Skip to main content Accessibility help
×
Home

Contents:

Information:

  • Access
  • Cited by 39

Actions:

      • Send article to Kindle

        To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

        Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

        Find out more about the Kindle Personal Document Service.

        Neural and metabolic regulation of macronutrient intake and selection
        Available formats
        ×

        Send article to Dropbox

        To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

        Neural and metabolic regulation of macronutrient intake and selection
        Available formats
        ×

        Send article to Google Drive

        To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

        Neural and metabolic regulation of macronutrient intake and selection
        Available formats
        ×
Export citation

Abstract

There is considerable disagreement regarding what constitutes a healthy diet. Ever since the influential work of Cannon and Richter, it was debated whether the ‘wisdom of the body’ will automatically direct us to the foods we need for healthy lives or whether we must carefully learn to eat the right foods, particularly in an environment of plenty. Although it is clear that strong mechanisms have evolved to prevent consumption of foods that have previously made us sick, it is less clear whether reciprocal mechanisms exist that reinforce the consumption of healthy diets. Here, we review recent progress in providing behavioural evidence for the regulation of intake and selection of proteins, carbohydrates and fats. We examine new developments in sensory physiology enabling recognition of macronutrients both pre- and post-ingestively. Finally, we propose a general model for central neural processing of nutrient-specific appetites. We suggest that the same basic neural circuitry responsible for the homoeostatic regulation of total energy intake is also used to control consumption of specific macro- and micronutrients. Similar to salt appetite, specific appetites for other micro- and macronutrients may be encoded by unique molecular changes in the hypothalamus. Gratification of such specific appetites is then accomplished by engaging the brain motivational system to assign the highest reward prediction to exteroceptive cues previously associated with consuming the missing ingredient. A better understanding of these nutrient-specific neural processes could help design drugs and behavioural strategies that promote healthier eating.

Abbreviations:
APC

anterior piriform cortex

BCAA

branched-chain amino acid

MA

mercaptoacetate

Obesity and malnutrition negatively affect the lives of millions of people, and despite intensive research, no easy cures are in sight. Among the many factors contributing to these diseases, consumption of imbalanced and unhealthy diets is of central importance. Yet, there is considerable disagreement regarding what constitutes a healthy diet, both to prevent weight gain in healthy individuals and to promote weight loss in settings of obesity. Some believe that the ‘wisdom of the body’( Reference Cannon 1 , Reference Richter 2 ) will automatically direct us to the foods we need for healthy lives, while others are more sceptical and believe that we must carefully learn to eat the right foods to avoid succumbing to unhealthy diets. It is clear that strong mechanisms have evolved to prevent consumption of foods that have previously made us severely sick (conditioned taste aversion). Do reciprocal mechanisms exist to promote the consumption of healthy diets?

Nutrients can be classified as essential and non-essential. By definition, essential nutrients cannot be manufactured in the body and thus must be consumed in the diet to maintain health. Non-essential nutrients can be synthesised by the body, although often at considerable cost( Reference Livesey 3 Reference Kalhan and Kilic 5 ). It is thus plausible that mechanisms have evolved to actively seek essential, and possibly non-essential, nutrients( Reference Rozin 6 Reference Liedtke, McKinley and Walker 9 ). It is within this context of the selection and consumption of individual nutrients that the concept of macronutrient selection takes shape. All diets contain a mixture of the three macronutrients (protein, carbohydrate and fat), and each of the three macronutrients is either glorified or vilified in one diet fad or another. Yet, implicit within each of these arguments is the hypothesis that there exists a macronutrient composition that is ideal for health. These concepts raise the question of whether we actively regulate our consumption of the individual macronutrients, and if so, can we tap into this mechanism to specifically change our macronutrient preference. Imagine if we could take a pill that selectively reduces appetite for fatty foods.

To accept that intake of a macronutrient is regulated, several predictions should be met. First, intake of that macronutrient should be relatively stable under steady state conditions. Second, this intake should not depend on the menu, even if the menu consists of many different foods with different macronutrient compositions and other properties such as palatability, hydration level, viscosity and smoothness. Third, intake of the specific macronutrient should depend on the physiological need state. Fourth, intake of the macronutrient must be sensed to provide a negative feedback signal. This could be the macronutrient itself, one of its metabolites or a unique consequence of its metabolism, such as a signature profile of gut hormone release, acting either directly on the brain or via sensory neural pathways. Specific brain circuits would then use this feedback information to either enhance or reduce intake of the macronutrient in question by affecting both appetitive and satiety mechanisms. Collectively, these mechanisms should result in a selective, macronutrient-specific appetitive drive (motivation), which results in the correct selection from a menu, as demonstrated for Na intake( Reference Tindell, Smith and Berridge 8 , Reference Berridge, Flynn and Schulkin 10 , Reference Tindell, Smith and Pecina 11 ). In other words, the subject has to ‘know’ exactly what it needs and to identify a source from a menu before ingesting large amounts of the other macronutrients. In human subjects, explicit knowledge about the composition and nutritional value of foods could be used to make the correct choice, yet such a solution is not available to animals, where implicit wanting would be the driving factor.

About 10 years ago, a multi-authored book was published asking the question whether the selection and consumption of the three macronutrients is regulated. Among the more than 30 chapters, both negative( Reference Galef, Berthoud and Seeley 12 , Reference Friedman and Berthoud HaS 13 ) and positive( Reference Simpson, Raubenheimer, Berthoud and Seeley 14 , Reference de Castro, Berthoud and Seeley 15 ) evidence were presented. It was concluded that protein is quite strongly defended, while carbohydrate and fat intake are only weakly defended or not at all( Reference Berthoud and Seeley 16 ). The purpose of this review is to provide a brief appraisal of current concepts on macronutrient-specific appetites with emphasis on progress made over the last 10 years.

Behavioural evidence for the regulation of intake and selection of specific macronutrients

Proteins: strong evidence for defence of situation-specific target intake

Proteins are crucially important for growth and most of their building blocks, the amino acids, cannot easily be synthesised by the body. Therefore, intake of protein needs to be defended similarly to salt and vitamins. A number of studies have focused on the effect of variations in dietary protein quality and quantity on food intake. The consensus of this literature is that dietary protein can have a profound impact on food intake, via two similar but potentially separate mechanisms. The first mechanism is via absolute protein content, with high-protein diets tending to suppress food intake and moderately low-protein diets increasing food intake. For instance, it is well described that protein is the most satiating macronutrient on a per energy basis, and a large number of studies suggest that high-protein diets can decrease food intake and promote weight loss while maintaining lean mass( Reference Westerterp-Plantenga, Nieuwenhuizen and Tome 17 , Reference Potier, Darcel and Tome 18 ). The second mechanism operates through protein quality: the amino acid profile. In particular, it seems clear that many species have the ability to rapidly detect and avoid diets that are severely imbalanced in their amino acid profile, and therefore unhealthy( Reference Sanahuja and Harper 19 Reference Harper and Peters 21 ). This phenomenon has been demonstrated following the depletion of multiple amino acids and appears to represent a learned aversion( Reference Koehnle, Russell and Gietzen 22 , Reference Gietzen, Hao and Anthony 23 ). Gietzen and co-workers have demonstrated that this learned aversion is mediated by critical molecular events within the anterior piriform cortex (APC), in particular, the accumulation of uncharged tRNA and the resulting activation of the kinase GCN2 (general control non-depressible-2)( Reference Hao, Sharp and Ross-Inta 24 , Reference Rudell, Rechs and Kelman 25 ). Thus, it is clear that variations in both dietary protein quantity and quality can have significant effects on food intake. It also seems likely that protein intake is regulated within general upper and lower limits to ensure a sufficient supply to support life( Reference Harper and Peters 21 ).

