Dib L, Tarusan L, Mariveles M, Roy M, Mohamed N, Simpson N, Yasmin N, Cullinan P, Daly P, Haq S, Moriera S, Fayzan T, Munawar U, Nwanguma U, Lingford-Hughes A, Altmann D, Johnston D, Mitchell J, Valabhji J, Price L, Molyneaux PL, Thwaites RS, Walsh S, Frankel A, Lightstone L, Wilkins M, Willicombe M, McAdoo S, Touyz R, Guerdette AM, Warwick K, Hewitt M, Reddy R, White S, McMahon A, Hoare A, Knighton A, Ramos A, Te A, Jolley CJ, Speranza F, Assefa-Kebede H, Peralta I, Breeze J, Shevket K, Powell N, Adeyemi O, Dulawan P, Adrego R, Byrne S, Patale S, Hayday A, Malim M, Pariante C, Sharpe C, Whitney J, Bramham K, Ismail K, Wessely S, Nicholson T, Ashworth A, Humphries A, Tan AL, Whittam B, Coupland C, Favager C, Peckham D, Wade E, Saalmink G, Clarke J, Glossop J, Murira J, Rangeley J, Woods J, Hall L, Dalton M, Window N, Beirne P, Hardy T, Coakley G, Turtle L, Berridge A, Cross A, Key AL, Rowe A, Allt AM, Mears C, Malein F, Madzamba G, Hardwick HE, Earley J, Hawkes J, Pratt J, Wyles J, Tripp KA, Hainey K, Allerton L, Lavelle-Langham L, Melling L, Wajero LO, Poll L, Noonan MJ, French N, Lewis-Burke N, Williams-Howard SA, Cooper S, Kaprowska S, Dobson SL, Marsh S, Highett V, Shaw V, Beadsworth M, Defres S, Watson E, Tiongson GF, Papineni P, Gurram S, Diwanji SN, Quaid S, Briggs A, Hastie C, Rogers N, Stensel D, Bishop L, McIvor K, Rivera-Ortega P, Al-Sheklly B, Avram C, Faluyi D, Blaikely J, Piper Hanley K, Radhakrishnan K, Buch M, Hanley NA, Odell N, Osbourne R, Stockdale S, Felton T, Gorsuch T, Hussell T, Kausar Z, Kabir T, McAllister-Williams H, Paddick S, Burn D, Ayoub A, Greenhalgh A, Sayer A, Young A, Price D, Burns G, MacGowan G, Fisher H, Tedd H, Simpson J, Jiwa K, Witham M, Hogarth P, West S, Wright S, McMahon MJ, Neill P, Dougherty A, Morrow A, Anderson D, Grieve D, Bayes H, Fallon K, Mangion K, Gilmour L, Basu N, Sykes R, Berry C, McInnes IB, Donaldson A, Sage EK, Barrett F, Welsh B, Bell M, Quigley J, Leitch K, Macliver L, Patel M, Hamil R, Deans A, Furniss J, Clohisey S, Elliott A, Solstice AR, Deas C, Tee C, Connell D, Sutherland D, George J, Mohammed S, Bunker J, Holmes K, Dipper A, Morley A, Arnold D, Adamali H, Welch H, Morrison L, Stadon L, Maskell N, Barratt S, Dunn S, Waterson S, Jayaraman B, Light T, Selby N, Hosseini A, Shaw K, Almeida P, Needham R, Thomas AK, Matthews L, Gupta A, Nikolaidis A, Dupont C, Bonnington J, Chrystal M, Greenhaff PL, Linford S, Prosper S, Jang W, Alamoudi A, Bloss A, Megson C, Nicoll D, Fraser E, Pacpaco E, Conneh F, Ogg G, McShane H, Koychev I, Chen J, Pimm J, Ainsworth M, Pavlides M, Sharpe M, Havinden-Williams M, Petousi N, Talbot N, Carter P, Kurupati P, Dong T, Peng Y, Burns A, Kanellakis N, Korszun A, Connolly B, Busby J, Peto T, Patel B, Nolan CM, Cristiano D, Walsh JA, Liyanage K, Gummadi M, Dormand N, Polgar O, George P, Barker RE, Patel S, Price L, Gibbons M, Matila D, Jarvis H, Lim L, Olaosebikan O, Ahmad S, Brill S, Mandal S, Laing C, Michael A, Reddy A, Johnson C, Baxendale H, Parfrey H, Mackie J, Newman J, Pack J, Parmar J, Paques K, Garner L, Harvey A, Summersgill C, Holgate D, Hardy E, Oxton J, Pendlebury J, McMorrow L, Mairs N, Majeed N, Dark P, Ugwuoke R, Knight S, Whittaker S, Strong-Sheldrake S, Matimba-Mupaya W, Chowienczyk P, Pattenadk D, Hurditch E, Chan F, Carborn H, Foot H, Bagshaw J, Hockridge J, Sidebottom J, Lee JH, Birchall K, Turner K, Haslam L, Holt L, Milner L, Begum M, Marshall M, Steele N, Tinker N, Ravencroft P, Butcher R, Misra S, Walker S, Coburn Z, Fairman A, Ford A, Holbourn A, Howell A, Lawrie A, Lye A, Mbuyisa A, Zawia A, Holroyd-Hind B, Thamu B, Clark C, Jarman C, Norman C, Roddis C, Foote D, Lee E, Ilyas F, Stephens G, Newell H, Turton H, Macharia I, Wilson I, Cole J, McNeill J, Meiring J, Rodger J, Watson J, Chapman K, Harrington K, Chetham L, Hesselden L, Nwafor L, Dixon M, Plowright M, Wade P, Gregory R, Lenagh R, Stimpson R, Megson S, Newman T, Cheng Y, Goodwin C, Heeley C, Sissons D, Sowter D, Gregory H, Wynter I, Hutchinson J, Kirk J, Bennett K, Slack K, Allsop L, Holloway L, Flynn M, Gill M, Greatorex M, Holmes M, Buckley P, Shelton S, Turner S, Sewell TA, Whitworth V, Lovegrove W, Tomlinson J, Warburton L, Painter S, Vickers C, Redwood D, Tilley J, Palmer S, Wainwright T, Breen G, Hotopf M, Dunleavy A, Teixeira J, Ali M, Mencias M, Msimanga N, Siddique S, Samakomva T, Tavoukjian V, Forton D, Ahmed R, Cook A, Thaivalappil F, Connor L, Rees T, McNarry M, Williams N, McCormick J, McIntosh J, Vere J, Coulding M, Kilroy S, Turner V, Butt AT, Savill H, Fraile E, Ugoji J, Landers G, Lota H, Portukhay S, Nasseri M, Daniels A, Hormis A, Ingham J, Zeidan L, Osborne L, Chablani M, Banerjee A, David A, Pakzad A, Rangelov B, Williams B, Denneny E, Willoughby J, Xu M, Mehta P, Batterham R, Bell R, Aslani S, Lilaonitkul W, Checkley A, Bang D, Basire D, Lomas D, Wall E, Plant H, Roy K, Heightman M, Lipman M, Merida Morillas M, Ahwireng N, Chambers RC, Jastrub R, Logan S, Hillman T, Botkai A, Casey A, Neal A, Newton-Cox A, Cooper B, Atkin C, McGee C, Welch C, Wilson D, Sapey E, Qureshi H, Hazeldine J, Lord JM, Nyaboko J, Short J, Stockley J, Dasgin J, Draxlbauer K, Isaacs K, Mcgee K, Yip KP, Ratcliffe L, Bates M, Ventura M, Ahmad Haider N, Gautam N, Baggott R, Holden S, Madathil S, Walder S, Yasmin S, Hiwot T, Jackson T, Soulsby T, Kamwa V, Peterkin Z, Suleiman Z, Chaudhuri N, Wheeler H, Djukanovic R, Samuel R, Sass T, Wallis T, Marshall B, Childs C, Marouzet E, Harvey M, Fletcher S, Dickens C, Beckett P, Nanda U, Daynes E, Charalambou A, Yousuf AJ, Lea A, Prickett A, Gooptu B, Hargadon B, Bourne C, Christie C, Edwardson C, Lee D, Baldry E, Stringer E, Woodhead F, Mills G, Arnold H, Aung H, Qureshi IN, Finch J, Skeemer J, Hadley K, Khunti K, Carr L, Ingram L, Aljaroof M, Bakali M, Bakau M, Baldwin M, Bourne M, Pareek M, Soares M, Tobin M, Armstrong N, Brunskill N, Goodman N, Cairns P, Haldar P, McCourt P, Dowling R, Russell R, Diver S, Edwards S, Glover S, Parker S, Siddiqui S, Ward TJC, Mcnally T, Thornton T, Yates T, Ibrahim W, Monteiro W, Thickett D, Wilkinson D, Broome M, McArdle P, Upthegrove R, Wraith D, Langenberg C, Summers C, Bullmore E, Heeney JL, Schwaeble W, Sudlow CL, Adeloye D, Newby DE, Rudan I, Shankar-Hari M, Thorpe M, Pius R, Walmsley S, McGovern A, Ballard C, Allan L, Dennis J, Cavanagh J, Petrie J, O'Donnell K, Spears M, Sattar N, MacDonald S, Guthrie E, Henderson M, Guillen Guio B, Zhao B, Lawson C, Overton C, Taylor C, Tong C, Mukaetova-Ladinska E, Turner E, Pearl JE, Sargant J, Wormleighton J, Bingham M, Sharma M, Steiner M, Samani N, Novotny P, Free R, Allen RJ, Finney S, Terry S, Brugha T, Plekhanova T, McArdle A, Vinson B, Spencer LG, Reynolds W, Ashworth M, Deakin B, Chinoy H, Abel K, Harvie M, Stanel S, Rostron A, Coleman C, Baguley D, Hufton E, Khan F, Hall I, Stewart I, Fabbri L, Wright L, Kitterick P, Morriss R, Johnson S, Bates A, Antoniades C, Clark D, Bhui K, Channon KM, Motohashi K, Sigfrid L, Husain M, Webster M, Fu X, Li X, Kingham L, Klenerman P, Miiler K, Carson G, Simons G, Huneke N, Calder PC, Baldwin D, Bain S, Lasserson D, Daines L, Bright E, Stern M, Crisp P, Dharmagunawardena R, Reddington A, Wight A, Bailey L, Ashish A, Robinson E, Cooper J, Broadley A, Turnbull A, Brookes C, Sarginson C, Ionita D, Redfearn H, Elliott K, Barman L, Griffiths L, Guy Z, Gill R, Nathu R, Harris E, Moss P, Finnigan J, Saunders K, Saunders P, Kon S, Kon SS, O'Brien L, Shah K, Shah P, Richardson E, Brown V, Brown M, Brown J, Brown J, Brown A, Brown A, Brown M, Choudhury N, Jones S, Jones H, Jones L, Jones I, Jones G, Jones H, Jones D, Davies F, Davies E, Davies K, Davies G, Davies GA, Howard K, Porter J, Rowland J, Rowland A, Scott K, Singh S, Singh C, Thomas S, Thomas C, Lewis V, Lewis J, Lewis D, Harrison P, Francis C, Francis R, Hughes RA, Hughes J, Hughes AD, Thompson T, Kelly S, Smith D, Smith N, Smith A, Smith J, Smith L, Smith S, Evans T, Evans RI, Evans D, Evans R, Evans H, Evans J. Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study. Lancet Respir Med 2023; 11:1003-1019. [PMID: 37748493 PMCID: PMC7615263 DOI: 10.1016/s2213-2600(23)00262-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 06/16/2023] [Accepted: 06/30/2023] [Indexed: 09/27/2023]
Abstract
INTRODUCTION The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. METHODS In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. FINDINGS Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2-6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5-5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4-10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32-4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23-11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. INTERPRETATION After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification. FUNDING UK Research and Innovation and National Institute for Health Research.
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McGurk KA, Zhang X, Theotokis P, Thomson K, Harper A, Buchan RJ, Mazaika E, Ormondroyd E, Wright WT, Macaya D, Pua CJ, Funke B, MacArthur DG, Prasad SK, Cook SA, Allouba M, Aguib Y, Yacoub MH, O'Regan DP, Barton PJR, Watkins H, Bottolo L, Ware JS. The penetrance of rare variants in cardiomyopathy-associated genes: A cross-sectional approach to estimating penetrance for secondary findings. Am J Hum Genet 2023; 110:1482-1495. [PMID: 37652022 PMCID: PMC10502871 DOI: 10.1016/j.ajhg.2023.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/21/2023] [Accepted: 08/04/2023] [Indexed: 09/02/2023] Open
Abstract
Understanding the penetrance of pathogenic variants identified as secondary findings (SFs) is of paramount importance with the growing availability of genetic testing. We estimated penetrance through large-scale analyses of individuals referred for diagnostic sequencing for hypertrophic cardiomyopathy (HCM; 10,400 affected individuals, 1,332 variants) and dilated cardiomyopathy (DCM; 2,564 affected individuals, 663 variants), using a cross-sectional approach comparing allele frequencies against reference populations (293,226 participants from UK Biobank and gnomAD). We generated updated prevalence estimates for HCM (1:543) and DCM (1:220). In aggregate, the penetrance by late adulthood of rare, pathogenic variants (23% for HCM, 35% for DCM) and likely pathogenic variants (7% for HCM, 10% for DCM) was substantial for dominant cardiomyopathy (CM). Penetrance was significantly higher for variant subgroups annotated as loss of function or ultra-rare and for males compared to females for variants in HCM-associated genes. We estimated variant-specific penetrance for 316 recurrent variants most likely to be identified as SFs (found in 51% of HCM- and 17% of DCM-affected individuals). 49 variants were observed at least ten times (14% of affected individuals) in HCM-associated genes. Median penetrance was 14.6% (±14.4% SD). We explore estimates of penetrance by age, sex, and ancestry and simulate the impact of including future cohorts. This dataset reports penetrance of individual variants at scale and will inform the management of individuals undergoing genetic screening for SFs. While most variants had low penetrance and the costs and harms of screening are unclear, some individuals with highly penetrant variants may benefit from SFs.
