AIM/HYPOTHESIS: The aim of our study was to characterize the human salivary proteome and determine the changes in protein expression in two different stages of diabetic retinopathy with type-2 diabetes mellitus: (1) with non-proliferative diabetic retinopathy (NPDR) and (2) with proliferative diabetic retinopathy (PDR). Type-2 diabetes mellitus without diabetic retinopathy (XDR) was designated as control.
METHOD: In this study, 45 saliva samples were collected (15 samples from XDR control group, 15 samples from NPDR disease group and 15 samples from PDR disease group). Salivary proteins were extracted, reduced, alkylated, trypsin digested and labeled with an isobaric tag for relative and absolute quantitation (iTRAQ) before being analyzed by an Orbitrap fusion tribrid mass spectrometer. Protein annotation, fold change calculation and statistical analysis were interrogated by Proteome Discoverer. Biological pathway analysis was performed by Ingenuity Pathway Analysis. Data are available via ProteomeXchange with identifiers PXD003723-PX003725.
RESULTS: A total of 315 proteins were identified from the salivary proteome and 119 proteins were found to be differentially expressed. The differentially expressed proteins from the NPDR disease group and the PDR disease group were assigned to respective canonical pathways indicating increased Liver X receptor/Retinoid X receptor (LXR/RXR) activation, Farnesoid X receptor/Retinoid X receptor (FXR/RXR) activation, acute phase response signaling, sucrose degradation V and regulation of actin-based motility by Rho in the PDR disease group compared to the NPDR disease group.
CONCLUSIONS/INTERPRETATION: Progression from non-proliferative to proliferative retinopathy in type-2 diabetic patients is a complex multi-mechanism and systemic process. Furthermore, saliva was shown to be a feasible alternative sample source for diabetic retinopathy biomarkers.
Diabetes mellitus (DM) is a pandemic and chronic metabolic disorder with substantial morbidity and mortality. In addition, osteoporosis (OP) is a silent disease with a harmful impact on morbidity and mortality. Therefore, this systematic review focuses on the relationship between OP and type 2 diabetes mellitus (T2DM). Systematic reviews of full-length articles published in English from January 1950 to October 2010 were identified in PubMed and other available electronic databases on the Universiti Sains Malaysia Library Database. The following keywords were used for the search: T2DM, OP, bone mass, skeletal. Studies of more than 50 patients with T2DM were included. Forty-seven studies were identified. The majority of articles (26) showed increased bone mineral density (BMD), while 13 articles revealed decreased BMD; moreover, eight articles revealed normal or no difference in bone mass. There were conflicting results concerning the influence of T2DM on BMD in association with gender, glycemic control, and body mass index. However, patients with T2DM display an increased fracture risk despite a higher BMD, which is mainly attributable to the increased risk of falling. As a conclusion, screening, identification, and prevention of potential risk factors for OP in T2DM patients are crucial and important in terms of preserving a good quality of life in diabetic patients and decreasing the risk of fracture. Patients with T2DM may additionally benefit from early visual assessment, regular exercise to improve muscle strength and balance, and specific measures for preventing falls. Patient education about an adequate calcium and vitamin D intake and regular exercise is important for improving muscle strength and balance. Furthermore, adequate glycemic control and the prevention of diabetic complications are the starting point of therapy in diabetic patients.
Ficus deltoidea from the Moraceae family has been scientifically proven to reduce hyperglycemia at different prandial states. In this study, we evaluate the mechanisms that underlie antihyperglycemic action of Ficus deltoidea. The results had shown that hot aqueous extract of Ficus deltoidea stimulated insulin secretion significantly with the highest magnitude of stimulation was 7.31-fold (P < 0.001). The insulin secretory actions of the hot aqueous extract involved K(+) (ATP) channel-dependent and K(+) (ATP)-channel-independent pathway. The extract also has the ability to induce the usage of intracellular Ca(2+) to trigger insulin release. The ethanolic and methanolic extracts enhanced basal and insulin-mediated glucose uptake into adipocytes cells. The extracts possess either insulin-mimetic or insulin-sensitizing property or combination of both properties during enhancing glucose uptake into such cells. Meanwhile, the hot aqueous and methanolic extracts augmented basal and insulin-stimulated adiponectin secretion from adipocytes cells. From this study, it is suggested that Ficus deltoidea has the potential to be developed as future oral antidiabetic agent.
As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.