65 resultados para Diabetes glucose metabolism


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RATIONALE: Thyroid hormones and their interactions with catecholamines play a potentially important role in alterations of mood and cognition. OBJECTIVES: This study aimed to examine the neurobiological effects of catecholamine depletion on thyroid hormones by measuring endocrine and cerebral metabolic function in unmedicated subjects with remitted major depressive disorder (RMDD) and in healthy controls. METHODS: This was a randomized, placebo-controlled, and double-blind crossover trial that included 15 unmedicated RMDD subjects and 13 healthy control subjects. The participants underwent two 3-day-long sessions at 1-week intervals; each participant was randomly administered oral α-methyl-para-tyrosine in one session (catecholamine depletion) and an identical capsule containing hydrous lactose (sham depletion) in the other session prior to a [(18)F]-fluorodeoxyglucose positron emission tomography scan. RESULTS: Serum concentrations of free T3 (FT3), free T4 (FT4), and TSH were obtained and assessed with respect to their relationship to regional cerebral glucose metabolism. Both serum FT3 (P = 0.002) and FT4 (P = 0.0009) levels were less suppressed after catecholamine depletion compared with placebo treatment in the entire study sample. There was a positive association between both FT3 (P = 0.0005) and FT4 (P = 0.002) and depressive symptoms measured using the Montgomery-Åsberg Depression Rating Scale. The relative elevation in FT3 level was correlated with a decrease in regional glucose metabolism in the right dorsolateral prefrontal cortex (rDLPFC; P < 0.05, corrected). CONCLUSIONS: This study provided evidence of an association between a thyroid-catecholamine interaction and mood regulation in the rDLPFC.

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Background: Despite immense efforts into development of new antidepressant drugs, the increases of serotoninergic and catechominergic neurotransmission have remained the two major pharmacodynamic principles of current drug treatments for depression. Consequently, psychopathological or biological markers that predict response to drugs that selectively increase serotonin and/or catecholamine neurotransmission hold the potential to optimize the prescriber’s selection among currently available treatment options. The aim of this study was to elucidate the differential symptomatology and neurophysiology in response to reductions in serotonergic versus catecholaminergic neurotransmission in subjects at high risk of depression recurrence. Methods: Using identical neuroimaging procedures with [18F] fluorodeoxyglucose positron emission tomography after tryptophan depletion (TD) and catecholamine depletion (CD), subjects with remitted depression were compared to healthy controls in a double-blind, randomized, crossover design. Results: While TD induced significantly more depressed mood, sadness and hopelessness than CD, CD induced more inactivity, concentration difficulties, lassitude and somatic anxiety than TD. CD specifically increased glucose metabolism in the bilateral ventral striatum and decreased glucose metabolism in the bilateral orbitofrontal cortex, whereas TD specifically increased metabolism in the right prefrontal cortex and the posterior cingulate cortex (PCC). While we found direct associations between changes in brain metabolism and induced depressive symptoms following CD, the relationship between neural activity and symptoms was less clear after TD. Conclusions: In conclusion, this study showed that serotonin and catecholamines play common and differential roles in the pathophysiology of depression.

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Despite immense efforts into development of new antidepressant drugs, the increases of serotoninergic and catecholaminergic neurotransmission have remained the two major pharmacodynamic principles of current drug treatments for depression. Consequently, psychopathological or biological markers that predict response to drugs that selectively increase serotonin and/or catecholamine neurotransmission hold the potential to optimize the prescriber's selection among currently available treatment options. The aim of this study was to elucidate the differential symptomatology and neurophysiology in response to reductions in serotonergic versus catecholaminergic neurotransmission in subjects at high risk of depression recurrence. Using identical neuroimaging procedures with [(18)F] fluorodeoxyglucose positron emission tomography after tryptophan depletion (TD) and catecholamine depletion (CD), subjects with remitted depression were compared with healthy controls in a double-blind, randomized, crossover design. Although TD induced significantly more depressed mood, sadness and hopelessness than CD, CD induced more inactivity, concentration difficulties, lassitude and somatic anxiety than TD. CD specifically increased glucose metabolism in the bilateral ventral striatum and decreased glucose metabolism in the bilateral orbitofrontal cortex, whereas TD specifically increased metabolism in the right prefrontal cortex and the posterior cingulate cortex. Although we found direct associations between changes in brain metabolism and induced depressive symptoms following CD, the relationship between neural activity and symptoms was less clear after TD. In conclusion, this study showed that serotonin and catecholamines have common and differential roles in the pathophysiology of depression.