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Affiliation(s)
- Kathryn A McGurk
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Xiaolei Zhang
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Pantazis Theotokis
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Kate Thomson
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Andrew Harper
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rachel J Buchan
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Erica Mazaika
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Elizabeth Ormondroyd
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - William T Wright
- Northern Ireland Regional Genetics Centre, Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, Northern Ireland, UK
| | | | - Chee Jian Pua
- National Heart Research Institute Singapore and Duke-National University of Singapore, Singapore, Singapore
| | - Birgit Funke
- Laboratory for Molecular Medicine, Partners Healthcare Center for Personalized Genetic Medicine, Boston, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Sanjay K Prasad
- National Heart and Lung Institute, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Stuart A Cook
- MRC London Institute of Medical Sciences, Imperial College London, London, UK; National Heart Research Institute Singapore and Duke-National University of Singapore, Singapore, Singapore
| | - Mona Allouba
- National Heart and Lung Institute, Imperial College London, London, UK; Aswan Heart Centre, Aswan, Egypt
| | - Yasmine Aguib
- National Heart and Lung Institute, Imperial College London, London, UK; Aswan Heart Centre, Aswan, Egypt
| | - Magdi H Yacoub
- National Heart and Lung Institute, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK; Aswan Heart Centre, Aswan, Egypt
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Paul J R Barton
- National Heart and Lung Institute, Imperial College London, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, UK; The Alan Turing Institute, London, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, 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 & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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] [What about the content of this article? (0)] [Affiliation(s)] [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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Saitta S, Maga L, Armour C, Votta E, O'Regan DP, Salmasi MY, Athanasiou T, Weinsaft JW, Xu XY, Pirola S, Redaelli A. Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta. Comput Methods Programs Biomed 2023; 233:107468. [PMID: 36921465 DOI: 10.1016/j.cmpb.2023.107468] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/15/2023] [Accepted: 03/05/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. METHODS Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. RESULTS Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. CONCLUSIONS We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.
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Affiliation(s)
- Simone Saitta
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Ludovica Maga
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Chemical Engineering, Imperial College London, London, UK
| | - Chloe Armour
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Emiliano Votta
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - M Yousuf Salmasi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Jonathan W Weinsaft
- Department of Medicine (Cardiology), Weill Cornell College, New York, NY, USA
| | - Xiao Yun Xu
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Selene Pirola
- Department of Chemical Engineering, Imperial College London, London, UK; Department of BioMechanical Engineering, 3mE Faculty, Delft University of Technology, Delft, Netherlands.
| | - Alberto Redaelli
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
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Hall M, de Marvao A, Schweitzer R, Cromb D, Colford K, Jandu P, O'Regan DP, Ho A, Price A, Chappell LC, Rutherford MA, Story L, Lamata P, Hutter J. Characterisation of placental, fetal brain and maternal cardiac structure and function in pre-eclampsia using MRI. medRxiv 2023:2023.04.24.23289069. [PMID: 37163073 PMCID: PMC10168502 DOI: 10.1101/2023.04.24.23289069] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Pre-eclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multi-system approach to MRI investigation of pre-eclampsia, with acquisition of maternal cardiac, placental, and fetal brain anatomical and functional imaging. Methods A prospective study was carried out recruiting pregnant women with pre-eclampsia, chronic hypertension, or no medical complications, and a non-pregnant female cohort. All women underwent a cardiac MRI, and pregnant women underwent a fetal-placental MRI. Cardiac analysis for structural, morphological and flow data was undertaken; placenta and fetal brain volumetric and T2* data were obtained. All results were corrected for gestational age. Results Seventy-eight MRIs were obtained during pregnancy. Pregnancies affected by pre-eclampsia demonstrated lower placental and fetal brain T2*. Within the pre-eclampsia group, three placental T2* results were within the normal range, these were the only cases with normal placental histopathology. Similarly, three fetal brain T2* results were within the normal range; these cases had no evidence of cerebral redistribution on fetal Dopplers. Cardiac MRI analysis demonstrated higher left ventricular mass in pre-eclampsia with 3D modelling revealing additional specific characteristics of eccentricity and outflow track remodelling. Conclusions We present the first holistic assessment of the immediate implications of pre-eclampsia on the placenta, maternal heart, and fetal brain. As well as having potential clinical implications for the risk-stratification and management of women with pre-eclampsia, this gives an insight into disease mechanism.
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Affiliation(s)
- Megan Hall
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Antonio de Marvao
- Department of Women and Children’s Health, King’s College London, UK
- School of Cardiovascular Medicine, King’s College London, UK
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Ronny Schweitzer
- School of Cardiovascular Medicine, King’s College London, UK
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Daniel Cromb
- Centre for the Developing Brain, King’s College London, UK
| | | | - Priya Jandu
- GKT School of Medical Education, King’s College London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Alison Ho
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Anthony Price
- Centre for the Developing Brain, King’s College London, UK
- Centre for Medical Engineering, King’s College London, UK
| | - Lucy C. Chappell
- Department of Women and Children’s Health, King’s College London, UK
| | | | - Lisa Story
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Pablo Lamata
- Centre for Medical Engineering, King’s College London, UK
| | - Jana Hutter
- Centre for the Developing Brain, King’s College London, UK
- Centre for Medical Engineering, King’s College London, UK
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Tadros R, Zheng SL, Grace C, Jordà P, Francis C, Jurgens SJ, Thomson KL, Harper AR, Ormondroyd E, West DM, Xu X, Theotokis PI, Buchan RJ, McGurk KA, Mazzarotto F, Boschi B, Pelo E, Lee M, Noseda M, Varnava A, Vermeer AM, Walsh R, Amin AS, van Slegtenhorst MA, Roslin N, Strug LJ, Salvi E, Lanzani C, de Marvao A, Roberts JD, Tremblay-Gravel M, Giraldeau G, Cadrin-Tourigny J, L'Allier PL, Garceau P, Talajic M, Pinto YM, Rakowski H, Pantazis A, Baksi J, Halliday BP, Prasad SK, Barton PJ, O'Regan DP, Cook SA, de Boer RA, Christiaans I, Michels M, Kramer CM, Ho CY, Neubauer S, Matthews PM, Wilde AA, Tardif JC, Olivotto I, Adler A, Goel A, Ware JS, Bezzina CR, Watkins H. Large scale genome-wide association analyses identify novel genetic loci and mechanisms in hypertrophic cardiomyopathy. medRxiv 2023:2023.01.28.23285147. [PMID: 36778260 PMCID: PMC9915807 DOI: 10.1101/2023.01.28.23285147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality with both monogenic and polygenic components. We here report results from the largest HCM genome-wide association study (GWAS) and multi-trait analysis (MTAG) including 5,900 HCM cases, 68,359 controls, and 36,083 UK Biobank (UKB) participants with cardiac magnetic resonance (CMR) imaging. We identified a total of 70 loci (50 novel) associated with HCM, and 62 loci (32 novel) associated with relevant left ventricular (LV) structural or functional traits. Amongst the common variant HCM loci, we identify a novel HCM disease gene, SVIL, which encodes the actin-binding protein supervillin, showing that rare truncating SVIL variants cause HCM. Mendelian randomization analyses support a causal role of increased LV contractility in both obstructive and non-obstructive forms of HCM, suggesting common disease mechanisms and anticipating shared response to therapy. Taken together, the findings significantly increase our understanding of the genetic basis and molecular mechanisms of HCM, with potential implications for disease management.
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Affiliation(s)
- Rafik Tadros
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Sean L Zheng
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Christopher Grace
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Paloma Jordà
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Catherine Francis
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Sean J Jurgens
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kate L Thomson
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Oxford Genetics Laboratories, Churchill Hospital, Oxford, UK
| | - Andrew R Harper
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Elizabeth Ormondroyd
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Dominique M West
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Xiao Xu
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Pantazis I Theotokis
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Rachel J Buchan
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Kathryn A McGurk
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Francesco Mazzarotto
- National Heart & Lung Institute, Imperial College London, London, UK
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | | | | | - Michael Lee
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Michela Noseda
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Amanda Varnava
- National Heart & Lung Institute, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Alexa Mc Vermeer
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Clinical Genetics, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
| | - Roddy Walsh
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ahmad S Amin
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marjon A van Slegtenhorst
- Department of Clinical Genetics, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Nicole Roslin
- The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Lisa J Strug
- Departments of Statistical Sciences and Computer Science, Data Sciences Institute, University of Toronto, Toronto, ON, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
- Ontario Regional Centre, Canadian Statistical Sciences Institute, University of Toronto, Toronto, ON, Canada
| | - Erika Salvi
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Chiara Lanzani
- Genomics of Renal Diseases and Hypertension Unit, Nephrology Operative Unit, IRCCS San Raffaele Hospital, Milan, Italy
- Chair of Nephrology, Vita-Salute San Raffaele University, Milan, Italy
| | - Antonio de Marvao
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Jason D Roberts
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, ON, Canada
| | - Maxime Tremblay-Gravel
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Genevieve Giraldeau
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Julia Cadrin-Tourigny
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Philippe L L'Allier
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Patrick Garceau
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Mario Talajic
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Yigal M Pinto
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Antonis Pantazis
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - John Baksi
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Brian P Halliday
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Sanjay K Prasad
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Paul Jr Barton
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Stuart A Cook
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- National Heart Centre Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore
| | - Rudolf A de Boer
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Imke Christiaans
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Michelle Michels
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Christopher M Kramer
- Department of Medicine, Cardiovascular Division, University of Virginia Health, Charlottesville, VA, USA
| | - Carolyn Y Ho
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, NIHR Oxford Health Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Paul M Matthews
- Department of Brain Sciences and UK Dementia Research Institute, Imperial College London, London, UK
| | - Arthur A Wilde
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- ECGen, Cardiogenetics Focus Group of EHRA, France
| | - Jean-Claude Tardif
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Iacopo Olivotto
- Department of Experimental and Clinical Medicine, Meyer Children Hospital, University of Florence, Florence, Italy
| | - Arnon Adler
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Anuj Goel
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - James S Ware
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
- Program in Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Connie R Bezzina
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
| | - Hugh Watkins
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
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Pillinger T, Osimo EF, de Marvao A, Shah M, Francis C, Huang J, D'Ambrosio E, Firth J, Nour MM, McCutcheon RA, Pardiñas AF, Matthews PM, O'Regan DP, Howes OD. Effect of polygenic risk for schizophrenia on cardiac structure and function: a UK Biobank observational study. Lancet Psychiatry 2023; 10:98-107. [PMID: 36632818 DOI: 10.1016/s2215-0366(22)00403-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/03/2022] [Accepted: 11/18/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Cardiovascular disease is a major cause of excess mortality in people with schizophrenia. Several factors are responsible, including lifestyle and metabolic effects of antipsychotics. However, variations in cardiac structure and function are seen in people with schizophrenia in the absence of cardiovascular disease risk factors and after accounting for lifestyle and medication. Therefore, we aimed to explore whether shared genetic causes contribute to these cardiac variations. METHODS For this observational study, we used data from the UK Biobank and included White British or Irish individuals without diagnosed schizophrenia with variable polygenic risk scores for the condition. To test the association between polygenic risk score for schizophrenia and cardiac phenotype, we used principal component analysis and regression. Robust regression was then used to explore the association between the polygenic risk score for schizophrenia and individual cardiac phenotypes. We repeated analyses with fibro-inflammatory pathway-specific polygenic risk scores for schizophrenia. Last, we investigated genome-wide sharing of common variants between schizophrenia and cardiac phenotypes using linkage disequilibrium score regression. The primary outcome was principal component regression. FINDINGS Of 33 353 individuals recruited, 32 279 participants had complete cardiac MRI data and were included in the analysis, of whom 16 625 (51·5%) were female and 15 654 (48·5%) were male. 1074 participants were excluded on the basis of incomplete cardiac MRI data (for all phenotypes). A model regressing polygenic risk scores for schizophrenia onto the first five cardiac principal components of the principal components analysis was significant (F=5·09; p=0·00012). Principal component 1 captured a pattern of increased cardiac volumes, increased absolute peak diastolic strain rates, and reduced ejection fractions; polygenic risk scores for schizophrenia and principal component 1 were negatively associated (β=-0·01 [SE 0·003]; p=0·017). Similar to the principal component analysis results, for individual cardiac phenotypes, we observed negative associations between polygenic risk scores for schizophrenia and indexed right ventricular end-systolic volume (β=-0·14 [0·04]; p=0·0013, pFDR=0·015), indexed right ventricular end-diastolic volume (β=-0·17 [0·08]); p=0·025; pFDR=0·082), and absolute longitudinal peak diastolic strain rates (β=-0·01 [0·003]; p=0·0024, pFDR=0·015), and a positive association between polygenic risk scores for schizophrenia and right ventricular ejection fraction (β=0·09 [0·03]; p=0·0041, pFDR=0·015). Models examining the transforming growth factor-β (TGF-β)-specific and acute inflammation-specific polygenic risk scores for schizophrenia found significant associations with the first five principal components (F=2·62, p=0·022; F=2·54, p=0·026). Using linkage disequilibrium score regression, we observed genetic overlap with schizophrenia for right ventricular end-systolic volume and right ventricular ejection fraction (p=0·0090, p=0·0077). INTERPRETATION High polygenic risk scores for schizophrenia are associated with decreased cardiac volumes, increased ejection fractions, and decreased absolute peak diastolic strain rates. TGF-β and inflammatory pathways might be implicated, and there is evidence of genetic overlap for some cardiac phenotypes. Reduced absolute peak diastolic strain rates indicate increased myocardial stiffness and diastolic dysfunction, which increases risk of cardiac disease. Thus, genetic risk for schizophrenia is associated with cardiac structural changes that can worsen cardiac outcomes. Further work is required to determine whether these associations are specific to schizophrenia or are also seen in other psychiatric conditions. FUNDING National Institute for Health Research, Maudsley Charity, Wellcome Trust, Medical Research Council, Academy of Medical Sciences, Edmond J Safra Foundation, British Heart Foundation.