Stronger evidence that protein selection is regulated comes from work testing whether protein selection is sensitive to changes in the need or demand for protein. These data collectively demonstrate that animals will increase their selection and consumption of protein when there is an increased need for protein, e.g., following a period of protein restriction, in growing animals, and during chronic injections of growth hormone, and that this regulation occurs independently from the regulation of energy intake( Reference Musten, Peace and Anderson 26 ).

Perhaps the most convincing data supporting the ability of an animal to navigate through ‘nutrient space’ derives from the Geometric Framework, developed by Steven Simpson and David Raubenheimer( Reference Cheng, Simpson and Raubenheimer 27 Reference Simpson and Raubenheimer 29 ). Studies of macronutrient selection are impaired by the fact that an increase of one macronutrient must be offset by a decrease in a different macronutrient in order to maintain diets that are isoenergetic. The geometric approach addresses this and other concerns by using a geometric state–space model to quantify the intake of individual nutrients across a range of diets and choices. Consumption of any individual nutrient can be plotted relative to other components in the diet (e.g. protein v. carbohydrate or protein v. energy), with this analysis easily extended to the selection between multiple diets. The data suggest that species as diverse as insects, fish, rodents and pigs seek to consume a fixed amount of both protein and carbohydrate, and thus regulate intake around a specific protein:carbohydrate target( Reference Simpson and Raubenheimer 28 ,30–Reference Sorensen, Mayntz and Raubenheimer 32 ). In addition, when faced with diets that do not allow an individual to simultaneously reach its protein and carbohydrate targets, evidence in insects and rodents indicates that protein intake is prioritised over carbohydrate intake. This effect has been termed ‘protein leveraging’, as small changes in protein content can induce profound changes in energy intake( Reference Simpson and Raubenheimer 28 , Reference Simpson and Raubenheimer 29 , Reference Sorensen, Mayntz and Raubenheimer 32 ). From this perspective, the hyperphagia detected on a low-protein diet is due to the leveraging in an effort to consume a target amount of protein. The concept of protein leveraging has recently been extended to human subjects, with reductions in dietary protein leading to increases in energy intake, at least over the short term( Reference Simpson, Batley and Raubenheimer 33 , Reference Gosby, Conigrave and Lau 34 ). Taken together, these data provide a compelling case for both the regulation of protein intake and a potential role for protein in the regulation of energy intake and obesity( Reference Simpson and Raubenheimer 29 , Reference Brooks, Simpson and Raubenheimer 35 ).

Carbohydrates: despite a strong basic attraction to sweets and learned avoidance if utilisation is chronically impaired, there is only weak defence of target intake

Glucose is the preferred nutrient for the brain and typically accounts for most of the energy requirements in human subjects. Furthermore, sugars are highly palatable and desired. Yet carbohydrate intake is not essential for survival per se, as energy and glucose can be derived from both fat and protein. However, a modest level of carbohydrate intake is necessary to avoid ketosis, and it seems likely that prolonged ketosis is at least undesirable. This may be the reason for animals to eat towards a carbohydrate target as determined in the geometric paradigm( Reference Simpson and Raubenheimer 28 , Reference Raubenheimer and Simpson 31 , Reference Sorensen, Mayntz and Raubenheimer 32 , Reference Mayntz, Raubenheimer and Salomon 36 ). However, protein takes priority over carbohydrate in this model.

Changes in food preferences when carbohydrate metabolism is impaired could also be interpreted as carbohydrate-specific regulation of intake. There is a large literature on food selection in diabetic rats( Reference Bartness and Rowland 37 Reference Tordoff, Tepper and Friedman 42 ), and the general consensus is that rats learn to avoid carbohydrates and instead prefer fat and protein, because of their inability to efficiently metabolise carbohydrates. Interestingly, in one report, induction of diabetes initially increased carbohydrate and protein intake, but after 3 weeks, rats switched to fat consumption( Reference Kanarek and Ho 40 ). This suggests an initial attempt to overcome blocked glucose utilisation by increased carbohydrate intake, before they learn to circumvent glucose utilisation as the better strategy. This interpretation is consistent with the increased carbohydrate intake after systemic 2-deoxy-glucose-induced acute blockade of glucose utilisation( Reference Singer, York and Bray 43 ) (but see( Reference Ritter, Ritter and Cromer 44 ) for different outcome).

There is also evidence that carbohydrate is not regulated. Although animals restricted for both energy and protein actively seek the missing nutrient, there is no evidence for carbohydrate-seeking in carbohydrate-restricted animals. Hamsters with restricted access to carbohydrate but provided with sufficient energy showed no preference for carbohydrate( Reference DiBattista 45 ), in contrast to a strong preference for protein following protein restriction. A similar observation was made for hypothalamic neuropeptide-Y expression, which increased in response to both protein and energy restriction, but not following the isoenergetic restriction of carbohydrate( Reference White, He and Dean 46 ). Taken together, the earlier data support the concept that carbohydrate is avoided if its utilisation is chronically impaired, but provide only limited support for the hypothesis that animals defend a specific intake of carbohydrate.

Fats: despite strong basic attraction to fatty foods, evidence for defence of target intake is lacking for fats in general, although it may exist for specific essential fatty acids

Fatty foods are very palatable and strongly preferred over dry foods with less creamy textures by human subjects and rodents. Most fatty acids are non-essential and thus can be synthesised by the body with the exception of α-linolenic and linoleic acids whose production requires desaturase enzymes that are lacking in many animal species including human subjects. Rats fed an n-3 fatty acid-deficient diet showed a robust preference to consume an n-3 fatty acid replete diet when given the choice over the n-3-deficient diet( Reference Dunlap and Heinrichs 47 ). Moreover, intake of the replete-diet progressively increased over 4 d of preference testing, indicating the contribution of post-ingestive learning. In another study, it was shown that mice with a genetic deletion of the fatty acid transporter CD36 were not able to detect and prefer another essential fatty acid, linoleic acid( Reference Gaillard, Laugerette and Darcel 48 ). These results provide evidence that rodents may possess a mechanism for regulating the intake of essential fatty acids just as they defend intake of thiamine.

In addition to essential fatty acids, there is some evidence for the regulation of total dietary lipids. For example, in free-living human subjects, it was found that fat intake on a given day was negatively correlated with fat intake 2 d later, suggesting some sort of delayed negative feedback regulation of fat intake( Reference de Castro, Berthoud and Seeley 15 ). The existence of a fat-specific appetite was suggested in studies using Pavlovian conditioning by pairing separate conditioned stimuli to either fatty or sweet food rewards acting as unconditioned stimuli. Specifically, rats treated with intracerebroventricular agouti-related protein were observed to show, in the absence of food intake, enhanced appetitive responding towards stimuli that had been previously paired with fat and reduced responding towards stimuli previously paired with sucrose( Reference Tracy, Clegg and Johnson 49 ). However, studies using the geometric model have consistently found a lack of support for regulation of fat intake in a number of species( Reference Simpson, Raubenheimer, Berthoud and Seeley 14 ). Therefore, with the exception of essential fatty acids, there is mixed evidence for physiological monitoring and precise regulation of fat intake.

When fats cannot be metabolised, the post-oral effects of ingested fat can become negative, thereby conditioning a reduction in fat intake. For example, a genetic mutation in Acads−/− mice renders them deficient in short-chain acyl-CoA dehydrogenase and therefore unable to oxidise SCFA. In a food choice situation, Acads-deficient mice shift consumption away from fat- and towards carbohydrate-containing diets, thus effectively preventing a reduction in total energies( Reference Smith Richards, Belton and York 50 ). This genetic model provides a tool for determining how signals from impaired SCFA oxidation are sensed and translated into feeding behaviour. The results from Acads−/− mice indicate that a deficiency in fatty acid oxidation can drive macronutrient selection and is similar to what has been reported in experimental diabetes where carbohydrate utilisation is impaired (see discussion earlier).

