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Background: Insulin resistance is one of the most common and important pathological features of type 2 diabetes (T2D). Recently, insulin resistance is increasingly considered to be associated with systemic chronic inflammation. Elevated levels of tumor necrosis factor (TNF)-α and interleukin (IL)-1β in blood are predictive indicators of the development of T2D. Mesenchymal stem cell (MSC)-based therapies have been proven to have potential immunomodulation and anti-inflammatory properties through their paracrine effects; however, the mechanism for the anti-inflammatory effect of MSCs in enhancing insulin sensitivity is still uncertain., Methods: In the present experiment, we used HepG2 cells, a human hepatoma cell line, and a MSC-HepG2 transwell culturing system to investigate the anti-inflammatory mechanism of human umbilical cord-derived MSCs (UC-MSCs) under palmitic acid (PA) and lipopolysaccharide (LPS)-induced insulin resistance in vitro. Insulin resistance was confirmed by glycogen assay kit and glucose assay kit. Inflammatory factor release was detected by ELISA, gene expression was tested by quantitative real-time PCR, and insulin signaling activation was determined by western blotting analysis. The changes of inflammatory factors and insulin signaling protein were also tested in T2D rats injected with UC-MSCs., Results: Treating HepG2 cells with PA-LPS caused NLRP3 inflammation activation, including overexpression of NLRP3 and caspase-1, and overproduction of IL-1β and IL-18 as well as TNF-α from HepG2 cells. The elevated levels of these inflammatory cytokines impaired insulin receptor action and thereby prevented downstream signaling pathways, exacerbating insulin resistance in HepG2 cells. Importantly, UC-MSCs cocultured with HepG2 could effectively alleviate PA and LPS-induced insulin resistance by blocking the NLRP3 inflammasome activation and inflammatory agents. Furthermore, knockdown of NLRP3 or IL-1β partially improved PA and LPS-induced insulin signaling impairments in the presence of UC-MSCs. Similarly, UC-MSC infusion significantly ameliorated hyperglycemia in T2D rats and decreased inflammatory activity, which resulted in improved insulin sensitivity in insulin target tissues., Conclusions: Our results indicated that UC-MSCs could attenuate insulin resistance and this regulation was correlated with their anti-inflammatory activity. Thus, MSCs might become a novel therapeutic strategy for insulin resistance and T2D in the near future.

Type 2 diabetes mellitus (T2DM), which is characterized by the combination of relative insulin deficiency and insulin resistance, cannot be reversed with existing therapeutic strategies. Transplantation of insulin-producing cells (IPCs) was once thought to be the most promising strategy for treating diabetes, but the pace from the laboratory to clinical application has been obstructed due to its drawbacks. Mesenchymal stem cells (MSCs) harbor differentiation potential, immunosuppressive properties, and anti-inflammatory effects, and they are considered an ideal candidate cell type for treatment of DM. MSC-related research has demonstrated exciting therapeutic effects in glycemic control both in vivo and in vitro, and these results now have been translated into clinical practice. However, some critical potential problems have emerged from current clinical trials. Multi-center, large-scale, double-blind, and placebo-controlled studies with strict supervision are required before MSC transplantation can become a routine therapeutic approach for T2DM. We briefly review the molecular mechanism of MSC treatment for T2DM as well as the merits and drawbacks identified in current clinical trials.

Aims: Analyze the clinical applicability of glucose metabolism indexes and continuous glucose monitoring data on the qualitative diagnosis of insulinoma., Methods: Involve 22 patients with insulinoma (insulinoma group), 11 patients with hypoglycemia (hypoglycemia group) and 31 people with normal glucose tolerance (control group). HbA1c, fasting blood glucose (FBG), insulin (FINS) and C-peptide (FCP) was tested. Using CGMS to monitor the blood glucose for three consecutive days and selecting the monitoring data of 24 h thereof, figuring out, with the aid of EasyGV Version 9.0, the mean glucose (MG), the standard deviation (SD) of blood glucose, CONGA (continuous overall net glycemic action), J-Index, LI (Lability Index), LBGI (Low Blood Glucose Index), HBGI (High Blood Glucose Index), GRADE (glycaemic risk assessment diabetes equation), MAGE (mean aplitude of glycaemic excursions), M value, MAG (mean absolute glucose)., Results: (1) FBG and LBG of insulinoma group are lower than those of control group and those of hypoglycemia group while FINS and FCP of insulinoma group are markedly higher than those of the other two groups; (2) the MG and CONGA of insulinoma group are lower than those of control group and its indexes like ST, LI, LBGI, GRADE, MAGE, M value and MAG are higher than those of control group; there are differences between the indexes of insulinoma group and those of hypoglycemia group in CONGA (lower than that of hypoglycemia group), LBGI (higher than that of hypoglycemia group), and M value (higher than that of hypoglycemia group). By drawing the ROC curve and calculating Youden index, the cut-off values of LBGI, M value, CONGA are respectively as 4.06, 7.79, 4.38, and the best index of differential diagnosis is LBGI., Conclusion: Continuous glucose monitoring data can be used to diagnose insulinoma and blood glucose fluctuation indicators such as LBGI, M value, CONGA might be useful to identify insulinoma.

Background: To explore the association between serum uric acid (SUA) levels, metabolic syndrome (MetS) and cardiovascular disease (CVD) in patients treated with lipid-lowering agents from multiple centers in China., Method: We investigated 15,478 participants who had been documented with recorded SUA in the DYSlipidemia International Study which included 25,697 patients, aged 45 years old or older, who were treated with lipid-lowering agents from 122 centers between April 2012 and October 2012. Logistic regression analysis was performed to examine the association between SUA levels, MetS and CVD., Result: After adjusting for multi-variables, hyperuricemia (the highest category of SUA level) showed a significantly higher risk of MetS compared to the lowest category[according to NCEP-ATPIII criteria, odds ratio (OR) 1.51, 95 % confidence interval (CI) (1.30,1.74) in men, OR 2.35 95 % CI (2.00,2.75) in women; and according to IDF criteria, OR 1.40 95 % CI (1.20,1.63) in men, OR 1.65 95 % CI (1.41,1.94) in women]. In addition, elevated SUA concentration was shown to be associated with coronary heart disease (CHD) (OR 1.26 95 % CI (1.09, 1.45) in men, and OR 1.27, 95 % CI (1.07, 1.50) in women) and heart failure (HF) (OR 1.61 95 % CI (1.15, 2.24) in men, and OR 1.91, 95 % CI (1.29, 2.82) in women)., Conclusion: Our research suggested a positive association between SUA levels and MetS in Chinese patients receiving lipid-lowering therapy. Elevated SU levels were positively associated independently of measured confounders to CHD and HF.

Background: Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public., Results: In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile., Conclusion: Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.