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Affiliation(s)
- Toby Pillinger
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK; Psychiatric Imaging Group, Imperial College London, London, UK.
| | - Emanuele F Osimo
- Department of Psychiatry, University of Cambridge, Cambridge, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK; Psychiatric Imaging Group, Imperial College London, London, UK
| | - Antonio de Marvao
- British Heart Foundation Centre of Research Excellence, School of Cardiovascular Medicine and Sciences, King's College London, London, UK; Department of Women and Children's Health, King's College London, London, UK
| | - Mit Shah
- Computational Cardiac Imaging Group, Imperial College London, London, UK
| | - Catherine Francis
- MRC London Institute of Medical Sciences, Department of Cardiovascular Genetics and Genomics, National Heart and Lung Institute, Imperial College London, London, UK; Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, Uxbridge, UK
| | - Jian Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK; Singapore Institute for Clinical Sciences (SICS), the Agency for Science, Technology and Research (A*STAR), Singapore
| | - Enrico D'Ambrosio
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK; Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari 'Aldo Moro', Italy
| | - Joseph Firth
- Division of Psychology and Mental Health, University of Manchester, and Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Matthew M Nour
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, and Wellcome Trust Centre for Human Neuroimaging, University College London, London, UK; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Robert A McCutcheon
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Paul M Matthews
- Department of Brain Sciences and UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Declan P O'Regan
- Computational Cardiac Imaging Group, Imperial College London, London, UK
| | - Oliver D Howes
- Department of Psychological Medicine, King's College London, London, UK; Psychiatric Imaging Group, Imperial College London, London, UK; H Lundbeck A/S, St Albans, UK
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13
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Siguero-Álvarez M, Salguero-Jiménez A, Grego-Bessa J, de la Barrera J, MacGrogan D, Prados B, Sánchez-Sáez F, Piñeiro-Sabarís R, Felipe-Medina N, Torroja C, Gómez MJ, Sabater-Molina M, Escribá R, Richaud-Patin I, Iglesias-García O, Sbroggio M, Callejas S, O'Regan DP, McGurk KA, Dopazo A, Giovinazzo G, Ibañez B, Monserrat L, Pérez-Pomares JM, Sánchez-Cabo F, Pendas AM, Raya A, Gimeno-Blanes JR, de la Pompa JL. A Human Hereditary Cardiomyopathy Shares a Genetic Substrate With Bicuspid Aortic Valve. Circulation 2023; 147:47-65. [PMID: 36325906 DOI: 10.1161/circulationaha.121.058767] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The complex genetics underlying human cardiac disease is evidenced by its heterogenous manifestation, multigenic basis, and sporadic occurrence. These features have hampered disease modeling and mechanistic understanding. Here, we show that 2 structural cardiac diseases, left ventricular noncompaction (LVNC) and bicuspid aortic valve, can be caused by a set of inherited heterozygous gene mutations affecting the NOTCH ligand regulator MIB1 (MINDBOMB1) and cosegregating genes. METHODS We used CRISPR-Cas9 gene editing to generate mice harboring a nonsense or a missense MIB1 mutation that are both found in LVNC families. We also generated mice separately carrying these MIB1 mutations plus 5 additional cosegregating variants in the ASXL3, APCDD1, TMX3, CEP192, and BCL7A genes identified in these LVNC families by whole exome sequencing. Histological, developmental, and functional analyses of these mouse models were carried out by echocardiography and cardiac magnetic resonance imaging, together with gene expression profiling by RNA sequencing of both selected engineered mouse models and human induced pluripotent stem cell-derived cardiomyocytes. Potential biochemical interactions were assayed in vitro by coimmunoprecipitation and Western blot. RESULTS Mice homozygous for the MIB1 nonsense mutation did not survive, and the mutation caused LVNC only in heteroallelic combination with a conditional allele inactivated in the myocardium. The heterozygous MIB1 missense allele leads to bicuspid aortic valve in a NOTCH-sensitized genetic background. These data suggest that development of LVNC is influenced by genetic modifiers present in affected families, whereas valve defects are highly sensitive to NOTCH haploinsufficiency. Whole exome sequencing of LVNC families revealed single-nucleotide gene variants of ASXL3, APCDD1, TMX3, CEP192, and BCL7A cosegregating with the MIB1 mutations and LVNC. In experiments with mice harboring the orthologous variants on the corresponding Mib1 backgrounds, triple heterozygous Mib1 Apcdd1 Asxl3 mice showed LVNC, whereas quadruple heterozygous Mib1 Cep192 Tmx3;Bcl7a mice developed bicuspid aortic valve and other valve-associated defects. Biochemical analysis suggested interactions between CEP192, BCL7A, and NOTCH. Gene expression profiling of mutant mouse hearts and human induced pluripotent stem cell-derived cardiomyocytes revealed increased cardiomyocyte proliferation and defective morphological and metabolic maturation. CONCLUSIONS These findings reveal a shared genetic substrate underlying LVNC and bicuspid aortic valve in which MIB1-NOTCH variants plays a crucial role in heterozygous combination with cosegregating genetic modifiers.
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Affiliation(s)
- Marcos Siguero-Álvarez
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
- Center for Chromosome Stability and Institut for Cellulær og Molekylær Medicin, University of Copenhagen, Denmark (M.S.)
| | - Alejandro Salguero-Jiménez
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
| | - Joaquim Grego-Bessa
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
| | - Jorge de la Barrera
- Bioinformatics Unit (J.d.l.B., C.T., M.J.G., F.S.-C.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Donal MacGrogan
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
| | - Belén Prados
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
- Pluripotent Cell Technology Unit (B.P., G.G.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Fernando Sánchez-Sáez
- Molecular Mechanisms Program, Centro de Investigación del Cáncer and Instituto de Biología Molecular y Celular del Cáncer Universidad de Salamanca, Spain (F.S.-S., N.F.-M., A.M.P.)
| | - Rebeca Piñeiro-Sabarís
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
| | - Natalia Felipe-Medina
- Molecular Mechanisms Program, Centro de Investigación del Cáncer and Instituto de Biología Molecular y Celular del Cáncer Universidad de Salamanca, Spain (F.S.-S., N.F.-M., A.M.P.)
| | - Carlos Torroja
- Bioinformatics Unit (J.d.l.B., C.T., M.J.G., F.S.-C.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Manuel José Gómez
- Genomics Unit (S.C., A.D.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
- Laboratorio de Cardiogenética, Instituto Murciano de Investigación Biosanitaria, European Reference Networks and Unidad de Referencia-European Reference Networks Guard Heart de Cardiopatias Familiares, Hospital Universitario Virgen de la Arrixaca-Universidad de Murcia, El Palmar, Spain (M.S.-M., J.R.G.-B.)
| | - María Sabater-Molina
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
| | - Rubén Escribá
- Regenerative Medicine Program, Bellvitge Institute for Biomedical Research, Program for Clinical Translation of Regenerative Medicine in Catalonia, Centre for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine and Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain (R.E., I.R.-P., O.I.-G., A.R.)
| | - Ivonne Richaud-Patin
- Regenerative Medicine Program, Bellvitge Institute for Biomedical Research, Program for Clinical Translation of Regenerative Medicine in Catalonia, Centre for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine and Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain (R.E., I.R.-P., O.I.-G., A.R.)
| | - Olalla Iglesias-García
- Regenerative Medicine Program, Bellvitge Institute for Biomedical Research, Program for Clinical Translation of Regenerative Medicine in Catalonia, Centre for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine and Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain (R.E., I.R.-P., O.I.-G., A.R.)
- Regenerative Medicine Program, Cima Universidad de Navarra, Navarra Institute for Health Research, Pamplona, Spain (O.I.-G.)
| | - Mauro Sbroggio
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
| | - Sergio Callejas
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
- Genomics Unit (S.C., A.D.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Declan P O'Regan
- Medical Research Council London Institute of Medical Sciences (D.P.O.' K.A.M.), Imperial College London, United Kingdom
| | - Kathryn A McGurk
- Medical Research Council London Institute of Medical Sciences (D.P.O.' K.A.M.), Imperial College London, United Kingdom
- National Heart and Lung Institute (K.A.M.), Imperial College London, United Kingdom
| | - Ana Dopazo
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
- Genomics Unit (S.C., A.D.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Giovanna Giovinazzo
- Pluripotent Cell Technology Unit (B.P., G.G.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Borja Ibañez
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
- Translational Laboratory (B.I.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
- Cardiology Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz Hospital, Madrid, Spain (B.I.)
| | - Lorenzo Monserrat
- Instituto de Investigación Biomédica de A Coruña and Departamento Científico, Health in Code S.L., A Coruña, Spain (L.M.)
| | - José María Pérez-Pomares
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
- Department of Animal Biology, Faculty of Sciences, Instituto de Investigación Biomédica de Málaga and Centro Andaluz de Nanomedicina y Biotecnología, Universidad de Málaga, Spain (J.M.P.-P.)
| | - Fátima Sánchez-Cabo
- Bioinformatics Unit (J.d.l.B., C.T., M.J.G., F.S.-C.), Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Alberto M Pendas
- Molecular Mechanisms Program, Centro de Investigación del Cáncer and Instituto de Biología Molecular y Celular del Cáncer Universidad de Salamanca, Spain (F.S.-S., N.F.-M., A.M.P.)
| | - Angel Raya
- Regenerative Medicine Program, Bellvitge Institute for Biomedical Research, Program for Clinical Translation of Regenerative Medicine in Catalonia, Centre for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine and Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain (R.E., I.R.-P., O.I.-G., A.R.)
| | - Juan R Gimeno-Blanes
- Laboratorio de Cardiogenética, Instituto Murciano de Investigación Biosanitaria, European Reference Networks and Unidad de Referencia-European Reference Networks Guard Heart de Cardiopatias Familiares, Hospital Universitario Virgen de la Arrixaca-Universidad de Murcia, El Palmar, Spain (M.S.-M., J.R.G.-B.)
| | - José Luis de la Pompa
- Intercellular Signaling in Cardiovascular Development & Disease Laboratory, Centro Nacional de Investigaciones Cardiovasculares and Ciber de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain (M.S.-A., A.S.-J., J.G.-B., D.M., B.P., R.P.-S., M.S., S.C.' A.D.' B.I., J.L.d.l.P.)
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14
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Patel K, Bajaj N, Statton B, Li X, Herath NS, Nyamakope K, Davidson R, Stoks J, Purkayastha S, Ware JS, O'Regan DP, Lambiase PD, Cluitmans M, Peters NS, Ng FS. Bariatric surgery reverses ventricular repolarisation heterogeneity in obesity: mechanistic insights into fat-related arrhythmic risk. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Obesity is a growing global health problem that confers higher risks of atrial arrhythmias and sudden cardiac death. Despite this, the proarrhythmic substrate in obesity and its reversibility with weight loss has not been studied in-depth.
Purpose
To characterise the proarrhythmic substrate in obese patients, and its reversibility with bariatric surgery, using electrocardiographic imaging (ECGi).
Methods
ECGi was performed in 16 obese patients pre-bariatric surgery (PreSurg; mean age 43±12 years, 13 female) and 16 age- and sex-matched non-obese (lean) individuals (42±11 years). 12 of the 16 obese patients also underwent ECGi after bariatric surgery (PostSurg). Over 2000 atrial and ventricular epicardial electrograms were computed using high density body surface mapping (256-lead ECG) and heart-torso geometries from cardiac magnetic resonance imaging, by solving the inverse problem of electrocardiography. Local atrial and ventricular epicardial activation times (AT) were calculated as the steepest downslope of their respective activation complexes, and local ventricular repolarisation times (RT) as the steepest upslope of the T-wave. Atrial activation gradients (ATG) and ventricular repolarisation gradients (RTG) were calculated as the maximum difference within 10 mm radius divided by the corresponding distance.
Results
Body mass index was greater in PreSurg vs lean (46.7±5.5 vs 22.8±2.6 kg/m2, p<0.0001) and decreased with surgery (PostSurg 36.8±6.5 kg/m2, p<0.0001). Epicardial adipose tissue (EAT) was greater in PreSurg vs lean (83±56 vs 28±13 ml, p<0.0001) and decreased post-surgery (PostSurg 69±45 ml, p=0.0010). Total atrial AT was prolonged in PreSurg vs lean (62±15 vs 46±12 ms, p=0.0028), which persisted post-surgery (PostSurg 67±15 ms, p=0.86). Atrial ATG were also greater in PreSurg vs lean (26±11 vs 14±8 ms, p=0.0007) and did not change with weight loss (PostSurg 25±12, p=0.44). Ventricular RTG were greater in PreSurg vs lean (26±11 vs 15±7 ms/mm, p=0.0024) and decreased with weight loss (PostSurg 19±8, p=0.0009). Ventricular RTG were similar between PostSurg and lean (p=0.20). EAT from lean and PreSurg individuals correlated with atrial ATG (r=0.36, p=0.044) and ventricular RTG (r=0.54, p=0.0014). Ventricular AT were similar between lean (31±6 ms), PreSurg (34±5 ms) and PostSurg (35±9 ms); all p>0.05.
Conclusion
Steep ventricular repolarisation gradients and prolonged atrial activation contribute to the proarrhythmic substrate in obesity. Ventricular repolarisation gradients correlate with epicardial adiposity and both regress post-bariatric surgery. By contrast, atrial activation remains prolonged after weight loss. These results provide mechanistic insights into obesity-related arrhythmic risks and their reversibility with weight loss following bariatric surgery.
Funding Acknowledgement
Type of funding sources: Other. Main funding source(s): British Heart FoundationNational Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC).
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Affiliation(s)
- K Patel
- National Heart and Lung Institute , London , United Kingdom
| | - N Bajaj
- National Heart and Lung Institute , London , United Kingdom
| | - B Statton
- Imperial College London , London , United Kingdom
| | - X Li
- National Heart and Lung Institute , London , United Kingdom
| | - N S Herath
- Imperial College London , London , United Kingdom
| | - K Nyamakope
- Imperial College London , London , United Kingdom
| | - R Davidson
- Imperial College London , London , United Kingdom
| | - J Stoks
- Maastricht University , Maastricht , The Netherlands
| | | | - J S Ware
- National Heart and Lung Institute , London , United Kingdom
| | - D P O'Regan
- Imperial College London , London , United Kingdom
| | - P D Lambiase
- University College London , London , United Kingdom
| | - M Cluitmans
- Maastricht University , Maastricht , The Netherlands
| | - N S Peters
- National Heart and Lung Institute , London , United Kingdom
| | - F S Ng
- National Heart and Lung Institute , London , United Kingdom
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15
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Mallon DH, McNamara CD, Rahmani GS, O'Regan DP, Amiras DG. Automated detection of enteric tubes misplaced in the respiratory tract on chest radiographs using deep learning with two centre validation. Clin Radiol 2022; 77:e758-e764. [PMID: 35850868 DOI: 10.1016/j.crad.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/25/2022] [Accepted: 06/17/2022] [Indexed: 11/27/2022]
Abstract
AIM To develop and test a model based on a convolutional neural network that can identify enteric tube position accurately on chest radiography. MATERIALS AND METHODS The chest radiographs of adult patients were classified by radiologists based on enteric tube position as either critically misplaced (within the respiratory tract) or not critically misplaced (misplaced within the oesophagus or safely positioned below the diaphragm). A deep-learning model based on the 121-layer DenseNet architecture was developed using a training and validation set of 4,693 chest radiographs. The model was evaluated on an external test data set from a separate institution that consisted of 1,514 consecutive radiographs with a real-world incidence of critically misplaced enteric tubes. RESULTS The receiver operator characteristic area under the curve was 0.90 and 0.92 for the internal validation and external test data sets, respectively. For the external data set with a prevalence of 4.4% of critically misplaced enteric tubes, the model achieved high accuracy (92%), sensitivity (80%), and specificity (92%) for identifying a critically misplaced enteric tube. The negative predictive value (99%) was higher than the positive predictive value (32%). CONCLUSION The present study describes the development and external testing of a model that accurately identifies an enteric tube misplaced within the respiratory tract. This model could help reduce the risk of the catastrophic consequences of feeding through a misplaced enteric tube.
