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Carbohydrate metabolism in brain

Carbohydrate metabolism in brain

Carbohydrate metabolism in brain is also substrate for the Carbohydrate metabolism in brain phosphate shunt and the barin of glycogen only in Insulin resistance and obesity cells. Article I PubMed Google Scholar Kim KS, Yoon YR, Lee HJ, Yoon S, Kim SY, Shin SW et al. Kim KS, Yoon YR, Lee HJ, Yoon S, Kim SY, Shin SW et al. Philos Trans R Soc Lond B Biol Sci ; : —

Carbohydrate metabolism in brain -

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Activation of hypothalamic S6 kinase mediates diet-induced hepatic insulin resistance in rats. CAS PubMed PubMed Central Google Scholar. Download references. This work was supported by grants from the National Research Foundation NRFR1A6A3A, NRFM3C7A for M-SK and the Asan Institute for Life Sciences Appeptite Regulation Laboratory, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, Korea.

Department of Medicine, University of Ulsan College of Medicine, Seoul, Korea. Division of Endocrinology and Metabolism, Asan Medical Center, Seoul, Korea. You can also search for this author in PubMed Google Scholar. Correspondence to Min-Seon Kim.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4. Reprints and permissions. Roh, E. Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism. Exp Mol Med 48 , e Download citation. Received : 20 November Revised : 07 December Accepted : 09 December Published : 11 March Issue Date : March Anyone you share the following link with will be able to read this content:.

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Abstract Accumulated evidence from genetic animal models suggests that the brain, particularly the hypothalamus, has a key role in the homeostatic regulation of energy and glucose metabolism.

Central regulation of energy metabolism In normal individuals, food intake and energy expenditure are tightly regulated by homeostatic mechanisms to maintain energy balance. Full size image. Brain regulation of glucose metabolism The earliest demonstration of the role of the brain in glucose homeostasis was provided by the physiologist Claude Bernard in Figure 2.

Figure 3. Concluding remarks This review highlights the role of the brain in the homeostatic regulation of energy and glucose metabolism. References Morton GJ, Meek TH, Schwartz MW.

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Cite Cite M. Select Format Select format. ris Mendeley, Papers, Zotero. enw EndNote. Patients with schizophrenic, manic-depressive, and involutional psychoses have a disorder of carbohydrate metabolism; the origin of this disorder is as yet unknown.

It also is known that patients with these psychoses commonly have blood glutathione levels that are in or below the lower range of normal.

In view of these facts, it was decided to study the effects of the administration of glutathione on the carbohydrate metabolism of patients with the above-mentioned psychoses.

ALTSCHULE MD , HENNEMAN DH , HOLLIDAY PD , GONCZ R. Carbohydrate Metabolism in Brain Disease : VII. The Effect of Glutathione on Carbohydrate Intermediary Metabolism in Schizophrenic and Manic-Depressive Psychoses.

AMA Arch Intern Med. Artificial Intelligence Resource Center. X Facebook LinkedIn. This Issue. Share X Facebook Email LinkedIn. January

Carbohydeate ground that I was called to cover is Carbohydrate metabolism in brain and Indian coffee beans unexplored. If you consider that most data on carbohydrate metabolism meyabolism nervous tissue Carbohydrwte lately undergone metabolsm reappraisal in the bdain of recent experimental Carbohydrate metabolism in brain, you will Carbohydrate metabolism in brain why my exposé will look somewhat fragmentary. My only hope is that the very nature of the subject and its intrinsic complexity will prompt my colleagues to take the few data I shall review as a starting point for fruitful and critical discussion. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in via an institution.

Thank you for visiting nature. You are using a browser version with limited bbrain for Cargohydrate. To obtain the best Natural athletic supplements, we recommend you use a more up to date browser or turn off compatibility mode Antioxidant benefits Internet Explorer.

In Carbhydrate meantime, bfain ensure continued support, we are displaying the site without styles and JavaScript. The correspondence between Weight management diet glucose metabolism indexing energy utilization and synchronous fluctuations in blood oxygenation indexing jn activity is relevant Gluten-free recipes neuronal Carbohydrxte and is affected by brain disorders.

Here, we define novel measures of Vitamin-rich Supplement power rPWR, Carbohydrahe of concurrent energy utilization Carbohydratd activity and relative cost rCST, extent that energy utilization exceeds activityCarbohydrzte from FDG-PET and fMRI.

We show that resting-state networks have distinct energetic signatures and Carboyydrate brain could be classified into major bilateral segments based on rPWR and rCST.

While medial-visual and im networks have the highest rPWR, frontoparietal networks have Carbohydratee highest Carbohydrate metabolism in brain. rPWR and rCST estimates are generalizable to other metwbolism of energy supply and neuronal activity, and are sensitive to ln effects of acute and metabollism alcohol exposure.

rPWR and rCST are informative metrics for characterizing brain pathology and alternative energy use, and may provide new ni biomarkers of Metabolism and calorie burning disorders. The Carbohydrate metabolism in brain brain has markedly complex and diverse structural characteristics that have evolved in accordance with the functional specialization of brain regions 1.

Notably, larger brain on, indicating metaboljsm cognitive capacity, Restful therapies been associated with higher energetic cost across species Carbkhydrate. In this respect, differences in regional morphometry, axonal and Importance of breakfast for athletic performance density, glia-to-neuron ratio, neurotransmitter distribution, and active African mango extract pills pathways, have led to different functional outcomes for different ,etabolism regions, including variations in meyabolism glucose metabolism 34 and differences in merabolism activity levels metabilism6.

It has been shown that regional glucose metabolism is coupled with braih Carbohydrate metabolism in brain at rest 7Carbohydrxte and task conditions 910metabllism In Carbohydarte relation, netabolism measures of Carbohydrrate activity, such Carboohydrate functional magnetic resonance imaging fMRI Self-help strategies for anxiety of Carbohydraate connectivity bfain13 or magnetic resonance spectroscopy measures of glutamatergic function 14have been associated with regional brain kn metabolism, wherein high and meetabolism neuronal activity demand were associated Carbkhydrate high and low metabolic supply, respectively.

