MATERIALS AND METHODS: In silico target prediction was first employed to predict the probability of the polyphenols interacting with key protein targets related to insulin signalling, based on a model trained on known bioactivity data and chemical similarity considerations. Next, CA was investigated in in vivo studies where induced type 2 diabetic rats were treated with CA for 28 days and the expression levels of genes regulating insulin signalling pathway, glucose transporters of hepatic (GLUT2) and muscular (GLUT4) tissue, insulin receptor substrate (IRS), phosphorylated insulin receptor (AKT), gluconeogenesis (G6PC and PCK-1), along with inflammatory mediators genes (NF-κB, IL-6, IFN-γ and TNF-α) and peroxisome proliferators-activated receptor gamma (PPAR-γ) were determined by qPCR.
RESULTS: In silico analysis shows that several of the top 20 enriched targets predicted for the constituents of CA are involved in insulin signalling pathways e.g. PTPN1, PCK-α, AKT2, PI3K-γ. Some of the predictions were supported by scientific literature such as the prediction of PI3K for epigallocatechin gallate. Based on the in silico and in vivo findings, we hypothesized that CA may enhance glucose uptake and glucose transporter expressions via the IRS signalling pathway. This is based on AKT2 and PI3K-γ being listed in the top 20 enriched targets. In vivo analysis shows significant increase in the expression of IRS, AKT, GLUT2 and GLUT4. CA may also affect the PPAR-γ signalling pathway. This is based on the CA-treated groups showing significant activation of PPAR-γ in the liver compared to control. PPAR-γ was predicted by the in silico target prediction with high normalisation rate although it was not in the top 20 most enriched targets. CA may also be involved in the gluconeogenesis and glycogenolysis in the liver based on the downregulation of G6PC and PCK-1 genes seen in CA-treated groups. In addition, CA-treated groups also showed decreased cholesterol, triglyceride, glucose, CRP and Hb1Ac levels, and increased insulin and C-peptide levels. These findings demonstrate the insulin secretagogue and sensitizer effect of CA.
CONCLUSION: Based on both an in silico and in vivo analysis, we propose here that CA mediates glucose/lipid metabolism via the PI3K signalling pathway, and influence AKT thereby causing insulin secretion and insulin sensitivity in peripheral tissues. CA enhances glucose uptake and expression of glucose transporters in particular via the upregulation of GLUT2 and GLUT4. Thus, based on its ability to modulate immunometabolic pathways, CA appears as an attractive long term therapy for T2DM even at relatively low doses.
Methods: A total of 150 CKD patients and 64 non-CKD patients were enrolled. The type 2 diabetic patients in the recruited study participants were categorised based on their glycaemic control; poor glycaemic control (GC) with haemoglobin A1c (HbA1c) > 7% and good GC with HbA1c ≤ 7%. The levels or activities of GPx, SOD and sRAGE in plasma were measured. These biochemical parameters were analysed using Mann-WhitneyUtest and two-way analysis of variance (ANOVA).
Results: The activities of GPx and SOD as well as plasma level of sRAGE were not significantly different among the CKD patients with varying glycaemic control status. Irrespective of diabetes status and glycaemic control status, CKD patients also exhibited lower plasma SOD activities compared with non-CKD patients. Among the non-CKD patients, SOD activities were significantly higher in diabetic patients with good GC than diabetic patients with poor GC. Two-way ANOVA revealed that both CKD status and glycaemic control had an interaction effect on SOD activities in diabetic subjects with and without CKD. Follow-up analysis showed that SOD activities were significantly higher in non-CKD patients with good GC. There were no overall significant differences in GPx activities among the study participants. Furthermore, plasma sRAGE levels were higher in diabetic patients with CKD than those without CKD, regardless of glycaemic control status. There were no interaction effects between CKD status and glycaemic control status on GPx and sRAGE. Instead, CKD status showed significant main effects on these parameters, indicating significant differences between diabetic subjects with CKD and diabetic subjects without CKD.
Conclusion: Glycaemic control did not quantitatively alter GPx, SOD and sRAGE in diabetic CKD patients. Despite the advantages of good glycaemic control, a well-controlled diabetes in CKD did not modulate the activities of enzymatic antioxidants and sRAGE levels, therefore may not be the primary mechanism to handle oxidative stress.
METHODS: Demographic and clinical variables were assessed at baseline, after three and six months in 73 type 2 diabetes patients. Regression analysis, using SPSS, evaluated the concurrent and longitudinal association of medication adherence and glycemic control. Potential confounders of variables were identified using bi-variate correlation analyses.
RESULTS: Concurrent Medication adherence and HbA1c association were significant after adjusting for ethnicity (P = 0.005). For longitudinal observation at 3 months, the association was significant after adjusting for ethnicity (P = 0.016); however, it became non-significant when baseline glycemic control was included in the model (P = 0.28).
CONCLUSION: Easy to administer MALMAS significantly predicted concurrent glycemic control independent of potential confounders. This association persisted in longitudinal observation after 3 months when adjusted for confounders and became non-significant after adjusting for baseline glycemic control.