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Affiliation(s)
- D H Mallon
- Imperial College Healthcare NHS Trust, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK.
| | - C D McNamara
- Imperial College Healthcare NHS Trust, London, UK
| | - G S Rahmani
- Galway University Hospitals, Galway, Ireland
| | - D P O'Regan
- Imperial College Healthcare NHS Trust, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - D G Amiras
- Imperial College Healthcare NHS Trust, London, UK
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16
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Alabed S, Alandejani F, Dwivedi K, Karunasaagarar K, Sharkey M, Garg P, de Koning PJH, Tóth A, Shahin Y, Johns C, Mamalakis M, Stott S, Capener D, Wood S, Metherall P, Rothman AMK, Condliffe R, Hamilton N, Wild JM, O'Regan DP, Lu H, Kiely DG, van der Geest RJ, Swift AJ. Validation of Artificial Intelligence Cardiac MRI Measurements: Relationship to Heart Catheterization and Mortality Prediction. Radiology 2022; 304:E56. [PMID: 35994400 PMCID: PMC9523681 DOI: 10.1148/radiol.229014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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17
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Tayal U, Verdonschot JAJ, Hazebroek MR, Howard J, Gregson J, Newsome S, Gulati A, Pua CJ, Halliday BP, Lota AS, Buchan RJ, Whiffin N, Kanapeckaite L, Baruah R, Jarman JWE, O'Regan DP, Barton PJR, Ware JS, Pennell DJ, Adriaans BP, Bekkers SCAM, Donovan J, Frenneaux M, Cooper LT, Januzzi JL, Cleland JGF, Cook SA, Deo RC, Heymans SRB, Prasad SK. Precision Phenotyping of Dilated Cardiomyopathy Using Multidimensional Data. J Am Coll Cardiol 2022; 79:2219-2232. [PMID: 35654493 PMCID: PMC9168440 DOI: 10.1016/j.jacc.2022.03.375] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND Dilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction. OBJECTIVES The purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification. METHODS Longitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years). RESULTS In total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005). CONCLUSIONS Three reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to traditional risk models. They may improve patient selection for novel interventions, thereby enabling precision medicine.
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Affiliation(s)
- Upasana Tayal
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom.
| | - Job A J Verdonschot
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands; Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Mark R Hazebroek
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | - James Howard
- National Heart Lung Institute, Imperial College London, London, United Kingdom
| | - John Gregson
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Simon Newsome
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ankur Gulati
- Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | | | - Brian P Halliday
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | - Amrit S Lota
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | - Rachel J Buchan
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | - Nicola Whiffin
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Medical Research Council London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Lina Kanapeckaite
- Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | - Resham Baruah
- Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | - Julian W E Jarman
- Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | - Declan P O'Regan
- Medical Research Council London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Paul J R Barton
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom; Medical Research Council London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - James S Ware
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom; Medical Research Council London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Dudley J Pennell
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | - Bouke P Adriaans
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Sebastiaan C A M Bekkers
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Jackie Donovan
- Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom
| | - Michael Frenneaux
- National Heart Lung Institute, Imperial College London, London, United Kingdom
| | | | - James L Januzzi
- Cardiology Division, Massachusetts General Hospital, Baim Insitute for Clinical Research, Boston, Massachusetts, USA
| | - John G F Cleland
- National Heart Lung Institute, Imperial College London, London, United Kingdom
| | - Stuart A Cook
- National Heart Centre, Singapore; Medical Research Council London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Rahul C Deo
- One Brave Idea and Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Stephane R B Heymans
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands; Centre for Molecular and Vascular Biology, Department of Cardiovascular Sciences, KU Leuven, Belgium
| | - Sanjay K Prasad
- National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom.
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18
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Thanaj M, Mielke J, McGurk KA, Bai W, Savioli N, de Marvao A, Meyer HV, Zeng L, Sohler F, Lumbers RT, Wilkins MR, Ware JS, Bender C, Rueckert D, MacNamara A, Freitag DF, O'Regan DP. Genetic and environmental determinants of diastolic heart function. Nat Cardiovasc Res 2022; 1:361-371. [PMID: 35479509 PMCID: PMC7612636 DOI: 10.1038/s44161-022-00048-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets.
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Affiliation(s)
- Marjola Thanaj
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Johanna Mielke
- 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
| | - Wenjia Bai
- Department of Computing, Imperial College London, London, UK.,Department of Brain Sciences, Imperial College London
| | - Nicolò Savioli
- MRC London Institute of Medical Sciences, Imperial College London, London, UK.,Department of Computing, Imperial College London, London, UK
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Hannah V Meyer
- Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, USA
| | - Lingyao Zeng
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Florian Sohler
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | | | - Martin R Wilkins
- National Heart and Lung Institute, Imperial 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
| | - Christian Bender
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK.,Institute for Artificial Intelligence and Informatics, Klinikum rechts der Isar, Technical University of Munich, Germany
| | - Aidan MacNamara
- 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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19
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McGurk KA, Zheng SL, Henry A, Josephs K, Edwards M, de Marvao A, Whiffin N, Roberts A, Lumbers TR, O'Regan DP, Ware JS. Correspondence on "ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG)" by Miller et al. Genet Med 2022; 24:744-746. [PMID: 34906520 DOI: 10.1016/j.gim.2021.10.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 10/26/2021] [Indexed: 11/17/2022] Open
Affiliation(s)
- Kathryn A McGurk
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Sean L Zheng
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Albert Henry
- British Heart Foundation Research Accelerator, and Institute of Health Informatics, University College London, London, United Kingdom
| | - Katherine Josephs
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Matthew Edwards
- Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Nicola Whiffin
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Angharad Roberts
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Thomas R Lumbers
- British Heart Foundation Research Accelerator, and Institute of Health Informatics, University College London, London, United Kingdom; Bart's Heart Centre, Barts Health NHS Trust, St. Bartholomew's Hospital, London, United Kingdom
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - James S Ware
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom; MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom.
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20
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Manchester EL, Pirola S, Salmasi MY, O'Regan DP, Athanasiou T, Xu XY. Evaluation of Computational Methodologies for Accurate Prediction of Wall Shear Stress and Turbulence Parameters in a Patient-Specific Aorta. Front Bioeng Biotechnol 2022; 10:836611. [PMID: 35402418 PMCID: PMC8987126 DOI: 10.3389/fbioe.2022.836611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/07/2022] [Indexed: 11/21/2022] Open
Abstract
Background: Recent studies suggest that blood flow in main arteries is intrinsically disturbed, even under healthy conditions. Despite this, many computational fluid dynamics (CFD) analyses of aortic haemodynamics make the assumption of laminar flow, and best practices surrounding appropriate modelling choices are lacking. This study aims to address this gap by evaluating different modelling and post-processing approaches in simulations of a patient-specific aorta. Methods: Magnetic resonance imaging (MRI) and 4D flow MRI from a patient with aortic valve stenosis were used to reconstruct the aortic geometry and derive patient-specific inlet and outlet boundary conditions. Three different computational approaches were considered based on assumed laminar or assumed disturbed flow states including low-resolution laminar (LR-Laminar), high-resolution laminar (HR-Laminar) and large-eddy simulation (LES). Each simulation was ran for 30 cardiac cycles and post-processing was conducted on either the final cardiac cycle, or using a phase-averaged approach which utilised all 30 simulated cycles. Model capabilities were evaluated in terms of mean and turbulence-based parameters. Results: All simulation types, regardless of post-processing approach could correctly predict velocity values and flow patterns throughout the aorta. Lower resolution simulations could not accurately predict gradient-derived parameters including wall shear stress and viscous energy loss (largest differences up to 44.6% and 130.3%, respectively), although phase-averaging these parameters improved predictions. The HR-Laminar simulation produced more comparable results to LES with largest differences in wall shear stress and viscous energy loss parameters up to 5.1% and 11.6%, respectively. Laminar-based parameters were better estimated than turbulence-based parameters. Conclusion: Our findings suggest that well-resolved laminar simulations can accurately predict many laminar-based parameters in disturbed flows, but there is no clear benefit to running a HR-Laminar simulation over an LES simulation based on their comparable computational cost. Additionally, post-processing "typical" laminar simulation results with a phase-averaged approach is a simple and cost-effective way to improve accuracy of lower-resolution simulation results.
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Affiliation(s)
| | - Selene Pirola
- Department of Chemical Engineering, Imperial College London, London, United Kingdom
| | - Mohammad Yousuf Salmasi
- Department of Surgery and Cancer, Imperial College London, St Mary's Hospital, London, United Kingdom
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, St Mary's Hospital, London, United Kingdom
| | - Xiao Yun Xu
- Department of Chemical Engineering, Imperial College London, London, United Kingdom
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21
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Sharkey MJ, Taylor JC, Alabed S, Dwivedi K, Karunasaagarar K, Johns CS, Rajaram S, Garg P, Alkhanfar D, Metherall P, O'Regan DP, van der Geest RJ, Condliffe R, Kiely DG, Mamalakis M, Swift AJ. Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning. Front Cardiovasc Med 2022; 9:983859. [PMID: 36225963 PMCID: PMC9549370 DOI: 10.3389/fcvm.2022.983859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/02/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA. Methods A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort. Results Dice similarity coefficients (DSC) for segmented structures were in the range 0.58-0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785-0.801) and 0.520 (0.482-0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases. Conclusion Fully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.
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Affiliation(s)
- Michael J Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,3D Imaging Lab, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Jonathan C Taylor
- 3D Imaging Lab, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Kavitasagary Karunasaagarar
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Radiology Department, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Christopher S Johns
- Radiology Department, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Smitha Rajaram
- Radiology Department, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Dheyaa Alkhanfar
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Peter Metherall
- 3D Imaging Lab, Sheffield Teaching Hospitals NHSFT, Sheffield, United Kingdom
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Rob J van der Geest
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Robin Condliffe
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom
| | - David G Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom.,Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom
| | - Michail Mamalakis
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom.,Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Andrew J Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom
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22
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Jia X, Thorley A, Chen W, Qiu H, Shen L, Styles IB, Chang HJ, Leonardis A, de Marvao A, O'Regan DP, Rueckert D, Duan J. Learning a Model-Driven Variational Network for Deformable Image Registration. IEEE Trans Med Imaging 2022; 41:199-212. [PMID: 34460369 DOI: 10.1109/tmi.2021.3108881] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this issue and meanwhile retain the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using a variable splitting optimization scheme, we first convert the image registration problem, established in a generic variational framework, into two sub-problems, one with a point-wise, closed-form solution and the other one being a denoising problem. We then propose two neural layers (i.e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net (termed generalized denoising layer) to formulate the denoising problem. Finally, we cascade the three neural layers multiple times to form our VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac magnetic resonance imaging datasets show that VR-Net outperforms state-of-the-art deep learning methods on registration accuracy, whilst maintaining the fast inference speed of deep learning and the data-efficiency of variational models.
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23
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Osimo EF, Sweeney M, de Marvao A, Berry A, Statton B, Perry BI, Pillinger T, Whitehurst T, Cook SA, O'Regan DP, Thomas EL, Howes OD. Adipose tissue dysfunction, inflammation, and insulin resistance: alternative pathways to cardiac remodelling in schizophrenia. A multimodal, case-control study. Transl Psychiatry 2021; 11:614. [PMID: 34873143 PMCID: PMC8648771 DOI: 10.1038/s41398-021-01741-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/17/2021] [Accepted: 11/23/2021] [Indexed: 12/12/2022] Open
Abstract
Cardiovascular diseases are the leading cause of death in schizophrenia. Patients with schizophrenia show evidence of concentric cardiac remodelling (CCR), defined as an increase in left-ventricular mass over end-diastolic volumes. CCR is a predictor of cardiac disease, but the molecular pathways leading to this in schizophrenia are unknown. We aimed to explore the relevance of hypertensive and non-hypertensive pathways to CCR and their potential molecular underpinnings in schizophrenia. In this multimodal case-control study, we collected cardiac and whole-body fat magnetic resonance imaging (MRI), clinical measures, and blood levels of several cardiometabolic biomarkers known to potentially cause CCR from individuals with schizophrenia, alongside healthy controls (HCs) matched for age, sex, ethnicity, and body surface area. Of the 50 participants, 34 (68%) were male. Participants with schizophrenia showed increases in cardiac concentricity (d = 0.71, 95% CI: 0.12, 1.30; p = 0.01), indicative of CCR, but showed no differences in overall content or regional distribution of adipose tissue compared to HCs. Despite the cardiac changes, participants with schizophrenia did not demonstrate activation of the hypertensive CCR pathway; however, they showed evidence of adipose dysfunction: adiponectin was reduced (d = -0.69, 95% CI: -1.28, -0.10; p = 0.02), with evidence of activation of downstream pathways, including hypertriglyceridemia, elevated C-reactive protein, fasting glucose, and alkaline phosphatase. In conclusion, people with schizophrenia showed adipose tissue dysfunction compared to body mass-matched HCs. The presence of non-hypertensive CCR and a dysmetabolic phenotype may contribute to excess cardiovascular risk in schizophrenia. If our results are confirmed, acting on this pathway could reduce cardiovascular risk and resultant life-years lost in people with schizophrenia.
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Affiliation(s)
- Emanuele F Osimo
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK. .,Department of Psychiatry, University of Cambridge, Cambridge, UK. .,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK. .,South London and Maudsley NHS Foundation Trust, London, UK.
| | - Mark Sweeney
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.,Imperial College London, London, UK
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Alaine Berry
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Ben Statton
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Toby Pillinger
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, UK.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Thomas Whitehurst
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Stuart A Cook
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Oliver D Howes
- MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK. .,South London and Maudsley NHS Foundation Trust, London, UK. .,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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24
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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: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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25
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Onwordi EC, Whitehurst T, Mansur A, Statton B, Berry A, Quinlan M, O'Regan DP, Rogdaki M, Marques TR, Rabiner EA, Gunn RN, Vernon AC, Natesan S, Howes OD. The relationship between synaptic density marker SV2A, glutamate and N-acetyl aspartate levels in healthy volunteers and schizophrenia: a multimodal PET and magnetic resonance spectroscopy brain imaging study. Transl Psychiatry 2021; 11:393. [PMID: 34282130 PMCID: PMC8290006 DOI: 10.1038/s41398-021-01515-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 06/22/2021] [Indexed: 02/07/2023] Open
Abstract
Glutamatergic excitotoxicity is hypothesised to underlie synaptic loss in schizophrenia pathogenesis, but it is unknown whether synaptic markers are related to glutamatergic function in vivo. Additionally, it has been proposed that N-acetyl aspartate (NAA) levels reflect neuronal integrity. Here, we investigated whether synaptic vesicle glycoprotein 2 A (SV2A) levels are related to glutamatergic markers and NAA in healthy volunteers (HV) and schizophrenia patients (SCZ). Forty volunteers (SCZ n = 18, HV n = 22) underwent [11C]UCB-J positron emission tomography and proton magnetic resonance spectroscopy (1H-MRS) imaging in the left hippocampus and anterior cingulate cortex (ACC) to index [11C]UCB-J distribution volume ratio (DVR), and creatine-scaled glutamate (Glu/Cr), glutamate and glutamine (Glx/Cr) and NAA (NAA/Cr). In healthy volunteers, but not patients, [11C]UCB-J DVR was significantly positively correlated with Glu/Cr, in both the hippocampus and ACC. Furthermore, in healthy volunteers, but not patients, [11C]UCB-J DVR was significantly positively correlated with Glx/Cr, in both the hippocampus and ACC. There were no significant relationships between [11C]UCB-J DVR and NAA/Cr in the hippocampus or ACC in healthy volunteers or patients. Therefore, an appreciable proportion of the brain 1H-MRS glutamatergic signal is related to synaptic density in healthy volunteers. This relationship is not seen in schizophrenia, which, taken with lower synaptic marker levels, is consistent with lower levels of glutamatergic terminals and/or a lower proportion of glutamatergic relative to GABAergic terminals in the ACC in schizophrenia.
