Epicardial adipose tissue from computed tomography: a missing link in premature coronary artery disease?
Abstract
AIMS: Epicardial adipose tissue (EAT) could contribute to the specific atherosclerosis profile observed in premature coronary artery disease (pCAD) characterized by accelerated plaque burden (calcified and non-calcified), high-risk plaque (HRP) features, and ischaemic recurrence. Our aims were to describe EAT volume and density in pCAD compared with asymptomatic individuals matched on cardiovascular risk factors and to study their relationship with coronary plaque severity extension and vulnerability.
METHODS AND RESULTS: Two hundred and eight patients who underwent coronary computed tomography angiography were analysed. It included 104 consecutive individuals with pCAD (acute or stable obstructive CAD before the age of 45 years) and 104 controls, matched 1:1 on age, sex, and cardiovascular risk factors. EAT volume, volume index (EATi), and density were measured with a semi-automated artificial-intelligence based segmentation method and CAD-RADS V2.0 determined according to guidelines. EAT volume and density were compared across groups, and associations with plaque burden and characteristics were investigated. EAT volume and EATi were significantly higher in patients with pCAD compared with matched controls (71.5 mL/m² [45.7;99.8] vs. 58.5 mL/m² [41.3;81.7] P = 0.002), and EAT density was significantly lower in patients with pCAD compared with matched controls (-82 HU [-87; -79] vs. -82 [-85; -78], P = 0.025). EATi was found to be positively correlated with increasing number…
How strong is the evidence?
Evidence Hub reads this as a case-control, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 104 participants or records.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.