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The neurobiology of food intake in an obesogenic environment

Published online by Cambridge University Press:  17 July 2012

Hans-Rudolf Berthoud*
Affiliation:
Neurobiology of Nutrition Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, USA
*
Corresponding author: Hans-Rudolf Berthoud, fax+1 225 763 0260, email berthohr@pbrc.edu
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Abstract

The objective of this non-systematic review of the literature is to highlight some of the neural systems and pathways that are affected by the various intake-promoting aspects of the modern food environment and explore potential modes of interaction between core systems such as hypothalamus and brainstem primarily receptive to internal signals of fuel availability and forebrain areas such as the cortex, amygdala and meso-corticolimbic dopamine system, primarily processing external signals. The modern lifestyle with its drastic changes in the way we eat and move puts pressure on the homoeostatic system responsible for the regulation of body weight, which has led to an increase in overweight and obesity. The power of food cues targeting susceptible emotions and cognitive brain functions, particularly of children and adolescents, is increasingly exploited by modern neuromarketing tools. Increased intake of energy-dense foods high in fat and sugar is not only adding more energy, but may also corrupt neural functions of brain systems involved in nutrient sensing as well as in hedonic, motivational and cognitive processing. It is concluded that only long-term prospective studies in human subjects and animal models with the capacity to demonstrate sustained over-eating and development of obesity are necessary to identify the critical environmental factors as well as the underlying neural systems involved. Insights from these studies and from modern neuromarketing research should be increasingly used to promote consumption of healthy foods.

Type
70th Anniversary Conference on ‘Body weight regulation – food, gut and brain signalling’
Copyright
Copyright © The Author 2012

Given the enormous amount of food eaten, it is remarkable that for most of us, body weight remains stable throughout adulthood. This weight stability is ascribed to a homoeostatic regulatory system in the hypothalamus that senses the nutritional and metabolic state of the body and controls energy intake and expenditure. Yet, an increasing portion of the population, including many children and adolescents develop obesity and the predisposition to a host of other debilitating diseases. The conundrum of high rates of obesity in the face of homoeostatic energy balance regulation has led to an intense scientific debate and at least three different views have emerged. The first is that in order for body weight (used here interchangeably with adiposity) to digress from the norm, there must be something wrong with the homoeostatic regulator located in the hypothalamus( Reference Guyenet and Schwartz 1 ). Another characteristic often associated with this view is a rigidly defended body weight ‘set point’. This view is supported by the fact that if there is something wrong with the homoeostatic regulator, e.g. impaired leptin and/or melanocortin-signalling, obesity is inevitable( Reference Farooqi and O'Rahilly 2 ). However, only a very small percentage of obesity can be allocated to defects in the presently known machinery of the homoeostatic regulator( Reference Bouchard 3 ). The overwhelming majority of obese people do not seem to have faulty genes presently associated with obesity.

A second view is that the homoeostatic regulator acts mainly to defend against undersupply but not oversupply of nutrients, that it is organised with considerable flexibility to accommodate different internal and external contingencies such as pregnancy and seasonal variations, and that there is no rigidly defended body weight ‘set point’( Reference Speakman 4 Reference Speakman, Levitsky and Allison 7 ). The implication would be that digressions from ideal body weight need not always be pathological, but can be physiological adaptations to special circumstances.

A third view is to include, besides the hypothalamus, other brain areas such as the brainstem, basal ganglia and cortico-limbic systems in the greater circuitry of the homoeostatic regulator( Reference Grill and Kaplan 8 Reference Berthoud 12 ). This view is supported by observations of lasting effects on food intake and energy balance by manipulating such extra-hypothalamic areas. It would also be much better to explain how obesity can develop in a rapidly changing environment that primarily interacts with the cognitive and emotional brain.

In the following non-systematic review, I will discuss how this greater neural circuitry, considered by the third view stated earlier, could be involved in managing the sometimes competing influences of intero- and extero-sensory signals in the control of food intake, energy expenditure and body weight regulation.

The modern environment: temptations to eat and avoid physical activity

The way we live, particularly what, when and how we eat and work has drastically changed with the gradual transformation from an agriculture based to a consumer society over the last 50 years or so. Foods are readily available to a large segment of the population, while the opportunity to work physically and expend energy has decreased. With the ascent of electronic communication, the brain plays a much more prominent role in the procurement and consumption of food and in the management of daily activities. There is a daily onslaught with cues associated with food and pictures of food( Reference Jones, Mannino and Green 13 , Reference Levitsky and Pacanowski 14 ). The advertisement and food industry relies more and more on expertise from neuroscientists and psychologists, and neuromarketing is the new buzzword. Neuromarketing in children is particularly profitable, as it generates loyal future buyers of brand name products. An unfiltered PubMed search using the terms ‘food marketing’ and ‘children’ yielded 756 papers, 600 of them published after the year 2000. Considering the many hours of daily exposure to media and electronic devices by children and adolescents( Reference Effertz and Wilcke 15 Reference Mink, Evans and Moore 17 ) and the persuasive techniques used( Reference Pettigrew, Roberts and Chapman 18 Reference Speers, Harris and Schwartz 21 ), the term being ‘brain-washed’ is not inaccurate. Of course, the same powerful methods could be used to induce children to consume healthy foods( Reference de Droog, Valkenburg and Buijzen 22 , Reference Corsini, Slater and Harrison 23 ), but this possibility remains little explored. Although cutting edge technology is applied by the food industry to find neurological markers for food-liking and wanting, much of this insight is unfortunately not shared with the research community.

Conditioned food intake in the absence of metabolic need

As we are increasingly exposed to cues evoking memories and images of foods throughout the day, this happens more and more frequently when we are satiated and metabolically replete. It is not clear how this hedonic hunger can be induced in the absence of metabolic depletion signals or during the postprandial phase when there is still plenty of absorbable energy in the gut. Why are we not simply ignoring such cues and stimuli? Several explanations are possible.

A model for cue-induced, conditioned food intake in satiated rats was developed by Weingarten( Reference Weingarten 24 ). After temporally pairing a tone or light (conditioned stimulus, CS+) with the presentation of a retractable food cup in food-restricted animals, rats learned quickly to go to the food cup every time the CS+ was on. After rats had been returned to ad libitum feeding and were fully satiated, the CS+ continued to elicit food cup approach and a small meal( Reference Weingarten 24 ), closely mimicking conditioned food intake through external cues in human subjects. In a series of elegant studies, Petrovich demonstrated the importance of a neural network including the amygdala, medial prefrontal cortex and lateral hypothalamus for this phenomenon to occur( Reference Petrovich, Setlow and Holland 25 Reference Petrovich, Ross and Holland 27 ). It appears that inputs to the hypothalamus from both the amygdala and medial prefrontal cortex (see Fig. 1) are necessary to link specific conditioned stimuli to appetitive action. It will be interesting to investigate the role of lateral hypothalamic orexin neurons and their projections to the mesolimbic dopamine system, as these neurons have been implicated in μ-opioid-induced food intake( Reference Zheng, Patterson and Berthoud 28 ), depletion-induced salt intake( Reference Liedtke, McKinley and Walker 29 ) and reinstatement of drug seeking( Reference Aston-Jones, Smith and Sartor 30 ). As the lateral hypothalamus is a major behavioural and autonomic output venue for the mediobasal hypothalamic integrative energy sensor, this modulatory input from the amygdala and prefrontal cortex may provide a basis for the overriding of homoeostatic regulation by external signals. However, it should be noted that neither the Weingarten( Reference Weingarten 24 ) nor the Petrovich studies( Reference Petrovich, Setlow and Holland 25 ) tested whether prolonged repetition of CS+ exposure led to chronic overeating and development of obesity and whether transection of the critical amygdala-hypothalamic projections prevented it.