Compelling evidence also Carbojydrate that brain regions may differ in their activity demand and metabolic supply associations Contrastingly, glucose metabolism may fall behind neuronal demand when relative Caffeine and energy levels other regions, there is brainn reliance on the Krebs cycle, rbain when Cwrbohydrate to glucose such as ketone bodies are metabolized as substrates Carbohydrate metabolism in brain energy generation 17 metabo,ism, Emerging evidence suggests that the coupling metabopism neuroglial demand and energy supply entails a bidirectional association 19 ; however, metabilism spatiotemporal metqbolism of these associations remain Cabrohydrate be meatbolism explored.

ACrbohydrate in how energy is Energizing lifestyle supplements and metabolized in different brain regions spatially 20 and under different stimulation and physiological mteabolism temporally 21 are Carbohyfrate high relevance in our understanding of brain physiology 22development 20Strategies to sustain athletic performance abilities braniand neuropsychiatric Carbohyxrate There are marked regional differences in glucose metabolism brainn25 and Carbohydrate metabolism in brain fMRI measures of brain activity 62627 during resting state metabllism are positively associated Carbohydate Carbohydrate metabolism in brain 12 However, without accounting for jn brain activity, regional Blueberry salsa recipe in glucose metabolism are hard to interpret.

Interestingly, the level of correspondence between glucose metabolism and neuroglial activity has been considered as a marker of functional specialization 16 Carbohydrats, and could be helpful for inferring alternative braij use vs.

activation of different metabolic pathways. Here we propose an approach to quantify match and mismatch between measured metabolic supply Carbohydrate metabolism in brain Carbobydrate observed level of activity Carbohydrate metabolism in brain the brain and assessed whether this Lifestyle changes for cholesterol control is relevant for studying distinct Carbohdyrate characteristics of brain regions and networks.

For this meetabolism, we measured cerebral metabolic rate of Metabolism-enhancing herbal blend CMRglc, indexed by 18 F-flurodeoxyglucose; fluorodeoxyglucose-positron Natural weight loss for high cholesterol tomography FDG-PETsee Methods and synchronous fluctuations in the blood oxygenation level Carbihydrate BOLD; measured by fMRI and indexed by local functional un density: lFCD, see Methods during resting state.

We studied Carbphydrate main unit-free and generalizable dimensions of associations. Vegetable juice recipes first dimension captured metabolis, positive association between glucose utilization and neuroglial activity and was ,etabolism relative metablism rPWRwhich represented the level Hydrostatic weighing and metabolic rate estimation concurrent metabolic need and observed activity, relative to the rest of the brain.

The second dimension captured the deviation between glucose utilization and neuroglial activity and was labeled relative cost rCSTwhich represented the extent to which glucose metabolic needs exceed or fall behind the observed activity, relative to the rest of the brain.

As in principal component analysis, when there is complete correspondence between measured neuroglial activity and glucose utilization across regions, all the common variance will be accounted for by the rPWR dimension.

But, more deviation between observed neuroglial activity and glucose utilization 16202128 i. b COMET connectivity-metabolism map. Red to blue indicate high to low-frequency counts, respectively.

c Average CMRglc map highlighting precuneus and lateral frontal lobe as the most metabolically demanding regions. e A hypothetical presentation of mean-variance normalized activity demand versus metabolic supply not to be confused with part b shown without mean-variance normalization with each circle representing one brain voxel.

Yellow-colored voxels correspond to higher rCST, blue to lower rCST, red to higher rPWR, and green to lower rPWR. For a representative voxel v i dark gray circlerPWR i and rCST i are shown on the plot. For v irPWR is negative and rCST is positive.

For visual demonstration purposes, voxels contributing the most to rPWR variability d and rCST variability f are highlighted. For this purpose, highlighting was performed by multiplying the radius of each voxel in polar coordinate system by its corresponding absolute rPWR d or absolute rCST f.

Group-average rPWR g and rCST h maps. Note the color scale in g resembles that used for the hypothetical voxels along the rPWR axis in d and the color scale in h resembles that used for the hypothetical voxels along the rCST axis in f.

Also see Fig. Here we perform a series of experiments and analyses in two independent cohorts. We test the hypothesis that different brain networks have distinct rPWR and rCST signatures.

We show the generalizability of rPWR and rCST to alternative measures of metabolic supply i. We also assess effects of temporal signal-to-noise ratio tSNR and brain morphometry on rPWR and rCST. We propose multimodal measures of rPWR and rCST to study regional variations in the correspondence between glucose metabolism and measures of functional activity, with potential implications for characterizing neuropsychiatric diseases.

Please refer to Table 1 for the list of acronyms. Spatial distributions of lFCD and CMRglc are highlighted in Fig. To quantify the regional differences in the coupling between lFCD indexing synchronous BOLD fluctuations and related to activity demand and CMRglc indexing glucose metabolic supplywe defined measures of rPWR and rCST.

While rPWR captured the level of concurrent lFCD and CMRglc, rCST captured the mismatch between lFCD and CMRglc, relative to the rest of the brain.

In a two-dimensional map of mean-variance-normalized lFCD-CMRglc Fig. Perpendicular to the rPWR axis, we define an rCST axis Fig. A positive correlation between lFCD measure of activity and CMRglc measure of metabolic supply indicates that more voxels are associated with high- and low-rPWR quadrants than high- and low-rCST quadrants.

Voxels contributing the most to rPWR and rCST variability in the hypothetical model are highlighted in Fig. We found marked regional differences in rPWR and in rCST Fig. Regions with higher rPWR included major sections of visual, parietal, and frontal cortices, putamen, caudate, and medial—dorsal nucleus of the thalamus Fig.