Several lines of evidence indicate that fat oxidation pathways are involved in the control of food intake, but whether or not these pathways regulate intake in a fat-specific manner is less clear. For example, the acute blockade of long-chain fatty acid oxidation potently stimulates food intake, and this response can be activated experimentally by using a variety of drugs that inhibit fatty acid oxidation including β-mercaptoacetate (MA). In a macronutrient choice paradigm, MA-treated rats increased their intake of protein and carbohydrate and decreased intake of fat( Reference Singer, York and Bray 43 ). MA-treated rats did not eat more fat, even if fat was the only macronutrient source available, thus, MA increased only the intake of nutrients that could be metabolised.

None of the earlier studies suggest a macronutrient-specific appetite because blocking oxidation of SCFA (Acads-deficient mice) or long-chain fatty acids (pharmacological antagonists) does not produce a specific need for fat. Instead, the behavioural feeding response appears to be directed at: (1) locating the diet or nutrient that can provide sufficient energy; (2) avoiding the diet/nutrient that cannot provide sufficient energy or (3) exhibiting hyperphagia in response to the only diet/nutrient available when there is no choice. One exception appears to be that MA-treated rats fail to increase their intake of fat when it is the only macronutrient source available( Reference Singer, York and Bray 43 ).

Exteroceptive and interoceptive cues for the detection of macronutrients

To ‘know’ which food source to select from, mechanisms must exist for detecting the necessary macronutrient before it is ingested. In the modern world, human subjects can rely on food labels and other explicit knowledge about foods, so that by just thinking about, or seeing foods( Reference Toepel, Knebel and Hudry 51 ), we can decide whether it is a good source for a given nutrient. Just as single-trial learning mechanisms have evolved to avoid toxic foods, learning helps select necessary and beneficial foods. Visual, olfactory, auditory and gustatory cues in the environment that are associated with specific foods become predictive for the beneficial post-ingestive consequences of eating this food through learning( Reference Davidson, Morell, Benoit, Berthoud and Seeley 52 , Reference Benoit, Davis and Davidson 53 ). The olfactory, and particularly the gustatory system, recognise certain nutrients through nutrient-specific receptor mechanisms, without the need for prior experience. Finally, once ingested, sensory mechanisms all along the alimentary canal and, after absorption, throughout the body, are used to encode the beneficial effects of their ingestion. Potential sites and mechanisms for the detection of the three macronutrients are discussed later.

Protein: there are excellent sensors for individual amino acids before and after ingestion, but quantitatively measuring protein intake is a challenging task

The umami (savory) flavour is often associated with protein-rich foods, and involves principally the detection of the amino acid glutamic acid or its salt, monosodium glutamate by members of the T1R taste receptor family, specifically the T1R1/T1R3 heterodimer( Reference Damak, Rong and Yasumatsu 54 , Reference Palmer 55 ), with additional involvement of the olfactory system. The complexity of this system should be noted in that: (1) umami is also represented by a rather diverse set of compounds( Reference Delay, Eddy and Eschle 56 ), (2) the T1R1/T1R3 heterodimer appears to be capable of responding to amino acids other than glutamate( Reference Nelson, Chandrashekar and Hoon 57 ) and (3) umami is also detected in the absence of T1R3( Reference Damak, Rong and Yasumatsu 54 ). Thus, there is likely more than one receptor mediating umami taste, and individual amino acids besides monosodium glutamate represent unique tastes( Reference Chaudhari, Pereira and Roper 58 , Reference Maruyama, Pereira and Margolskee 59 ).

Protein selection is complicated by the fact that protein is more than a single substrate and because it is unclear whether animals are selecting for sensory cues from specific amino acids or crude protein (nitrogen). Infusing protein directly into the stomach or small intestine, thereby bypassing the oral cavity, is sufficient to condition learned flavour preferences similar to fat or carbohydrate( Reference Perez, Ackroff and Sclafani 60 Reference Sclafani 62 ). Because attempts to produce the same effects with infusion of glucose into the hepatic portal system have been less successful, it is thought that sensors at pre-absorptive sites, or linked to the absorptive process, can detect the arrival of all three macronutrients in the gut. The same sweet, umami, fat and bitter taste receptors found in the mouth are also expressed in select enterocytes throughout the small and large intestines where they can signal to primary afferent nerves or stimulate the release of gut hormones. In addition, after being absorbed into the bloodstream, macronutrients and their metabolites can generate hormonal signals by acting on the pancreas, liver and other organs, and can act directly on the brain. Collectively, these signalling mechanisms are thought to represent the post-ingestive consequences of a specific food or macronutrient which can be learned to be liked.

Learned associations between the post-ingestive effects of protein and flavours specific to that protein source may strongly influence protein intake and selection( Reference Miller and Teates 63 ). For instance, DiBattista demonstrated that the strong protein preference demonstrated by protein-deprived hamsters was driven by the association of the high-protein diet with flavours associated with that diet( Reference DiBattista and Mercier 64 ). These data suggest that protein intake is not a specific, hard-wired appetite for protein, but instead a learned association between dietary cues and post-ingestive consequences.

Carbohydrates: while the human subjects gustatory system is largely blind to complex non-sweet carbohydrates there is abundant sensing of glucose, the major common currency of carbohydrates, in periphery and brain

The pre-ingestive detection of sugars is thought to involve both olfaction and taste( Reference Zukerman, Touzani and Margolskee 65 , Reference Rhinehart-Doty, Schumm and Smith 66 ). The T1R2/T1R3 heterodimer is responsible for mammalian sweet taste perception, and its location in the oral cavity mediates at least short-term preference for sugars( Reference Zukerman, Touzani and Margolskee 65 , Reference Nelson, Hoon and Chandrashekar 67 ). However, intragastric glucose infusions, bypassing the oral cavity, can also condition flavour preferences in rats( Reference Sclafani, Glass and Margolskee 68 ). Post-oral sugar conditioning could depend on sweet taste receptors or, alternatively, the Na-GLUT, expressed in specialised gut epithelial cells that release specific gut hormones to communicate with the brain. For instance, sweet taste receptors are expressed on enteroendocrine cells within the gut, providing a potential mechanism for carbohydrate to induce changes in gut hormone secretion( Reference Steinert, Gerspach and Gutmann 69 , Reference Kokrashvili, Mosinger and Margolskee 70 ). Whether any of these identified or yet unidentified receptor mechanisms for the detection of simple carbohydrates in the gut contributes to the regulation of carbohydrate intake or balance remains an open question.

Fats: novel lipid sensors found in mouth and gut have not yet been characterised in the brain

Historically, the orosensory perception of fat has been attributed mainly to trigeminal( Reference Laugerette, Gaillard and Passilly-Degrace 71 ) or olfactory( Reference Kinney and Antill 72 , Reference Ramirez 73 ) mechanisms. More recently, two lines of evidence for fat taste transduction mechanisms in taste receptor cells have been described: the delayed-rectifying K channel which is sensitive to PUFA( Reference Gilbertson, Fontenot and Liu 74 ), and fatty acid translocase (CD36) which is localised in lingual taste buds. In addition, the transient receptor potential type M5 was shown to be essential for fat taste in the mouse( Reference Liu, Shah and Croasdell 75 ), suggesting involvement of G-protein-coupled receptors, possibly GPR40 and GPR120( Reference Cartoni, Yasumatsu and Ohkuri 76 ). CD36 knockout mice do not prefer a fatty acid emulsion as wild-type mice do, in two bottle 48 h preference tests( Reference Laugerette, Passilly-Degrace and Patris 77 , Reference Sclafani, Ackroff and Abumrad 78 ) but do learn to prefer a flavoured solution paired with intragastric soyabean oil infusions( Reference Sclafani, Ackroff and Abumrad 78 ), thus supporting a role of CD36 as a signalling protein for fat taste but not required for post-oral fat conditioning.