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Affiliation(s)
- Ellis Chika Onwordi
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK.
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, W12 0NN, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK.
- South London and Maudsley NHS Foundation Trust, Camberwell, London, SE5 8AF, UK.
| | - Thomas Whitehurst
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Ayla Mansur
- Department of Brain Sciences, Imperial College London, The Commonwealth Building, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
- Invicro, Burlington Danes Building, Du Cane Road, London, W12 0NN, UK
| | - Ben Statton
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
| | - Alaine Berry
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
| | - Marina Quinlan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
| | - Maria Rogdaki
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, W12 0NN, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK
- South London and Maudsley NHS Foundation Trust, Camberwell, London, SE5 8AF, UK
| | - Tiago Reis Marques
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK
- South London and Maudsley NHS Foundation Trust, Camberwell, London, SE5 8AF, UK
| | - Eugenii A Rabiner
- Invicro, Burlington Danes Building, Du Cane Road, London, W12 0NN, UK
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK
| | - Roger N Gunn
- Department of Brain Sciences, Imperial College London, The Commonwealth Building, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
- Invicro, Burlington Danes Building, Du Cane Road, London, W12 0NN, UK
| | - Anthony C Vernon
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, 5 Cutcombe Road, London, SE5 9RT, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK
| | - Sridhar Natesan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK
| | - Oliver D Howes
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK.
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, W12 0NN, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK.
- South London and Maudsley NHS Foundation Trust, Camberwell, London, SE5 8AF, UK.
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26
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Garnier S, Harakalova M, Weiss S, Mokry M, Regitz-Zagrosek V, Hengstenberg C, Cappola TP, Isnard R, Arbustini E, Cook SA, van Setten J, Calis JJA, Hakonarson H, Morley MP, Stark K, Prasad SK, Li J, O'Regan DP, Grasso M, Müller-Nurasyid M, Meitinger T, Empana JP, Strauch K, Waldenberger M, Marguiles KB, Seidman CE, Kararigas G, Meder B, Haas J, Boutouyrie P, Lacolley P, Jouven X, Erdmann J, Blankenberg S, Wichter T, Ruppert V, Tavazzi L, Dubourg O, Roizes G, Dorent R, de Groote P, Fauchier L, Trochu JN, Aupetit JF, Bilinska ZT, Germain M, Völker U, Hemerich D, Raji I, Bacq-Daian D, Proust C, Remior P, Gomez-Bueno M, Lehnert K, Maas R, Olaso R, Saripella GV, Felix SB, McGinn S, Duboscq-Bidot L, van Mil A, Besse C, Fontaine V, Blanché H, Ader F, Keating B, Curjol A, Boland A, Komajda M, Cambien F, Deleuze JF, Dörr M, Asselbergs FW, Villard E, Trégouët DA, Charron P. Genome-wide association analysis in dilated cardiomyopathy reveals two new players in systolic heart failure on chromosomes 3p25.1 and 22q11.23. Eur Heart J 2021; 42:2000-2011. [PMID: 33677556 PMCID: PMC8139853 DOI: 10.1093/eurheartj/ehab030] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/13/2020] [Accepted: 01/14/2021] [Indexed: 12/31/2022] Open
Abstract
AIMS Our objective was to better understand the genetic bases of dilated cardiomyopathy (DCM), a leading cause of systolic heart failure. METHODS AND RESULTS We conducted the largest genome-wide association study performed so far in DCM, with 2719 cases and 4440 controls in the discovery population. We identified and replicated two new DCM-associated loci on chromosome 3p25.1 [lead single-nucleotide polymorphism (SNP) rs62232870, P = 8.7 × 10-11 and 7.7 × 10-4 in the discovery and replication steps, respectively] and chromosome 22q11.23 (lead SNP rs7284877, P = 3.3 × 10-8 and 1.4 × 10-3 in the discovery and replication steps, respectively), while confirming two previously identified DCM loci on chromosomes 10 and 1, BAG3 and HSPB7. A genetic risk score constructed from the number of risk alleles at these four DCM loci revealed a 3-fold increased risk of DCM for individuals with 8 risk alleles compared to individuals with 5 risk alleles (median of the referral population). In silico annotation and functional 4C-sequencing analyses on iPSC-derived cardiomyocytes identify SLC6A6 as the most likely DCM gene at the 3p25.1 locus. This gene encodes a taurine transporter whose involvement in myocardial dysfunction and DCM is supported by numerous observations in humans and animals. At the 22q11.23 locus, in silico and data mining annotations, and to a lesser extent functional analysis, strongly suggest SMARCB1 as the candidate culprit gene. CONCLUSION This study provides a better understanding of the genetic architecture of DCM and sheds light on novel biological pathways underlying heart failure.
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Affiliation(s)
- Sophie Garnier
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris 75013, France
| | - Magdalena Harakalova
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Regenerative Medicine Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Michal Mokry
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Laboratory of Clinical Chemistry and Haematology, University Medical Center, Heidelberglaan 100, Utrecht, the Netherlands
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, the Netherlands
| | - Vera Regitz-Zagrosek
- Institute of Gender in Medicine and Center for Cardiovascular Research, Charite University Hospital, Berlin, Germany
- DZHK (German Center for Cardiovascular Research), Berlin, Germany
| | - Christian Hengstenberg
- Department of Internal Medicine, Division of Cardiology, Medical University of Vienna, Austria
- Department of Internal Medicine, Medical University of Regensburg, Germany
| | - Thomas P Cappola
- Penn Cardiovascular Institute and Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard Isnard
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris 75013, France
- Cardiology Department, APHP, Pitié-Salpêtrière Hospital, Paris, France
| | | | - Stuart A Cook
- National Heart and Lung Institute, Imperial College London, London, UK
- National Heart Centre Singapore, Singapore
- Duke-NUS, Singapore
| | - Jessica van Setten
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jorg J A Calis
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Regenerative Medicine Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hakon Hakonarson
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Michael P Morley
- Penn Cardiovascular Institute and Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Klaus Stark
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Sanjay K Prasad
- National Heart Centre Singapore, Singapore
- Royal Brompton Hospital, London, UK
| | - Jin Li
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Declan P O'Regan
- Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Maurizia Grasso
- Centre for Inherited Cardiovascular Diseases—IRCCS Fondazione Policlinico San Matteo, Pavia, Italy
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Germany
- Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Thomas Meitinger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Germany
- Institute of Human Genetics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Jean-Philippe Empana
- Université de Paris, INSERM, UMR-S970, Integrative Epidemiology of cardiovascular disease, Paris, France
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz 55101, Germany
| | - Melanie Waldenberger
- Research unit of Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Kenneth B Marguiles
- Penn Cardiovascular Institute and Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christine E Seidman
- Department of Medicine and Genetics Harvard Medical School, Boston, MA, USA
- Brigham & Women's Cardiovascular Genetics Center, Boston, MA, USA
| | - Georgios Kararigas
- Department of Physiology, Faculty of Medicine, University of Iceland, Vatnsmýrarvegur 16, 101 Reykjavík, Iceland
| | - Benjamin Meder
- Institute for Cardiomyopathies Heidelberg, Heidelberg University, Germany
- Stanford Genome Technology Center, Department of Genetics, Stanford Medical School, CA, USA
| | - Jan Haas
- Institute for Cardiomyopathies Heidelberg, Heidelberg University, Germany
| | - Pierre Boutouyrie
- Université de Paris, INSERM, UMR-S970, Integrative Epidemiology of cardiovascular disease, Paris, France
- Cardiology Department, APHP, Georges Pompidou European Hospital, Paris, France
| | | | - Xavier Jouven
- Université de Paris, INSERM, UMR-S970, Integrative Epidemiology of cardiovascular disease, Paris, France
- Cardiology Department, APHP, Georges Pompidou European Hospital, Paris, France
| | - Jeanette Erdmann
- Medizinische Klinik und Poliklinik, Universitätsmedizin der Johannes-Gutenberg Universität Mainz, Mainz, Germany
| | | | - Thomas Wichter
- Dept. of Cardiology and Angiology, Niels-Stensen-Kliniken Marienhospital Osnabrück, Heart Centre Osnabrück/Bad Rothenfelde, Osnabrück 49074, Germany
| | - Volker Ruppert
- Klinik für Innere Medizin-Kardiologie UKGM GmbH Standort Marburg Baldingerstrasse, Marburg, Germany
| | - Luigi Tavazzi
- Maria Cecilia Hospital, GVM Care and Research, Cotignola, Italy
| | - Olivier Dubourg
- Université de Versailles-Saint Quentin, Hôpital Ambroise Paré, AP-HP, Boulogne, France
| | - Gérard Roizes
- Institut de Génétique Humaine, UPR 1142, CNRS, Montpellier, France
| | | | | | - Laurent Fauchier
- Service de Cardiologie, Centre Hospitalier Universitaire Trousseau, Tours, France
| | - Jean-Noël Trochu
- Université de Nantes, CHU Nantes, CNRS, INSERM, l’institut du thorax, Nantes 44000, France
| | - Jean-François Aupetit
- Département de pathologie cardiovasculaire, Hôpital Saint-Joseph-Saint-Luc, Lyon, France
| | - Zofia T Bilinska
- Unit for Screening Studies in Inherited Cardiovascular Diseases, National Institute of Cardiology, Warsaw, Poland
| | - Marine Germain
- Univ. Bordeaux, INSERM, BPH, U1219, Bordeaux 33000, France
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Daiane Hemerich
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ibticem Raji
- AP-HP, Département de Génétique, Centre de Référence Maladies Cardiaques Héréditaires, Hôpital Pitié-Salpêtrière, Paris, France
| | - Delphine Bacq-Daian
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry 91057, France
- Laboratory of Excellence GENMED (Medical Genomics)
| | - Carole Proust
- Univ. Bordeaux, INSERM, BPH, U1219, Bordeaux 33000, France
| | - Paloma Remior
- Department of Cardiology, Hospital Universitario Puerta de Hierro, CIBERCV, Madrid, Spain
| | - Manuel Gomez-Bueno
- Department of Cardiology, Hospital Universitario Puerta de Hierro, CIBERCV, Madrid, Spain
| | - Kristin Lehnert
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Renee Maas
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Regenerative Medicine Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Robert Olaso
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry 91057, France
- Laboratory of Excellence GENMED (Medical Genomics)
| | - Ganapathi Varma Saripella
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- SLU Bioinformatics Infrastructure (SLUBI), PlantLink, Department of Plant Breeding, Swedish University of Agricultural Sciences, Almas Allé 8, 750 07 Uppsala, Sweden
| | - Stephan B Felix
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Steven McGinn
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry 91057, France
- Laboratory of Excellence GENMED (Medical Genomics)
| | - Laëtitia Duboscq-Bidot
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris 75013, France
| | - Alain van Mil
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Regenerative Medicine Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Céline Besse
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry 91057, France
- Laboratory of Excellence GENMED (Medical Genomics)
| | - Vincent Fontaine
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris 75013, France
| | - Hélène Blanché
- Laboratory of Excellence GENMED (Medical Genomics)
- Centre d'Etude du Polymorphisme Humain, Fondation Jean Dausset, Paris, France
| | - Flavie Ader
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- APHP, UF Cardiogénétique et Myogénétique, service de Biochimie métabolique, Hôpital universitaire Pitié-Salpêtrière Paris, France
- Faculté de Pharmacie Paris Descartes, Département 3, Paris 75006, France
| | - Brendan Keating
- Division of Transplantation, Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Angélique Curjol
- AP-HP, Département de Génétique, Centre de Référence Maladies Cardiaques Héréditaires, Hôpital Pitié-Salpêtrière, Paris, France
| | - Anne Boland
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry 91057, France
- Laboratory of Excellence GENMED (Medical Genomics)
| | - Michel Komajda
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris 75013, France
- Cardiology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | | | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry 91057, France
- Laboratory of Excellence GENMED (Medical Genomics)
- Centre d'Etude du Polymorphisme Humain, Fondation Jean Dausset, Paris, France
| | - Marcus Dörr
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Folkert W Asselbergs
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Eric Villard
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris 75013, France
| | - David-Alexandre Trégouët
- Univ. Bordeaux, INSERM, BPH, U1219, Bordeaux 33000, France
- Laboratory of Excellence GENMED (Medical Genomics)
| | - Philippe Charron
- Sorbonne Université, INSERM, UMR-S1166, Research Unit on Cardiovascular Disorders, Metabolism and Nutrition, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris 75013, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris 75013, France
- Cardiology Department, APHP, Pitié-Salpêtrière Hospital, Paris, France
- AP-HP, Département de Génétique, Centre de Référence Maladies Cardiaques Héréditaires, Hôpital Pitié-Salpêtrière, Paris, France
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27
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Keenan NG, Captur G, McCann GP, Berry C, Myerson SG, Fairbairn T, Hudsmith L, O'Regan DP, Westwood M, Greenwood JP. Regional variation in cardiovascular magnetic resonance service delivery across the UK. Heart 2021; 107:1974-1979. [PMID: 33766986 PMCID: PMC8639953 DOI: 10.1136/heartjnl-2020-318667] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/16/2021] [Accepted: 02/20/2021] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES To examine service provision in cardiovascular magnetic resonance (CMR) in the UK. Equitable access to diagnostic imaging is important in healthcare. CMR is widely available in the UK, but there may be regional variations. METHODS An electronic survey was sent by the British Society of CMR to the service leads of all CMR units in the UK in 2019 requesting data from 2017 and 2018. Responses were analysed by region and interpreted alongside population statistics. RESULTS The survey response rate was 100% (82 units). 100 386 clinical scans were performed in 2017 and 114 967 in 2018 (15% 1-year increase; 5-fold 10-year increase compared with 2008 data). In 2018, there were 1731 CMR scans/million population overall, with significant regional variation, for example, 4256 scans/million in London vs 396 scans/million in Wales. Median number of clinical scans per unit was 780, IQR 373-1951, range 98-10 000, with wide variation in mean waiting times (median 41 days, IQR 30-49, range 5-180); median 25 days in London vs 180 days in Northern Ireland). Twenty-five units (30%) reported mean elective waiting times in excess of 6 weeks, and 8 (10%) ≥3 months. There were 351 consultants reporting CMR, of whom 230 (66%) were cardiologists and 121 (34%) radiologists; 81% of units offered a CMR service for patients with pacemakers and defibrillators. CONCLUSIONS This survey provides a unique, contemporary insight into national CMR delivery with 100% centre engagement. The 10-year growth in CMR usage at fivefold has been remarkable but heterogeneous across the UK, with some regions still reporting low usage or long waiting times which may be of clinical concern.
