5 resultados para GLUCOSE-TRANSPORTER-1


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Pancreatic beta-cell apoptosis is known to participate in the beta-cell destruction process that occurs in diabetes. It has been described that high glucose level induces a hyperfunctional status which could provoke apoptosis. This phenomenon is known as glucotoxicity and has been proposed that it can play a role in type 1 diabetes mellitus pathogenesis. In this study we develop an experimental design to sensitize pancreatic islet cells by high glucose to streptozotocin (STZ) and proinflammatory cytokines [interleukin (IL)-1beta, tumor necrosis factor (TNF)-alpha and interferon (IFN)-gamma]-induced apoptosis. This method is appropriate for subsequent quantification of apoptotic islet cells stained with Tdt-mediated dUTP Nick-End Labeling (TUNEL) and protein expression assays by Western Blotting (WB).

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Hyperuricaemia is one of the components of metabolic syndrome. Both oxidative stress and hyperinsulinism are important variables in the genesis of this syndrome and have a close association with uric acid (UA). We evaluated the effect of an oral glucose challenge on UA concentrations. The study included 656 persons aged 18 to 65 years. Glycaemia, insulin, UA and plasma proteins were measured at baseline and 120 min after an oral glucose tolerance test (OGTT). The baseline sample also included measurements of total cholesterol, triacylglycerol (TAG) and HDL-cholesterol. Insulin resistance was calculated with the homeostasis model assessment. UA levels were significantly lower after the OGTT (281.93 (sd 92.19) v. 267.48 (sd 90.40) micromol/l; P < 0.0001). Subjects with a drop in UA concentrations >40.86 micromol/l (>75th percentile) had higher plasma TAG levels (P = 0.0001), baseline insulin (P = 0.02) and greater insulin resistance (P = 0.034). Women with a difference in plasma concentrations of UA above the 75th percentile had higher baseline insulin levels (P = 0.019), concentration of plasma TAG (P = 0.0001) and a greater insulin resistance index (P = 0.029), whereas the only significant difference in men was the level of TAG. Multiple regression analysis showed that the basal TAG levels, insulin at 120 min, glycaemia at 120 min and waist:hip ratio significantly predicted the variance in the UA difference (r2 0.077). Levels of UA were significantly lower after the OGTT and the individuals with the greatest decrease in UA levels are those who have greater insulin resistance and higher TAG levels.

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The mechanisms underlying the increased risk of cardiovascular disease associated with diabetes mellitus (DM) are not fully defined. Insulin resistance in human metabolic syndrome patients is associated with decreased expression of the insulin receptor substrate-2- (Irs2-) AKT2 axis in mononuclear leukocytes (MLs). Moreover, acute coronary syndrome (ACS) has been linked through genome-wide association studies to the 2q36-q37.3 locus, which contains the Irs1 gene. Here, we investigated the expression of insulin-signaling pathway genes in MLs from patients with DM, ACS, and ACS plus DM. Quantitative real-time PCR expression studies showed no differences in the mRNA levels of Irs2, Akt2, and Akt1 among all patients. However, Irs1 mRNA expression was significantly increased in patients with ACS-diabetics and nondiabetics-compared with diabetic patients without ACS (P < .02 and P < .005, resp.). The present study reveals for the first time an association between increased Irs1 mRNA levels in MLs of patients with ACS which is not related to DM.

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BACKGROUND A recent study using a rat model found significant differences at the time of diabetes onset in the bacterial communities responsible for type 1 diabetes modulation. We hypothesized that type 1 diabetes in humans could also be linked to a specific gut microbiota. Our aim was to quantify and evaluate the difference in the composition of gut microbiota between children with type 1 diabetes and healthy children and to determine the possible relationship of the gut microbiota of children with type 1 diabetes with the glycemic level. METHODS A case-control study was carried out with 16 children with type 1 diabetes and 16 healthy children. The fecal bacteria composition was investigated by polymerase chain reaction-denaturing gradient gel electrophoresis and real-time quantitative polymerase chain reaction. RESULTS The mean similarity index was 47.39% for the healthy children and 37.56% for the children with diabetes, whereas the intergroup similarity index was 26.69%. In the children with diabetes, the bacterial number of Actinobacteria and Firmicutes, and the Firmicutes to Bacteroidetes ratio were all significantly decreased, with the quantity of Bacteroidetes significantly increased with respect to healthy children. At the genus level, we found a significant increase in the number of Clostridium, Bacteroides and Veillonella and a significant decrease in the number of Lactobacillus, Bifidobacterium, Blautia coccoides/Eubacterium rectale group and Prevotella in the children with diabetes. We also found that the number of Bifidobacterium and Lactobacillus, and the Firmicutes to Bacteroidetes ratio correlated negatively and significantly with the plasma glucose level while the quantity of Clostridium correlated positively and significantly with the plasma glucose level in the diabetes group. CONCLUSIONS This is the first study showing that type 1 diabetes is associated with compositional changes in gut microbiota. The significant differences in the number of Bifidobacterium, Lactobacillus and Clostridium and in the Firmicutes to Bacteroidetes ratio observed between the two groups could be related to the glycemic level in the group with diabetes. Moreover, the quantity of bacteria essential to maintain gut integrity was significantly lower in the children with diabetes than the healthy children. These findings could be useful for developing strategies to control the development of type 1 diabetes by modifying the gut microbiota.

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Aims: To evaluate the impact on glycemic control and quality of life of a bolus calculator. Methods: Multicentre randomized prospective crosssectional study. Patients were randomized to control phase (3 months; calculation of prandial insulin according to insulinto-carbohydrate ratio and insulin sensitivity factor using a single strip meter) or intervention phase (3 months; calculation of prandial insulin with a bolus advisor), with a washout period (3 months). Patients wore a continuous glucosensor (7 days) and answered a quality of life questionnaire at the beginning and at the end of each phase. A questionnaire of satisfaction was obtained at the end of both phases. Inclusion criteria: Adults; T1DM> 1 year, HbA1c > 7.5%, basal-bolus therapy with insulin analogs, experience with carbohydrate Results: Data from the first 32 subjects with at least 1 ended phase (27 females, age 38 – 11 years, diabetes duration 16.8 – 7.5 years). Basal characteristics were comparable independently of the starting phase. No differences were found between phases in terms of mean blood glucose, standard deviation (from meter neither from sensor) and satisfaction. Conclusions: The use of a bolus calculator improves glycemic control and quality of life of T1DM subjects.