915 resultados para horse, laminitis, glucose, insulin, GLUT, insulin resistance


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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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High consumption of polyunsaturated fatty acids, such as sunflower oil has been associated to beneficial effects in plasma lipid profile, but its role on inflammation and insulin resistance is not fully elucidated yet. We evaluated the effect of sunflower oil supplementation on inflammatory state and insulin resistance condition in HFD-induced obese mice. C57BL/ 6 male mice (8 weeks) were divided in four groups: (a) control diet (CD), (b) HFD, (c) CD supplemented with n-6 (CD + n-6), and (d) HFD supplemented with n-6 (HFD + n-6). CD + n-6 and HFD + n-6 were supplemented with sunflower oil by oral gavage at 2 g/ Kg of body weight, three times per week. CD and HFD were supplemented with water instead at the same dose. HFD induced whole andmuscle-specific insulin resistance associated with increased inflammatory markers in insulin-sensitive tissues andmacrophage cells. Sunflower oil supplementation was not efficient in preventing or reducing these parameters. In addition, the supplementation increased pro-inflammatory cytokine production by macrophages and tissues. Lipid profile, on the other hand, was improved with the sunflower oil supplementation in animals fed HFD. In conclusion, sunflower oil supplementation improves lipid profile, but it does not prevent or attenuate insulin resistance and inflammation induced by HFD in C57BL/ 6 mice.

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In this study, we evaluated the effects of obesity and insulin resistance induced by a high-fat diet on prostate morphophysiology, focusing on cell proliferation, expression of androgen (AR) and estrogen receptors (ER) and proteins of the insulin signaling pathway. Adult male Wistar rats were fed a high-fat diet (20% fat) for 15 weeks, whereas control animals received a balanced diet (4% fat). Both groups were then divided and treated for 2 weeks with 1 mg/kg body weight/day of the aromatase inhibitor letrozole or vehicle only. The ventral prostate was analyzed with immunohistochemical, histopathological, stereological, and Western blotting methods. Obese rats showed insulin resistance, hyperinsulinemia, and reduced plasma testosterone levels. The incidence of prostatic intraepithelial neoplasia (PIN) was 2.7 times higher in obese rats and affected 0.4% of the gland compared with 0.1% PIN areas found in control rats. Obesity doubled cell proliferation in both prostate epithelium and stroma. AR content decreased in the prostate of obese rats and estrogen receptor beta (ER beta) increased in this group. Protein levels of insulin receptor substrate 1 and protein kinase B diminished in the obese group, whereas phosphatidylinositol 3-kinase (PI3K) increased significantly. Most structural changes observed in the prostate of obese rats normalized after letrozole treatment, except for increased stromal cell proliferation and ER beta expression, which might be associated with insulin resistance. This experimental model of obesity and insulin resistance induced by a high-fat diet increases cell proliferation in rat prostate. Such alterations are associated with decreased levels of AR and increased ER beta and PI3K proteins. This change can facilitate the establishment of proliferative lesions in rat prostate.

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Insulin resistance condition is associated to the development of several syndromes, such as obesity, type 2 diabetes mellitus and metabolic syndrome. Although the factors linking insulin resistance to these syndromes are not precisely defined yet, evidence suggests that the elevated plasma free fatty acid (FFA) level plays an important role in the development of skeletal muscle insulin resistance. Accordantly, in vivo and in vitro exposure of skeletal muscle and myocytes to physiological concentrations of saturated fatty acids is associated with insulin resistance condition. Several mechanisms have been postulated to account for fatty acids-induced muscle insulin resistance, including Randle cycle, oxidative stress, inflammation and mitochondrial dysfunction. Here we reviewed experimental evidence supporting the involvement of each of these propositions in the development of skeletal muscle insulin resistance induced by saturated fatty acids and propose an integrative model placing mitochondrial dysfunction as an important and common factor to the other mechanisms.

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Insulin resistance is a metabolic disorder in which target cells fail to respond to normal levels of circulating insulin. Insulin resistance has been associated with presence of acanthosis nigricans and acrochordons. It is known that early diagnosis and early initial treatment are of paramount importance to prevent a series of future complications. These dermatoses may represent an easily identifiable sign of insulin resistance and non-insulin-dependent diabetes.

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FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo)

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Macrophage-mediated chronic inflammation is mechanistically linked to insulin resistance and atherosclerosis. Although arginase I is considered antiinflammatory, the role of arginase II (Arg-II) in macrophage function remains elusive. This study characterizes the role of Arg-II in macrophage inflammatory responses and its impact on obesity-linked type II diabetes mellitus and atherosclerosis.

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AIMS/HYPOTHESIS: Retinol-binding protein 4 (RBP4) has recently been reported to be associated with insulin resistance and the metabolic syndrome. This study tested the hypothesis that RBP4 is a marker of insulin resistance and the metabolic syndrome in patients with type 2 diabetes or coronary artery disease (CAD) or in non-diabetic control subjects without CAD. METHODS: Serum RBP4 was measured in 365 men (126 with type 2 diabetes, 143 with CAD and 96 control subjects) and correlated with the homeostasis model assessment of insulin resistance index (HOMA-IR), components of the metabolic syndrome and lipoprotein metabolism. RBP4 was detected by ELISA and validated by quantitative Western blotting. RESULTS: RBP4 concentrations detected by ELISA were shown to be strongly associated with the results gained in quantitative Western blots. There were no associations of RBP4 with HOMA-IR or HbA(1c) in any of the groups studied. In patients with type 2 diabetes there were significant positive correlations of RBP4 with total cholesterol, LDL-cholesterol, VLDL-cholesterol, plasma triacylglycerol and hepatic lipase activity. In patients with CAD, there were significant associations of RBP4 with VLDL-cholesterol, plasma triacylglycerol and hepatic lipase activity, while non-diabetic control subjects without CAD showed positive correlations of RBP4 with VLDL-cholesterol and plasma triacylglycerol. CONCLUSIONS/INTERPRETATION: RBP4 does not seem to be a valuable marker for identification of the metabolic syndrome or insulin resistance in male patients with type 2 diabetes or CAD. Independent associations of RBP4 with pro-atherogenic lipoproteins and enzymes of lipoprotein metabolism indicate a possible role of RBP4 in lipid metabolism.

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In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.