Conversely, parts of the anterior and posterior cerebellar lobes, medial—frontal, and precentral gyri, hippocampus and parahippocampal gyrus, and ventral anterior and lateral nuclei of the thalamus had lower rPWR Fig.

Many brain regions with higher rPWR had higher rCST Fig. Higher rCST regions included inferior, middle, superior frontal, precentral, and postcentral gyri, as well as insula, putamen, and middle, and superior temporal gyri.

In contrast, caudate, cerebellum CBlimbic lobe, midbrain, pons, and ventral anterior and lateral thalamic nuclei had lower rCST Fig. Figure 2a, b shows individual differences in lFCD and CMRglc within 10 predefined resting-state networks Plotting these values against each other showed low segregation of these networks when considering individual differences Fig.

Similarly, we compared individual differences in rPWR and rCST of these 10 networks Fig. In addition, differences in rPWR or in rCST in some networks were not significant e. Highest rPWR corresponded to the MV and default mode networks, whereas lowest rPWR corresponded to the CB network.

The left and right frontoparietal networks had the highest rCST, whereas the MV and CB networks had the lowest rCST. Since rPWR and rCST were calculated from mean-variance normalized lFCD and CMRglc, both measures contributed to variance in rPWR and rCST see Methods. ab Within-subject averages 28 circles and between-subject average thick line of lFCD and CMRglc for each of 10 resting-state networks 33 including medial visual MVoccipital pole OPlateral visual LVdefault mode DMcerebellum CBsensorimotor SMauditory ADexecutive control ECright and left frontoparietal RFP and LFP networks.

c Within-subject averages of lFCD and CMRglc plotted against each other each circle represents one participant for the networks shown in ab. The network colors in c match those shown in ab. de Within-subject averages 28 circles for 28 participants and between-subject average thick lines of rPWR and rCST for each of the 10 resting-state networks shown in parts ab.

f Within-subject averages of rPWR and rCST plotted against each other for the networks shown in de.

The network colors in f match those shown in de. Figure 2f shows subject-level averages of rPWR and rCST when plotted against each other, which in contrast to Fig. This resulted in better segregation of networks based on rPWR and rCST properties than lFCD and CMRglc measures Fig.

We quantified the level of segregation of brain networks in the two spaces i. Specifically, in a given two-dimensional space and for each brain network, NSI was defined as the ratio of the average of between network distances to the average of within network distances see Methods.

There were notable differences in regional distributions of rPWR and rCST Fig. The largest differences between rPWR and rCST maps were in the visual cortex, which despite having high rPWR had low rCST, while many temporal and limbic regions showed the opposite pattern Supplementary Table 3.

Variations in the regional rPWR and rCST measures motivated segmenting the brain into groups of voxels, each with most similar rPWR and rCST than the rest of the brain. A k -means clustering approach identified four reproducible clusters of voxels see Methods from the across-subject average of rPWR and rCST measures Fig.

The clusters corresponded to: higher rPWR, lower to intermediate rCST red ; intermediate rPWR, higher rCST yellow ; lower rPWR, intermediate rCST blue ; and lower rPWR, lower rCST green regions Fig.

Projection of these clusters into the across-subject average of lFCD-CMRglc map Fig. Voxels corresponding to each of the clusters primarily highlighted sensorimotor bluecerebellar-limbic greenvisual redand frontoparietal yellow regions Fig. a rCST versus rPWR contrast showing precuneus and visual cortices with significantly lower rCST than rPWR, whereas superior frontal and insular cortices showed higher rCST than rPWR.

b Scatter plot of rPWR versus rCST for all the brain voxels, showing the four k -means clusters with different colors. Numbers represent percentage of voxels falling within clusters. c Same clusters projected into the space of local functional connectivity density lFCD and cerebral metabolic rate of glucose CMRglc.

The thick gray lines mark the average of log lFCD and average of CMRglc with the number in each quadrant showing percentage of voxels falling within that quadrant. These quadrants are defined in Fig. It is important to note that having a higher percentage of voxels associated with the high- and low-rPWR quadrants is consistent with log lFCD and CMRglc being positively correlated see Fig.

d The four k -means clusters in b on a surface rendering of the brain. We further assessed whether rPWR and rCST are generalizable to alternative measures of neuronal activity i.

In sum, there were high correlations between regional differences in CMRglc and CBF PWI and between regional differences log lFCD and fALFF Supplementary Fig.

There was excellent agreement between rPWR based on lFCD-CMRglc, and rPWR based on fALFF-CBF PWI Supplementary Fig. Same effect was observed for rCST Supplementary Fig. Across regions, rPWR and rCST did not amplify the effects of tSNR and brain morphometry on lFCD and on CMRglc Supplementary Tables 8— See Supplementary Results for more details.

: Carbohydrate metabolism in brain

Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism

received funding from the Päivikki and Sakari Sohlberg Foundation. Duality of Interest. No potential conflicts of interest relevant to this article were reported. Author Contributions. Sa, and L. researched data, contributed to discussion, and wrote, reviewed, and edited the manuscript.

Sh, H. contributed to discussion and reviewed and edited the manuscript. All authors approved the final version of the manuscript. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Volume 46, Issue 2. Previous Article Next Article. Article Information. Article Navigation. e-Letters — Observations December 15 Brain Glucose Metabolism and Aging: A 5-Year Longitudinal Study in a Large Positron Emission Tomography Cohort Kyoungjune Pak Kyoungjune Pak. Corresponding author: Kyoungjune Pak, ilikechopin me.

This Site. Google Scholar. Tuulia Malén Tuulia Malén. Severi Santavirta Severi Santavirta. Seunghyeon Shin In this chapter, we will describe the fueling and sensing properties of glucose and other carbohydrates on the brain and present some physiological brain functions impacted by these sugars.

