By Sérgio Fontinhas | Reading Time: 41 minutes |
There are many risk factors for weight gain in adults and children, one being sleep duration. Sleep duration of less than 7 hours per night is associated with higher body weight than sleeping approximately 7 to 8 hours per night.
Averaged, age-adjusted weight of the Nurses’ Health Study cohort from 1986 to 2002 as a function of usual sleep duration in 1986 (Graphic Source: Patel et al., 2006)
For example findings from a population study involving 1030 persons indicated that restricted duration of sleep was associated with reduced leptin levels, increased ghrelin levels, and elevated body mass index.
Potential mechanisms that increase the risk of obesity through sleep deprivation. (Graphic Source: Patel & Hu, 2018)
Systematic reviews with meta-analysis of prospective cohort studies have also indicated that both short sleep (<6h/day) and long sleep (>9h/day) is associated with higher mortality, diabetes mellitus, cardiovascular disease, coronary heart disease, obesity and hypertension.
Relative mortality risks and health effects compared to “normal sleepers”. NOS = Newcastle-Ottawa Scale. No meta-analyses were performed for the results of dyslipidemia and depression. (Graphic Source: Itani et al., 2017)
Energy balance, the relationship between energy intake and expenditure, is tightly regulated by the hormonal system. Hormones such as insulin, leptin and ghrelin are among the main signals involved in this system, communicating with brain regions that control energy intake and expenditure. For example, leptin signals satiety and ghrelin signals hunger. These hormones can be altered by sleep duration.
Negative feedback model for the regulation of body fat percentage: Leptin and insulin are signals that circulate in relation to body fat and stimulate and inhibit catabolic (e.g. POMC/CART) and anabolic (e.g. NPY/AgRP) effectors in the hypothalamus. These pathways have opposite effects on both energy intake and energy consumption – and thus on the amount of fuel stored in the body as fat. Weight loss through calorie reduction lowers insulin and leptin levels, which in turn activates and inhibits anabolic and catabolic effectors, thereby promoting the recovery of lost weight. (Graphic Source: Schwartz et al., 2003)
It has been observed that shortened sleep reduces leptin levels and increases ghrelin levels, resulting in increased hunger and appetite.
Therefore sleep deprivation can lead to a hormonal state that increases the susceptibility for overeating and increased body weight overtime, consistent with observational studies.
Sleep Deprivation, Circadian Disruption & Dietary Patterns
Brain regions regulating energy balance
Hypothalamus & Hypothalamic arcuate nucleus
A region of the brain called the hypothalamus is deeply involved in several homeostatic processes, including energy balance and circadian rhythms (the suprachiasmatic nucleus). Hunger and satiety signals from the bloodstream, mainly ghrelin, leptin and insulin, seem to be processed in by the brain  in the hypothalamic arcuate nucleus.
Studies have shown that leptin supplementation alters brain responses to food cues in genetically leptin-deficient adults and reverse leptin weight loss-induced changes in regional neural activity in brain areas known to be involved in the regulatory, emotional, and cognitive control of food intake.
This last study provided data consistent with a model of the weight-reduced state as one of relative leptin deficiency in several areas of the brain. Both leptin and insulin administration into the brain causes a dose-dependent reduction of both food intake and body weight.
Role of the nucleus arcuatus in obesity signalling. a.) Activity of leptin/insulin sensitive obesity signalling pathways in the hypothalamus under conditions of leptin/insulin deficiency. b.) Activity of the leptin/insulin sensitive obesity signalling pathways in the hypothalamus under conditions of leptin/insulin deficiency. Increased activity of lepttin/insulin in the nucleus arcuatus inhibits the anabolic NPY/AGRP signaling pathway and stimulates the catabolic POMC signaling pathway, resulting in decreased food intake and anorexia (appetite inhibition). (Graphic Source: Schwartz et al., 2000)
Thalamus & Hippocampus
The hypothalamus, via hypothalamic cells, project to other areas of the brain, including the cortex, thalamus, brainstem, limbic structures, and forebrain.
The hypothalamus is also connected to the hippocampus, which also contains insulin and leptin receptors. The hippocampus is involved in food-related behaviors as a processor of energy state signals and a mediator of reward valuation, therefore the hippocampus plays an important role in motivation and appetite. The thalamus may also integrate visual signals of food, which leads to an increase in appetite and in the motivation to eat.
The use of fMRI allows us to assess neuronal activity in response to food stimuli and examine neuroendocrine processes regulating food intake, while also examine the reward-driven, hedonic components of food intake.
Nucleus accumbens & Caudade nucleus
The nucleus accumbens is also another important region; a new study showed that fasting enhanced responses to food cues in the nucleus accumbens, while food cravings are also related to nucleus accumbens and caudade nucleus activity.
There is a relationship between leptin and neural activity in the nucleus accumbens. In genetically leptin-deficient adolescents neural activity in that area is very high after elicited by visual food stimuli but returns to normal after leptin administration.
Insula & Orbitofrontal cortex
The insula and orbitofrontal cortex (OFC) are also activated in response to food stimuli. The dorsolateral prefrontal cortex is an area of the brain heavily implicated in the dopaminergic system, and it can be activated in response to unhealthy foods  and by high calorie foods.
It has been established that the hypothalamic neurocircuitry is crucial for body energy homeostasis. In particular, the mediobasal hypothalamus is the main hub for sensing availability of nutrients via AMP-kinase. AMP-kinase regulates food intake by responding to hormonal and nutrient signals in the hypothalamus  to regulate energy balance through effects in the hypothalamus and peripheral tissues.