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Affiliation(s)
- Niall G Keenan
- Cardiology, West Hertfordshire Hospitals NHS Trust, Watford, Hertfordshire, UK .,MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Gabriella Captur
- University College London Institute of Cardiovascular Science, London, UK.,Centre for Inherited Heart Muscle Conditions, Royal Free Hospital, London, UK
| | - Gerry P McCann
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK.,NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Colin Berry
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.,Cardiology, Golden Jubilee National Hospital, Clydebank, UK
| | - Saul G Myerson
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Timothy Fairbairn
- Cardiology, Liverpool Heart and Chest Hospital NHS Trust, Liverpool, Merseyside, UK
| | - Lucy Hudsmith
- Adult Congenital Heart Disease Unit, University Hospitals Birmingham, Birmingham, West Midlands, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | | | - John P Greenwood
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.,Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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28
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Tadros R, Francis C, Xu X, Vermeer AMC, Harper AR, Huurman R, Kelu Bisabu K, Walsh R, Hoorntje ET, Te Rijdt WP, Buchan RJ, van Velzen HG, van Slegtenhorst MA, Vermeulen JM, Offerhaus JA, Bai W, de Marvao A, Lahrouchi N, Beekman L, Karper JC, Veldink JH, Kayvanpour E, Pantazis A, Baksi AJ, Whiffin N, Mazzarotto F, Sloane G, Suzuki H, Schneider-Luftman D, Elliott P, Richard P, Ader F, Villard E, Lichtner P, Meitinger T, Tanck MWT, van Tintelen JP, Thain A, McCarty D, Hegele RA, Roberts JD, Amyot J, Dubé MP, Cadrin-Tourigny J, Giraldeau G, L'Allier PL, Garceau P, Tardif JC, Boekholdt SM, Lumbers RT, Asselbergs FW, Barton PJR, Cook SA, Prasad SK, O'Regan DP, van der Velden J, Verweij KJH, Talajic M, Lettre G, Pinto YM, Meder B, Charron P, de Boer RA, Christiaans I, Michels M, Wilde AAM, Watkins H, Matthews PM, Ware JS, Bezzina CR. Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect. Nat Genet 2021; 53:128-134. [PMID: 33495596 PMCID: PMC7611259 DOI: 10.1038/s41588-020-00762-2] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 12/10/2020] [Indexed: 01/29/2023]
Abstract
The heart muscle diseases hypertrophic (HCM) and dilated (DCM) cardiomyopathies are leading causes of sudden death and heart failure in young, otherwise healthy, individuals. We conducted genome-wide association studies and multi-trait analyses in HCM (1,733 cases), DCM (5,521 cases) and nine left ventricular (LV) traits (19,260 UK Biobank participants with structurally normal hearts). We identified 16 loci associated with HCM, 13 with DCM and 23 with LV traits. We show strong genetic correlations between LV traits and cardiomyopathies, with opposing effects in HCM and DCM. Two-sample Mendelian randomization supports a causal association linking increased LV contractility with HCM risk. A polygenic risk score explains a significant portion of phenotypic variability in carriers of HCM-causing rare variants. Our findings thus provide evidence that polygenic risk score may account for variability in Mendelian diseases. More broadly, we provide insights into how genetic pathways may lead to distinct disorders through opposing genetic effects.
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Affiliation(s)
- Rafik Tadros
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada.
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
| | - Catherine Francis
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Xiao Xu
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Alexa M C Vermeer
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Clinical Genetics, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART)
| | - Andrew R Harper
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Oxford, UK
| | - Roy Huurman
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ken Kelu Bisabu
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Roddy Walsh
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Edgar T Hoorntje
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Wouter P Te Rijdt
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Rachel J Buchan
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Hannah G van Velzen
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Marjon A van Slegtenhorst
- Department of Clinical Genetics, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jentien M Vermeulen
- Department of Psychiatry, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Joost Allard Offerhaus
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wenjia Bai
- Data Science Institute, Imperial College London, London, UK
- Department of Brain Sciences and UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, Imperial College London, London, UK
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Najim Lahrouchi
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Leander Beekman
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Jacco C Karper
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jan H Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Elham Kayvanpour
- Institute for Cardiomyopathies, Heidelberg Heart Center, University of Heidelberg, Heidelberg, Germany
- DZHK (German Center for Cardiovascular Research), Berlin, Germany
| | - Antonis Pantazis
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
| | - A John Baksi
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Nicola Whiffin
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Francesco Mazzarotto
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
| | - Geraldine Sloane
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Hideaki Suzuki
- Department of Brain Sciences and UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, Imperial College London, London, UK
- Department of Cardiovascular Medicine, Tohoku University Hospital, Seiryo, Aoba, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Seiryo, Aoba, Sendai, Japan
| | - Deborah Schneider-Luftman
- The Francis Crick Institute, London, UK
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Pascale Richard
- Service de biochimie métabolique, UF de cardiogénétique et myogénétique moléculaire et cellulaire, APHP, Hôpital Pitié-Salpêtrière, Paris, France
- INSERM, UMR_S 1166 and ICAN Institute for Cardiometabolism and Nutrition, Faculté de Médecine, Sorbonne Université, Paris, France
| | - Flavie Ader
- Service de biochimie métabolique, UF de cardiogénétique et myogénétique moléculaire et cellulaire, APHP, Hôpital Pitié-Salpêtrière, Paris, France
- INSERM, UMR_S 1166 and ICAN Institute for Cardiometabolism and Nutrition, Faculté de Médecine, Sorbonne Université, Paris, France
- Faculté de Pharmacie, Université de Paris, Paris, France
| | - Eric Villard
- INSERM, UMR_S 1166 and ICAN Institute for Cardiometabolism and Nutrition, Faculté de Médecine, Sorbonne Université, Paris, France
| | - Peter Lichtner
- Institute of Human Genetics, Helmholtz Zentrum Muenchen, Neuherberg, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum Muenchen, Neuherberg, Germany
- Klinikum rechts der Isar der TU Muenchen School of Medicine, Institute of Human Genetics, Munich, Germany
- DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Michael W T Tanck
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam Public Health (APH), Amsterdam UMC, Amsterdam, the Netherlands
| | - J Peter van Tintelen
- Department of Clinical Genetics, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Andrew Thain
- Department of Medicine and Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - David McCarty
- Department of Medicine and Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Robert A Hegele
- Department of Medicine and Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jason D Roberts
- Department of Medicine and Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Julie Amyot
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Marie-Pierre Dubé
- Montreal Heart Institute Research Center, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Julia Cadrin-Tourigny
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Geneviève Giraldeau
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Philippe L L'Allier
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Patrick Garceau
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Jean-Claude Tardif
- Montreal Heart Institute Research Center, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - S Matthijs Boekholdt
- Department of Cardiology, University of Amsterdam, Heartcenter, Amsterdam UMC, Amsterdam, the Netherlands
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, Gibbs Building, London, UK
- Barts Heart Centre, Saint Bartholomew's Hospital, London, UK
| | - Folkert W Asselbergs
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Institute of Cardiovascular Science and Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
| | - Paul J R Barton
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Stuart A Cook
- National Heart and Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- National Heart Research Institute Singapore, National Heart Center Singapore, Singapore, Singapore
- Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Sanjay K Prasad
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Jolanda van der Velden
- Department of Physiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Amsterdam, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Mario Talajic
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Guillaume Lettre
- Montreal Heart Institute Research Center, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada
| | - Yigal M Pinto
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART)
| | - Benjamin Meder
- Institute for Cardiomyopathies, Heidelberg Heart Center, University of Heidelberg, Heidelberg, Germany
| | - Philippe Charron
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART)
- INSERM, UMR_S 1166 and ICAN Institute for Cardiometabolism and Nutrition, Faculté de Médecine, Sorbonne Université, Paris, France
- Département de Génétique, Centre de référence des maladies cardiaques héréditaires ou rares, APHP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Rudolf A de Boer
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Imke Christiaans
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Michelle Michels
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART)
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Oxford, UK
| | - Paul M Matthews
- Department of Brain Sciences and UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, Imperial College London, London, UK
| | - James S Ware
- Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK.
- National Heart and Lung Institute, Imperial College London, London, UK.
- MRC London Institute of Medical Sciences, Imperial College London, London, UK.
| | - Connie R Bezzina
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART), .
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29
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Zhang X, Walsh R, Whiffin N, Buchan R, Midwinter W, Wilk A, Govind R, Li N, Ahmad M, Mazzarotto F, Roberts A, Theotokis PI, Mazaika E, Allouba M, de Marvao A, Pua CJ, Day SM, Ashley E, Colan SD, Michels M, Pereira AC, Jacoby D, Ho CY, Olivotto I, Gunnarsson GT, Jefferies JL, Semsarian C, Ingles J, O'Regan DP, Aguib Y, Yacoub MH, Cook SA, Barton PJR, Bottolo L, Ware JS. Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions. Genet Med 2021; 23:69-79. [PMID: 33046849 PMCID: PMC7790749 DOI: 10.1038/s41436-020-00972-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene-disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. METHODS We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost's ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. RESULTS CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4-24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11-29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. CONCLUSIONS A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions ( https://www.cardiodb.org/cardioboost/ ), highlighting broad opportunities for improved pathogenicity prediction through disease specificity.
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Affiliation(s)
- Xiaolei Zhang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Roddy Walsh
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Nicola Whiffin
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Rachel Buchan
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - William Midwinter
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Alicja Wilk
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Risha Govind
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Nicholas Li
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Mian Ahmad
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Francesco Mazzarotto
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
- Department of Clinical and Experimental Medicine, University of Florence, Florence, Italy
| | - Angharad Roberts
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Pantazis I Theotokis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Erica Mazaika
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Mona Allouba
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Aswan Heart Centre, Magdi Yacoub Heart Foundation, Aswan, Egypt
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | | | - Sharlene M Day
- Division of Cardiovascular Medicine and Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Euan Ashley
- Division of Cardiovascular Medicine, Stanford University Medical Center, Stanford, CA, USA
| | - Steven D Colan
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
| | - Michelle Michels
- Department of Cardiology, Thoraxcenter, Erasmus MC Rotterdam, Rotterdam, Netherlands
| | - Alexandre C Pereira
- Heart Institute (InCor), University of Sao Paulo Medical School, Sao Paulo, Brazil
| | - Daniel Jacoby
- Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Carolyn Y Ho
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Iacopo Olivotto
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
| | | | - John L Jefferies
- The Cardiovascular Institute, University of Tennessee, Memphis, TN, USA
| | - Chris Semsarian
- Centenary Institute, The University of Sydney, Sydney, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Jodie Ingles
- Centenary Institute, The University of Sydney, Sydney, Australia
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Yasmine Aguib
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Aswan Heart Centre, Magdi Yacoub Heart Foundation, Aswan, Egypt
| | - Magdi H Yacoub
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Aswan Heart Centre, Magdi Yacoub Heart Foundation, Aswan, Egypt
| | - Stuart A Cook
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
- National Heart Centre, Singapore, Singapore
- Duke-National University of Singapore, Singapore, Singapore
| | - Paul J R Barton
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom.
- Alan Turing Institute, London, United Kingdom.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS, Foundation Trust London, London, United Kingdom.
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom.
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30
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Salmasi MY, Pirola S, Sasidharan S, Fisichella SM, Redaelli A, Jarral OA, O'Regan DP, Oo AY, Moore JE, Xu XY, Athanasiou T. High Wall Shear Stress can Predict Wall Degradation in Ascending Aortic Aneurysms: An Integrated Biomechanics Study. Front Bioeng Biotechnol 2021; 9:750656. [PMID: 34733832 PMCID: PMC8558434 DOI: 10.3389/fbioe.2021.750656] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/24/2021] [Indexed: 01/16/2023] Open
Abstract
Background: Blood flow patterns can alter material properties of ascending thoracic aortic aneurysms (ATAA) via vascular wall remodeling. This study examines the relationship between wall shear stress (WSS) obtained from image-based computational modelling with tissue-derived mechanical and microstructural properties of the ATAA wall using segmental analysis. Methods: Ten patients undergoing surgery for ATAA were recruited. Exclusions: bicuspid aortopathy, connective tissue disease. All patients had pre-operative 4-dimensional flow magnetic resonance imaging (4D-MRI), allowing for patient-specific computational fluid dynamics (CFD) analysis and anatomically precise WSS mapping of ATAA regions (6-12 segments per patient). ATAA samples were obtained from surgery and subjected to region-specific tensile and peel testing (matched to WSS segments). Computational pathology was used to characterize elastin/collagen abundance and smooth muscle cell (SMC) count. Results: Elevated values of WSS were predictive of: reduced wall thickness [coef -0.0489, 95% CI (-0.0905, -0.00727), p = 0.022] and dissection energy function (longitudinal) [-15,0, 95% CI (-33.00, -2.98), p = 0.048]. High WSS values also predicted higher ultimate tensile strength [coef 0.136, 95% CI (0 0.001, 0.270), p = 0.048]. Additionally, elevated WSS also predicted a reduction in elastin levels [coef -0.276, 95% (CI -0.531, -0.020), p = 0.035] and lower SMC count ([oef -6.19, 95% CI (-11.41, -0.98), p = 0.021]. WSS was found to have no effect on collagen abundance or circumferential mechanical properties. Conclusions: Our study suggests an association between elevated WSS values and aortic wall degradation in ATAA disease. Further studies might help identify threshold values to predict acute aortic events.