Fig. 1. (colour online) Major neural systems and pathways involved in the control of ingestive behaviour and energy balance regulation with emphasis on interactions between the classical homoeostatic energy regulatory system in the hypothalamus and brainstem (blue boxes and arrows in lower half) and cognitive/emotional brain systems (red boxes and arrows in upper half). Bottom-up modulation of cognitive and emotional processes by metabolic signals and their derivatives is accomplished by (a) circulating hormones and metabolites acting not only on the hypothalamus and brainstem but also on external sensory processing pathways as well as on components of the corticolimbic system (open blue arrows with broken lines), (b) a stream of vagal and spinal sensory information from within the body to all levels of the neuraxis, including the cortex (full blue arrows with solid lines) and (c) neural signals generated by the integrative hypothalamic energy sensor and distributed to areas involved in reward-based decision making (full blue arrows with solid lines). Together, these ascending modulatory influences determine the level of incentive salience directed to specific nutrients. Top-down modulation of food intake and energy expenditure by cognitive and emotional/reward systems is accomplished by (a) direct external (taste and smell) sensory input to the hypothalamic energy sensor and response allocator (dark yellow lines), (b) input from the amygdala, cortex and reward processing systems to mainly the lateral hypothalamus, responsible for conditioned external signals to elicit food intake (full red lines and arrows), (c) inputs from cortex, amygdala and basal ganglia to midbrain extrapyramidal motor pathways (emotional motor system, broken red lines and full arrows) and (d) pyramidal motor system for voluntary behavioural control (broken red lines on the right). N. Accumbens, nucleus accumbens; SMA, supplemental motor area; BLA, basolateral amygdala; CeA, central nucleus of the amygdala; VTA, ventral tegmental area; PAG, periaqueductal gray; GLP-1, glucgon-like-peptide-1; PYY, peptide YY; AT, adipose tissue; SPA, spontaneous physical activity. Adapted from( Reference Berthoud 12 ).

The phenomenon of sensory-specific satiety( Reference Rolls, Rolls and Rowe 31 ) may facilitate conditioned food intake in the satiated state. An example of this facilitation is the appeal of a new sensory food experience, typically dessert, at the end of a satiating meal. Little is known regarding the neural mechanisms involved in this phenomenon, but it has been shown that a reduction in the electrical activity of neurons in the orbitofrontal cortex, a part of the frontal cortex, of macaque monkeys, can reflect sensory-specific satiety( Reference Rolls, Sienkiewicz and Yaxley 32 ). It is conceivable that some of the neurons in the orbitofrontal cortex direct their output to the lateral hypothalamus and thereby amplify vulnerability to conditioned food cues between meals.

It is also possible that the so-called cephalic phase responses to the sight and smell (or just thinking about) food can trigger appetitive behaviour ( Reference Parra-Covarrubias, Rivera-Rodriguez and Almaraz-Ugalde 33 , Reference Powley 34 ). Perhaps the small increases in saliva, gastric acid, insulin and ghrelin secretion that constitute the cephalic response stimulate appetitive drive by acting on sensory nerves or directly on the brain and thereby enhance the neural effects of conditioned stimuli. We may also be more vulnerable to conditioned food cues when under stress. Food consumption as a form of self-medication to relieve stress has been demonstrated( Reference Dallman, Pecoraro and Akana 35 ), although we do not know the neural mechanisms involved. Finally, a history of uncertainty about the food supply could also increase reactivity to food cues in the absence of direct metabolic hunger.

In summary, it has been clearly shown that conditioned stimuli can induce food intake in satiated rats and some of the critical neural circuitry has been identified. Thus, stimuli from the environment clearly have the capacity to temporarily overwhelm homoeostatic regulation. However, there is no animal or human study directly demonstrating that long-term exposure to conditioned stimuli leads to obesity.

Amplification of hedonic hunger by metabolic need

When conditioned cues such as food advertisements are present at times of metabolic depletion such as shortly before or during a meal, they are more likely to stimulate overingestion, because metabolic depletion amplifies their incentive salience( Reference Berridge, Ho and Richard 36 , Reference Berridge 37 ). It is well known that metabolic hunger makes us more responsive to cues signalling food and drug reward( Reference Highfield, Mead and Grimm 38 , Reference Carr 39 ). The neural pathways and mechanisms involved in this attribution of salience are not completely understood, but progress has recently been made. Specifically, it has been demonstrated that metabolic depletion signals in the form of high levels of circulating ghrelin as well as low levels of leptin, insulin, gut hormones and various metabolites can act not only on the classical brain areas involved in energy balance homoeostasis such as the hypothalamus and brainstem but also on brain areas involved in sensory processing, cognition and reward (Fig. 1; also see( Reference Berthoud 40 ) for a more detailed discussion).

Modern eating habits: increased availability, variety and portion size

Even in the absence of food advertisements, we are finding ourselves more and more exposed to opportunities to eat. Compared with the relatively fixed-meal patterns of the past, availability of food has drastically increased at home, at the work place and in the larger community. In addition to the birthday cakes and vending machines at work and school and the increasing number of fast food places, the refrigerator at home is also always stacked with ready to eat foods. In addition, typical plate and serving size has increased dramatically and self-serve buffets are common( Reference Rolls 41 ). Although there are plenty of studies showing that manipulations of availability, variety and portion size have short-term effects on food intake in human subjects( Reference Levitsky and Youn 42 Reference Wansink and Payne 45 ), few studies have looked at the longer-term consequences on intake and weight gain. In one such controlled clinical study, it was clearly demonstrated that increasing portion size resulted in sustained increase in food intake and weight gain over an 11 d observation period( Reference Rolls, Roe and Meengs 46 ). However, it is inherently difficult and expensive to measure food intake in human subjects accurately in long-term studies. Thus, direct evidence that availability, opportunity and variety of food can cause human obesity is not as strong as commonly assumed. Furthermore, indirect evidence from cross-sectional studies comparing lean and obese subjects( Reference Wansink and Payne 45 ) is limited by the fact that it cannot distinguish cause and effect.

Animal studies provide much better experimental control over longer time periods. Clearly, exposing animals to ad libitum high-fat and variety (cafeteria) diets can cause hyperphagia and obesity( Reference Sclafani and Springer 47 ). Standardised high-fat diets have now been commercially available for more than a decade and thousands of studies have been conducted; the role of diet composition and palatability is discussed in the next section. In stark contrast, there is only one study examining the role of availability in rodents. Rats that had access to four drinking spouts of sucrose and one spout of water ingest more energy and gained more weight over a 30 d observation period than rats that had access to one spout of sucrose and four spouts of water( Reference Tordoff 48 ). These findings are truly startling. Although the acute overingestion could be easily explained by the initial curiosity to sample from each available spout, it is difficult to understand why there is no adaptation over time and why the homoeostatic regulatory feedback mechanisms failed. The authors entitled the paper ‘Obesity by Choice’, suggesting that it is the rat's failure to make the sensible choice( Reference Tordoff 48 ). It is critical to verify the results of this experiment, as it could not be replicated by another group of scientists (A Sclafani, personal communication).