Based on systemic treatment with the fatty acid β-oxidation blocker mercaptoacetate, it has been proposed that mice may be able to detect fat via its oxidation products within taste receptor cells, independent of post-oral conditioning( Reference Matsumura, Saitou and Miyaki 79 ). Contrary to this idea is the finding that mice with genetically impaired SCFA oxidation respond normally to maize oil in 5-s lick tests, where post-ingestive learning is unlikely, but reduce responding in longer-term tests( Reference Smith Richards, Belton and York 50 ). However, such a mechanism needs to be verified by demonstrating a direct action of fatty acid oxidation inhibitors on taste receptor cell function.

Rats and mice do not discriminate between the oral effects of a nutritive or non-nutritive fat solution during brief presentations. However, rodents prefer a nutritive fat solution when post-oral consequences are allowed, e.g., when solutions are presented for a longer period of time to allow for post-oral consequences to occur( Reference Ackroff, Vigorito and Sclafani 80 ), and during gastric conditioning when an oral flavour is paired with intragastric infusions of fat solutions (thus avoiding oral effects)( Reference Lucas, Ackroff and Sclafani 81 , Reference Ackroff, Lucas and Sclafani 82 ). These data support the view that post-oral processes communicate the nutritional value of ingested fat solutions to the body. In gastric conditioning paradigms, fats that are high in PUFA and low in SCFA content are the most reinforcing( Reference Ackroff, Lucas and Sclafani 82 ), supporting the idea that oral as well as post-oral sensing helps to satisfy the evolutionary pressure to ingest sufficient amounts of the essential PUFA.

Post-oral effects of fat may involve processes in the stomach and gastrointestinal tract which could regulate the release of gut hormones (ghrelin, peptide YY and glucagon-like peptide-1), modulation of vagal afferents, absorption of nutrients into the circulation and subsequent communication with liver, pancreas or other peripheral tissues, which would then alter the release of hormones relevant to fat storage and glucose homoeostasis (e.g. leptin, insulin and glucagon). Individual nutrients affect incretin secretion( Reference Wu, Rayner and Jones 83 ). Enterocytes may sense TAG via fatty acid oxidation and influence eating through changes in intestinal vagal afferent activity( Reference Langhans, Leitner and Arnold 84 ).

Potential neural mechanisms integrating sensory inputs and leading to the expression of specific macronutrient appetite

In the earlier two sections, we have reviewed the behavioural evidence for regulation, as well as the potential external and internal cues for the detection of specific macronutrients and their components. Here, we discuss the scarce knowledge about potential neural mechanisms that might be responsible for expression of the specific appetites and behavioural selection process. Accepting the neural circuitry for the homoeostatic control of energy balance as the prototype, we can distinguish at least two steps of neural processing. First, relevant sensory information is integrated by dedicated neural circuits generating a need state or hunger for energy. The major circuitry for this function is attributed to areas of the brainstem and hypothalamus (see( Reference Berthoud and Morrison 85 ) for a recent review). Neurons sensing the availability of all three energy providing macronutrients (see( Reference Levin, Magnan and Dunn-Meynell 86 ) for a recent review) ultimately determine activity of the agouti-related protein/neuropeptide-Y and melanocortin systems via the ancient fuel gauge AMP-regulated kinase. Activation of basomedial hypothalamic agouti-related protein neurons by low fuel availability is essential for the basic hunger drive to occur( Reference Luquet, Perez and Hnasko 87 , Reference Krashes, Koda and Ye 88 ). In a second step, the motivational system residing in cortico-limbic structures is engaged through the heightened incentive provided by the nutritional need state( Reference Kelley and Berridge 89 , Reference Berridge 90 ). This reward-based decision-making system takes both interoceptive and extroceptive sensory information into account and relies on earlier experience stored as ‘food memories’( Reference Davidson, Morell, Benoit, Berthoud and Seeley 52 , Reference Benoit, Davis and Davidson 53 ) (Fig. 1). As a result, attention is shifted from any other behaviour towards finding and eating food, and reward generated from gratification of the specific need provides the necessary reinforcement.

Fig. 1. Schematic flow diagram showing possible neural processing of external and internal food cues leading to nutrient-specific appetites. Representations of experience with a particular food (food memories) take into account (a) exteroceptive cues including taste, available before ingestion of significant amounts, (b) post-ingestive consequences elicited by ingesting the food (digestion, absorption and metabolism), and (c) the prevailing deprivation state for the particular nutrient at the time of replenishment. The hypothalamic energy sensor may be involved in generating a general hunger signal (incentive), and the cortico-limbic, reward-based decision-making circuitry may confer the behavioural specificity for the selection process. Amy, amygdala; Hipp, hippocampal complex; NAcb, nucleus accumbens; OFC, orbitofrontal cortex; PFC, prefrontral cortex; VTA, ventral tegmental area.

We suggest that this same basic two-step neural processing model is responsible for the homoeostatic-like regulation of individual macro- and micronutrients (Fig. 1). The difference is that instead of satisfying a general energy deficit, the system satisfies nutrient-specific appetites. For example, it has been shown that similar to food seeking in general( Reference Kelley and Berridge 89 ), sodium appetite depends on the mesolimbic dopamine system( Reference Lucas, Pompei and McEwen 91 ). An intriguing possibility is that lateral hypothalamic orexin neurons provide the connection between steps one and two of the model( Reference Liedtke, McKinley and Walker 9 ). Orexin neurons connect the hypothalamus, where the specific need state is generated, with the mesolimbic dopamine system, which confers selectivity of behavioural action. In a seminal paper, Liedtke et al.( Reference Liedtke, McKinley and Walker 9 ) have recently demonstrated the molecular changes occurring in hypothalamic orexin neurons of Na depleted mice. They went on to show that pharmacological prevention of these changes greatly and selectively reduced Na appetite, without affecting thirst and hunger( Reference Liedtke, McKinley and Walker 9 ), suggesting that distinct molecular signatures of specific need states may be generated within hypothalamic orexin neurons. It is well known that orexin neurons are stimulated by hypoglycaemia( Reference Cai, Evans and Lister 92 ) and fasting( Reference Diano, Horvath and Urbanski 93 ), and activation of orexin neurons is associated with conditioned reward seeking for foods and drugs( Reference Harris, Wimmer and Aston-Jones 94 ). Furthermore, local administration of orexin to the ventral tegmental area, the home of mesolimbic dopamine neurons, reinstates extinct reward seeking for foods and drugs( Reference Harris, Wimmer and Aston-Jones 94 , Reference Cason, Smith and Tahsili-Fahadan 95 ). Thus, this pathway could account for the expression of the ‘wisdom of the body’, by reinforcing only the behavioural actions that contribute to general well-being.

Protein: individual amino acid sensing in the piriform cortex and metabolism-coupled protein effects in the hypothalamus – search for the missing link

The most straight-forward mechanism for protein detection would be a direct effect of amino acids on brain areas regulating food intake( Reference Mellinkoff, Frankland and Boyle 96 ), as both dietary and circulating amino acids clearly have access to the brain( Reference Choi, Chang and Fletcher 97 , Reference Hawkins, O'Kane and Simpson 98 ). As described previously, exposure to a diet that is devoid of an essential amino acid induces a rapid, learned aversion that requires critical signalling events within the APC( Reference Hao, Sharp and Ross-Inta 24 , Reference Rudell, Rechs and Kelman 25 ). Lesions of the APC block the aversive response to an imbalanced diet, as does replacement of the missing amino acid locally within the APC( Reference Hao, Sharp and Ross-Inta 24 ,99–Reference Leung and Rogers 101 ), thus indicating the APC as a direct detector of essential amino acids. Subsequent studies indicate that a build-up of uncharged tRNA and the activation of the general control non-depressible-2 kinase are the key cellular mechanism involved. Indeed, general control non-depressible-2-deficient mice fail to exhibit the aversive response to a diet devoid of essential amino acids( Reference Maurin, Jousse and Averous 99 ). These studies provide a clear neuroanatomical and cellular model for the avoidance of imbalanced and thus unhealthy diets, but it is unclear whether this mechanism contributes more generally to the selection of protein.

