Excessive accumulation of epicardial adipose tissue promotes microvascular obstruction formation after myocardial ischemia/reperfusion through modulating macrophages polarization.

BACKGROUND: Owing to its unique location and multifaceted metabolic functions, epicardial adipose tissue (EAT) is gradually emerging as a new metabolic target for coronary artery disease risk stratification. Microvascular obstruction (MVO) has been recognized as an independent risk factor for unfavorable prognosis in acute myocardial infarction patients. However, the concrete role of EAT in the pathogenesis of MVO formation in individuals with ST-segment elevation myocardial infarction (STEMI) remains unclear. The objective of the study is to evaluate the correlation between EAT accumulation and MVO formation measured by cardiac magnetic resonance (CMR) in STEMI patients and clarify the underlying mechanisms involved in this relationship. METHODS: Firstly, we utilized CMR technique to explore the association of EAT distribution and quantity with MVO formation in patients with STEMI. Then we utilized a mouse model with EAT depletion to explore how EAT affected MVO formation under the circumstances of myocardial ischemia/reperfusion (I/R) injury. We further investigated the immunomodulatory effect of EAT on macrophages through co-culture experiments. Finally, we searched for new therapeutic strategies targeting EAT to prevent MVO formation. RESULTS: The increase of left atrioventricular EAT mass index was independently associated with MVO formation. We also found that increased circulating levels of DPP4 and high DPP4 activity seemed to be associated with EAT increase. EAT accumulation acted as a pro-inflammatory mediator boosting the transition of macrophages towards inflammatory phenotype in myocardial I/R injury through secreting inflammatory EVs. Furthermore, our study declared the potential therapeutic effects of GLP-1 receptor agonist and GLP-1/GLP-2 receptor dual agonist for MVO prevention were at least partially ascribed to its impact on EAT modulation. CONCLUSIONS: Our work for the first time demonstrated that excessive accumulation of EAT promoted MVO formation by promoting the polarization state of cardiac macrophages towards an inflammatory phenotype. Furthermore, this study identified a very promising therapeutic strategy, GLP-1/GLP-2 receptor dual agonist, targeting EAT for MVO prevention following myocardial I/R injury.

Automated detection and classification of coronary atherosclerotic plaques on coronary CT angiography using deep learning algorithm.

Background: Coronary artery disease (CAD) is the leading cause of mortality worldwide. Recent advances in deep learning technology promise better diagnosis of CAD and improve assessment of CAD plaque buildup. The purpose of this study is to assess the performance of a deep learning algorithm in detecting and classifying coronary atherosclerotic plaques in coronary computed tomographic angiography (CCTA) images. Methods: Between January 2019 and September 2020, CCTA images of 669 consecutive patients with suspected CAD from Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine were included in this study. There were 106 patients included in the retrospective plaque detection analysis, which was evaluated by a deep learning algorithm and four independent physicians with varying clinical experience. Additionally, 563 patients were included in the analysis for plaque classification using the deep learning algorithm, and their results were compared with those of expert radiologists. Plaques were categorized as absent, calcified, non-calcified, or mixed. Results: The deep learning algorithm exhibited higher sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy {92% [95% confidence interval (CI): 89.5-94.1%], 87% (95% CI: 84.2-88.5%), 79% (95% CI: 76.1-82.4%), 95% (95% CI: 93.4-96.3%), and 89% (95% CI: 86.9-90.0%)} compared to physicians with ≤5 years of clinical experience in CAD diagnosis for the detection of coronary plaques. The algorithm's overall sensitivity, specificity, PPV, NPV, accuracy, and Cohen's kappa for plaque classification were 94% (95% CI: 92.3-94.7%), 90% (95% CI: 88.8-90.3%), 70% (95% CI: 68.3-72.1%), 98% (95% CI: 97.8-98.5%), 90% (95% CI: 89.8-91.1%) and 0.74 (95% CI: 0.70-0.78), indicating strong performance. Conclusions: The deep learning algorithm has demonstrated reliable and accurate detection and classification of coronary atherosclerotic plaques in CCTA images. It holds the potential to enhance the diagnostic capabilities of junior radiologists and junior intervention cardiologists in the CAD diagnosis, as well as to streamline the triage of patients with acute coronary symptoms.