Key result
An algorithm combining atrioventricular groove epicardial adipose tissue volume, T1 relaxation, and peak left atrial strain identified type-2 diabetes patients with an AUC of 0.81 (P<0.0001).
Why the study?
Use of epicardial adipose tissue as a biomarker of atrial fibrillation is limited by difficulties isolating it from paracardial adipose tissues, prompting evaluation of measuring pure atrioventricular groove epicardial adipose tissue on CMR.
Does CMR-derived atrioventricular groove epicardial adipose tissue (GEAT) correlate with atrial EAT and identify atrial cardiomyopathy in patients with metabolic disorders?
Cohort (n=100)
Does CMR-derived atrioventricular groove epicardial adipose tissue (GEAT) correlate with atrial EAT and identify atrial cardiomyopathy in patients with metabolic disorders?
Atrioventricular groove adipose tissue measured by routine CMR strongly correlates with atrial epicardial adipose tissue and can be integrated into a multi-parametric biomarker to identify atrial cardiomyopathy in metabolic disorders.
Aims 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. Methods and results CMR was performed on 100 patients from the MetaCardis cohort: obese (n = 18), metabolic syndrome (MSD) (n = 25), type-2 diabetes (T2D) (n = 42), and age- and gender-matched healthy controls (n = 15). GEAT volume measured from long-axis views was obtained in all patients with a strong correlation between GEAT and atrial EAT (r = 0.95; P < 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, P < 0.0001). Conclusion 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.
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Bialobroda et al. (2024) conducted a cohort in Metabolic disorders (obesity, metabolic syndrome, type-2 diabetes) (n=100). Measurement of atrioventricular groove epicardial adipose tissue (GEAT) using CMR vs. Healthy controls was evaluated on Identification of T2D patients from other groups using an algorithm combining GEAT volume, GEAT T1, and peak LA strain (AUC 0.81, 95% CI 0.72-0.91, p=<0.0001). An algorithm combining atrioventricular groove epicardial adipose tissue volume, T1 relaxation, and peak left atrial strain identified type-2 diabetes patients with an AUC of 0.81 (P<0.0001).
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