We will also highlight the scientific questions that need to be answered in order to better understand the impact of sugars on the brain.

The mammalian brain essentially depends on glucose for its energy needs. Because neurons have the highest energy demand in the adult brain, they require continuous delivery of glucose from the blood. As a consequence, a tight regulation of glucose metabolism is critical for brain physiology.

The brain needs a precise and clear feedback on the metabolic state of the whole body [ 4 ]. To achieve this aim, various brain areas, especially the brainstem and the hypothalamus, integrate peripheral signals delivered by neural input from various organs, as well as by metabolites glucose, fatty acids and hormones leptin, insulin, ghrelin via the blood [ 2 , 3 , 4 ].

Thus, specialized nutrients- and hormones-sensing neurons in which the firing rate varies in response to changes in extra-cellular nutrients or hormones concentration have been described. Thus, we will describe in this chapter that in the central nervous system, glucose has a dual role and is considered as a fueling as well as a sensing metabolite to ensure glucose homeostasis and appropriate fueling of brain cells.

Role of the brain in the control of energy homeostasis. The brain integrates peripheral signals delivered by neural input from various organs, as well as by metabolites glucose and fatty acids and hormones leptin, insulin, and ghrelin via the blood. However, one has to keep in mind that given the dietary mutations that occurred in recent decades, sugars other than glucose are part of our diet and could influence brain fueling and sensing.

This is indeed the case for example of fructose. Fructose and glucose are rather simple molecules but there are differences in the way the body processes them.

This is definitely true for the way the brain uses and reacts to them. These differences could explain the consequences observed after a high consumption of fructose, on food intake and whole-body glucose metabolism.

Although the endocrine pancreas is the main regulator of blood glucose level via the secretion of insulin and glucagon, the brain plays a major role in controlling glycemia. This is achieved through different pathways involving the autonomic nervous system and its projection to several organs and tissues such as the endocrine pancreas, the adrenal gland, the liver, skeletal muscles, and white and brown adipose tissues.

As illustrated in Figure 2 , in case of a drop in blood glucose, there is an activation of sympathetic nerves and consequently an increase in glucagon secretion by the alpha cells and a decrease in that of insulin by the beta cells of the pancreas, as well as an increase in epinephrine and cortisol secretion by the adrenal gland.

These changes in hormone levels together with a direct effect of the sympathetic system will lead to an increased glucose production by the liver, and a decreased glucose utilization by fat deposits and muscles, leading thus to a normalization of blood glucose.

Neuroendocrine pathways involved in the counter-regulatory response to hypoglycemia. Decreased blood glucose is detected by central hypothalamus and hindbrain and peripheral pancreas, hepatoportal vein, and carotid body glucose sensors.

Together, these glucose sensors coordinate physiological responses, which raise blood glucose levels. The initial response to hypoglycemia involves activation of the autonomic nervous system ANS , inhibition of insulin secretion, and stimulation of pituitary ACTH secretion.

Activation of the autonomic nervous system increases glucagon and epinephrine secretion from the pancreas and adrenal medulla, respectively. ACTH stimulates cortisol release from the adrenal cortex. Increased glucagon, epinephrine, and cortisol together with decreased insulin stimulate hepatic glucose production and decrease adipose and muscle glucose uptake.

The net result of the neuroendocrine counter-regulatory response to hypoglycemia is to increase blood glucose levels and restore euglycemia.

Brain function and glucose metabolism are intimately linked [ 1 ]. Indeed, glucose is the main, if not the only, energy substrate of this organ. Hypoglycemia below 0. With regard to hyperglycemia, acute situations such as ketoacidosis and hyperosmolarity can lead to a coma, with significant mortality.

The chronic effects of hyperglycemia on the brain remain unclear, apart from the risk of ischemic stroke. However, microangiopathy is intimately linked to chronic hyperglycemia, and can cause irreversible diffuse vascular lesions and cerebral ischemia, resulting in cortical atrophy and diabetic encephalopathy.

The brain uses glucose as its main source of energy, although it can utilize other metabolites mainly ketone bodies in special situations such as fasting. It has very high energy consumption for its size, mainly due to the high energy supply needed to maintain its functions potential difference across nerve cell membranes, transport along axons and dendrites, tissue plasticity and repair.

Glucose enters the brain by facilitated diffusion across the blood-brain barrier, and enters brain cells mainly via a range of glucose transporters. Most human cells import glucose by members of the GLUT SLC2A family of membrane transport proteins see review [ 5 ].

Of these, GLUT1 is abundant at the BBB and in astrocytes, regulated mainly by steady-state levels of plasma glucose. GLUT2 appears to serve glucose sensors in the brain. GLUT3 ensures efficient glucose uptake by neurons.

Although the brain is considered as a non-insulin-dependent organ, insulin crosses the blood-brain barrier and binds to receptors on neurons and glial cells [ 6 ]. GLUT5 and GLUT7 are present at low levels in the brain and have specificity for fructose.

GLUT6 is expressed in the brain but has low affinity to glucose. Studies of mice suggest roles of GLUT8 in hippocampal neuronal proliferation. GLUT13 is a myoinositol transporter expressed primarily in the brain and is the only GLUT protein that appears to function as a proton-coupled symporter see review [ 5 ].

Once transported into the cell, glucose is phosphorylated by a hexokinase, an enzyme with such high affinity toward glucose that it rapidly transforms glucose into glucosephosphate. Glucosephosphate is metabolized further, mainly in the glycolytic pathway, where it is converted to pyruvate.

Glucosephosphate is also substrate for the pentose phosphate shunt and the generation of glycogen only in glial cells. Pyruvate is metabolized either in the Krebs cycle after transport into the mitochondria, or converted to lactate by means of the lactate dehydrogenase.

