Bavachin protects against diet-induced hepatic steatosis and obesity in mice.

Non-alcoholic fatty liver disease (NAFLD) is a major health concern worldwide, and the incidence of metabolic disorders associated with NAFLD is rapidly increasing because of the obesity epidemic. There are currently no approved drugs that prevent or treat NAFLD. Recent evidence shows that bavachin, a flavonoid isolated from the seeds and fruits of Psoralea corylifolia L., increases the transcriptional activity of PPARγ and insulin sensitivity during preadipocyte differentiation, but the effect of bavachin on glucose and lipid metabolism remains unclear. In the current study we investigated the effects of bavachin on obesity-associated NAFLD in vivo and in vitro. In mouse primary hepatocytes and Huh7 cells, treatment with bavachin (20 µM) significantly suppressed PA/OA or high glucose/high insulin-induced increases in the expression of fatty acid synthesis-related genes and the number and size of lipid droplets. Furthermore, bavachin treatment markedly elevated the phosphorylation levels of AKT and GSK-3ß, improving the insulin signaling activity in the cells. In HFD-induced obese mice, administration of bavachin (30 mg/kg, i.p. every other day for 8 weeks) efficiently attenuated the increases in body weight, liver weight, blood glucose, and liver and serum triglyceride contents. Moreover, bavachin administration significantly alleviated hepatic inflammation and ameliorated HFD-induced glucose intolerance and insulin resistance. We demonstrated that bavachin protected against HFD-induced obesity by inducing fat thermogenesis and browning subcutaneous white adipose tissue (subWAT). We revealed that bavachin repressed the expression of lipid synthesis genes in the liver of obese mice, while promoting the expression of thermogenesis, browning, and mitochondrial respiration-related genes in subWAT and brown adipose tissue (BAT) in the mice. In conclusion, bavachin attenuates hepatic steatosis and obesity by repressing de novo lipogenesis, inducing fat thermogenesis and browning subWAT, suggesting that bavachin is a potential drug for NAFLD therapy.

Synergistic prognostic value of coronary distensibility index and fractional flow reserve based cCTA for major adverse cardiac events in patients with Coronary artery disease.

BACKGROUND: Coronary distensibility index (CDI), as an early predictor of cardiovascular diseases, has the potential to complement coronary computed tomography angiography (cCTA)-derived fractional flow reserve (CT-FFR) for predicting major adverse cardiac events (MACEs). Thus, the prognostic value of CT-FFR combined with CDI for MACEs is worth exploring. METHODS: Patients with a moderate or severe single left anterior descending coronary artery stenosis were included and underwent FFR and CDI analysis based on cCTA, followed up at least 1 year, and recorded MACEs. Multivariate logistic regression analysis was performed to determine independent predictors of MACEs. The area under of receiver operating characteristic (ROC) curve was used to evaluated evaluate the diagnostic performance of CT-FFR, CDI, and a combination of the two. RESULTS: All the vessel-specific data were from LAD. 150 patients were analysed. 55 (37%) patients experienced MACEs during follow-up. Patients with CT-FFR ≤ 0.8 had higher percentage of MACEs compared with CT-FFR > 0.8 (56.3% vs.7.3%, p < 0.05). Patients' CDI was significantly decreased in MACEs group compared with non-MACEs group (p < 0.05). Multivariate analysis revealed that diabetes (p = 0.025), triglyceride (p = 0.015), CT-FFR ≤ 0.80 (p = 0.038), and CDI (p < 0.001) are independent predictors of MACEs. According to ROC curve analysis, CT-FFR combined CDI showed incremental diagnostic performance over CT-FFR alone for prediction of MACEs (AUC = 0.831 vs. 0.656, p = 0.0002). CONCLUSION: Our study provides initial evidence that combining CDI with CT-FFR shows incremental discriminatory power for MACEs over CT-FFR alone, independent of clinical risk factors. Diabetes and triglyceride are also associated with MACEs.

Synergistic value of fractional flow reserve and low­density non­calcified plaque based on coronary computed tomography angiography for the identification of lesion­specific ischemia.

Increasing evidence has suggested that plaque characteristics are closely associated with ischemia, and coronary computed tomography (CT) angiography-derived fractional flow reserve (FFRCT) based on deep machine learning algorithms has also been used to identify lesion-specific ischemia. Therefore, the aim of the present study was to explore the predictive ability of plaque characteristics in combination with deep learning-based FFRCT for lesion-specific ischemia. To meet this end, invasive FFR was used as a reference standard, with the joint aims of the early prediction of ischemic lesions and guiding clinical treatment. In the present study, the plaque characteristics, including non-calcified plaque (NCP), low-density NCP (LD-NCP), plaque length, total plaque volume (TPV), remodeling index, calcified plaque, fibrous plaque and plaque burden, were obtained using a semi-automated program. The FFRCT values were derived based on a deep machine learning algorithm. On the basis of the data obtained, differences among the values between the atopic ischemia and the non-significant lesions groups were analyzed to further determine the predictive value of independent predictors for atopic ischemia. Of the plaque features, FFRCT, LD-NCP, NCP, TPV and plaque length differed significantly when comparing between the lesion-specific ischemia and no hemodynamic abnormality groups, and LD-NCP and FFRCT were both independent predictors for ischemia. Additionally, FFRCT combined with LD-NCP showed a greater ability at discriminating ischemia compared with FFRCT or LD-NCP alone. Taken together, the findings of the present study suggest that the combination of FFRCT and LD-NCP has a synergistic effect in terms of predicting ischemia, thereby facilitating the identification of specific ischemia in patients with coronary artery disease.