In addition, there is strong evidence that the branched-chain amino acid (BCAA) leucine acts locally within the hypothalamus to suppress food intake( Reference Blouet, Jo and Li 102 Reference Ropelle, Pauli and Fernandes 105 ), at least in part via activation of mammalian target of rapamycin/S6 kinase 1, inhibition of AMP-regulated kinase signalling( Reference Cota, Proulx and Smith 103 , Reference Ropelle, Pauli and Fernandes 105 , Reference Morrison, White and Wang 106 ), and via activation of BCAA metabolism( Reference Blouet, Jo and Li 102 , Reference Purpera, Shen and Taghavi 107 ). Thus the hypothalamus is clearly capable of responding to amino acids, suggesting that BCAA may provide a unique circulating signal of dietary protein content( Reference Ropelle, Pauli and Fernandes 105 , Reference Newgard, An and Bain 108 ). However, anorectic leucine effects are primarily observed when it is added in excess, and whether circulating amino acids provide a specific signal of protein status remains unclear. Mice with defects in BCAA metabolism and resulting increases in circulating BCAA show normal protein intake, even when allowed to self-select between high- and low-protein diets supplemented with or without BCAA( Reference Purpera, Shen and Taghavi 107 ). Thus, despite the clear anorexigenic effects of pharmacological leucine doses on food intake, its role as a physiological protein signal is unclear.

The intriguing possibility that orexin and other lateral hypothalamic neurons might provide a link between the specific protein-deficient state and the motivational system has not been explored. However, both the APC( Reference Blevins, Truong and Gietzen 109 ) and the medial hypothalamic sites responding to leucine, project directly to the lateral hypothalamus, including orexin neurons( Reference Elias, Saper and Maratos-Flier 110 ). Thus, protein-deficiency may engage similar neural pathways as Na deficiency( Reference Liedtke, McKinley and Walker 9 ).

Carbohydrate: hypothalamic glucose sensing may generate need state and drive specific appetite by engaging with mesolimbic dopamine system

As discussed earlier, the presence of carbohydrates in the diet and in the circulation are is detected by at least two distinct mechanisms, sweet taste and the metabolic effects of glucose and other simple sugars. Glucose-sensing neurons, through a mechanism involving GLUT2 and glucokinase, are either excited or inhibited by surrounding glucose (see( Reference Levin, Magnan and Dunn-Meynell 86 ) for a recent review). If brain glucose-sensing would be critically involved in the regulation or defence of carbohydrate intake, one would expect that loss-of-function manipulation would selectively stimulate carbohydrate seeking. Unfortunately, in the majority of studies that demonstrate increased food intake induced by impairment of brain glucosensing such as insulin or 2-deoxy-d-glucose( Reference Berthoud and Mogenson 111 ) administration and genetic deletion of glucokinase( Reference Bady, Marty and Dallaporta 112 ), only total food intake, but not macronutrient selection, was measured. In only one experiment, systemic administration of 2-deoxy-d-glucose induced a selective hunger for carbohydrate( Reference Singer, York and Bray 43 ), but it is not clear whether impaired brain glucosensing was responsible. However, third ventricular administration of insulin which, in contrast to 2-deoxy-glucose, enhances glucosensing and decreases total food intake( Reference Woods, Lotter and McKay 113 ), selectively reduced fat, not carbohydrate, intake in a three-choice paradigm( Reference Chavez, Riedy and Van Dijk 114 ). Thus, although brain glucosensing would be in an ideal position to modulate carbohydrate intake selectively, there is no direct evidence for such a mechanism.

As glucose on the tongue and carbohydrates in the gut can powerfully stimulate the mesolimbic dopamine system and condition flavour preferences( Reference Lenoir, Serre and Cantin 115 Reference Sclafani, Touzani and Bodnar 117 ), it will be interesting to look for a role of hypothalamic orexin neurons as potential mediators.

Fat: are fat-specific neuropeptides pharmacological artefacts or do they serve regulation of fat intake?

The possibility that essential fatty acids are detected directly in the brain, similar to amino acids( Reference Gietzen, Hao and Anthony 23 ), is suggested by the observation that consumption of an n-3 fatty acid deficient diet was accompanied by a decline in forebrain n-3 fatty acid content( Reference Dunlap and Heinrichs 47 ); however, this possibility needs to be validated by targeted repletion of the missing fatty acid in specific brain sites. In analogy to Na depletion, it would be particularly interesting to look for molecular changes in orexin and other hypothalamic neurons possibly encoding the specific need state.

Initially, there was excitement about the possibility that specific neurotransmitters and peptides drive selective intake of macronutrients, e.g. that the neuropeptides galanin and orexin stimulate lipid intake, and that norepinephrine via the α2-receptor as well as neuropeptide-Y stimulate carbohydrate intake( Reference Tempel, Leibowitz and Leibowitz 118 , Reference Clegg, Air and Woods 119 ). It would be interesting to test the effects of these neuropeptides with the geometric model to determine whether the observed preferences were mainly due to the specific sensory properties of the macronutrient samples rather than a defended target intake. It is clear that certain neuropeptides and transmitters change their expression levels with diets enriched with certain macronutrients. In particular, high-fat diets via elevated circulating TAG levels increase expression of galanin, enkephalin and dynorphin in paraventricular neurons, as well as orexin in lateral hypothalamic neurons. However, rather than providing negative feedback to curb further fat intake as would be expected in a regulated system, these mechanisms are apparently working in a positive feedback fashion to further enhance high-fat intake( Reference Wortley, Chang and Davydova 120 , Reference Chang, Karatayev and Ahsan 121 ).

A more likely mechanism that could be responsible for a homoeostatic-like negative feedback regulation of fat intake may use the same general system outlined earlier (Fig. 1). Low-fat oxidation detected in the periphery( Reference Langhans, Leitner and Arnold 84 ) and/or directly in the brain( Reference Obici, Feng and Morgan 122 ) may activate hypothalamic energy sensor neurons via AMP-regulated kinase phosphorylation. This would in turn lead to activation of the agouti-related protein system and possibly fat-specific molecular changes in orexin and other lateral hypothalamic neurons as demonstrated for Na deficiency( Reference Liedtke, McKinley and Walker 9 ). In support of such molecular changes, our preliminary observations suggest that mice with genetic deletion of the ability to oxidise SCFA show elevated hypothalamic AMP-regulated kinase expression on high-fat diet compared with wild-type mice (BK Richards, unpublished results).

Conclusions and unanswered questions

It is now commonly accepted that the consumption of energy and essential nutrients such as Na, vitamins and certain amino- and fatty acids is at least controlled and defended, if not regulated, in a homeostatic-like manner, is now commonly accepted. A much weaker case for such ‘wisdom of the body’ can be made for the non-essential macronutrients, carbohydrate, fat and protein, although a review of the recent literature provides some new insights encouraging continued inquiry of this question. We propose that the same basic neural circuitry responsible for the homoeostatic-regulation of total energy intake when energy expenditure is held constant is also used to control consumption of specific macro- and micronutrients. Thus, in addition to a general appetitive drive, specific appetites are likely encoded by unique molecular changes in the hypothalamus( Reference Liedtke, McKinley and Walker 9 ) that occur via largely unknown changes in interoceptive signalling. Gratification of such specific appetites is then accomplished by engaging the brain motivational system. The cortico-limbic circuitry assigns the highest reward prediction to exteroceptive cues previously associated with consuming the missing ingredient. Thus, we would argue that the depletion of a given nutrient results in an increased rewarding value (incentive salience) of that nutrient. For example, protein-deprived animals would find protein particularly rewarding, much more than any other nutrient, and more than a protein-replete animal. The increased incentive salience would result in a heightened reinforcement of cues associated with that food source, particularly in the deplete state, such that the depleted animal specifically seeks food sources that provide the needed nutrient, and particularly those sources that previously met this need. It will be interesting to test this model under conditions of relative protein-, carbohydrate-, or fat-deficiencies. In particular, it will be interesting to (1) search for possible nutrient-specific molecular changes within hypothalamic circuits, (2) characterise the sensitivity and selectivity of the midbrain dopamine system in response to nutrient-specific cues and (3) identify the nature and location of the relevant ‘food memories’. The potential ‘reward’ of such research could be big, as it could lead to the development of drugs that make us want to eat less fatty foods, or help design behavioural strategies that promote healthier eating.