A large part of the pyruvate transported into brain mitochondria is devoted to the oxidative phosphorylation of ADP to ATP. The energy supply to the brain is provided by blood vessels. In most brain structures, these vessels are surrounded by a blood-brain barrier which does not allow molecules to cross it and as a consequence isolates the brain from the circulatory network.

Under these conditions, the energy input is partly indirect and passes partly through the cells that constitute this barrier, namely the astrocytes [ 9 ].

These cells can store energy as glycogen or transform it as lactate. This energy is released on demand, when the neurons need it [ 10 ]. This lactate is produced in astrocytes by degradation of glucose in pyruvate when the neurons need it.

The lactate is then sent to neurons, which synthetize pyruvate and use it in the Krebs cycle. This role of astrocytes and lactate as the main energy substrate of neurons is still a matter of debates. In the previous part, we discussed the fact that the brain relies on glucose to function.

This implies that blood glucose level must remain stable. Any decrease in blood glucose level would have immediate consequences on brain functions. Increased blood level will not have acute consequences but sustained hyperglycemia will be deleterious in the long term as seen in patients with uncontrolled diabetes mellitus.

The brain plays a critical role in the regulation of blood glucose level to ensure whole-body glucose homeostasis. Thus, to be able to control the level of blood glucose, the brain must be able to sense any change. In this part, we will discuss the idea that glucose is more than a fueling molecule and it is able to play the role of a signaling molecule in some neurons or brain cells called glucose-sensing cells.

While these studies suggested that neurons able to detect glucose were present in the brain, they did not prove that glucose could directly affect these neurons since glucose was injected intravenously.

Thus, later, Oomura demonstrated the presence of specialized glucose-sensing neurons in showing that the direct application of glucose in the lateral hypothalamus of rats altered the activity of specific neurons [ 13 ]. These so-called glucose-sensing neurons are now defined as cells able to adapt their electrical activity in response to changes in extracellular glucose level.

By definition, glucose-excited GE neurons increase their electrical activity, whereas glucose-inhibited GI neurons decrease their activity when glucose level rises. By opposition, when glucose level decreases, GE neurons decrease their electrical activity whereas GI neurons increase it Figure 3.

Schematic representation of the electrical activity of glucose-sensing neurons in response to changes in glucose level. Glucose-excited GE neurons increase their electrical activity depolarization and increased action potential frequency , whereas glucose-inhibited GI neurons decrease their activity hyperpolarization and decreased firing rate when glucose level rises.

By opposition, when glucose level decreases, GE neurons decrease their electrical activity whereas GI neurons increase it. Abbreviations: glucose or glc, extracellular glucose level; Vm, basal membrane potential. It is important to note that glucose-sensing neurons use glucose, not only as fuel, but as a signaling molecule that modulates their electrical activity.

In addition, it must be mentioned that glucose-sensing neurons directly detect changes in glucose level and not through indirect presynaptic modulation. Brain glucose level : The notion that, by definition, glucose-sensing neurons respond to physiological changes in brain glucose level raises the question of the glucose level in the brain.

The level of brain glucose is a process finely regulated by GLUT1, the glucose transporter expressed at the BBB.

Thus, several studies using glucose oxidase electrode methods or zero net flux method for microdialysis consistently indicate that physiological levels of glucose within the brain vary within a fairly tight range from 0.

On the other hand, extracellular brain glucose levels below 0. This is the case in all brain areas where it has been measured including the hypothalamus, the hippocampus, and striatum for instance [ 14 , 15 , 16 , 17 , 18 ] Figure 4. Extracellular brain glucose levels versus plasma glucose levels.

Adapted from Ref. Location and role of glucose-sensing neurons : Most of the glucose-sensing neurons have been described in the hypothalamus in response to changes in the window between 0.

Nevertheless, our group found that within the arcuate nucleus ARC , four populations of glucose-sensing neurons actually exist. Interestingly, the electrical activity of HGE and HGI neurons is only changed in response to glucose change below 2.

Similarly, we found that HGE and HGI neurons only change their electrical activity in response to changes in glucose level above 5 mM but not below this level [ 23 ]. Finding these different subpopulations of glucose-sensing neurons raised the question of the actual glucose level present in the arcuate nucleus of the hypothalamus in which the BBB is fenestrated [ 16 , 26 ] and suggested that, in confined areas, glucose level could be increased closer to levels found in the blood.

Not everything is known yet regarding these different populations of glucose-sensing neurons. Their proportion within the different nuclei of the hypothalamus is difficult to estimate since not every study uses the same changes in glucose level. A question which has been poorly addressed is their interconnection.

We think that some HGE or HGI neurons from the ARC may connect some VMN neurons found to be indirectly modulated to increased glucose level above 5 mM [ 20 , 23 , 27 ].

Nevertheless, no study has directly studied their interconnection to determine whether they could work as a synchronous network. By opposition, the molecular mechanisms involved in their detection to changes in glucose level are pretty much known see for review [ 20 , 21 ].

The nature of these glucose-sensing neurons in terms of neurotransmitter expressed and released, however, is not clear for all the subpopulations [ 20 ]. Knowing better the identity of glucose-sensing neurons will be necessary to better understand the physiological functions they control, which are not fully understood yet.

It is however clear that these neurons are involved in the control of food intake, thermogenesis, and glucose homeostasis glucose tolerance, insulin secretion, and hepatic glucose production.

Several studies have described that inhibiting molecular mechanisms involved in their glucose sensitivity alters some of these functions. Glucose-sensing neurons can be found in extra-hypothalamic areas Figure 5.

To our knowledge, HGE and HGI neurons have only been found in so-called circumventricular organs, brain areas where the BBB is fenestrated including the area postrema of the hindbrain, the subfornical organ and the vascular organ of lamina terminalis.

All the other brain areas where glucose-sensing have been found present neurons modulated by glucose changes below 2. This raises the question of the physiological role of these neurons in these extra-hypothalamic areas.