What are the neural mechanisms responsible for eating more energetic food when availability, variety and portion size is high? Availability-induced hyperphagia in normal-weight subjects is likely to depend on neural mechanisms similar to those involved in food cue-induced hyperphagia as discussed earlier. The difference is that with cue-induced overeating, the stimuli are more immediate. That is, if signals indicating food availability coincide with signals of metabolic depletion shortly before a meal, their salience will be amplified resulting in an earlier start of the meal. Under metabolically replete conditions, the circuitry including amygdala, prefrontal cortex and lateral hypothalamus, shown to be responsible for conditioned food intake in satiated rats( Reference Petrovich, Setlow and Holland 25 , Reference Petrovich, Ross and Holland 27 , Reference Petrovich and Gallagher 49 ) is likely to be involved.

Modern foods: from palatable to addictive

Palatability is clearly one of the main drivers of food intake and it can lead to the development of obesity in susceptible individuals. However, the link between palatability and development of obesity is still not clear. Known as the ‘French Paradox’, the consumption of highly palatable French/Mediterranean cuisine produces less risk for obesity, suggesting that there are factors other than palatability that lead to chronic overconsumption. Energy-dense foods that are high in sugar and fat, and low in vitamins and minerals (also called empty energies), may be a more important factor. Foods such as this may be addictive.

Neural representations of the pleasure of eating

It is clear that the reward value of food is not only represented by its taste and flavour during the consumatory phase. A variety of sensory stimuli and emotional states or feelings with vastly different temporal profiles contribute to the experience of reward. Specifically, during the post-consumatory phase, nutrients interact with sensors in the gastrointestinal tract, other peripheral organs and the brain itself. It has recently been demonstrated that when all taste processing is eliminated by genetic manipulation, mice still learn to prefer sugar over water, suggesting the generation of food reward by processes of glucose utilisation( Reference de Araujo, Oliveira-Maia and Sotnikova 50 ).

Given the multifaceted involvement of pleasure and reward in ingestive behaviour, it is clear that multiple neural systems are involved (for a more detailed analysis, see( Reference Berthoud, Lenard and Shin 51 )). Briefly, the most primitive form of liking and disliking appears to be inherent to components of the peripheral gustatory pathways in the brainstem( Reference Grill and Norgren 52 Reference Berridge and Kringelbach 55 ). However, for the full sensory impact of palatable food and the subjective feeling of pleasure in human subjects, taste is integrated with other sensory modalities such as smell and mouth-feel. Integration takes place in forebrain areas including the amygdala, as well as primary and higher order sensory cortical areas including the insular and orbitofrontal cortex, where sensory representations of particular foods are formed( Reference Verhagen 56 Reference de Araujo, Rolls and Kringelbach 62 ). The exact neural pathways through which such sensory percepts or representations lead to the generation of subjective pleasure are not clear. Neuroimaging studies in human subjects suggest that pleasure, as measured by subjective ratings, is computed within portions of the orbitofrontal and perhaps insular cortex( Reference Berridge and Kringelbach 55 , Reference Kringelbach 63 ).

Neural systems representing the motivation to eat

The ultimate goal of food advertisement is to entice an individual to buy a specific food product and get hooked on it. This goal can be linked to what happens in addiction to drugs and alcohol, and it is not surprising that similar neural mechanisms have been implicated. Although ‘liking’ a branded food item seems necessary, ‘wanting’ it and buying it is more important for successful marketing. According to the liking/wanting distinction in food reward, it is possible to ‘want’ something that is not liked( Reference Berridge, Robinson and Aldridge 64 ). Berridge defined wanting as ‘Incentive salience, or motivation for reward typically triggered by reward-related cues’( Reference Berridge, Ho and Richard 36 ). The mesolimbic dopamine system with projections from the ventral tegmental area to the nucleus accumbens, prefrontal cortex, amygdala and hippocampus seems to be a key neural substrate for wanting (Fig. 1). Phasic activity of dopamine neurons projecting from the ventral tegmental area to the nucleus accumbens in the ventral striatum is involved in the decision-making process during the preparatory (appetitive) phase of ingestive behaviour( Reference Schultz, Dayan and Montague 65 , Reference Carelli 66 ). In addition, when palatable foods such as sucrose are actually consumed, a sustained and sweetness-dependent increase and turnover in dopamine levels occurs in the nucleus accumbens( Reference Hernandez and Hoebel 67 Reference Smith 69 ). Dopamine signalling in the nucleus accumbens thus appears to play a role in both the appetitive and consumatory phases of an ingestive bout. The nucleus accumbens shell is thereby part of a neural loop including the lateral hypothalamus and the ventral tegmental area, with orexin neurons playing a key role( Reference Zheng, Patterson and Berthoud 28 , Reference Stratford and Kelley 70 Reference Korotkova, Sergeeva and Eriksson 74 ). This loop appears to be important for transmitting metabolic state signals from the lateral hypothalamus and thus attributing incentive salience to goal objects, as discussed earlier.

Eating and ‘free will’

In human subjects, there is also wanting at a more conscious level, described by Berridge as a ‘cognitive desire for a declarative goal in the ordinary sense of the word wanting’( Reference Berridge, Ho and Richard 36 ). In addition to the mesolimbic dopamine system, a number of cortical areas, such as the dorsolateral prefrontal cortex and other components of a decision-making system are likely involved( Reference Hare, O'Doherty and Camerer 75 ). Ultimately, a conscious decision can be made to eat a food item or to abstain from eating it. Although this appears to be up to the ‘free will’ of every individual, even apparently conscious decisions may have a subconscious component. This was demonstrated in a neuroimaging study in human subjects which was designed to decode the outcome of decisions before and after they reached awareness( Reference Soon, Brass and Heinze 76 ). Notably, when the subject's decision reached conscious awareness, it already had been influenced for up to 10 s by unconscious (unaware) brain activity in the lateral and medial frontopolar as well as anterior cingulate cortex and the precuneus( Reference Soon, Brass and Heinze 76 ). That prefrontal activity is necessary to choose advantageously in a gambling task was shown in a study in patients with prefrontal lesions( Reference Bechara, Damasio and Tranel 77 ). Normal subjects began to choose advantageously before they realised which strategy worked best, and they exhibited anticipatory skin conductance responses before they knew explicitly that it was a risky choice. In contrast, prefrontal patients continued to make disadvantageous choices and never showed an anticipatory autonomic response( Reference Bechara, Damasio and Tranel 77 ). These findings strongly suggest that subconscious neural activity can guide ingestive behaviour before conscious explicit knowledge does. The neural pathways for behavioural and autonomic control that escapes awareness is not well understood. Nevertheless, pathways from various prefrontal cortical areas and particularly strong descending pathways from the amygdala to areas in the midbrain (including the periaqueductal grey), brain stem and spinal cord are known to be part of the emotional motor system that exist outside the bounds of conscious control( Reference Hurley, Herbert and Moga 78 Reference Tettamanti, Rognoni and Cafiero 80 ) (Fig. 1). Interestingly, many areas of the limbic system, including the cortex have direct, monosynaptic inputs to autonomic preganglionic neurons( Reference Westerhaus and Loewy 81 ), providing an avenue for subconscious modulation of peripheral organs involved in metabolic processes (Fig. 1).

Overlap of neural pathways for food intake and drug addiction

Based on the observation that dopamine receptor-2 availability within the dorsal striatum is similarly reduced in both obese subjects and cocaine addicts( Reference Volkow and Wise 82 ), a heated discussion about the similarities between food and drug addiction has ensued( Reference Volkow, Wang and Fowler 83 Reference Epstein and Shaham 92 ).