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It is still controversial which mediators regulate energy provision to activated neural cells, as insulin does in peripheral tissues. Interleukin-1β (IL-1β) may mediate this effect as it can affect glucoregulation, it is overexpressed in the 'healthy' brain during increased neuronal activity, and it supports high-energy demanding processes such as long-term potentiation, memory and learning. Furthermore, the absence of sustained neuroendocrine and behavioral counterregulation suggests that brain glucose-sensing neurons do not perceive IL-1β-induced hypoglycemia. Here, we show that IL-1β adjusts glucoregulation by inducing its own production in the brain, and that IL-1β-induced hypoglycemia is myeloid differentiation primary response 88 protein (MyD88)-dependent and only partially counteracted by Kir6.2-mediated sensing signaling. Furthermore, we found that, opposite to insulin, IL-1β stimulates brain metabolism. This effect is absent in MyD88-deficient mice, which have neurobehavioral alterations associated to disorders in glucose homeostasis, as during several psychiatric diseases. IL-1β effects on brain metabolism are most likely maintained by IL-1β auto-induction and may reflect a compensatory increase in fuel supply to neural cells. We explore this possibility by directly blocking IL-1 receptors in neural cells. The results showed that, in an activity-dependent and paracrine/autocrine manner, endogenous IL-1 produced by neurons and astrocytes facilitates glucose uptake by these cells. This effect is exacerbated following glutamatergic stimulation and can be passively transferred between cell types. We conclude that the capacity of IL-1β to provide fuel to neural cells underlies its physiological effects on glucoregulation, synaptic plasticity, learning and memory. However, deregulation of IL-1β production could contribute to the alterations in brain glucose metabolism that are detected in several neurologic and psychiatric diseases.Molecular Psychiatry advance online publication, 8 December 2015; doi:10.1038/mp.2015.174.

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The adaptive response to extreme endurance exercise might involve transcriptional and translational regulation by microRNAs (miRNAs). Therefore, the objective of the present study was to perform an integrated analysis of the blood transcriptome and miRNome (using microarrays) in the horse before and after a 160 km endurance competition. A total of 2,453 differentially expressed genes and 167 differentially expressed microRNAs were identified when comparing pre- and post-ride samples. We used a hypergeometric test and its generalization to gain a better understanding of the biological functions regulated by the differentially expressed microRNA. In particular, 44 differentially expressed microRNAs putatively regulated a total of 351 depleted differentially expressed genes involved variously in glucose metabolism, fatty acid oxidation, mitochondrion biogenesis, and immune response pathways. In an independent validation set of animals, graphical Gaussian models confirmed that miR-21-5p, miR-181b-5p and miR-505-5p are candidate regulatory molecules for the adaptation to endurance exercise in the horse. To the best of our knowledge, the present study is the first to provide a comprehensive, integrated overview of the microRNA-mRNA co-regulation networks that may have a key role in controlling post-transcriptomic regulation during endurance exercise.

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Postprandial metabolism is impaired in patients with type 2 diabetes (T2Dm). Two thiazolidinediones pioglitazone (PGZ) and rosiglitazone (RGZ) have similar effects on glycaemic control but differ in their effects on fasting lipids. This study investigated the effects of RGZ and PGZ on postprandial metabolism in a prospective, randomized crossover trial.

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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.

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In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

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Hypopituitarism with adult-onset growth hormone deficiency (GHD) is associated with increased cardiovascular morbidity and mortality due to premature and progressive atherosclerosis. An underlying cause of atherosclerosis is increased insulin resistance. Elevated fasting and postprandial glucose and lipid levels may contribute to premature atherosclerosis. We studied effects of growth hormone replacement (GHRT) on fasting and postprandial metabolic parameters as well as on insulin sensitivity in patients with adult-onset GHD.