One hypothesis is that these neurons present in different places of the brain detect decreased glucose level, which could be associated to hypoglycemia. Nevertheless, we cannot exclude that these neurons take part in physiological functions including memory, motivation olfaction, in view of their location in areas such as the hippocampus, striatum, olfactory bulb for instance.

Significant work is still needed to fully understand the functions controlled by these hypothalamic or extra-hypothalamic neurons. Author Affiliations Boston; Waverley, Mass. visual abstract icon Visual Abstract. Access through your institution. Add or change institution.

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PITUITARY AND CARBOHYDRATE METABOLISM OF THE BRAIN | Endocrinology | Oxford Academic Outdoor temperature, age, sex, body mass index, and diabetic status determine the prevalence, mass, and glucose-uptake activity of 18F-FDG-detected BAT in humans. Article CAS PubMed Google Scholar Asilmaz E, Cohen P, Miyazaki M, Dobrzyn P, Ueki K, Fayzikhodjaeva G et al. Article CAS PubMed Google Scholar Kubota N, Terauchi Y, Tobe K, Yano W, Suzuki R, Ueki K et al. Lauri Nummenmaa Zuo, X. Ozcan L, Ergin AS, Lu A, Chung J, Sarkar S, Nie D et al.
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These neurons project to other hypothalamic nuclei and to the nucleus of the solitary tract in the brain stem to control multiple aspects of the homeostatic regulation of energy balance.

ARC, arcuate nucleus; CCK, cholecystokinin; GLP-1, glucagon-like peptide-1; IL-6, interleukin-6; PP, pancreatic polypeptide; PVN, paraventricular nucleus; PYY, peptide YY. The hypothalamus is considered a key organ in the regulation of food intake.

The hypothalamic arcuate nucleus ARC is adjacent to the median eminence, one of the circumventricular organs, and surrounds the third cerebroventricle. Thus, hormones and nutrients in the systemic circulation and the cerebrospinal fluid can easily access the ARC.

Anatomically, the ARC is considered a hypothalamic area that primarily senses metabolic signals from the periphery via the systemic circulation. These neurons are the first-order neurons on which peripheral metabolic hormones, including leptin, insulin, ghrelin and nutrients, primarily act.

The anorexigenic neuropeptide α-melanocyte-stimulating hormone α-MSH is produced by posttranscriptional processing of POMC and is released from the presynaptic terminals of POMC neurons. Upon binding to the melanocortin-3 and -4 receptors MC3R and MC4R on second-order neurons, α-MSH activates catabolic pathways, leading to reduced food intake and increased energy expenditure.

Targeted deletion of the MC4R in mice induces hyperphagia, reduces energy expenditure and leads to obesity. Administration of NPY stimulates food intake via Y1 or Y5 receptors. PVN neurons synthesize and secrete neuropeptides that have a net catabolic action, including corticotrophin-releasing hormone, thyrotropin-releasing hormone, somatostatin, vasopressin and oxytocin.

On the other hand, PVN neurons control sympathetic outflow to peripheral metabolic organs, resulting in increased fatty acid oxidation and lipolysis. The VMH mainly receives neuronal projections from the ARC and projects their axons to the ARC, dorsomedial nucleus DMN , LH and brain stem regions.

The VMH contains neurons that sense glucose and leptin. The DMN contains a high level of NPY terminals and α-MSH terminals originating from the ARC. In contrast to the PVN, VMH and DMN, destruction of the LH leads to hypophagia and weight loss.

Therefore, LH is considered a feeding center. LH contains two neuronal populations producing the orexigenic neuropeptides melanin-concentrating hormone MCH and orexin, also called hypocretin.

The brain stem is another key brain area involved in the regulation of food intake. Satiety signals from the gastrointestinal tract are relayed to the nucleus tractus solitaries NTS through the sensory vagus nerve, a major neuronal connection between the gut and brain.

Transection of sensory vagal fibers decreases meal size and meal duration, confirming that vagal afferents transfer meal-related signals to the brain. Meanwhile, the NTS receives extensive neuronal projections from the PVN and vice versa, 28 indicating that there are intimate communications between the hypothalamus and the brain stem.

Like hypothalamic neurons, NTS neurons produce appetite-regulating glucagon-like peptide-1 GLP-1 , NPY and POMC, and sense peripheral metabolic signals. On the other hand, the brain reward system is involved in the control of hedonic feeding, that is, intake of palatable foods.

Like other addiction behaviors, the mesolimbic and mesocortical dopaminergic pathways are involved in hedonic feeding. Intake of palatable foods elicits dopamine release in the ventral tegmental area VTA , which in turn activates the neural pathways from the VTA to the nucleus accumbens via the medial forebrain bundles.

Interestingly, hedonic feeding is modulated by metabolic signals. Leptin acts on the dopaminergic neurons in the VTA to suppress feeding. Mice lacking the D 2 receptor are more sensitive to leptin. The brain modulates various processes that consume energy, such as locomotor activity, fatty acid oxidation in the skeletal muscle and thermogenesis.

Leptin also enhances fatty acid oxidation in skeletal muscle via both central and peripheral mechanisms. Thermogenesis is theprocess that dissipates energy as heat to maintain body temperature. Thermogenesis mainly occurs in brown adipose tissue BAT.

Cold exposure or intracerebroventricular ICV coinjection of insulin and leptin induces WAT browning. Conversely, inhibition of WAT browning by depletion of Prdm16 leads to obesity. The brain regulates BAT thermogenesis through modulation of the sympathetic nervous system.

Norepinephrine released from sympathetic nervous terminals acts on the β3-adrenergic receptors in adipocytes in the BAT and inguinal fat pads. Activation of adrenergic receptors triggers cyclic-adenosine monophosphate signaling, which in turn increases the expression of uncoupling protein-1 in the mitochondria.