Acknowledgements

We thank Katie Bailey for editorial assistance. Supported by National Institutes of Health Grants DK047348 and DK071082 (H.R.B.), DK081563 (C.D.M.), DK053113 (B.K.R.), RR02195 and DK092587 (H.M.). None of the authors declares a conflict of interest. All authors contributed equally to the writing of this review paper.

References

1. Cannon, W (1939) The Wisdom of the Body. New York: Norton.
2. Richter, C (1942–1943) Total self-regulatory functions of animals and human beings. Harvey Lect Ser 38, 63103.
3. Livesey, G (1984) The energy equivalents of ATP and the energy values of food proteins and fats. Br J Nutr 51, 1528.
4. Veldhorst, MA, Westerterp-Plantenga, MS & Westerterp, KR (2009) Gluconeogenesis and energy expenditure after a high-protein, carbohydrate-free diet. Am J Clin Nutr 90, 519526.
5. Kalhan, SC & Kilic, I (1999) Carbohydrate as nutrient in the infant and child: range of acceptable intake. Eur J Clin Nutr 53, Suppl. 1, S94100.
6. Rozin, P (1969) Adaptive food sampling patterns in vitamin deficient rats. J Comp Physiol Psychol 69, 126132.
7. Fromentin, G, Gietzen, DW & Nicolaidis, S (1997) Aversion-preference patterns in amino acid- or protein-deficient rats: a comparison with previously reported responses to thiamin-deficient diets. Br J Nutr 77, 299314.
8. Tindell, AJ, Smith, KS, Berridge, KC et al. (2009) Dynamic computation of incentive salience: “wanting” what was never “liked”. J Neurosci 29, 1222012228.
9. Liedtke, WB, McKinley, MJ, Walker, LL et al. (2011) Relation of addiction genes to hypothalamic gene changes subserving genesis and gratification of a classic instinct, sodium appetite. Proc Natl Acad Sci USA 108, 1250912514.
10. Berridge, KC, Flynn, FW, Schulkin, J et al. (1984) Sodium depletion enhances salt palatability in rats. Behav Neurosci 98, 652660.
11. Tindell, AJ, Smith, KS, Pecina, S et al. (2006) Ventral pallidum firing codes hedonic reward: when a bad taste turns good. J Neurophysiol 96, 23992409.
12. Galef, BGJ (2000) Is There a specific appetite for Protein? In Neural and Metabolic Control of Macronutrient Intake , pp. 1928 [Berthoud, HR and Seeley, RJ, editors]. Boca Raton, FL: CRC Press.
13. Friedman, M (2000) Too many choices? Acritical essay on macronutrient selection. In Neural and Metabolic Control of Macronutrient Intake , pp. 1128 [Berthoud HaS, RJ, editor]. Boca Raton, FL: CRC Press.
14. Simpson, SJ & Raubenheimer, D (2000) Geometric models of macronutrient selection. In Neural and Metabolic Control of Macronutrient Intake , pp. 2942 [Berthoud, HR and Seeley, RJ, editors]. Boca Raton, FL: CRC Press.
15. de Castro, JM (2000) Macronutrient selection in free-feeding humans: evidence for long-term regulation. In Neural and Metabolic Control of Macronutrient Intake , pp. 4359 [Berthoud, HR and Seeley, RJ, editors]. Boca Raton, FL: CRC Press.
16. Berthoud, HR & Seeley, RJ. (2000) Neural and Metabolic Control of Macronutrient Intake. Boca Raton, FL: CRC Press.
17. Westerterp-Plantenga, MS, Nieuwenhuizen, A, Tome, D et al. (2009) Dietary protein, weight loss, and weight maintenance. Annu Rev Nutr 29, 2141.
18. Potier, M, Darcel, N & Tome, D (2009) Protein, amino acids and the control of food intake. Curr Opin Clin Nutr Metab Care 12, 5458.
19. Sanahuja, JC & Harper, AE (1963) Effect of dietary amino acid pattern on plasma amino acid pattern and food intake. Am J Physiol 204, 686690.
20. Leung, PM, Rogers, QR & Harper, AE (1968) Effect of amino acid imbalance on dietary choice in the rat. J Nutr 95, 483492.
21. Harper, AE & Peters, JC (1989) Protein intake, brain amino acid and serotonin concentrations and protein self-selection. J Nutr 119, 677689.
22. Koehnle, TJ, Russell, MC & Gietzen, DW (2003) Rats rapidly reject diets deficient in essential amino acids. J Nutr 133, 23312335.
23. Gietzen, DW, Hao, S & Anthony, TG (2007) Mechanisms of food intake repression in indispensable amino acid deficiency. Annu Rev Nutr 27, 6378.
24. Hao, S, Sharp, JW, Ross-Inta, CM et al. (2005) Uncharged tRNA and sensing of amino acid deficiency in mammalian piriform cortex. Science 307, 17761778.
25. Rudell, JB, Rechs, AJ, Kelman, TJ et al. (2011) The anterior piriform cortex is sufficient for detecting depletion of an indispensable amino acid, showing independent cortical sensory function. J Neurosci 31, 15831590.
26. Musten, B, Peace, D & Anderson, GH (1974) Food intake regulation in the weanling rat: self-selection of protein and energy. J Nutr 104, 563572.
27. Cheng, K, Simpson, SJ & Raubenheimer, D (2008) A geometry of regulatory scaling. Am Nat 172, 681693.
28. Simpson, SJ & Raubenheimer, D (1997) Geometric analysis of macronutrient selection in the rat. Appetite 28, 201213.
29. Simpson, SJ & Raubenheimer, D (2005) Obesity: the protein leverage hypothesis. Obes Rev 6, 133142.
30. Kyriazakis, I & Emmans, GC (1991) Diet selection in pigs: dietary choices made by growing pigs following a period of underfeeding with protein. Anim Prod 52, 337346.
31. Raubenheimer, D & Simpson, SJ (1997) Integrative models of nutrient balancing: application to insects and vertebrates. Nutr Res Rev 10, 151179.
32. Sorensen, A, Mayntz, D, Raubenheimer, D et al. (2008) Protein-leverage in mice: the geometry of macronutrient balancing and consequences for fat deposition. Obesity (Silver Spring) 16, 566571.
33. Simpson, SJ, Batley, R & Raubenheimer, D (2003) Geometric analysis of macronutrient intake in humans: the power of protein? Appetite 41, 123140.
34. Gosby, AK, Conigrave, AD, Lau, NS et al. (2011) Testing protein leverage in lean humans: a randomised controlled experimental study. PLoS ONE 6, e25929.
35. Brooks, RC, Simpson, SJ & Raubenheimer, D (2010) The price of protein: combining evolutionary and economic analysis to understand excessive energy consumption. Obes Rev 11, 887894.
36. Mayntz, D, Raubenheimer, D, Salomon, M et al. (2005) Nutrient-specific foraging in invertebrate predators. Science 307, 111113.
37. Bartness, TJ & Rowland, NE (1983) Diet selection and metabolic fuels in three models of diabetes mellitus. Physiol Behav 31, 539545.
38. Epstein, LH, Salvy, SJ, Carr, KA et al. (2010) Food reinforcement, delay discounting and obesity. Physiol Behav 100, 438445.
39. Bellush, LL & Rowland, NE (1986) Dietary self-selection in diabetic rats: an overview. Brain Res Bull 17, 653661.
40. Kanarek, RB & Ho, L (1984) Patterns of nutrient selection in rats with streptozotocin-induced diabetes. Physiol Behav 32, 639645.
41. Tepper, BJ & Kanarek, RB (1989) Selection of protein and fat by diabetic rats following separate dilution of the dietary sources. Physiol Behav 45, 4961.
