Association of epicardial fat volume index with coronary artery disease phenotypes based on anatomical and functional imaging: a linkage with the severity of coronary artery disease.

Background: Epicardial adipose tissue (EAT) plays an important role in the pathogenesis of coronary artery disease (CAD). The association between EAT and obstructive CAD or myocardial ischemia has been established, but its relationship with CAD phenotypes based on anatomical and functional imaging remains unclear.

Methods: A total of 495 suspected CAD patients who underwent both single-photon emission computed tomography/computed tomography myocardial perfusion imaging (SPECT/CT MPI) and coronary angiography (CAG/CTA) were enrolled in this retrospective study. Epicardial fat volume (EFV) and epicardial fat volume indexed to body surface (EFVi) were measured on non-contrast CT. CAD phenotypes were categorized into 4 groups based on the presence or absence of obstructive CAD (any epicardial coronary diameter stenosis ≥ 50% by CAG/CTA) and myocardial ischemia (diagnosed by MPI): Group1 (non-obstructive CAD without ischemia, n = 165), Group2 (ischemia with non-obstructive CAD, INOCA, n = 69), Group3 (obstructive CAD without ischemia, n = 149), Group4 (obstructive CAD with ischemia, n = 112).

Results: Both EFV and EFVi had an increasing trend across 4 groups [EFVi: median (interquartile range), cm³/m²: 80.54 (68.10-102.37) vs. 84.44 (73.57-100.93) vs. 89.63 (75.39-103.15) vs. 91.67 (76.48-111.66), p = 0.007, p for trend < 0.001]. In adjusted ordered logistic regression model, EFVi was independently associated with more advanced CAD phenotype levels (per SD unit change: OR = 1.26, 95% CI:1.03-1.55, p = 0.024). Subgroup analysis showed diabetes subgroup had the strongest correlation between EFVi and CAD phenotype levels in ordered logistic regression model (OR = 3.45, 95%CI:1.52-7.82, p = 0.003). In adjusted unordered multinomial logistic regression model, with Group1 as reference group, maximizing Youden index method was used to find the optimal cutoff values for EFV/EFVi on Group2 to Group4. Both EFV and EFVi were independently associated with Group3 and Group4 but only EFVi was independently associated with INOCA (EFV: EFV >134.47cm³ for INOCA, OR = 1.94, 95%CI:0.97-3.88, p = 0.058, EFVi: EFVi >80.67cm³/m² for INOCA, OR = 2.53, 95%CI:1.25-5.12, p = 0.010). Net reclassification improvement (NRI) showed that EFVi was more effective than EFV in diabetes subgroup to differentiate CAD phenotypes over traditional cardiovascular risk factors.

Conclusion: EFVi was correlated with the severity of CAD phenotype levels based on anatomical and functional imaging. EFVi had the strongest correlation with CAD phenotypes levels in diabetes subgroup. Notably, EFVi rather than EFV exhibits a distinct linkage with INOCA. EFVi was more effective to provide incremental value of differentiating CAD phenotypes over traditional cardiovascular risk factors than EFV in diabetes subgroup.