BAT thermogenesis is important for maintaining body temperature in response to cold exposure and dissipating excess energy after high-calorie intake. Because metabolic fuel substrates such as glucose and fatty acid are consumed during BAT thermogenesis, BAT thermogenesis can affect body weight and body fat mass.

However, 18 F-fluorodeoxyglucose positron emission tomography revealed the presence of BAT in the adult humans. Human BAT depots are distributed in the supraclavicular area and in perivascular and periviscus areas for example, around the heart, airway, gut, liver and adrenal gland of the chest and abdomen.

Because the amount of BAT is inversely correlated with body mass index, especially in older subjects, a potential role of BAT in adult human metabolism has been suggested.

In thermogenic regulation, the hypothalamus integrates the sensation of body temperature with efferent sympathetic outflow.

Hypothalamic areas such as the prooptic area, VMH, DMN and ARC modulate thermogenic activity by influencing the sympathetic nervous system. The DMN also contains sympathoexcitatory neurons, 46 which regulate thermogenic activity.

Hormonal- and nutrient-mediated metabolic signals can influence sympathetic outflow to the BAT. Central administration of high doses of insulin increases sympathetic nerve activity in the BAT, whereas low doses of insulin decrease it. Although the mechanism of postprandial thermogenesis is unclear, norepinephrine turnover in the BAT is increased after a meal.

Low-protein diet and high-fat diet increase BAT activity. Adiposity signals refer to the peripheral signals that circulate in proportion to the total amount of stored fat and inform the brain about the stored energy state.

They modulate energy balance through the regulation of food intake and energy expenditure. Plasma insulin concentrations increase in proportion to the amount of stored fat. In hypothalamic neurons, insulin activates the insulin receptor substrate-2 IRS2 —phosphatidylinositol 3-kinase PI3K signaling pathway.

Neuronal deletion of insulin receptor and IRS2 results in increased food intake and susceptibility to diet-induced obesity. The adipose tissue-derived hormone leptin was discovered by positional cloning of the obesity locus ob in Injection of leptin directly into the ARC reduces food intake and body weight.

Nutrients such as glucose, fatty acids and amino acids provide information on nutrient availability to the brain. Glucose signals the presence of anenergy supply to the brain, whereas hypoglycemia signals an energy deficit.

An increased hypothalamic LCFA-CoA level due to ICV long-chain fatty acid LCFA administration leads to decreased food intake. Hormones secreted by the gut in response to a meal provide information on energy intake.

Cholecystokinin, peptide YY and GLP-1 released from the gut induce satiety by acting on the vagus nerve or in the brain. GLP-1 receptors are prevalent in vagus nerve terminals, 76 as well as in the central nucleus of the amygdala, the PVN and ARC of the hypothalamus, and the caudal brain stem.

Interleukin-6 IL-6 is synthesized and released from contracting skeletal muscle during exercise. The elevation in the plasma IL-6 concentration during exercise correlates with exercise intensity and duration and the muscle mass recruited.

IL-6 may mobilize fat from storage sites to provide energy to the muscle. ICV administration of IL-6 stimulates energy expenditure, and mice lacking IL-6 develop mature-onset obesity. Hormones secreted from the endocrine pancreas are also involved in energy homeostasis.

Insulin and amylin are co-secreted by β-cells. Like insulin, amylin acts as a satiety signal and reduces food intake via amylin receptors in the area postrema. Other brain sites mediating amylin action include the NTS and the lateral parabrachial nucleus.

Glucagon, a counter-regulatory hormone to insulin, is secreted from α-cells. Glucagon reduces meal size by acting on the vagus nerves and stimulates energy expenditure through central and peripheral mechanisms.

Pancreatic polypeptide regulates gastric motility, pancreatic exocrine secretion and food intake. Systemic administration of pancreatic polypeptide reduces food intake and weight gain.

The earliest demonstration of the role of the brain in glucose homeostasis was provided by the physiologist Claude Bernard in Dr Bernard demonstrated that a puncture in the floor of the fourth ventricle of the rabbit brain resulted in glycosuria.

The major sites of convergence of these metabolic signals are the hypothalamus and brain stem Figure 2. Brain regulation of glucose homeostasis. The brain senses peripheral metabolic signals through hormones insulin, leptin and so on and nutrients glucose, free fatty acids and so on to regulate glucose metabolism.

The sites of the convergence of these metabolic signals are the hypothalamus and brain stem. AP, area postrema; ARC, arcuate nucleus; BLM, basolateral medulla; DMN, dorsomedial nucleus; DMNX, dorsal motor nucleus of the vagus; FFA, free fatty acids; LH, lateral hypothalamus; NTS, nucleus of the solitary tract; PNS, parasympathetic nervous system; PVN, paraventricular nucleus; SNS, sympathetic nervous system; VMH, ventromedial hypothalamus.

Brain regions related to the control of glucose metabolism contain neurons whose excitability changes with alterations in glucose concentrations in the extracellular fluid. These glucose-sensing neurons are found in the hypothalamic nuclei and brain stem, which are also important areas in the control of energy balance.

Glucose-sensing neurons are subgrouped into two types. Glucose-excited neurons are excited when extracellular glucose levels increase. In contrast, glucose-inhibited neurons are activated by a fall in extracellular glucose concentrations.

During the past decade, the brain has been recognized to be a site of insulin action with regard to glucose homeostasis. Obici et al. They showed, by injecting insulin receptor antisense oligonucleotides into the cerebroventricle, that inhibition of central insulin action impaired insulin-mediated suppression of hepatic glucose production HGP during hyperinsulinemic clamp studies in rats.

They also demonstrated that infusion of insulin into the cerebroventricle suppressed HGP, irrespective of circulating insulin levels. Moreover, central administration of insulin antibodies or inhibitors of the downstream signaling of insulin diminished the ability of insulin to inhibit glucose production.

