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Perivascular adipose tissue as a source of therapeutic targets and clinical biomarkers A clinical consensus statement from the European Society of Cardiology Working Group on Coronary Pathophysiology and Micro-circulation

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Date: 21 August 2023
Journal: European Heart Journal , Volume 44 , Issue 38 , Pages 3827 - 3844
Topic: IMAGING, Cardiac Computed Tomography (CT), PREVENTIVE CARDIOLOGY, Risk Factors and Prevention
Authors: C. Antoniades , D. Tousoulis , M. Vavlukis , I. Fleming , D. Duncker , E. Eringa , O. Manfrini , A. Antonopoulos , E. Oikonomou , T. Padró , D. Trifunovic-Zamaklar , G. De Luca , T. Guzik , E. Cenko , A. Djordjevic-Dikic , F. Crea

ESC Journals

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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About the contributors

Charalambos Antoniades

Role: Author

Dimitris Tousoulis

Role: Author

Marija Vavlukis

Role: Author