Multiparametric Coronary CT Angiography-Derived Imaging Biomarkers for Risk Stratification in Nonobstructive Coronary Artery Disease: Incremental Prognostic Value in Patients with Diabetes

Background: Patients with diabetes mellitus and nonobstructive coronary artery disease (NOCAD) may remain at increased cardiovascular risk despite the absence of flow-limiting stenosis. Quantitative coronary CT angiography (CCTA) enables comprehensive assessment of anatomical, functional, and inflammatory imaging biomarkers beyond luminal stenosis. This study aimed to evaluate the prognostic value of an automated multiparametric CCTA-derived imaging framework for risk stratification in patients with NOCAD, with exploratory assessment in those with diabetes mellitus. Methods: This retrospective single-center study included 485 patients with NOCAD who underwent CCTA between January 2020 and December 2021. Automated CCTA analysis was performed to quantify plaque burden, high-risk plaque features, CT-derived fractional flow reserve (CT-FFR), and perivascular fat attenuation index. The primary endpoint was major adverse cardiovascular events (MACE) during follow-up. Prognostic associations were assessed using Kaplan–Meier analysis, Cox regression, and hierarchical models. Results: During a median follow-up of approximately three years, MACE occurred in 56 patients. Patients with diabetes had a higher event rate than those without diabetes. Increased plaque burden, high-risk plaque features, elevated perivascular fat attenuation index, and reduced CT-FFR were associated with adverse outcomes. The fully integrated model combining anatomical, functional, and inflammatory CCTA-derived biomarkers improved risk stratification compared with plaque-based assessment alone. Conclusions: Automated multiparametric CCTA phenotyping may provide complementary prognostic information for risk stratification in patients with NOCAD. The diabetes-specific findings should be considered exploratory and require validation in larger prospective cohorts.