42. Tordoff, MG, Tepper, BJ & Friedman, MI (1987) Food flavor preferences produced by drinking glucose and oil in normal and diabetic rats: evidence for conditioning based on fuel oxidation. Physiol Behav 41, 481487.
43. Singer, LK, York, DA & Bray, GA (1998) Macronutrient selection following 2-deoxy-d-glucose and mercaptoacetate administration in rats. Physiol Behav 65, 115121.
44. Ritter, S, Ritter, JB & Cromer, L (1999) 2-Deoxy-d-glucose and mercaptoacetate induce different patterns of macronutrient ingestion. Physiol Behav 66, 709715.
45. DiBattista, D (1991) Effects of time-restricted access to protein and to carbohydrate in adult mice and rats. Physiol Behav 49, 263269.
46. White, BD, He, B, Dean, RG et al. (1994) Low protein diets increase neuropeptide Y gene expression in the basomedial hypothalamus of rats. J Nutr 124, 11521160.
47. Dunlap, S, Heinrichs, SC (2009) Neuronal depletion of omega-3 fatty acids induces flax seed dietary self-selection in the rat. Brain Res 1250, 113119.
48. Gaillard, D, Laugerette, F, Darcel, N et al. (2008) The gustatory pathway is involved in CD36-mediated orosensory perception of long-chain fatty acids in the mouse. FASEB J 22, 14581468.
49. Tracy, AL, Clegg, DJ, Johnson, JD et al. (2008) The melanocortin antagonist AgRP (83–132) increases appetitive responding for a fat, but not a carbohydrate, reinforcer. Pharmacol Biochem Behav 89, 263271.
50. Smith Richards, BK, Belton, BN, York, B et al. (2004) Mice bearing Acads mutation display altered postingestive but not 5-s orosensory response to dietary fat. Am J Physiol Regul Integr Comp Physiol 286, R311R319.
51. Toepel, U, Knebel, JF, Hudry, J et al. (2009) The brain tracks the energetic value in food images. Neuroimage 44, 967974.
52. Davidson, TL, Morell, JR & Benoit, SC (2000) Memory and macronutrient regulation. In Neural and Metabolic Control of Macronutrient Intake , pp. 203217 [Berthoud, HR and Seeley, RJ, editors]. Boca Raton, FL: CRC Press.
53. Benoit, SC, Davis, JF & Davidson, TL (2010) Learned and cognitive controls of food intake. Brain Res 1350, 7176.
54. Damak, S, Rong, M, Yasumatsu, K et al. (2003) Detection of sweet and umami taste in the absence of taste receptor T1r3. Science 301, 850853.
55. Palmer, RK (2007) The pharmacology and signaling of bitter, sweet, and umami taste sensing. Mol Interv 7, 8798.
56. Delay, ER, Eddy, MC & Eschle, BK (2009) Behavioral studies of umami: tales told by mice and rats. Ann NY Acad Sci 1170, 4145.
57. Nelson, G, Chandrashekar, J, Hoon, MA et al. (2002) An amino-acid taste receptor. Nature 416, 199202.
58. Chaudhari, N, Pereira, E & Roper, SD (2009) Taste receptors for umami: the case for multiple receptors. Am J Clin Nutr 90, 738S742S.
59. Maruyama, Y, Pereira, E, Margolskee, RF et al. (2006) Umami responses in mouse taste cells indicate more than one receptor. J Neurosci 26, 22272234.
60. Perez, C, Ackroff, K & Sclafani, A (1996) Carbohydrate- and protein-conditioned flavor preferences: effects of nutrient preloads. Physiol Behav 59, 467474.
61. Perez, C, Lucas, F & Sclafani, A (1995) Carbohydrate, fat, and protein condition similar flavor preferences in rats using an oral-delay procedure. Physiol Behav 57, 549554.
62. Sclafani, A (2001) Psychobiology of food preferences. Int J Obes Relat Metab Disord 25, Suppl. 5, S13S16.
63. Miller, MG & Teates, JF (1985) Acquisition of dietary self-selection in rats with normal and impaired oral sensation. Physiol Behav 34, 401408.
64. DiBattista, D & Mercier, S (1999) Role of learning in the selection of dietary protein in the golden hamster (Mesocricetus auratus). Behav Neurosci 113, 574586.
65. Zukerman, S, Touzani, K, Margolskee, RF et al. (2009) Role of olfaction in the conditioned sucrose preference of sweet-ageusic T1R3 knockout mice. Chem Senses 34, 685694.
66. Rhinehart-Doty, JA, Schumm, J, Smith, JC et al. (1994) A non-taste cue of sucrose in short-term taste tests in rats. Chem Senses 19, 425431.
67. Nelson, G, Hoon, MA, Chandrashekar, J et al. (2001) Mammalian sweet taste receptors. Cell 106, 381390.
68. Sclafani, A, Glass, DS, Margolskee, RF et al. (2010) Gut T1R3 sweet taste receptors do not mediate sucrose-conditioned flavor preferences in mice. Am J Physiol Regul Integr Comp Physiol 299, R1643R1650.
69. Steinert, RE, Gerspach, AC, Gutmann, H et al. (2011) The functional involvement of gut-expressed sweet taste receptors in glucose-stimulated secretion of glucagon-like peptide-1 (GLP-1) and peptide YY (PYY). Clin Nutr 30, 524532.
70. Kokrashvili, Z, Mosinger, B & Margolskee, RF (2009) Taste signaling elements expressed in gut enteroendocrine cells regulate nutrient-responsive secretion of gut hormones. Am J Clin Nutr 90, 822S825S.
71. Laugerette, F, Gaillard, D, Passilly-Degrace, P et al. (2007) Do we taste fat? Biochimie 89, 265269.
72. Kinney, NE & Antill, RW (1996) Role of olfaction in the formation of preference for high-fat foods in mice. Physiol Behav 59, 475478.
73. Ramirez, I (1993) Role of olfaction in starch and oil preference. Am J Physiol 265, R1404R1409.
74. Gilbertson, TA, Fontenot, DT, Liu, L et al. (1997) Fatty acid modulation of K+ channels in taste receptor cells: gustatory cues for dietary fat. Am J Physiol 272, C1203C1210.
75. Liu, P, Shah, BP, Croasdell, S et al. (2011) Transient receptor potential channel type M5 is essential for fat taste. J Neurosci 31, 86348642.
76. Cartoni, C, Yasumatsu, K, Ohkuri, T et al. (2010) Taste preference for fatty acids is mediated by GPR40 and GPR120. J Neurosci 30, 83768382.
77. Laugerette, F, Passilly-Degrace, P, Patris, B et al. (2005) CD36 involvement in orosensory detection of dietary lipids, spontaneous fat preference, and digestive secretions. J Clin Invest 115, 31773184.
78. Sclafani, A, Ackroff, K & Abumrad, NA (2007) CD36 gene deletion reduces fat preference and intake but not post-oral fat conditioning in mice. Am J Physiol Regul Integr Comp Physiol 293, R1823R1832.
79. Matsumura, S, Saitou, K, Miyaki, T et al. (2008) Mercaptoacetate inhibition of fatty acid beta-oxidation attenuates the oral acceptance of fat in BALB/c mice. Am J Physiol Regul Integr Comp Physiol 295, R82R91.
80. Ackroff, K, Vigorito, M & Sclafani, A (1990) Fat appetite in rats: the response of infant and adult rats to nutritive and non-nutritive oil emulsions. Appetite 15, 171188.
81. Lucas, F, Ackroff, K & Sclafani, A (1989) Dietary fat-induced hyperphagia in rats as a function of fat type and physical form. Physiol Behav 45, 937946.
82. Ackroff, K, Lucas, F & Sclafani, A (2005) Flavor preference conditioning as a function of fat source. Physiol Behav 85, 448460.
