Decoding the radiomic and proteomic phenotype of epicardial adipose tissue associated with adverse left atrial remodelling and post-operative atrial fibrillation in aortic stenosis

Abstract

Aims Epicardial adipose tissue (EAT) volume and attenuation on computed tomography (CT) have been associated with atrial fibrillation. Beyond these conventional CT measures, radiomics allows extraction of high-dimensional data and deep quantitative adipose tissue phenotyping, which may capture its underlying biology. We aimed to explore the EAT proteomic and CT-radiomic signatures associated with impaired left atrial (LA) remodelling and post-operative atrial fibrillation (POAF). Methods and results We prospectively included 132 patients with severe aortic stenosis with no prior atrial fibrillation referred for aortic valve replacement. Pre-operative non-contrast CT images were obtained for extraction of EAT volume and other radiomic features describing EAT texture. The LA function was assessed by 2D-speckle-tracking echocardiography peak atrial longitudinal strain and peak atrial contraction strain. The EAT biopsies were performed during surgery for proteomic analysis by sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS). The POAF incidence was monitored from surgery until discharge. Impaired LA function and incident POAF were associated with EAT up-regulation of inflammatory and thrombotic proteins, and down-regulation of cardioprotective proteins with anti-inflammatory and anti-lipotoxic properties. The EAT volume was independently associated with LA enlargement, impaired function, and POAF risk. On CT images, EAT texture of patients with POAF was heterogeneous and exhibited higher maximum grey-level values than sinus rhythm patients, which correlated with up-regulation of inflammatory and down-regulation of lipid droplet-formation EAT proteins. The CT radiomics of EAT provided an area under the curve of 0.80 (95% confidence interval: 0.68–0.92) for discrimination between patients with POAF and sinus rhythm. Conclusion Pre-operative CT-radiomic profile of EAT detected adverse EAT proteomics and identified patients at risk of developing POAF.

Influence of epicardial adipose tissue inflammation and adipocyte size on postoperative atrial fibrillation in patients after cardiovascular surgery

“…Although increased adipocyte size is generally associated with detrimental condition including inflammation (Song & Kuang, 2019), recent studies using peri-coronary EAT and EAT distributed around the heart have suggested that adipocytes may become smaller in relation to the increased release of proinflammatory cytokines in AF patients (Ishii et al, 2021;Mocanu et al, 2020). Interestingly, Mancio et al (2022) have suggested that impaired EAT adipocyte differentiation and lipid droplet formation are associated with POAF. Although the mechanistic insight into the relationship between adipocyte size and AF largely remains undiscovered, prior studies examining the EAT profile in coronary artery stenosis may help explain the mechanism involved.…”

Radiomics signature of epicardial adipose tissue for predicting postoperative atrial fibrillation after pulmonary endarterectomy

“…To the best of our knowledge, the present study is the first study to construct a radiomics signature of EAT in the prediction of POAF after PEA. A recent study, however, did find that the radiomics profile of EAT could predict POAF in aortic valve replacement patients ( 24 ). This suggests that the radiomics signature of EAT can potentially be used to identify patients at high risk of developing POAF.…”

“…To a certain extent, the EAT volume and radiodensity reflect its pathological status. It is reported that EAT volume and radiodensity were found to be associated with the occurrence, severity, and recurrence of AF ( 17 – 20 , 24 ). Across our whole cohort, the EAT volume was significantly different between patients with and without POAF, but not so much so that it was an independent predictor according to multivariate analysis.…”

“…Radiomics quantitatively assesses tissue heterogeneity, which is an objective measure but not visually recognizable, by reflecting the distribution of gray level values and spatial arrangement of the pixels ( 34 ). Radiomics analysis of EAT has been previously shown to be useful in identifying AF ( 23 , 24 ), differentiating AF characteristics, and predicting AF recurrence ( 14 ). These studies revealed that radiomics analysis of EAT may have the potential to provide an accurate prediction for POAF after PEA.…”

“…It can also provide and uncover quantitative disease characteristics that fail to be detected by observational measures ( 22 ). Radiomics analysis of EAT has been previously proven to be useful in identifying AF ( 23 , 24 ), differentiating AF characteristics, and predicting AF recurrence ( 14 ). Therefore, radiomics may provide additional information beyond EAT volume and radiodensity.…”