Overexpression of the insulin signaling molecules IRS2 and Akt in the hypothalamus enhances the glucose-lowering effect of insulin in streptozotocin-induced diabetic rats.

The ATP-sensitive potassium K ATP channel mediates insulin actions in hypothalamic neurons. Leptin has an important role in the control of glucose metabolism.

The hypothalamus is a key site of action of leptin-mediated control of glucose metabolism. ICV administration of leptin in the lipodystrophy mice model corrects insulin resistance and improves impaired insulin signaling in the liver.

In contrast, peripheral injection of the same dose of leptin did not have a similar effect. These data demonstrate that leptin signaling in the ARC is critical for the maintenance of glucose homeostasis.

Leptin-mediated regulation of glucose metabolism is mediated by hypothalamic STAT3 and PI3K signaling pathways. Glucose sensing in the hypothalamus is important in glucose homeostasis. Injection of 2-deoxy- D -glucose into the VMH increases plasma glucose levels by elevating plasma glucagon and catecholamine levels.

The glucose-sensing mechanisms in hypothalamic neurons are similar to those in pancreatic β-cells. ICV administration of glucose suppresses feeding via inhibition of hypothalamic AMPK activity. LCFA signals nutrient availability to the brain and modulates peripheral glucose metabolism.

ICV administration of K ATP channel blocker attenuates the inhibitory effect of oleic acid on glucose production, indicating an involvement of brain K ATP channels in this process. In rodents, direct action of insulin on the liver is necessary, but is insufficient to inhibit HGP, unless the indirect brain pathway is not fully functional.

Restoration of insulin receptor expression in either the liver or brain of insulin receptor-null mice does not completely restore the ability of insulin to inhibit HGP. ICV insulin infusion in the dog augments hepatic glucose uptake and glycogen synthesis without altering HGP, indicating that the regulation of gluconeogenesis by brain insulin signaling may differ among species.

The basal HGP rate per weight is almost 5—10 times higher in rodents than in dogs and humans. Electrical stimulation of VMH neurons and local injection of leptin into the VMH increases glucose uptake in the skeletal muscle of rats independently of circulating insulin levels.

In the skeletal muscle, AMPK activation is induced by muscle contraction and adrenergic agonist and mediates insulin-independent glucose uptake.

On the other hand, orexin-producing neurons in the LH are activated by sweet foods. Orexin regulates skeletal muscle glucose uptake through VMH neurons expressing orexin receptors and the sympathetic nervous system. The autonomic nervous system controls the secretion of insulin and glucagon in the pancreas.

Sympathetic and parasympathetic nerve endings are foundin pancreatic islets. In contrast, parasympathetic branches stimulate insulin secretion, whereas sympathetic branches inhibit it. Insulin regulates whole-body glucose metabolism by acting on the brain, and modulating insulin and glucagon secretion.

ICV administration of insulin increases pancreatic insulin output, demonstrating that pancreatic β-cells are influenced by insulin-sensitive cells of the brain. In healthy conditions, energy intake matches energy expenditure to maintain normal body weight.

Impaired ability of the brain to maintain energy homeostasis may underlie pathological weight gain and obesity Figure 3. Several defects in the negative-feedback pathway in energy homeostatic mechanisms have been suggested.

Because leptin primarily acts on hypothalamic neurons to regulate the energy balance, leptin transfer to the brain may be critical for its action. However, the increase in leptin concentrations in the cerebrospinal fluid of obese individuals is less than that of plasma leptin concentrations.

Pathogenesis of obesity and type 2 diabetes due to defective central regulation of energy and glucose homeostasis. Reduced nutrient sensing and impaired insulin and leptin signaling in the hypothalamus may result in a positive energy balance and predispose weight gain, causing insulin resistance in peripheral metabolic organs.

Obesity-associated insulin resistance may lead to type 2 diabetes when it is combined with β-cell dysfunction. IRS, insulin receptor substrate; PI3K, phosphatidylinositol 3 kinase; STAT3, signal transducer and activator of transcription 3.

Defective hypothalamic sensing of these hormones favors a positive energy balance because loss of leptin receptors in the hypothalamus leads to obesity in mice. Disruption of the hypothalamic IRS—PI3K signaling pathway causes resistance to peripheral metabolic signals and leads to obesity.

In rodents, long-term high-fat feeding reduces the anorectic response and hypothalamic STAT3 activation induced by leptin, which is called leptin resistance. Ablation of suppressor of cytokine signaling 3 expression in neurons mitigates high-fat diet-induced weight gain and hyperleptinemia and improves glucose tolerance and insulin sensitivity.

Neuronal Protein-tyrosine phosphatase 1B knockout mice are hypersensitive to exogenous leptin and insulin, and display improved glucose tolerance during chronic high-fat feeding.

Experimental evidence suggests that defective metabolic sensing in hypothalamic neurons may lead to dysregulation of glucose homeostasis and diabetes Figure 3. Hypothalamic insulin—PI3K signaling is markedly impaired in rats with streptozotocin-induced diabetes. Conversely, enhanced hypothalamic PI3K signaling via adenoviral gene therapy potentiates insulin-induced glucose lowering.

This review highlights the role of the brain in the homeostatic regulation of energy and glucose metabolism. The brain detects energy intake by sensing gut hormones released in response to food intake and detecting nutrients in circulating blood. The brain also monitors body energy stores by sensing adiposity-related signals.

Information on nutrient availability and stored fat is transferred to specialized neurons in the hypothalamus and brain stem.

In the control of the energy balance, outflow pathways from the brain regulate food intake and energy expenditure thermogenesis or locomotor activity.

The autonomic nervous system constitutes the outflow pathways from the brain to peripheral metabolic organs. Defective crosstalk between the brain and peripheral metabolic organs observed in the obese condition may lead to type 2 diabetes development and obesity progression.

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