As repeated exposure to drugs of abuse causes neuro-adaptive changes leading to elevations in reward thresholds (tolerance resulting in decreased reward) that drive accelerated drug intake( Reference Ahmed, Kenny and Koob 93 Reference Koob and Le Moal 98 ), similar neural and behavioural changes can be predicted from repeated exposure to addictive foods. For example, repeated sucrose access is known to up-regulate dopamine release( Reference Avena, Rada and Hoebel 99 ) and dopamine transporter expression( Reference Bello, Sweigart and Lakoski 100 ), as well as to change dopamine D1 and D2-receptor availability in the nucleus accumbens( Reference Avena, Rada and Hoebel 99 , Reference Bello, Lucas and Hajnal 101 ). These changes may be responsible for the observed escalation of sucrose binging, cross-sensitisation to amphetamine-induced locomotor activity, withdrawal symptoms, such as increased anxiety and depression( Reference Avena, Rada and Hoebel 99 ) and the reduced reinforcing efficacy of normal foods( Reference Cottone, Sabino and Steardo 102 ).

Exposure to a palatable cafeteria diet in Wistar rats led to sustained hyperphagia over 40 d and lateral hypothalamic electrical self-stimulation threshold increased in parallel to body weight gain( Reference Johnson and Kenny 103 ). A similar insensitivity of the reward system was previously seen in addicted rats that self-administered intravenous cocaine or heroin( Reference Ahmed, Kenny and Koob 93 , Reference Markou and Koob 94 ). Dopamine D2-receptor expression in the dorsal striatum was significantly reduced, in parallel to worsening of the reward threshold( Reference Johnson and Kenny 103 ), to levels found in cocaine addicted rats( Reference Dalley, Fryer and Brichard 104 ). Interestingly, after 14 d of abstinence from the palatable diet, reward threshold did not normalise even though the rats were hypophagic and lost about 10% body weight( Reference Johnson and Kenny 103 ). This is in contrast to the relatively rapid (about 48 h) normalisation in reward thresholds in rats that abstained from cocaine self-administration( Reference Markou and Koob 94 ), and may indicate the presence of irreversible changes caused by the high-fat content of the diet (see next section). Given the observation that cocaine addicts and obese human subjects exhibit low D2-receptor availability in the dorsal striatum( Reference Wang, Volkow and Thanos 105 ), dopamine plasticity due to repeated consumption of palatable food may be similar to what occurs with repeated consumption of drugs of abuse. On the other hand, there is less convincing evidence for development of dependence on high-fat food( Reference Boggiano, Chandler and Viana 106 , Reference Corwin 107 ), although intermittent access to corn oil can stimulate dopamine release in the nucleus accumbens( Reference Liang, Hajnal and Norgren 108 ).

Modern foods: from energy dense to toxic

There is mounting evidence from rodent studies that eating a high-fat diet not only puts pressure on energy balance by providing extra energy, but that it can cause brain damage. The very brain area that is supposed to tightly regulate energy balance, the hypothalamus, appears to get corrupted by eating high-fat food( Reference De Souza, Araujo and Bordin 109 Reference Benoit, Kemp and Elias 115 ). The complex cascades of molecular changes through which high-fat feeding appears to impair leptin and insulin signalling, most critical for body weight regulation and glucose homoeostasis have recently been reviewed by Ryan et al.( Reference Ryan, Woods and Seeley 116 ).

Observations from experiments using fatty acid administration or blockade of fatty acid-induced inflammation in the brain suggest that a short period of fat feeding( Reference Benoit, Kemp and Elias 115 , Reference Thaler, Yi and Schur 117 ) and even a single high-fat meal( Reference Zhang, Zhang and Zhang 118 , Reference Posey, Clegg and Printz 119 ) are enough to rapidly inflict hypothalamic injury and impairment of normal nutrient-sensing and energy balance functions of the hypothalamus. An even worse scenario is that fetal exposure to the mouse dam's high-fat diet is apparently enough to cause hypothalamic dysfunction( Reference Rother, Kuschewski and Alcazar 120 ). Thus, pro-inflammatory signalling is no longer regarded as a consequence of the obese state, but appears to be one of the first causative steps in high-fat diet-induced obesity. The only encouraging news is that unsaturated fatty acids directly infused into the brain of mice appear to almost completely reverse hypothalamic inflammation and obesity induced by eating a high-fat diet rich in saturated fats for 8 weeks( Reference Cintra, Ropelle and Moraes 121 ). It is thus possible that specifically saturated fats can cause these debilitating effects to the brain( Reference Gupta, Knight and Keller 122 ).

In addition to direct deleterious effects on the hypothalamus, high-fat diets also appear to disrupt normal satiety-signalling from the gut. High-fat diets can stimulate inflammatory signalling via increased mucosal permeability and Toll-like receptors in rats that become hyperphagic and obese, but not in rats that are resistant( Reference de La Serre, Ellis and Lee 123 ). It looks more and more like a distinct possibility that changes in the composition of the gut microbiota via stimulation of the innate immune response, the inflammasome, are at the origin of the intestinal and eventually systemic and brain inflammation( Reference Mohammed, Tang and Jahangiri 124 Reference Elinav, Strowig and Kau 127 ); and see recent review by Harris et al.( Reference Harris, Kassis and Major 128 ). As microbiota can be transferred between subjects, the resulting obesity and fatty-liver disease may even be looked at as a communicable disease( Reference Vijay-Kumar and Gewirtz 129 ). The sensitivity of vagal afferent chemo- and mechano-sensors communicating to the brain is also reduced in high-fat diet obese rats and mice( Reference Paulino, Barbier de la and Knotts 130 Reference Daly, Park and Valinsky 135 ).

These new findings discussed earlier raise a lot of new questions. It is hard to believe that eating one fat-rich meal should start a cascade of events that eventually lead to obesity, diabetes and dementia. Why should eating the macronutrient fat that provides valuable energy and prevents starvation have such clear-cut maladaptive consequences? It is unlikely that eating just one ‘forbidden fruit’ is a nutritional sin, and it remains to be seen whether the acute effects obtained with pharmacological manipulations in the brain mimic real physiological mechanisms. Furthermore, it is not known whether such acute effects occur in human subjects. If they do occur, acute numbing of hypothalamic nutrient sensing by fat-rich meals might have been adaptive in the past by providing a mechanism to take advantage of rare moments of nutritional abundance.

The chronic effects of high-fat eating are more difficult to ignore, although they seem just as maladaptive as the acute effects. Why does the mouse not avoid high-fat food that apparently makes it sick? What happened to the ‘wisdom of the body'? How is it that animals and man evolved elaborate taste perception and rapid learning mechanisms to avoid toxic foods, but they are easily fooled by toxic fat?

Modern environment: less opportunity to burn energy

This review has almost entirely focused on energy intake, but it is clear that the modern environment also affects energy expenditure in a number of ways. Although we are beginning to understand the neurobiology of food intake in the modern world, we remain almost completely ignorant regarding the neurobiological controls of physical activity and exercise and the integrative processes that comprise the regulation of energy balance( Reference Garland, Schutz and Chappell 136 ). One reason might be that we have a limited understanding of hormonal (or neural) inter-organ communication. Although we know a lot about gut–brain and adipose tissue–brain signalling, we know virtually nothing about communication between the exercising muscle and the brain and other organs. Only very recently, the muscle-derived hormone irisin was discovered which appears to induce browning of white adipose tissue( Reference Bostrom, Wu and Jedrychowski 137 ). It will be interesting to see whether this hormone also signals to the brain systems regulating energy balance.