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Affiliation(s)
- M Yousuf Salmasi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Selene Pirola
- Department of Chemical Engineering, Imperial College London, London, United Kingdom
| | - Sumesh Sasidharan
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Serena M Fisichella
- Department of Chemical Engineering, Imperial College London, London, United Kingdom.,Politecnico di Milano, Milan, Italy
| | | | - Omar A Jarral
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Aung Ye Oo
- Barts Heart Centre, London, United Kingdom
| | - James E Moore
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Xiao Yun Xu
- Department of Chemical Engineering, Imperial College London, London, United Kingdom
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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31
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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32
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Bai W, Suzuki H, Huang J, Francis C, Wang S, Tarroni G, Guitton F, Aung N, Fung K, Petersen SE, Piechnik SK, Neubauer S, Evangelou E, Dehghan A, O'Regan DP, Wilkins MR, Guo Y, Matthews PM, Rueckert D. A population-based phenome-wide association study of cardiac and aortic structure and function. Nat Med 2020; 26:1654-1662. [PMID: 32839619 PMCID: PMC7613250 DOI: 10.1038/s41591-020-1009-y] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 07/07/2020] [Indexed: 12/14/2022]
Abstract
Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart-brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.
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Affiliation(s)
- Wenjia Bai
- Data Science Institute, Imperial College London, London, UK. .,Department of Brain Sciences, Imperial College London, London, UK.
| | - Hideaki Suzuki
- Department of Brain Sciences, Imperial College London, London, UK.,Department of Cardiovascular Medicine, Tohoku University Hospital, Sendai, Japan.,Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Jian Huang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.,UK Dementia Research Institute, Imperial College London, London, UK
| | - Catherine Francis
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Shuo Wang
- Data Science Institute, Imperial College London, London, UK
| | - Giacomo Tarroni
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK.,CitAI Research Centre, Department of Computer Science, City University of London, London, UK
| | | | - Nay Aung
- NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Kenneth Fung
- NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Steffen E Petersen
- NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Stefan K Piechnik
- NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- NIHR Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Evangelos Evangelou
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.,Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Abbas Dehghan
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.,UK Dementia Research Institute, Imperial College London, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Martin R Wilkins
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Yike Guo
- Data Science Institute, Imperial College London, London, UK
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London, London, UK.,UK Dementia Research Institute, Imperial College London, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
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33
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Osimo EF, Brugger SP, de Marvao A, Pillinger T, Whitehurst T, Statton B, Quinlan M, Berry A, Cook SA, O'Regan DP, Howes OD. Cardiac structure and function in schizophrenia: cardiac magnetic resonance imaging study. Br J Psychiatry 2020; 217:450-457. [PMID: 31915079 PMCID: PMC7511899 DOI: 10.1192/bjp.2019.268] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Heart disease is the leading cause of death in schizophrenia. However, there has been little research directly examining cardiac function in schizophrenia. AIMS To investigate cardiac structure and function in individuals with schizophrenia using cardiac magnetic resonance imaging (CMR) after excluding medical and metabolic comorbidity. METHOD In total, 80 participants underwent CMR to determine biventricular volumes and function and measures of blood pressure, physical activity and glycated haemoglobin levels. Individuals with schizophrenia ('patients') and controls were matched for age, gender, ethnicity and body surface area. RESULTS Patients had significantly smaller indexed left ventricular (LV) end-diastolic volume (effect size d = -0.82, P = 0.001), LV end-systolic volume (d = -0.58, P = 0.02), LV stroke volume (d = -0.85, P = 0.001), right ventricular (RV) end-diastolic volume (d = -0.79, P = 0.002), RV end-systolic volume (d = -0.58, P = 0.02), and RV stroke volume (d = -0.87, P = 0.001) but unaltered ejection fractions relative to controls. LV concentricity (d = 0.73, P = 0.003) and septal thickness (d = 1.13, P < 0.001) were significantly larger in the patients. Mean concentricity in patients was above the reference range. The findings were largely unchanged after adjusting for smoking and/or exercise levels and were independent of medication dose and duration. CONCLUSIONS Individuals with schizophrenia show evidence of concentric cardiac remodelling compared with healthy controls of a similar age, gender, ethnicity, body surface area and blood pressure, and independent of smoking and activity levels. This could be contributing to the excess cardiovascular mortality observed in schizophrenia. Future studies should investigate the contribution of antipsychotic medication to these changes.
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Affiliation(s)
- Emanuele F. Osimo
- Academic Clinical Fellow in Psychiatry, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus; and Department of Psychiatry, University of Cambridge; and Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Stefan P. Brugger
- Academic Clinical Fellow in Psychiatry, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, UK
| | - Antonio de Marvao
- Clinical Lecturer in Cardiology, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, UK
| | - Toby Pillinger
- Academic Clinical Fellow in Psychiatry, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus; and Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Thomas Whitehurst
- Clinical Research Fellow, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, UK
| | - Ben Statton
- Lead MR Radiographer, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, UK
| | - Marina Quinlan
- MR Radiographer, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, UK
| | - Alaine Berry
- MR Radiographer, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, UK
| | - Stuart A. Cook
- Professor of Clinical and Molecular Cardiology, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, UK
| | - Declan P. O'Regan
- Reader in Imaging Sciences (Consultant Radiologist), MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, UK
| | - Oliver D. Howes
- Professor of Molecular Psychiatry, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus; and Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK,Correspondence: Professor Oliver Howes.
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Biffi C, Cerrolaza JJ, Tarroni G, Bai W, de Marvao A, Oktay O, Ledig C, Le Folgoc L, Kamnitsas K, Doumou G, Duan J, Prasad SK, Cook SA, O'Regan DP, Rueckert D. Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models. IEEE Trans Med Imaging 2020; 39:2088-2099. [PMID: 31944949 PMCID: PMC7269693 DOI: 10.1109/tmi.2020.2964499] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging.
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Grapsa J, Tan TC, Nunes MCP, O'Regan DP, Durighel G, Howard LS, Gibbs JSR, Nihoyannopoulos P. Prognostic impact of right ventricular mass change in patients with idiopathic pulmonary arterial hypertension. Int J Cardiol 2020; 304:172-174. [DOI: 10.1016/j.ijcard.2020.01.052] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 01/04/2020] [Accepted: 01/22/2020] [Indexed: 02/01/2023]
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Jarral OA, Tan MKH, Salmasi MY, Pirola S, Pepper JR, O'Regan DP, Xu XY, Athanasiou T. Phase-contrast magnetic resonance imaging and computational fluid dynamics assessment of thoracic aorta blood flow: a literature review. Eur J Cardiothorac Surg 2020; 57:438-446. [PMID: 31638698 DOI: 10.1093/ejcts/ezz280] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 09/06/2019] [Accepted: 09/17/2019] [Indexed: 11/14/2022] Open
Abstract
The death rate from thoracic aortic disease is on the rise and represents a growing global health concern as patients are often asymptomatic before acute events, which have devastating effects on health-related quality of life. Biomechanical factors have been found to play a major role in the development of both acquired and congenital aortic diseases. However, much is still unknown and translational benefits of this knowledge are yet to be seen. Phase-contrast cardiovascular magnetic resonance imaging of thoracic aortic blood flow has emerged as an exceptionally powerful non-invasive tool enabling visualization of complex flow patterns, and calculation of variables such as wall shear stress. This has led to multiple new findings in the areas of phenotype-dependent bicuspid valve flow patterns, thoracic aortic aneurysm formation and aortic prosthesis performance assessment. Phase-contrast cardiovascular magnetic resonance imaging has also been used in conjunction with computational fluid modelling techniques to produce even more sophisticated analyses, by allowing the calculation of haemodynamic variables with exceptional temporal and spatial resolution. Translationally, these technologies may potentially play a major role in the emergence of precision medicine and patient-specific treatments in patients with aortic disease. This clinically focused review will provide a systematic overview of key insights from published studies to date.
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Affiliation(s)
- Omar A Jarral
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Matthew K H Tan
- Department of Surgery and Cancer, Imperial College London, London, UK
| | | | - Selene Pirola
- Department of Chemical Engineering, Imperial College London, London, UK
| | - John R Pepper
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Xiao Y Xu
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, UK
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38
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Esslinger U, Garnier S, Korniat A, Proust C, Kararigas G, Müller-Nurasyid M, Empana JP, Morley MP, Perret C, Stark K, Bick AG, Prasad SK, Kriebel J, Li J, Tiret L, Strauch K, O'Regan DP, Marguiles KB, Seidman JG, Boutouyrie P, Lacolley P, Jouven X, Hengstenberg C, Komajda M, Hakonarson H, Isnard R, Arbustini E, Grallert H, Cook SA, Seidman CE, Regitz-Zagrosek V, Cappola TP, Charron P, Cambien F, Villard E. Correction: Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy. PLoS One 2020; 15:e0229472. [PMID: 32059048 PMCID: PMC7021299 DOI: 10.1371/journal.pone.0229472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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40
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Affiliation(s)
- Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Timothy Jw Dawes
- MRC London Institute of Medical Sciences, Imperial College London, London, UK.,National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
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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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42
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Annio G, Torii R, Ariff B, O'Regan DP, Muthurangu V, Ducci A, Tsang V, Burriesci G. Enhancing Magnetic Resonance Imaging With Computational Fluid Dynamics. ACTA ACUST UNITED AC 2019. [DOI: 10.1115/1.4045493] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Abstract
The analysis of the blood flow in the great thoracic arteries does provide valuable information about the cardiac function and can diagnose the potential development of vascular diseases. Flow-sensitive four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR) is often used to characterize patients' blood flow in the clinical environment. Nevertheless, limited spatial and temporal resolution hinders a detailed assessment of the hemodynamics. Computational fluid dynamics (CFD) could expand this information and, integrated with experimental velocity field, enable to derive the pressure maps. However, the limited resolution of the 4D flow CMR and the simplifications of CFD modeling compromise the accuracy of the computed flow parameters. In this article, a novel approach is proposed, where 4D flow CMR and CFD velocity fields are integrated synergistically to obtain an enhanced MR imaging (EMRI). The approach was first tested on a two-dimensional (2D) portion of a pipe, to understand the behavior of the parameters of the model in this novel framework, and afterwards in vivo, to apply it to the analysis of blood flow in a patient-specific human aorta. The outcomes of EMRI are assessed by comparing the computed velocities with the experimental one. The results demonstrate that EMRI preserves flow structures while correcting for experimental noise. Therefore, it can provide better insights into the hemodynamics of cardiovascular problems, overcoming the limitations of MRI and CFD, even when considering a small region of interest. EMRI confirmed its potential to provide more accurate noninvasive estimation of major cardiovascular risk predictors (e.g., flow patterns, endothelial shear stress) and become a novel diagnostic tool.
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Affiliation(s)
- Giacomo Annio
- Department Medical Physics and Bioengineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Ryo Torii
- UCL Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Ben Ariff
- MRC London Institute of Medical Sciences, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Declan P. O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Vivek Muthurangu
- UCL Institute of Cardiovascular Science, Centre for Cardiovascular Imaging, University College London, 62 Huntley Street, Fitzrovia, London WC1E 6DD, UK; Great Ormond Street Hospital for Children, Great Ormond Street, Holborn, London WC1N 3JH, UK
| | - Andrea Ducci
- UCL Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Victor Tsang
- Cardiothoracic Surgery Unit, Great Ormond Street Hospital for Children, Great Ormond Street, Holborn, London WC1N 3JH, UK
| | - Gaetano Burriesci
- UCL Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK; Ri.MED Foundation, Via Bandiera, 11, Palermo 90133, Italy
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43
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Orini M, Graham AJ, Martinez-Naharro A, Andrews CM, de Marvao A, Statton B, Cook SA, O'Regan DP, Hawkins PN, Rudy Y, Fontana M, Lambiase PD. Noninvasive Mapping of the Electrophysiological Substrate in Cardiac Amyloidosis and Its Relationship to Structural Abnormalities. J Am Heart Assoc 2019; 8:e012097. [PMID: 31496332 PMCID: PMC6818012 DOI: 10.1161/jaha.119.012097] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background The relationship between structural pathology and electrophysiological substrate in cardiac amyloidosis is unclear. Differences between light‐chain (AL) and transthyretin (ATTR) cardiac amyloidosis may have prognostic implications. Methods and Results ECG imaging and cardiac magnetic resonance studies were conducted in 21 cardiac amyloidosis patients (11 AL and 10 ATTR). Healthy volunteers were included as controls. With respect to ATTR, AL patients had lower amyloid volume (51.0/37.7 versus 73.7/16.4 mL, P=0.04), lower myocardial cell volume (42.6/19.1 versus 58.5/17.2 mL, P=0.021), and higher T1 (1172/64 versus 1109/80 ms, P=0.022) and T2 (53.4/2.9 versus 50.0/3.1 ms, P=0.003). ECG imaging revealed differences between cardiac amyloidosis and control patients in virtually all conduction‐repolarization parameters. With respect to ATTR, AL patients had lower epicardial signal amplitude (1.07/0.46 versus 1.83/1.26 mV, P=0.026), greater epicardial signal fractionation (P=0.019), and slightly higher dispersion of repolarization (187.6/65 versus 158.3/40 ms, P=0.062). No significant difference between AL and ATTR patients was found using the standard 12‐lead ECG. T1 correlated with epicardial signal amplitude (cc=−0.78), and extracellular volume with epicardial signal fractionation (cc=0.48) and repolarization time (cc=0.43). Univariate models based on single features from both cardiac magnetic resonance and ECG imaging classified AL and ATTR patients with an accuracy of 70% to 80%. Conclusions In this exploratory study cardiac amyloidosis was associated with ventricular conduction and repolarization abnormalities, which were more pronounced in AL than in ATTR. Combined ECG imaging–cardiac magnetic resonance analysis supports the hypothesis that additional mechanisms beyond infiltration may contribute to myocardial damage in AL amyloidosis. Further studies are needed to assess the clinical impact of this approach.
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Affiliation(s)
- Michele Orini
- Barts Heart Centre Barts Health NHS Trust London United Kingdom.,Institute of Cardiovascular Science University College London London United Kingdom
| | - Adam J Graham
- Barts Heart Centre Barts Health NHS Trust London United Kingdom
| | | | - Christopher M Andrews
- Cardiac Bioelectricity and Arrhythmia Center Washington University in St Louis St. Louis MO
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences Imperial College London London United Kingdom
| | - Ben Statton
- MRC London Institute of Medical Sciences Imperial College London London United Kingdom
| | - Stuart A Cook
- MRC London Institute of Medical Sciences Imperial College London London United Kingdom
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences Imperial College London London United Kingdom
| | - Philip N Hawkins
- The Royal Free Hospital UCL Hospitals Trust London United Kingdom
| | - Yoram Rudy
- Cardiac Bioelectricity and Arrhythmia Center Washington University in St Louis St. Louis MO
| | - Marianna Fontana
- The Royal Free Hospital UCL Hospitals Trust London United Kingdom
| | - Pier D Lambiase
- Barts Heart Centre Barts Health NHS Trust London United Kingdom.,Institute of Cardiovascular Science University College London London United Kingdom
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Duan J, Bello G, Schlemper J, Bai W, Dawes TJW, Biffi C, de Marvao A, Doumoud G, O'Regan DP, Rueckert D. Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach. IEEE Trans Med Imaging 2019; 38:2151-2164. [PMID: 30676949 PMCID: PMC6728160 DOI: 10.1109/tmi.2019.2894322] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialize atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artifacts in input CMR volumes.
