Treatment with DPP-4I Anagliptin or α-GI Miglitol Reduces IGT Development and the Expression of CVD Risk Factors in OLETF Rats

Combining bioinformatics, network pharmacology and artificial intelligence to predict the mechanism of celastrol in the treatment of type 2 diabetes

BACKGROUND

Type 2 diabetes (T2D) is a common chronic disease with many serious complications. Celastrol can prevent and treat type 2 diabetes by reversing insulin resistance in a number of ways. However, the specific mechanisms by which celastrol prevents and treats T2D are not well understood. The aim of this study was to explore the key gene targets and potential signaling pathway mechanisms of celastrol for the treatment of T2D.

METHODS

GSE184050 was downloaded from the Gene Expression Omnibus online database. Blood samples from patients and healthy individuals with T2D were analyzed to identify differentially expressed genes (DEGs), and a protein-protein interaction network (PPI) was constructed. Key gene analysis of DEGs was performed using the MCODE plugin in Cystoscope as well as the Hubba plugin, and intersections were taken to obtain hub genes, which were displayed using a Venn diagram. Enrichment analysis was then performed via the ClueGo plugin in Cytoscape and validated using Gene Set Enrichment Analysis. The therapeutic targets of celastrol were then analyzed by pharmacophore network pharmacology, intersected to identify the therapeutic targets of celastrol, enriched for all targets, and intersected to obtain the signaling pathways for celastrol treatment. The protein structures of the therapeutic targets were predicted using the artificial intelligence AlphaFold2. Finally, molecular docking was used to verify whether celastrol could be successfully docked to the predicted targets.

RESULTS

618 DEGs were obtained, and 9 hub genes for T2D were identified by the MCODE and Hubba plug-ins, including ADAMTS15, ADAMTS7, ADAMTSL1, SEMA5B, ADAMTS8, THBS2, HBB, HBD and HBG2. The DEG-enriched signaling pathways mainly included the ferroptosis and TGF-beta signaling pathways. A total of 228 target genes were annotated by pharmacophore target analysis, and the therapeutic targets were identified, including S100A11, RBP3, HBB, BMP7 and IQUB, and 9 therapeutic signaling pathways were obtained by an intersectional set. The protein structures of the therapeutic targets were successfully predicted by AlphaFold2, and docking was validated using molecular docking.

CONCLUSION

Celastrol may prevent and treat T2D through key target genes, such as HBB, as well as signaling pathways, such as the TGF-beta signaling pathway and type II diabetes mellitus.

Undernutrition in Pregnant Rats Induces Glucose Intolerance with Enhanced Expression of Inflammation-Related Genes in Peripheral Leukocytes of the Offspring

Impaired glucose tolerance (IGT) induces chronic inflammation and subsequent development of complications triggered by arteriosclerosis. Moreover, undernutrition in pregnant rodents can induce IGT in their offspring. Here, we assessed whether undernutrition in pregnant rats would induce chronic inflammation in their offspring by measuring the expression levels of inflammation-related genes in peripheral blood leukocytes. Pregnant Wistar rats were divided into two groups: the control group received an American Institute of Nutrition Rodent diet (AIN-93G) ad libitum, and the undernutrition group had their diet restricted by 50% (w/w) compared with the control group from day 10 of pregnancy until birth of the offspring. Subsequently, mothers and pups were allowed to access the AIN-93G diet freely. At day 35 after birth, male pups were fasted for 4 h and subsequently orally administered with glucose solution (2 g/kg body weight). Blood glucose area under the curve (AUC) after glucose loading was significantly greater in the undernutrition group than the control group. The mRNA levels for inflammatory cytokines were increased by glucose loading especially in the undernutrition group. Expressions of genes encoding S100A9 and cell adhesion molecule CD11b were increased by glucose loading in the undernutrition group. Thus, undernutrition of pregnant rats during mid to late gestation induced the expression of inflammation-related genes in peripheral blood leukocytes of their offspring, with the development of IGT and impaired insulin secretion.

Effects of the Mean Amplitude of Glycemic Excursions and Vascular Endothelial Dysfunction on Cardiovascular Events in Nondiabetic Patients With Coronary Artery Disease

Background

Mean amplitude of glycemic excursion (MAGE) is commonly used to gauge the degree of glucose level fluctuations. MAGE plays a significant role in vascular endothelial dysfunction and cardiovascular events in patients with diabetes mellitus (DM), but its significance is not clear in non‐DM patients. Thus, we examined the impact of MAGE and vascular endothelial dysfunction on clinical outcomes in non‐DM patients with coronary artery disease.

Methods and Results

We followed non‐DM patients (n=65) for 12 months who underwent percutaneous coronary intervention and assessed the relationship among MAGE, reactive hyperemia index (RHI) measured by reactive hyperemia peripheral arterial tonometry as endothelial function, and cardiovascular events. Cardiovascular events analyzed were cardiovascular death, myocardial infarction, unstable angina, and revascularizations. Compared with patients with MAGE <65 mg/dL (normal glycemic excursions), the group with MAGE ≥65 mg/dL (high glycemic excursions) had significantly higher high‐sensitivity C‐reactive protein (0.10±0.11 mg/dL versus 0.18±0.13 mg/dL, P=0.006) and lower RHI (0.64±0.21 versus 0.51±0.22, P=0.035). The multivariable analysis identified high MAGE and low RHI (≤0.56) as risk factors associated with cardiovascular events (hazard ratio, 5.6; 95% RI, 1.72–18.4 [P=0.004] versus hazard ratio, 4.5; 95% RI, 1.37–14.9 [P=0.013]). When the prognosis was classified by combination with MAGE and RHI, the incidence of cardiovascular events was 46.7% (high MAGE+low RHI), 26.7% (high MAGE+high RHI), 20.0% (low MAGE+low RHI), and 6.6% (low MAGE+high RHI) in descending order (P=0.014). Receiver operating characteristic curve analysis revealed that MAGE, RHI, and MAGE+RHI were each associated with cardiovascular events (area under the curve 0.780, 0.727, and 0.796, respectively).

Conclusions

MAGE was associated with cardiovascular events in non‐DM patients with coronary artery disease. Furthermore, the combination with MAGE and RHI was useful for further subdivision of the risk of cardiovascular events.