Conclusions

Clearly, appetitive drive and food intake are affected by signals from inside the body and the environment, and the latter are exploited by the food industry through the newly established field of neuromarketing. Although these techniques would be just as powerful to stimulate eating of healthy foods, not much effort has been made towards this goal. Environmental signals affecting food intake interact almost exclusively with corticolimbic brain areas involved in cognition, emotion, motivation and decision making. These systems, although modulated in a bottom-up manner by metabolic signals, can exert strong and overpowering top-down control of food intake and energy balance regulation, as demonstrated by eating in the complete absence of nutritional need. However, most of these demonstrations of top-down control act only in an acute fashion, and more long-term studies are necessary to demonstrate a lasting impact on body weight. Finally, the neural pathways linking corticolimbic functions with hypothalamic and brainstem structures involved in the control of food intake and energy balance need to be better defined. Specifically, the respective contributions of conscious and subconcious determinants of behavioural action and autonomic control should be further investigated.

Acknowledgements

I would like to thank Katie Bailey for editorial assistance and Christopher Morrison, Heike Münzberg and Brenda Richards for valuable comments on an earlier draft of this manuscript. This work was supported by the National Institutes of Health Grants DK047348 and DK0871082. The author declares no conflict of interest.

References

1. Guyenet, SJ & Schwartz, MW (2012) Clinical review + #: regulation of food intake, energy balance, and body fat mass: implications for the pathogenesis and treatment of obesity. J Clin Endocrinol Metab 97, 745755.Google Scholar
2. Farooqi, S & O'Rahilly, S (2006) Genetics of obesity in humans. Endocr Rev 27, 710718.Google Scholar
3. Bouchard, C (1995) Genetics of obesity: an update on molecular markers. Int J Obes Relat Metab Disord 19, Suppl. 3, S10S13.Google Scholar
4. Speakman, JR (2008) Thrifty genes for obesity, an attractive but flawed idea, and an alternative perspective: the ‘drifty gene’ hypothesis. Int J Obes (Lond) 32, 16111617.Google Scholar
5. Harris, RB (1990) Role of set-point theory in regulation of body weight. FASEB J 4, 33103318.Google Scholar
6. Hall, KD, Heymsfield, SB, Kemnitz, JW et al. (2012) Energy balance and its components: implications for body weight regulation. Am J Clin Nutr 95, 989994.Google Scholar
7. Speakman, JR, Levitsky, DA, Allison, DB et al. (2011) Set points, settling points and some alternative models: theoretical options to understand how genes and environments combine to regulate body adiposity. Dis Model Mech 4, 733745.Google Scholar
8. Grill, HJ & Kaplan, JM (2002) The neuroanatomical axis for control of energy balance. Front Neuroendocrinol 23, 240.Google Scholar
9. Berthoud, HR (2002) Multiple neural systems controlling food intake and body weight. Neurosci Biobehav Rev 26, 393428.Google Scholar
10. Berthoud, HR (2004) Mind versus metabolism in the control of food intake and energy balance. Physiol Behav 81, 781793.Google Scholar
11. Berthoud, HR & Morrison, C (2008) The brain, appetite, and obesity. Annu Rev Psychol 59, 5592.Google Scholar
12. Berthoud, HR (2011) Metabolic and hedonic drives in the neural control of appetite: who is the boss? Curr Opin Neurobiol 21, 888896.Google Scholar
13. Jones, SC, Mannino, N & Green, J (2010) ‘Like me, want me, buy me, eat me’: relationship-building marketing communications in children's magazines. Public Health Nutr 13, 21112118.Google Scholar
14. Levitsky, DA & Pacanowski, CR (2011) Free will and the obesity epidemic. Public Health Nutr 19, 116.Google Scholar
15. Effertz, T & Wilcke, AC (2011) Do television food commercials target children in Germany? Public Health Nutr 14, 18.Google Scholar
16. Powell, LM, Szczypka, G & Chaloupka, FJ (2010) Trends in exposure to television food advertisements among children and adolescents in the United States. Arch Pediatr Adolesc Med 164, 794802.Google Scholar
17. Mink, M, Evans, A, Moore, CG et al. (2010) Nutritional imbalance endorsed by televised food advertisements. J Am Diet Assoc 110, 904910.Google Scholar
18. Pettigrew, S, Roberts, M, Chapman, K et al. (2012) The use of negative themes in television food advertising. Appetite 58, 496503.Google Scholar
19. Boyland, EJ, Harrold, JA, Kirkham, TC et al. (2012) Persuasive techniques used in television advertisements to market foods to UK children. Appetite 58, 658664.Google Scholar
20. Hebden, L, King, L & Kelly, B (2011) Art of persuasion: an analysis of techniques used to market foods to children. J Paediatr Child Health 47, 776782.Google Scholar
21. Speers, SE, Harris, JL & Schwartz, MB (2011) Child and adolescent exposure to food and beverage brand appearances during prime-time television programming. Am J Prev Med 41, 291296.Google Scholar
22. de Droog, SM, Valkenburg, PM & Buijzen, M (2011) Using brand characters to promote young children's liking of and purchase requests for fruit. J Health Commun 16, 7989.Google Scholar
23. Corsini, N, Slater, A, Harrison, A et al. (2011) Rewards can be used effectively with repeated exposure to increase liking of vegetables in 4–6-year-old children. Public Health Nutr 7, 110.Google Scholar
24. Weingarten, HP (1983) Conditioned cues elicit feeding in sated rats: a role for learning in meal initiation. Science 220, 431433.Google Scholar
25. Petrovich, GD, Setlow, B, Holland, PC et al. (2002) Amygdalo-hypothalamic circuit allows learned cues to override satiety and promote eating. J Neurosci 22, 87488753.Google Scholar
26. Petrovich, GD, Holland, PC & Gallagher, M (2005) Amygdalar and prefrontal pathways to the lateral hypothalamus are activated by a learned cue that stimulates eating. J Neurosci 25, 82958302.Google Scholar
27. Petrovich, GD, Ross, CA, Holland, PC et al. (2007) Medial prefrontal cortex is necessary for an appetitive contextual conditioned stimulus to promote eating in sated rats. J Neurosci 27, 64366441.Google Scholar
28. Zheng, H, Patterson, LM & Berthoud, HR (2007) Orexin signaling in the ventral tegmental area is required for high-fat appetite induced by opioid stimulation of the nucleus accumbens. J Neurosci 27, 1107511082.Google Scholar
29. 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.Google Scholar
30. Aston-Jones, G, Smith, RJ, Sartor, GC et al. (2010) Lateral hypothalamic orexin/hypocretin neurons: a role in reward-seeking and addiction. Brain Res 1314, 7490.Google Scholar
31. Rolls, BJ, Rolls, ET, Rowe, EA et al. (1981) Sensory specific satiety in man. Physiol Behav 27, 137142.Google Scholar
32. Rolls, ET, Sienkiewicz, ZJ & Yaxley, S (1989) Hunger modulates the responses to gustatory stimuli of single neurons in the caudolateral orbitofrontal cortex of the macaque monkey. Eur J Neurosci 1, 5360.Google Scholar
33. Parra-Covarrubias, A, Rivera-Rodriguez, I & Almaraz-Ugalde, A (1971) Cephalic phase of insulin secretion in obese adolescents. Diabetes 20, 800802.Google Scholar
34. Powley, TL (1977) The ventromedial hypothalamic syndrome, satiety, and a cephalic phase hypothesis. Psychol Rev 84, 89126.Google Scholar
35. Dallman, MF, Pecoraro, N, Akana, SF et al. (2003) Chronic stress and obesity: a new view of ‘comfort food’. Proc Natl Acad Sci USA 100, 1169611701.Google Scholar
36. Berridge, KC, Ho, CY, Richard, JM et al. (2010) The tempted brain eats: pleasure and desire circuits in obesity and eating disorders. Brain Res 1350, 4364.Google Scholar
37. Berridge, KC (2007) The debate over dopamine's role in reward: the case for incentive salience. Psychopharmacology (Berl) 191, 391431.Google Scholar
38. Highfield, DA, Mead, AN, Grimm, JW et al. (2002) Reinstatement of cocaine seeking in 129X1/SvJ mice: effects of cocaine priming, cocaine cues and food deprivation. Psychopharmacology (Berl) 161, 417424.Google Scholar
39. Carr, KD (2007) Chronic food restriction: enhancing effects on drug reward and striatal cell signaling. Physiol Behav 91, 459472.Google Scholar
40. Berthoud, HR (2007) Interactions between the ‘cognitive’ and ‘metabolic’ brain in the control of food intake. Physiol Behav 91, 486498.Google Scholar