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The decreased incidence of cardiovascular disease in premenopausal women has been attributed, at least partially, to protective effects of estrogens. However, premenopausal women with diabetes mellitus are no longer selectively protected. High-glucose (HG) conditions have previously been shown to abolish the antimitogenic effects of 17β-estradiol (E(2)) in vascular smooth muscle cells (VSMCs).

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Intramyocellular acetylcarnitine (IMAC) is involved in exercise-related fuel metabolism. It is not known whether levels of systemic glucose influence IMAC levels in type 1 diabetes.

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Self-monitoring of blood glucose (SMBG) in type 2 diabetes has increasingly been shown to display beneficial effects on glycemic control. SMBG is not only associated with a reduction of hemoglobin A1c but has also been demonstrated to increase patients' awareness of the disease. SMBG has also the potential to visualize and predict hypoglycemic episodes. International guidelines by the International Diabetes Federation, the European Society of Cardiology, and the European Association for the Study of Diabetes and also the International Society for Pediatric and Adolescent Diabetes emphasize that SMBG is an integral part of self-management. More recently, two European consensus documents have been published to give recommendations for frequency and timing of SMBG also for various clinical scenarios. Recently, a European expert panel was held to further facilitate and enhance standardized approaches to SMBG. The aim was to present simple, clinically meaningful, and standardized SMBG strategies for type 2 diabetes. The panel recommended a less intensive and an intensive scheme for SMBG across the type 2 diabetes continuum. The length and frequency of SMBG performance depend on the clinical circumstances and the quality of glycemic control. The expert panel also recommended further evaluation of various schemes for SMBG in type 2 diabetes in clinical studies.

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Abstract Background and Aims: Data on the influence of calibration on accuracy of continuous glucose monitoring (CGM) are scarce. The aim of the present study was to investigate whether the time point of calibration has an influence on sensor accuracy and whether this effect differs according to glycemic level. Subjects and Methods: Two CGM sensors were inserted simultaneously in the abdomen on either side of 20 individuals with type 1 diabetes. One sensor was calibrated predominantly using preprandial glucose (calibration(PRE)). The other sensor was calibrated predominantly using postprandial glucose (calibration(POST)). At minimum three additional glucose values per day were obtained for analysis of accuracy. Sensor readings were divided into four categories according to the glycemic range of the reference values (low, ≤4 mmol/L; euglycemic, 4.1-7 mmol/L; hyperglycemic I, 7.1-14 mmol/L; and hyperglycemic II, >14 mmol/L). Results: The overall mean±SEM absolute relative difference (MARD) between capillary reference values and sensor readings was 18.3±0.8% for calibration(PRE) and 21.9±1.2% for calibration(POST) (P<0.001). MARD according to glycemic range was 47.4±6.5% (low), 17.4±1.3% (euglycemic), 15.0±0.8% (hyperglycemic I), and 17.7±1.9% (hyperglycemic II) for calibration(PRE) and 67.5±9.5% (low), 24.2±1.8% (euglycemic), 15.5±0.9% (hyperglycemic I), and 15.3±1.9% (hyperglycemic II) for calibration(POST). In the low and euglycemic ranges MARD was significantly lower in calibration(PRE) compared with calibration(POST) (P=0.007 and P<0.001, respectively). Conclusions: Sensor calibration predominantly based on preprandial glucose resulted in a significantly higher overall sensor accuracy compared with a predominantly postprandial calibration. The difference was most pronounced in the hypo- and euglycemic reference range, whereas both calibration patterns were comparable in the hyperglycemic range.

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A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are (i) simultaneous adjustment of both the insulin basal rate and the bolus dose, (ii) initialization based on clinical procedures, and (iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of ±25% in the estimation of carbohydrates. The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children. The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycemic control. After optimization of the algorithm, the controller will be tested in a clinical trial.