Bialobroda Jonathan, Bouazizi Khaoula, Ponnaiah Maharajah, Kachenoura Nadjia, Charpentier Etienne, Zarai Mohamed, Clement Karine, Andreelli Fabrizio, Aron-Wisnewsky Judith, Hatem Stéphane N, Redheuil Alban
Institute of Cardiology, Foundation for Innovation in Cardiometabolism and Nutrition-ICAN, INSERM UMRS 1166, Sorbonne Université, AP-HP Pitié-Salpêtrière University Hospital, 47-83, Boulevard de l'Hôpital, 75013 Paris, France.
Laboratoire d'Imagerie Biomédicale, CNRS, INSERM UMR 1146, Sorbonne Université, Paris, France.
Eur Heart J Imaging Methods Pract. 2024 Jun 14;2(1):qyae057. doi: 10.1093/ehjimp/qyae057. eCollection 2024 Jan.
The growing interest in epicardial adipose tissue (EAT) as a biomarker of atrial fibrillation is limited by the difficulties in isolating EAT from other paracardial adipose tissues. We tested the feasibility and value of measuring the pure EAT contained in the atrioventricular groove (GEAT) using cardiovascular magnetic resonance (CMR) imaging in patients with distinct metabolic disorders.
CMR was performed on 100 patients from the MetaCardis cohort: obese ( = 18), metabolic syndrome (MSD) ( = 25), type-2 diabetes (T2D) ( = 42), and age- and gender-matched healthy controls ( = 15). GEAT volume measured from long-axis views was obtained in all patients with a strong correlation between GEAT and atrial EAT ( = 0.95; < 0.0001). GEAT volume was higher in the three groups of patients with metabolic disorders and highest in the MSD group compared with controls. GEAT volume, as well as body mass and body fat, allowed obese, T2D, and MSD patients to be distinguished from controls. GEAT T1 relaxation and peak longitudinal left atrial (LA) strain in CMR were decreased in T2D patients. Logistic regression and random forest machine learning methods were used to create an algorithm combining GEAT volume, GEAT T1, and peak LA strain to identify T2D patients from other groups with an area under curve (AUC) of 0.81 (Se: 77%, Spe: 80%; 95% confidence interval 0.72-0.91, < 0.0001).
Atrioventricular groove adipose tissue characteristics measured during routine CMR can be used as a proxy of atrial EAT and integrated in a multi-parametric CMR biomarker for early identification of atrial cardiomyopathy.
心外膜脂肪组织（EAT）作为心房颤动生物标志物的研究兴趣日益增加，但从其他心包旁脂肪组织中分离EAT存在困难，限制了相关研究。我们测试了在患有不同代谢紊乱的患者中，使用心血管磁共振（CMR）成像测量房室沟（GEAT）中纯EAT的可行性和价值。
对来自MetaCardis队列的100名患者进行了CMR检查：肥胖患者（n = 18）、代谢综合征（MSD）患者（n = 25）、2型糖尿病（T2D）患者（n = 42）以及年龄和性别匹配的健康对照者（n = 15）。从长轴视图测量所有患者的GEAT体积，GEAT与心房EAT之间具有很强的相关性（r = 0.95；P < 0.0001）。与对照组相比，三组代谢紊乱患者的GEAT体积更高，其中MSD组最高。GEAT体积以及体重和体脂能够区分肥胖、T2D和MSD患者与对照组。T2D患者的CMR中GEAT T1弛豫和左心房（LA）纵向峰值应变降低。使用逻辑回归和随机森林机器学习方法创建了一种算法，该算法结合GEAT体积、GEAT T1和LA峰值应变，以从其他组中识别T2D患者，曲线下面积（AUC）为0.81（敏感性：77%，特异性：80%；95%置信区间0.72 - 0.91，P < 0.0001）。
在常规CMR检查期间测量的房室沟脂肪组织特征可作为心房EAT的替代指标，并整合到多参数CMR生物标志物中，用于早期识别心房心肌病。