41. Rolls, BJ (2003) The supersizing of America: portion size and the obesity epidemic. Nutr Today 38, 4253.Google Scholar
42. Levitsky, DA & Youn, T (2004) The more food young adults are served, the more they overeat. J Nutr 134, 25462549.Google Scholar
43. Wansink, B & Kim, J (2005) Bad popcorn in big buckets: portion size can influence intake as much as taste. J Nutr Educ Behav 37, 242245.Google Scholar
44. Wansink, B, van Ittersum, K & Painter, JE (2006) Ice cream illusions bowls, spoons, and self-served portion sizes. Am J Prev Med 31, 240243.Google Scholar
45. Wansink, B & Payne, CR (2008) Eating behavior and obesity at Chinese buffets. Obesity (Silver Spring) 16, 19571960.Google Scholar
46. Rolls, BJ, Roe, LS & Meengs, JS (2006) Larger portion sizes lead to a sustained increase in energy intake over 2 days. J Am Diet Assoc 106, 543549.Google Scholar
47. Sclafani, A & Springer, D (1976) Dietary obesity in adult rats: similarities to hypothalamic and human obesity syndromes. Physiol Behav 17, 461471.Google Scholar
48. Tordoff, MG (2002) Obesity by choice: the powerful influence of nutrient availability on nutrient intake. Am J Physiol Regul Integr Comp Physiol 282, R1536R1539.Google Scholar
49. Petrovich, GD & Gallagher, M (2003) Amygdala subsystems and control of feeding behavior by learned cues. Ann N Y Acad Sci 985, 251262.Google Scholar
50. de Araujo, IE, Oliveira-Maia, AJ, Sotnikova, TD et al. (2008) Food reward in the absence of taste receptor signaling. Neuron 57, 930941.Google Scholar
51. Berthoud, HR, Lenard, NR & Shin, AC (2011) Food reward, hyperphagia, and obesity. Am J Physiol Regul Integr Comp Physiol 300, R1266R1277.Google Scholar
52. Grill, HJ & Norgren, R (1978) The taste reactivity test. I. Mimetic responses to gustatory stimuli in neurologically normal rats. Brain Res 143, 263279.Google Scholar
53. Steiner, JE (1973) The gustofacial response: observations on normal and anancephalic newborn infants. Bethesda, MD: U. S. Department of Health, Education, and Welfare.Google Scholar
54. Berridge, KC (2000) Measuring hedonic impact in animals and infants: microstructure of affective taste reactivity patterns. Neurosci Biobehav Rev 24, 173198.Google Scholar
55. Berridge, KC & Kringelbach, ML (2008) Affective neuroscience of pleasure: reward in humans and animals. Psychopharmacology (Berl) 199, 457480.Google Scholar
56. Verhagen, JV (2006) The neurocognitive bases of human multimodal food perception: cconsciousness. Brain Res Brain Res Rev 53, 271286.Google Scholar
57. Rolls, ET, Verhagen, JV & Kadohisa, M (2003) Representations of the texture of food in the primate orbitofrontal cortex: neurons responding to viscosity, grittiness, and capsaicin. J Neurophysiol 90, 37113724.Google Scholar
58. Rolls, ET (2000) The orbitofrontal cortex and reward. Cereb Cortex 10, 284294.Google Scholar
59. Small, DM, Jones-Gotman, M, Zatorre, RJ et al. (1997) A role for the right anterior temporal lobe in taste quality recognition. J Neurosci 17, 51365142.Google Scholar
60. Small, DM, Zald, DH, Jones-Gotman, M et al. (1999) Human cortical gustatory areas: a review of functional neuroimaging data. Neuroreport. 10, 714.Google Scholar
61. de Araujo, IE, Kringelbach, ML, Rolls, ET et al. (2003) Representation of umami taste in the human brain. J Neurophysiol 90, 313319.Google Scholar
62. de Araujo, IE, Rolls, ET, Kringelbach, ML et al. (2003) Taste-olfactory convergence, and the representation of the pleasantness of flavour, in the human brain. Eur J Neurosci 18, 20592068.Google Scholar
63. Kringelbach, ML (2004) Food for thought: hedonic experience beyond homeostasis in the human brain. Neuroscience 126, 807819.Google Scholar
64. Berridge, KC, Robinson, TE & Aldridge, JW (2009) Dissecting components of reward: ‘liking’, ‘wanting’, and learning. Curr Opin Pharmacol 9, 6573.Google Scholar
65. Schultz, W, Dayan, P & Montague, PR (1997) A neural substrate of prediction and reward. Science 275, 15931599.Google Scholar
66. Carelli, RM (2002) The nucleus accumbens and reward: neurophysiological investigations in behaving animals. Behav Cogn Neurosci Rev 1, 281296.Google Scholar
67. Hernandez, L & Hoebel, BG (1988) Feeding and hypothalamic stimulation increase dopamine turnover in the accumbens. Physiol Behav 44, 599606.Google Scholar
68. 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.Google Scholar
69. Smith, GP (2004) Accumbens dopamine mediates the rewarding effect of orosensory stimulation by sucrose. Appetite 43, 11–3.Google Scholar
70. Stratford, TR & Kelley, AE (1999) Evidence of a functional relationship between the nucleus accumbens shell and lateral hypothalamus subserving the control of feeding behavior. J Neurosci 19, 1104011048.Google Scholar
71. Harris, GC, Wimmer, M & Aston-Jones, G (2005) A role for lateral hypothalamic orexin neurons in reward seeking. Nature 437, 556559.Google Scholar
72. Peyron, C, Tighe, DK, van den Pol, AN et al. (1998) Neurons containing hypocretin (orexin) project to multiple neuronal systems. J Neurosci 18, 999610015.Google Scholar
73. Nakamura, T, Uramura, K, Nambu, T et al. (2000) Orexin-induced hyperlocomotion and stereotypy are mediated by the dopaminergic system. Brain Res 873, 181187.Google Scholar
74. Korotkova, TM, Sergeeva, OA, Eriksson, KS et al. (2003) Excitation of ventral tegmental area dopaminergic and nondopaminergic neurons by orexins/hypocretins. J Neurosci 23, 711.Google Scholar
75. Hare, TA, O'Doherty, J, Camerer, CF et al. (2008) Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors. J Neurosci 28, 56235630.Google Scholar
76. Soon, CS, Brass, M, Heinze, HJ et al. (2008) Unconscious determinants of free decisions in the human brain. Nat Neurosci 11, 543545.Google Scholar
77. Bechara, A, Damasio, H, Tranel, D et al. (1997) Deciding advantageously before knowing the advantageous strategy. Science 275, 12931295.Google Scholar
78. Hurley, KM, Herbert, H, Moga, MM et al. (1991) Efferent projections of the infralimbic cortex of the rat. J Comp Neurol 308, 249276.Google Scholar
79. Ghashghaei, HT & Barbas, H (2001) Neural interaction between the basal forebrain and functionally distinct prefrontal cortices in the rhesus monkey. Neuroscience 103, 593614.Google Scholar
80. Tettamanti, M, Rognoni, E, Cafiero, R et al. (2012) Distinct pathways of neural coupling for different basic emotions. Neuroimage 59, 18041817.Google Scholar
81. Westerhaus, MJ & Loewy, AD (2001) Central representation of the sympathetic nervous system in the cerebral cortex. Brain Res 903, 117127.Google Scholar
82. Volkow, ND & Wise, RA (2005) How can drug addiction help us understand obesity? Nat Neurosci 8, 555560.Google Scholar
83. Volkow, ND, Wang, GJ, Fowler, JS et al. (2008) Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology. Philos Trans R Soc Lond B Biol Sci 363, 31913200.Google Scholar
84. Pelchat, ML (2002) Of human bondage: food craving, obsession, compulsion, and addiction. Physiol Behav 76, 347352.Google Scholar
85. Levine, AS, Kotz, CM & Gosnell, BA (2003) Sugars: hedonic aspects, neuroregulation, and energy balance. Am J Clin Nutr 78, 834S842S.Google Scholar
86. Kelley, AE & Berridge, KC (2002) The neuroscience of natural rewards: relevance to addictive drugs. J Neurosci 22, 33063311.Google Scholar
87. Grigson, PS (2002) Like drugs for chocolate: separate rewards modulated by common mechanisms? Physiol Behav 76, 389395.Google Scholar
88. Del Parigi, A, Chen, K, Salbe, AD et al. (2003) Are we addicted to food? Obes Res 11, 493495.Google Scholar
89. Corwin, RL & Grigson, PS (2009) Symposium overview – Food addiction: fact or fiction? J Nutr 139, 617619.Google Scholar
90. Rogers, PJ & Smit, HJ (2000) Food craving and food ‘addiction’: a critical review of the evidence from a biopsychosocial perspective. Pharmacol Biochem Behav 66, 314.Google Scholar
91. Davis, C & Carter, JC (2009) Compulsive overeating as an addiction disorder. A review of theory and evidence. Appetite 53, 18.Google Scholar
92. Epstein, DH & Shaham, Y (2010) Cheesecake-eating rats and the question of food addiction. Nat Neurosci 13, 529531.Google Scholar
93. Ahmed, SH, Kenny, PJ, Koob, GF et al. (2002) Neurobiological evidence for hedonic allostasis associated with escalating cocaine use. Nat Neurosci 5, 625626.Google Scholar
94. Markou, A & Koob, GF (1991) Postcocaine anhedonia. An animal model of cocaine withdrawal. Neuropsychopharmacology 4, 1726.Google Scholar