83. Wu, T, Rayner, CK, Jones, K et al. (2010) Dietary effects on incretin hormone secretion. Vitam Horm 84, 81110.
84. Langhans, W, Leitner, C & Arnold, M (2011) Dietary fat sensing via fatty acid oxidation in enterocytes: possible role in the control of eating. Am J Physiol Regul Integr Comp Physiol 300, R554R565.
85. Berthoud, HR & Morrison, C (2008) The brain, appetite, and obesity. Annu Rev Psychol 59, 5592.
86. Levin, BE, Magnan, C, Dunn-Meynell, A et al. (2011) Metabolic sensing and the brain: who, what, where, and how? Endocrinology 152, 25522557.
87. Luquet, S, Perez, FA, Hnasko, TS et al. (2005) NPY/AgRP neurons are essential for feeding in adult mice but can be ablated in neonates. Science 310, 683685.
88. Krashes, MJ, Koda, S, Ye, C et al. (2011) Rapid, reversible activation of AgRP neurons drives feeding behavior in mice. J Clin Invest 121, 14241428.
89. Kelley, AE & Berridge, KC (2002) The neuroscience of natural rewards: relevance to addictive drugs. J Neurosci 22, 33063311.
90. Berridge, KC (1996) Food reward: brain substrates of wanting and liking. Neurosci Biobehav Rev 20, 125.
91. Lucas, LR, Pompei, P & McEwen, BS (1999) Correlates of deoxycorticosterone-induced salt appetite behavior and basal ganglia neurochemistry. Ann NY Acad Sci 897, 423428.
92. Cai, XJ, Evans, ML, Lister, CA et al. (2001) Hypoglycemia activates orexin neurons and selectively increases hypothalamic orexin-B levels: responses inhibited by feeding and possibly mediated by the nucleus of the solitary tract. Diabetes 50, 105112.
93. Diano, S, Horvath, B, Urbanski, HF et al. (2003) Fasting activates the nonhuman primate hypocretin (orexin) system and its postsynaptic targets. Endocrinology 144, 37743778.
94. Harris, GC, Wimmer, M & Aston-Jones, G (2005) A role for lateral hypothalamic orexin neurons in reward seeking. Nature 437, 556559.
95. Cason, AM, Smith, RJ, Tahsili-Fahadan, P et al. (2010) Role of orexin/hypocretin in reward-seeking and addiction: implications for obesity. Physiol Behav 100, 419428.
96. Mellinkoff, SM, Frankland, M, Boyle, D et al. (1956) Relationship between serum amino acid concentration and fluctuations in appetite. J Appl Physiol 8, 535538.
97. Choi, YH, Chang, N, Fletcher, PJ et al. (2000) Dietary protein content affects the profiles of extracellular amino acids in the medial preoptic area of freely moving rats. Life Sci 66, 11051118.
98. Hawkins, RA, O'Kane, RL, Simpson, IA et al. (2006) Structure of the blood-brain barrier and its role in the transport of amino acids. J Nutr 136, 218S226S.
99. Maurin, AC, Jousse, C, Averous, J et al. (2005) The GCN2 kinase biases feeding behavior to maintain amino acid homeostasis in omnivores. Cell Metab 1, 273277.
100. Russell, MC, Koehnle, TJ, Barrett, JA et al. (2003) The rapid anorectic response to a threonine imbalanced diet is decreased by injection of threonine into the anterior piriform cortex of rats. Nutr Neurosci 6, 247251.
101. Leung, PM & Rogers, QR (1971) Importance of prepyriform cortex in food-intake response of rats to amino acids. Am J Physiol 221, 929935.
102. Blouet, C, Jo, YH, Li, X et al. (2009) Mediobasal hypothalamic leucine sensing regulates food intake through activation of a hypothalamus-brainstem circuit. J Neurosci 29, 83028311.
103. Cota, D, Proulx, K, Smith, KA et al. (2006) Hypothalamic mTOR signaling regulates food intake. Science 312, 927930.
104. Morrison, CD, Xi, X, White, CL et al. (2007) Amino acids inhibit Agrp gene expression via an mTOR-dependent mechanism. Am J Physiol Endocrinol Metab 293, E165E171.
105. Ropelle, ER, Pauli, JR, Fernandes, MF et al. (2008) A central role for neuronal AMP-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) in high-protein diet-induced weight loss. Diabetes 57, 594605.
106. Morrison, CD, White, CL, Wang, Z et al. (2007) Increased hypothalamic protein tyrosine phosphatase 1B contributes to leptin resistance with age. Endocrinology 148, 433440.
107. Purpera, MN, Shen, L, Taghavi, M et al. (2012) Impaired branched chain amino acid metabolism alters feeding behavior and increases orexigenic neuropeptide expression in the hypothalamus. J Endocrinol 212, 8594.
108. Newgard, CB, An, J, Bain, JR et al. (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9, 311326.
109. Blevins, JE, Truong, BG & Gietzen, DW (2004) NMDA receptor function within the anterior piriform cortex and lateral hypothalamus in rats on the control of intake of amino acid-deficient diets. Brain Res 1019, 124133.
110. Elias, CF, Saper, CB, Maratos-Flier, E et al. (1998) Chemically defined projections linking the mediobasal hypothalamus and the lateral hypothalamic area. J Comp Neurol 402, 442459.
111. Berthoud, HR & Mogenson, GJ (1997) Ingestive behavior after intracerebral and intracerebroventricular infusions of glucose and 2-deoxy-d-glucose. Am J Physiol 233, R127R133.
112. Bady, I, Marty, N, Dallaporta, M et al. (2006) Evidence from glut2-null mice that glucose is a critical physiological regulator of feeding. Diabetes 55, 988995.
113. Woods, SC, Lotter, EC, McKay, LD et al. (1979) Chronic intracerebroventricular infusion of insulin reduces food intake and body weight of baboons. Nature 282, 503505.
114. Chavez, M, Riedy, CA, Van Dijk, G et al. (1996) Central insulin and macronutrient intake in the rat. Am J Physiol 271, R727R731.
115. Lenoir, M, Serre, F, Cantin, L et al. (2007) Intense sweetness surpasses cocaine reward. PLoS ONE 2, e698.
116. Hajnal, A, Smith, GP & Norgren, R (2004) Oral sucrose stimulation increases accumbens dopamine in the rat. Am J Physiol Regul Integr Comp Physiol 286, R31R37.
117. Sclafani, A, Touzani, K & Bodnar, RJ (2011) Dopamine and learned food preferences. Physiol Behav 104, 6468.
118. Tempel, DL, Leibowitz, KJ & Leibowitz, SF (1988) Effects of PVN galanin on macronutrient selection. Peptides 9, 309314.
119. Clegg, DJ, Air, EL, Woods, SC et al. (2002) Eating elicited by orexin-a, but not melanin-concentrating hormone, is opioid mediated. Endocrinology 143, 29953000.
120. Wortley, KE, Chang, GQ, Davydova, Z et al. (2003) Peptides that regulate food intake: orexin gene expression is increased during states of hypertriglyceridemia. Am J Physiol Regul Integr Comp Physiol 284, R1454R1465.
121. Chang, GQ, Karatayev, O, Ahsan, R et al. (2007) Dietary fat stimulates endogenous enkephalin and dynorphin in the paraventricular nucleus: role of circulating triglycerides. Am J Physiol Endocrinol Metab 292, E561E570.
122. Obici, S, Feng, Z, Morgan, K et al. (2002) Central administration of oleic acid inhibits glucose production and food intake. Diabetes 51, 271275.