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Lee AWC, O'Regan DP, Gould J, Sidhu B, Sieniewicz B, Plank G, Warriner DR, Lamata P, Rinaldi CA, Niederer SA. Sex-Dependent QRS Guidelines for Cardiac Resynchronization Therapy Using Computer Model Predictions. Biophys J 2019; 117:2375-2381. [PMID: 31547974 PMCID: PMC6990372 DOI: 10.1016/j.bpj.2019.08.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/16/2019] [Accepted: 08/12/2019] [Indexed: 01/30/2023] Open
Abstract
Cardiac resynchronization therapy (CRT) is an important treatment for heart failure. Low female enrollment in clinical trials means that current CRT guidelines may be biased toward males. However, females have higher response rates at lower QRS duration (QRSd) thresholds. Sex differences in the left ventricle (LV) size could provide an explanation for the improved female response at lower QRSd. We aimed to test if sex differences in CRT response at lower QRSd thresholds are explained by differences in LV size and hence predict sex-specific guidelines for CRT. We investigated the effect that LV size sex difference has on QRSd between male and females in 1093 healthy individuals and 50 CRT patients using electrophysiological computer models of the heart. Simulations on the healthy mean shape models show that LV size sex difference can account for 50–100% of the sex difference in baseline QRSd in healthy individuals. In the CRT patient cohort, model simulations predicted female-specific guidelines for CRT, which were 9–13 ms lower than current guidelines. Sex differences in the LV size are able to account for a significant proportion of the sex difference in QRSd and provide a mechanistic explanation for the sex difference in CRT response. Simulations accounting for the smaller LV size in female CRT patients predict 9–13 ms lower QRSd thresholds for female CRT guidelines.
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Affiliation(s)
- Angela W C Lee
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Baldeep Sidhu
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Benjamin Sieniewicz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gernot Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - David R Warriner
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Pablo Lamata
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
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Ware JS, Amor-Salamanca A, Tayal U, Govind R, Serrano I, Salazar-Mendiguchía J, García-Pinilla JM, Pascual-Figal DA, Nuñez J, Guzzo-Merello G, Gonzalez-Vioque E, Bardaji A, Manito N, López-Garrido MA, Padron-Barthe L, Edwards E, Whiffin N, Walsh R, Buchan RJ, Midwinter W, Wilk A, Prasad S, Pantazis A, Baski J, O'Regan DP, Alonso-Pulpon L, Cook SA, Lara-Pezzi E, Barton PJ, Garcia-Pavia P. Genetic Etiology for Alcohol-Induced Cardiac Toxicity. J Am Coll Cardiol 2019; 71:2293-2302. [PMID: 29773157 PMCID: PMC5957753 DOI: 10.1016/j.jacc.2018.03.462] [Citation(s) in RCA: 161] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 02/14/2018] [Accepted: 03/01/2018] [Indexed: 01/23/2023]
Abstract
BACKGROUND Alcoholic cardiomyopathy (ACM) is defined by a dilated and impaired left ventricle due to chronic excess alcohol consumption. It is largely unknown which factors determine cardiac toxicity on exposure to alcohol. OBJECTIVES This study sought to evaluate the role of variation in cardiomyopathy-associated genes in the pathophysiology of ACM, and to examine the effects of alcohol intake and genotype on dilated cardiomyopathy (DCM) severity. METHODS The authors characterized 141 ACM cases, 716 DCM cases, and 445 healthy volunteers. The authors compared the prevalence of rare, protein-altering variants in 9 genes associated with inherited DCM. They evaluated the effect of genotype and alcohol consumption on phenotype in DCM. RESULTS Variants in well-characterized DCM-causing genes were more prevalent in patients with ACM than control subjects (13.5% vs. 2.9%; p = 1.2 ×10-5), but similar between patients with ACM and DCM (19.4%; p = 0.12) and with a predominant burden of titin truncating variants (TTNtv) (9.9%). Separately, we identified an interaction between TTN genotype and excess alcohol consumption in a cohort of DCM patients not meeting ACM criteria. On multivariate analysis, DCM patients with a TTNtv who consumed excess alcohol had an 8.7% absolute reduction in ejection fraction (95% confidence interval: -2.3% to -15.1%; p < 0.007) compared with those without TTNtv and excess alcohol consumption. The presence of TTNtv did not predict phenotype, outcome, or functional recovery on treatment in ACM patients. CONCLUSIONS TTNtv represent a prevalent genetic predisposition for ACM, and are also associated with a worse left ventricular ejection fraction in DCM patients who consume alcohol above recommended levels. Familial evaluation and genetic testing should be considered in patients presenting with ACM.
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Affiliation(s)
- James S Ware
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom; MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Almudena Amor-Salamanca
- Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro, Madrid, Spain
| | - Upasana Tayal
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - Risha Govind
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom; Institute of Psychiatry, Psychology and Neuroscience, Social Genetic and Developmental Psychiatry Centre, King's College London, London, United Kingdom
| | - Isabel Serrano
- Department of Cardiology, Hospital Universitario de Tarragona Joan XXIII, IISPV, Rovira Virgili University, Tarragona, Spain
| | - Joel Salazar-Mendiguchía
- Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Bellvitge, Barcelona, Spain; Genetics Department, Universidad Autónoma de Barcelona, Barcelona, Spain
| | - Jose Manuel García-Pinilla
- CIBER in Cardiovascular Diseases, Madrid, Spain; Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Virgen de la Victoria, IBIMA, Málaga, Spain
| | - Domingo A Pascual-Figal
- CIBER in Cardiovascular Diseases, Madrid, Spain; Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, IMIB-Arrixaca, University of Murcia, Murcia, Spain
| | - Julio Nuñez
- CIBER in Cardiovascular Diseases, Madrid, Spain; Cardiology Department, Hospital Clínico Universitario, INCLIVA Universitat de Valencia, Valencia, Spain
| | - Gonzalo Guzzo-Merello
- Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro, Madrid, Spain
| | | | - Alfredo Bardaji
- Department of Cardiology, Hospital Universitario de Tarragona Joan XXIII, IISPV, Rovira Virgili University, Tarragona, Spain
| | - Nicolas Manito
- Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Bellvitge, Barcelona, Spain
| | - Miguel A López-Garrido
- CIBER in Cardiovascular Diseases, Madrid, Spain; Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Virgen de la Victoria, IBIMA, Málaga, Spain
| | - Laura Padron-Barthe
- Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro, Madrid, Spain; CIBER in Cardiovascular Diseases, Madrid, Spain
| | - Elizabeth Edwards
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - Nicola Whiffin
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom; MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Roddy Walsh
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - Rachel J Buchan
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - William Midwinter
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - Alicja Wilk
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - Sanjay Prasad
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - Antonis Pantazis
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - John Baski
- Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Luis Alonso-Pulpon
- Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro, Madrid, Spain; CIBER in Cardiovascular Diseases, Madrid, Spain
| | - Stuart A Cook
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom; National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore; Division of Cardiovascular & Metabolic Disorders, Duke-National University of Singapore, Singapore
| | - Enrique Lara-Pezzi
- CIBER in Cardiovascular Diseases, Madrid, Spain; Myocardial Biology Programme, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Paul J Barton
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton and Harefield NHS Foundation Trust London, London, United Kingdom.
| | - Pablo Garcia-Pavia
- Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro, Madrid, Spain; CIBER in Cardiovascular Diseases, Madrid, Spain; University Francisco de Vitoria (UFV), Pozuelo de Alarcón, Madrid, Spain.
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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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Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, de Marvao A, O'Regan DP, Cook S, Glocker B, Matthews PM, Rueckert D. Learning-Based Quality Control for Cardiac MR Images. IEEE Trans Med Imaging 2019; 38:1127-1138. [PMID: 30403623 DOI: 10.1109/tmi.2018.2878509] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation; 2) inter-slice motion detection; 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method-integrating both regression and structured classification models-to extract landmarks and probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank and on 100 cases from the UK Digital Heart Project and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88% and 99% for heart coverage estimation and 85% and 95% for motion detection), allowing their exclusion from the analyzed dataset or the triggering of a new acquisition.
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Dawes TJW, de Marvao A, Shi W, Rueckert D, Cook SA, O'Regan DP. Identifying the optimal regional predictor of right ventricular global function: a high-resolution three-dimensional cardiac magnetic resonance study. Anaesthesia 2018; 74:312-320. [PMID: 30427059 PMCID: PMC6767156 DOI: 10.1111/anae.14494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2018] [Indexed: 12/17/2022]
Abstract
Right ventricular (RV) function has prognostic value in acute, chronic and peri‐operative disease, although the complex RV contractile pattern makes rapid assessment difficult. Several two‐dimensional (2D) regional measures estimate RV function, however the optimal measure is not known. High‐resolution three‐dimensional (3D) cardiac magnetic resonance cine imaging was acquired in 300 healthy volunteers and a computational model of RV motion created. Points where regional function was significantly associated with global function were identified and a 2D, optimised single‐point marker (SPM‐O) of global function developed. This marker was prospectively compared with tricuspid annular plane systolic excursion (TAPSE), septum‐freewall displacement (SFD) and their fractional change (TAPSE‐F, SFD‐F) in a test cohort of 300 patients in the prediction of RV ejection fraction. RV ejection fraction was significantly associated with systolic function in a contiguous 7.3 cm2 patch of the basal RV freewall combining transverse (38%), longitudinal (35%) and circumferential (27%) contraction and coinciding with the four‐chamber view. In the test cohort, all single‐point surrogates correlated with RV ejection fraction (p < 0.010), but correlation (R) was higher for SPM‐O (R = 0.44, p < 0.001) than TAPSE (R = 0.24, p < 0.001) and SFD (R = 0.22, p < 0.001), and non‐significantly higher than TAPSE‐F (R = 0.40, p < 0.001) and SFD‐F (R = 0.43, p < 0.001). SPM‐O explained more of the observed variance in RV ejection fraction (19%) and predicted it more accurately than any other 2D marker (median error 2.8 ml vs 3.6 ml, p < 0.001). We conclude that systolic motion of the basal RV freewall predicts global function more accurately than other 2D estimators. However, no markers summarise 3D contractile patterns, limiting their predictive accuracy.
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Affiliation(s)
- T J W Dawes
- National Heart and Lung Institute, Imperial College London, London, UK
| | - A de Marvao
- Medical Research Council London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - W Shi
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
| | - D Rueckert
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
| | - S A Cook
- Department of Clinical and Molecular Cardiology, Medical Research Council London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, London, UK.,Department of Cardiology, National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore
| | - D P O'Regan
- Medical Research Council London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, London, UK
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Shirvani S, Tokarczuk P, Statton B, Quinlan M, Berry A, Tomlinson J, Weale P, Kühn B, O'Regan DP. Motion-corrected multiparametric renal arterial spin labelling at 3 T: reproducibility and effect of vasodilator challenge. Eur Radiol 2018; 29:232-240. [PMID: 29992384 PMCID: PMC6291439 DOI: 10.1007/s00330-018-5628-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 06/14/2018] [Accepted: 06/22/2018] [Indexed: 02/02/2023]
Abstract
OBJECTIVES We investigated the feasibility and reproducibility of free-breathing motion-corrected multiple inversion time (multi-TI) pulsed renal arterial spin labelling (PASL), with general kinetic model parametric mapping, to simultaneously quantify renal perfusion (RBF), bolus arrival time (BAT) and tissue T1. METHODS In a study approved by the Health Research Authority, 12 healthy volunteers (mean age, 27.6 ± 18.5 years; 5 male) gave informed consent for renal imaging at 3 T using multi-TI ASL and conventional single-TI ASL. Glyceryl trinitrate (GTN) was used as a vasodilator challenge in six subjects. Flow-sensitive alternating inversion recovery (FAIR) preparation was used with background suppression and 3D-GRASE (gradient and spin echo) read-out, and images were motion-corrected. Parametric maps of RBF, BAT and T1 were derived for both kidneys. Agreement was assessed using Pearson correlation and Bland-Altman plots. RESULTS Inter-study correlation of whole-kidney RBF was good for both single-TI (r2 = 0.90), and multi-TI ASL (r2 = 0.92). Single-TI ASL gave a higher estimate of whole-kidney RBF compared to multi-TI ASL (mean bias, 29.3 ml/min/100 g; p <0.001). Using multi-TI ASL, the median T1 of renal cortex was shorter than that of medulla (799.6 ms vs 807.1 ms, p = 0.01), and mean whole-kidney BAT was 269.7 ± 56.5 ms. GTN had an effect on systolic blood pressure (p < 0.05) but the change in RBF was not significant. CONCLUSIONS Free-breathing multi-TI renal ASL is feasible and reproducible at 3 T, providing simultaneous measurement of renal perfusion, haemodynamic parameters and tissue characteristics at baseline and during pharmacological challenge. KEY POINTS • Multiple inversion time arterial spin labelling (ASL) of the kidneys is feasible and reproducible at 3 T. • This approach allows simultaneous mapping of renal perfusion, bolus arrival time and tissue T 1 during free breathing. • This technique enables repeated measures of renal haemodynamic characteristics during pharmacological challenge.
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Affiliation(s)
- Saba Shirvani
- Medical Research Council (MRC), London Institute of Medical Sciences (LMS), Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
- Department of Chemistry, Imperial College London, South Kensington Campus, Exhibition Road, London, UK
| | - Paweł Tokarczuk
- Medical Research Council (MRC), London Institute of Medical Sciences (LMS), Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Ben Statton
- Medical Research Council (MRC), London Institute of Medical Sciences (LMS), Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Marina Quinlan
- Medical Research Council (MRC), London Institute of Medical Sciences (LMS), Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Alaine Berry
- Medical Research Council (MRC), London Institute of Medical Sciences (LMS), Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - James Tomlinson
- Medical Research Council (MRC), London Institute of Medical Sciences (LMS), Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | | | - Bernd Kühn
- Siemens Healthcare GmbH, Erlangen, Germany
| | - Declan P O'Regan
- Medical Research Council (MRC), London Institute of Medical Sciences (LMS), Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK.
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