METHODS: This cross-sectional study included 95 breast cancer survivors (age 53.7 ± 7.6 years) who have completed main cancer treatments for ≥6 months. Cancer survivors were recruited from two main government hospitals in Kelantan and Terengganu using a purposive sampling method.
RESULTS: According to the Harmonized criteria, the metabolic syndrome prevalence was 50.5%. Among those with metabolic syndrome, the most prevalent abnormal metabolic components were triglycerides (91.2%), fasting blood glucose (79.6%) and HDL-c level (78.4%). Except for total cholesterol and LDL-c, all other metabolic syndrome components were significantly different (p
OBJECTIVE: This study examines how patients with diabetes mellitus responded towards their clinical treatments, where the probability distribution of patients and the types of treatment received were derived from the Rasch probabilistic model.
METHODS: This is a retrospective study wherein data were collected from patients' medical records at a local public hospital in Selangor, Malaysia. Clinical and demographic information such as fasting blood glucose, hemoglobin A1c (HbA1c), family history, type of diabetes (type 1 or type 2), types of medication (oral or insulin), compliance with treatments, gender, race and age were chosen as the agents of measurement.
RESULTS: The use of Rasch analysis in the present study helped to compare the patients' responses towards the DM treatments and identify the types of treatment they received. Results from the Wright map show that a majority of the diabetes mellitus patients who were diagnosed with type 2 diabetes have no controlled readings of HbA1c during their first and second visits to the medical center. However, patients with a family history of diabetes mellitus who took oral medication have controlled readings of fasting blood glucose based on the probabilistic outcomes of the treatment received by the patients.
CONCLUSION: Controlled readings were found only in the readings of fasting blood glucose during the first and second visits, followed by family history, types of medication received, and compliance with the treatment. This study has recommended that type 2 patients with diabetes without a family history of diabetes mellitus need to exercise more control over the readings of HbA1c.
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.