95. Russo, SJ, Dietz, DM, Dumitriu, D et al. (2010) The addicted synapse: mechanisms of synaptic and structural plasticity in nucleus accumbens. Trends Neurosci 33, 267276.Google Scholar
96. Hyman, SE, Malenka, RC & Nestler, EJ (2006) Neural mechanisms of addiction: the role of reward-related learning and memory. Annu Rev Neurosci 29, 565598.Google Scholar
97. Koob, GF & Le Moal, M (2005) Plasticity of reward neurocircuitry and the ‘dark side’ of drug addiction. Nat Neurosci 8, 14421444.Google Scholar
98. Koob, GF & Le Moal, M (2008) Addiction and the brain antireward system. Annu Rev Psychol 59, 2953.Google Scholar
99. Avena, NM, Rada, P & Hoebel, BG (2008) Evidence for sugar addiction: behavioral and neurochemical effects of intermittent, excessive sugar intake. Neurosci Biobehav Rev 32, 2039.Google Scholar
100. Bello, NT, Sweigart, KL, Lakoski, JM et al. (2003) Restricted feeding with scheduled sucrose access results in an upregulation of the rat dopamine transporter. Am J Physiol Regul Integr Comp Physiol 284, R1260R1268.Google Scholar
101. Bello, NT, Lucas, LR & Hajnal, A (2002) Repeated sucrose access influences dopamine D2 receptor density in the striatum. Neuroreport 13, 15751578.Google Scholar
102. Cottone, P, Sabino, V, Steardo, L et al. (2008) Intermittent access to preferred food reduces the reinforcing efficacy of chow in rats. Am J Physiol Regul Integr Comp Physiol 295, R1066R1076.Google Scholar
103. Johnson, PM & Kenny, PJ (2010) Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat Neurosci 13, 635641.Google Scholar
104. Dalley, JW, Fryer, TD, Brichard, L et al. (2007) Nucleus accumbens D2/3 receptors predict trait impulsivity and cocaine reinforcement. Science 315, 12671270.Google Scholar
105. Wang, GJ, Volkow, ND, Thanos, PK et al. (2004) Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. J Addict Dis 23, 3953.Google Scholar
106. Boggiano, MM, Chandler, PC, Viana, JB et al. (2005) Combined dieting and stress evoke exaggerated responses to opioids in binge-eating rats. Behav Neurosci 119, 12071214.Google Scholar
107. Corwin, RL (2006) Bingeing rats: a model of intermittent excessive behavior? Appetite 46, 11–5.Google Scholar
108. Liang, NC, Hajnal, A & Norgren, R (2006) Sham feeding corn oil increases accumbens dopamine in the rat. Am J Physiol Regul Integr Comp Physiol 291: R1236R1239.Google Scholar
109. De Souza, CT, Araujo, EP, Bordin, S et al. (2005) Consumption of a fat-rich diet activates a proinflammatory response and induces insulin resistance in the hypothalamus. Endocrinology 146, 41924199.Google Scholar
110. Milanski, M, Degasperi, G, Coope, A et al. (2009) Saturated fatty acids produce an inflammatory response predominantly through the activation of TLR4 signaling in hypothalamus: implications for the pathogenesis of obesity. J Neurosci 29, 359370.Google Scholar
111. Milanski, M, Arruda, AP, Coope, A et al. (2012) Inhibition of hypothalamic inflammation reverses diet-induced insulin resistance in the liver. Diabetes 61, 14551462.Google Scholar
112. Arruda, AP, Milanski, M, Coope, A et al. (2011) Low-grade hypothalamic inflammation leads to defective thermogenesis, insulin resistance, and impaired insulin secretion. Endocrinology 152, 13141326.Google Scholar
113. Calegari, VC, Torsoni, AS, Vanzela, EC et al. (2011) Inflammation of the hypothalamus leads to defective pancreatic islet function. J Biol Chem 286, 1287012880.Google Scholar
114. Clegg, DJ, Gotoh, K, Kemp, C et al. (2011) Consumption of a high-fat diet induces central insulin resistance independent of adiposity. Physiol Behav 103, 1016.Google Scholar
115. Benoit, SC, Kemp, CJ, Elias, CF et al. (2009) Palmitic acid mediates hypothalamic insulin resistance by altering PKC-theta subcellular localization in rodents. J Clin Invest 119, 25772589.Google Scholar
116. Ryan, KK, Woods, SC & Seeley, RJ (2012) Central nervous system mechanisms linking the consumption of palatable high-fat diets to the defense of greater adiposity. Cell Metab 15, 137149.Google Scholar
117. Thaler, JP, Yi, CX, Schur, EA et al. (2012) Obesity is associated with hypothalamic injury in rodents and humans. J Clin Invest 122, 153162.Google Scholar
118. Zhang, X, Zhang, G, Zhang, H et al. (2008) Hypothalamic IKKbeta/NF-kappaB and ER stress link overnutrition to energy imbalance and obesity. Cell 135, 6173.Google Scholar
119. Posey, KA, Clegg, DJ, Printz, RL et al. (2009) Hypothalamic proinflammatory lipid accumulation, inflammation, and insulin resistance in rats fed a high-fat diet. Am J Physiol Endocrinol Metab 296, E1003E1012.Google Scholar
120. Rother, E, Kuschewski, R, Alcazar, MA et al. (2012) Hypothalamic JNK1 and IKKbeta activation and impaired early postnatal glucose metabolism after maternal perinatal high-fat feeding. Endocrinology 153, 770781.Google Scholar
121. Cintra, DE, Ropelle, ER, Moraes, JC et al. . (2012) Unsaturated fatty acids revert diet-induced hypothalamic inflammation in obesity. PLoS ONE 7, e30571.Google Scholar
122. Gupta, S, Knight, AG, Keller, JN et al. (2012) Saturated long-chain fatty acids activate inflammatory signaling in astrocytes. J Neurochem 120, 1060–71.Google Scholar
123. de La Serre, CB, Ellis, CL, Lee, J et al. (2010) Propensity to high-fat diet-induced obesity in rats is associated with changes in the gut microbiota and gut inflammation. Am J Physiol Gastrointest Liver Physiol 299, G440G448.Google Scholar
124. Mohammed, N, Tang, L, Jahangiri, A et al. (2012) Elevated IgG levels against specific bacterial antigens in obese patients with diabetes and in mice with diet-induced obesity and glucose intolerance. Metabolism. Epublication ahead of print.Google Scholar
125. Lam, YY, Ha, CW, Campbell, CR et al. . (2012) Increased gut permeability and microbiota change associate with mesenteric fat inflammation and metabolic dysfunction in diet-induced obese mice. PLoS ONE 7, e34233.Google Scholar
126. Henao-Mejia, J, Elinav, E, Jin, C et al. (2012) Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482, 179185.Google Scholar
127. Elinav, E, Strowig, T, Kau, AL et al. (2011) NLRP6 inflammasome regulates colonic microbial ecology and risk for colitis. Cell 145, 745757.Google Scholar
128. Harris, K, Kassis, A, Major, G et al. (2012) Is the gut microbiota a new factor contributing to obesity and its metabolic disorders? J Obes 2012, 879151.Google Scholar
129. Vijay-Kumar, M & Gewirtz, AT (2012) Is predisposition to NAFLD and obesity communicable? Cell Metab 15, 419420.Google Scholar
130. Paulino, G, Barbier de la, Serre C, Knotts, TA et al. (2009) Increased expression of receptors for orexigenic factors in nodose ganglion of diet-induced obese rats. Am J Physiol Endocrinol Metab 296, E898E903.Google Scholar
131. de Lartigue, G, Barbier de la, Serre C, Espero, E et al. (2011) Diet-induced obesity leads to the development of leptin resistance in vagal afferent neurons. Am J Physiol Endocrinol Metab 301, E187E195.Google Scholar
132. Donovan, MJ, Paulino, G & Raybould, HE (2009) Activation of hindbrain neurons in response to gastrointestinal lipid is attenuated by high fat, high energy diets in mice prone to diet-induced obesity. Brain Res 1248, 136140.Google Scholar
133. Nefti, W, Chaumontet, C, Fromentin, G et al. (2009) A high-fat diet attenuates the central response to within-meal satiation signals and modifies the receptor expression of vagal afferents in mice. Am J Physiol Regul Integr Comp Physiol 296, R1681R1686.Google Scholar
134. Kentish, S, Li, H, Philp, LK, O'Donnell, TA et al. (2012) Diet-induced adaptation of vagal afferent function. J Physiol 590, 209221.Google Scholar
135. Daly, DM, Park, SJ, Valinsky, WC et al. (2011) Impaired intestinal afferent nerve satiety signalling and vagal afferent excitability in diet induced obesity in the mouse. J Physiol 589, 28572870.Google Scholar
136. Garland, T Jr, Schutz, H, Chappell, MA et al. (2011) The biological control of voluntary exercise, spontaneous physical activity and daily energy expenditure in relation to obesity: human and rodent perspectives. J Exp Biol 214, 206229.Google Scholar
137. Bostrom, P, Wu, J, Jedrychowski, MP et al. (2012) A PGC1-alpha-dependent myokine that drives brown-fat-like development of white fat and thermogenesis. Nature 481, 463468.Google Scholar
Figure 0

