This study aimed to uncover underlying mechanisms and promising intervention targets of heart failure (HF)-related stroke. HF-related dataset GSE42955 and stroke-related dataset GSE58294 were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub genes. Gene Ontology (GO) and pathway enrichment analyses were performed on genes in the key modules. Genes in HF-and stroke-related key modules were intersected to obtain common genes for HF-related stroke, which were further intersected with hub genes of stroke-related key modules to obtain key genes in HF-related stroke. Key genes were functionally annotated through GO in the Reactome and Cytoscape databases. Finally, key genes were validated in these two datasets and other datasets. HF-and stroke-related datasets each identified two key modules. Functional enrichment analysis indicated that protein ubiquitination, Wnt signaling, and exosomes were involved in both HF-and stroke-related key modules. Additionally, ten hub genes were identified in stroke-related key modules and 155 genes were identified as common genes in HF-related stroke. OTU deubiquitinase with linear linkage specificity (OTULIN) and nuclear factor interleukin 3-regulated (NFIL3) were determined to be the key genes in HF-related stroke. Through functional annotation, OTULIN was involved in protein ubiquitination and Wnt signaling, and NFIL3 was involved in DNA binding and transcription. Importantly, OTULIN and NFIL3 were also validated to be differentially expressed in all HF and stroke groups. Protein ubiquitination, Wnt signaling, and exosomes were involved in HF-related stroke. OTULIN and NFIL3 may play a key role in HF-related stroke through regulating these processes, and thus serve as promising intervention targets.

ObjectiveTo investigate the association between estimated glucose disposal rate （eGDR） and the severity of coronary heart disease. MethodsWe conducted a hospital-based cross-sectional study that included 1258 patients （mean age： 62（53-68） years） who underwent coronary angiography for suspected coronary artery disease （53.9% were male）. Insulin resistance level （IR） was calculated according to eGDR formula： eGDR = 21.158 - （0.09 × WC） - （3.407 × hypertension） - （0.551 × HbA1c） ［hypertension （yes = 1 / no = 0）， HbA1c = HbA1c （%）］. Subjects were grouped according to the eGDR quantile. CAD severity was determined by the number of narrowed vessels： no-obstructive CAD group （all coronary stenosis were<50%， n=704）， Single-vessel CAD group （only one involved major coronary artery stenosis≥50%， n=205）， Multi-vessel CAD group （two or more involved major coronary arteries stenosis≥50%， n=349）； Multivariate logistic regression model was used to analyze the association between eGDR and CAD severity. The linear relationship between eGDR and CAD in the whole range of eGDR was analyzed using restricted cubic spline. Subgroup analyses were used to assess the association between eGDR and CAD severity in different diabetic states. Receiver operating characteristic （ROC） curve analysis were used to evaluate the value of eGDR in improving CAD recognition. ResultsA decrease in the eGDR index was significantly associated with an increased risk of CAD severity （OR： 2.79； 95%CI： 1.72~4.55； P<0.001）. In multivariate logistic regression models， individuals with the lowest quantile of eGDR （T1） were 2.79 times more likely to develop multi-vessel CAD than those with the highest quantile of eGDR （T3） （OR： 2.79； 95%CI： 1.72~4.55； P<0.001）. Multivariate restricted cubic spline analysis showed that eGDR was negatively associated with CAD and multi-vessel CAD （P-nonlinear>0.05）. In non-diabetic patients， compared with the reference group （T3）， the T1 group had a significantly increased risk of CAD （OR： 1.42； 95% CI： 1.00~2.01； P<0.05） and multi-vessel CAD （OR： 1.86； 95%CI： 1.21~2.86； P<0.05）. No statistical association was found between eGDR and CAD in diabetic patients. In ROC curve analysis， when eGDR was added to traditional model for CAD， significant improvements were observed in the model's recognition of CAD and multi-vessel CAD. ConclusionOur study shows eGDR levels are inversely associated with CAD and CAD severity. eGDR， as a non-insulin measure to assess IR， could be a valuable indicator of CAD severity for population.

Currently, there is still no effective curative treatment for the development of late-stage liver fibrosis. Here, we have illustrated that TB001, a dual glucagon-like peptide-1 receptor/glucagon receptor (GLP-1R/GCGR) agonist with higher affinity towards GCGR, could retard the progression of liver fibrosis in various rodent models, with remarkable potency, selectivity, extended half-life and low toxicity. Four types of liver fibrosis animal models which were induced by CCl₄, α-naphthyl-isothiocyanate (ANIT), bile duct ligation (BDL) and Schistosoma japonicum were used in our study. We found that TB001 treatment dose-dependently significantly attenuated liver injury and collagen accumulation in these animal models. In addition to decreased levels of extracellular matrix (ECM) accumulation during hepatic injury, activation of hepatic stellate cells was also inhibited via suppression of TGF-β expression as well as downstream Smad signaling pathways particularly in CCl₄-and S. japonicum-induced liver fibrosis. Moreover, TB001 attenuated liver fibrosis through blocking downstream activation of pro-inflammatory nuclear factor kappa B/NF-kappa-B inhibitor alpha (NFκB/IKBα) pathways as well as c-Jun N-terminal kinase (JNK)-dependent induction of hepatocyte apoptosis. Furthermore, GLP-1R and/or GCGR knock-down results represented GCGR played an important role in ameliorating CCl₄-induced hepatic fibrosis. Therefore, TB001 can be used as a promising therapeutic candidate for the treatment of multiple causes of hepatic fibrosis demonstrated by our extensive pre-clinical evaluation of TB001.