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Antoniades C, Tousoulis D, Vavlukis M, Fleming I, Duncker DJ, Eringa E, Manfrini O, Antonopoulos AS, Oikonomou E, Padró T, Trifunovic-Zamaklar D, De Luca G, Guzik T, Cenko E, Djordjevic-Dikic A, Crea F. Perivascular adipose tissue as a source of therapeutic targets and clinical biomarkers. Eur Heart J 2023; 44:3827-3844. [PMID: 37599464 PMCID: PMC10568001 DOI: 10.1093/eurheartj/ehad484] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/03/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Obesity is a modifiable cardiovascular risk factor, but adipose tissue (AT) depots in humans are anatomically, histologically, and functionally heterogeneous. For example, visceral AT is a pro-atherogenic secretory AT depot, while subcutaneous AT represents a more classical energy storage depot. Perivascular adipose tissue (PVAT) regulates vascular biology via paracrine cross-talk signals. In this position paper, the state-of-the-art knowledge of various AT depots is reviewed providing a consensus definition of PVAT around the coronary arteries, as the AT surrounding the artery up to a distance from its outer wall equal to the luminal diameter of the artery. Special focus is given to the interactions between PVAT and the vascular wall that render PVAT a potential therapeutic target in cardiovascular diseases. This Clinical Consensus Statement also discusses the role of PVAT as a clinically relevant source of diagnostic and prognostic biomarkers of vascular function, which may guide precision medicine in atherosclerosis, hypertension, heart failure, and other cardiovascular diseases. In this article, its role as a 'biosensor' of vascular inflammation is highlighted with description of recent imaging technologies that visualize PVAT in clinical practice, allowing non-invasive quantification of coronary inflammation and the related residual cardiovascular inflammatory risk, guiding deployment of therapeutic interventions. Finally, the current and future clinical applicability of artificial intelligence and machine learning technologies is reviewed that integrate PVAT information into prognostic models to provide clinically meaningful information in primary and secondary prevention.
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Affiliation(s)
- Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
| | - Dimitris Tousoulis
- 1st Cardiology Department, National and Kapodistrian University of Athens, Greece
| | - Marija Vavlukis
- Medical Faculty, University Clinic for Cardiology, University Ss’ Cyril and Methodius in Skopje, Skopje, North Macedonia
| | - Ingrid Fleming
- Institute for Vascular Signalling, Centre of Molecular Medicine, Goethe University, Frankfurt, Germany
| | - Dirk J Duncker
- Department of Cardiology, Thorax Center, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Etto Eringa
- Cardiovascular-Program ICCC, Research Institute—Hospital Santa Creu i Sant Pau, IIB-Sant Pau, Barcelona, Spain
| | - Olivia Manfrini
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Alexios S Antonopoulos
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
- 1st Cardiology Department, National and Kapodistrian University of Athens, Greece
| | - Evangelos Oikonomou
- 1st Cardiology Department, National and Kapodistrian University of Athens, Greece
| | - Teresa Padró
- Cardiovascular Program-ICCC, Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain
- CiberCV, Institute Carlos III, Madrid, Spain
| | | | - Giuseppe De Luca
- Division of Cardiology, AOU Policlinico G. Martino, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
- Cardiologia Ospedaliera, Nuovo Galeazzi-Sant’Ambrogio, Milan, Italy
| | - Tomasz Guzik
- Cardiovascular Science, Queens Medical Research Institute, University of Edinburgh, UK
- Department of Medicine, Jagiellonian University, Collegium Medicum, Krakow, Poland
| | - Edina Cenko
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Ana Djordjevic-Dikic
- Medical Faculty, Cardiology Clinic, University Clinical Center, University of Belgrade, Serbia
| | - Filippo Crea
- Department of Cardiology and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
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Bhagirath P, Campos FO, Postema PG, Kemme MJB, Wilde AAM, Prassl AJ, Neic A, Rinaldi CA, Götte MJW, Plank G, Bishop MJ. Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling. Europace 2023; 25:euad198. [PMID: 37421339 PMCID: PMC10481251 DOI: 10.1093/europace/euad198] [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] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/26/2023] [Accepted: 06/21/2023] [Indexed: 07/10/2023] Open
Abstract
AIMS Substrate assessment of scar-mediated ventricular tachycardia (VT) is frequently performed using late gadolinium enhancement (LGE) images. Although this provides structural information about critical pathways through the scar, assessing the vulnerability of these pathways for sustaining VT is not possible with imaging alone.This study evaluated the performance of a novel automated re-entrant pathway finding algorithm to non-invasively predict VT circuit and inducibility. METHODS Twenty post-infarct VT-ablation patients were included for retrospective analysis. Commercially available software (ADAS3D left ventricular) was used to generate scar maps from 2D-LGE images using the default 40-60 pixel-signal-intensity (PSI) threshold. In addition, algorithm sensitivity for altered thresholds was explored using PSI 45-55, 35-65, and 30-70. Simulations were performed on the Virtual Induction and Treatment of Arrhythmias (VITA) framework to identify potential sites of block and assess their vulnerability depending on the automatically computed round-trip-time (RTT). Metrics, indicative of substrate complexity, were correlated with VT-recurrence during follow-up. RESULTS Total VTs (85 ± 43 vs. 42 ± 27) and unique VTs (9 ± 4 vs. 5 ± 4) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with area under the curve of 0.820 and 0.770, respectively. VITA was robust to scar threshold variations with no significant impact on total and unique VTs, and mean RTT between the four models. Simulation metrics derived from PSI 45-55 model had the highest number of parameters predictive for post-ablation VT-recurrence. CONCLUSION Advanced computational metrics can non-invasively and robustly assess VT substrate complexity, which may aid personalized clinical planning and decision-making in the treatment of post-infarction VT.
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Affiliation(s)
- Pranav Bhagirath
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
- Department of Cardiology, St Thomas' Hospital, London SE1 7EH, UK
| | - Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
| | - Pieter G Postema
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Michiel J B Kemme
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Aurel Neic
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Marco J W Götte
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
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