Fig. 1. (colour online) Major neural systems and pathways involved in the control of ingestive behaviour and energy balance regulation with emphasis on interactions between the classical homoeostatic energy regulatory system in the hypothalamus and brainstem (blue boxes and arrows in lower half) and cognitive/emotional brain systems (red boxes and arrows in upper half). Bottom-up modulation of cognitive and emotional processes by metabolic signals and their derivatives is accomplished by (a) circulating hormones and metabolites acting not only on the hypothalamus and brainstem but also on external sensory processing pathways as well as on components of the corticolimbic system (open blue arrows with broken lines), (b) a stream of vagal and spinal sensory information from within the body to all levels of the neuraxis, including the cortex (full blue arrows with solid lines) and (c) neural signals generated by the integrative hypothalamic energy sensor and distributed to areas involved in reward-based decision making (full blue arrows with solid lines). Together, these ascending modulatory influences determine the level of incentive salience directed to specific nutrients. Top-down modulation of food intake and energy expenditure by cognitive and emotional/reward systems is accomplished by (a) direct external (taste and smell) sensory input to the hypothalamic energy sensor and response allocator (dark yellow lines), (b) input from the amygdala, cortex and reward processing systems to mainly the lateral hypothalamus, responsible for conditioned external signals to elicit food intake (full red lines and arrows), (c) inputs from cortex, amygdala and basal ganglia to midbrain extrapyramidal motor pathways (emotional motor system, broken red lines and full arrows) and (d) pyramidal motor system for voluntary behavioural control (broken red lines on the right). N. Accumbens, nucleus accumbens; SMA, supplemental motor area; BLA, basolateral amygdala; CeA, central nucleus of the amygdala; VTA, ventral tegmental area; PAG, periaqueductal gray; GLP-1, glucgon-like-peptide-1; PYY, peptide YY; AT, adipose tissue; SPA, spontaneous physical activity. Adapted from(12).