Proposed model for the role of AMPK in anorexigenic signalling in the hypothalamus: Anorexigenic signals activate POMC neurons in the ARH (hypothalamic nucleus) via STAT3 and possibly also PI3 kinase, thereby producing a second anorexogenic signal mediated by the a-melanocyte stimulating hormone (a-MSH). In contrast, the anorexigenic signals suppress the activity of NPY/AGRP neurons, partly via STAT3 and possibly also PI3 kinase, and reduce AMPK activity in these neurons. The reduced AMPK activity enhances the suppression of NPY/AGRP effects, which leads to the activation of MC4 receptor signalling in PVH (paraventricular hypothalamus) neurons. Activation of the MC4 receptor decreases AMPK activity in PVH, which probably further increases neurotransmission, which is necessary for the regulation of food intake and energy balance. A decreased NPY signaling pathway in PVH functionally improves the MC4 receptor signaling pathway. In addition, decreased AMPK activity in other hypothalamic regions may enhance the MC4 receptor signaling pathway by projections into ARH or PVH (e.g., NPY neurons in DMH) and recruit additional signaling pathways that may regulate food intake. (Graphic Source: Minokoshi et al., 2004)
With this mechanism the hypothalamic neurocircuitry is capable of integrating adaptive responses to deviations from adiposity levels that are appropriate for a set of given internal and external conditions.
In other words, this neurocircuitry helps maintain body fat levels within a narrow set point (like a thermostat), or as more recent evidence suggests it can also act as a flexible regulator to changing environmental factors (floating set point).
Ventral striatum & Ventral tegmental area
Another hormone that communicates with the brain the lower gut hormone Peptide YY. Peptide YY can suppress food intake  and modulate activity of the ventral tegmental area (VTA) and ventral striatum.
The ventral striatum have been strongly implicated in governing the motivation to eat. Activity in the ventral striatum in response to foods accurately predicts immediate food intake, binge eating  as well as real world weight gain.
Other subcortical brain regions
Several subcortical regions have established roles in food stimulus evaluation and appetite.
For example, the anterior insula cortex, lateral orbital frontal cortex and anterior cingulate cortex are related to appetitive choices and odor and flavor evaluation. There is activation in the insula and medial frontal cortex in response to food odors after food deprivation  and insular activation in response to thoughts of high-fat and high-sugar foods. Brain areas activated by food odors are similar to those elicited by cues of addictive substances, such as alcohol.
The midorbitofrontal and anterior cingulate cortex, have a crucial role in evaluating different food representations such as oral fat texture and the pleasantness of flavor. The combination of high fat and a pleasant, sweet flavor results in a supralinear response in the pregenual cingulate cortex. The lateral hypothalamus and amygdala is also more strongly activated by high- versus low-fat stimuli.
Schematic representation of the most important factors that determine food intake and energy balance in restrictive and modern environments The availability of nutrients (internal milieu) is detected by a variety of distributed sensors and controls food intake directly via classical hypothalamic brain stem pathways and indirectly by modulating food reward processes in corticolimbic structures (blue arrows). Low nutrient availability, as signalled by low leptin levels, for example, causes a very strong sensitisation of the cognitive and hedonic mechanisms that enable food acquisition and intake and generate high reward and satisfaction. This system is designed to ensure adequate nutritional supply in restrictive environments requiring high levels of physical activity. The modern environment and modern lifestyle are characterized by high food availability, abundant food references and high palatability of food (red arrows), all of which promote food intake either directly or through the same corticolimbic systems, which are easily sensitized by signals of nutrient depletion. In addition, the built environment, sedentary lifestyles and low procurement costs lead to reduced physical activity and, in return, increased nutrient availability (green arrows). Obesity develops in susceptible individuals who either efficiently convert excessive hedonic, cognitive and/or emotional pressures exerted by the modern environment and lifestyle into increased eating, or in individuals in whom energy saturation signals are prominent, who suppress hedonic eating, or both. (Graphic Source: Zheng et al., 2009)
It is also known that foods high in fats and sugars activate the brain’s dopaminergic system through the mesoaccumbens-dopaminergic pathway. Interestingly, in one study participants with greater activation in the medial orbital frontal cortex, left amygdala, left insula, and nucleus accumbens chose buffet foods with higher fat content.
In another meta-analysis, the most concurrent brain regions activated in response to viewing food pictures were the bilateral posterior fusiform gyrus, the left lateral orbitofrontal cortex (OFC) and the left middle insula. Hunger modulated the response to food pictures in the right amygdala and left lateral OFC, and energy content modulated the response in the hypothalamus/ventral striatum.
The increased availability of large amounts of energy-dense foods and increased presence of powerful food cues affect cortico-limbic brain areas concerned with learning and memory, reward, mood and emotion. Low leptin levels trigger strong signals to induce adaptive biological actions such as increased energy intake and reduced energy expenditure.
Multiple peripheral and brain systems are activated to restore energy balance. Neurons in the arcuate nucleus, located in the hypothalamus, are affected by low leptin levels and promote energy intake, while other brain systems involved in finding potential food sources, smelling and tasting food, and learning to maximize rewarding effects of foods are also activated. This leads to a ‘hungry’ brain, preoccupied with food.
8 areas of the human brain that are activated in response to tasty food or food-related signals: It is believed that the orbitofrontal cortex and amygdala encode information related to the reward value of food. The insula processes information related to the taste of food and its hedonic rating. The nucleus accumbens and the dorsal striatum, which receive dopaminergic input from the ventral tegmental region and the substantia nigra, regulate the motivational and stimulatory properties of food. The lateral hypothalamus can regulate rewarding responses to tasty food and control the behaviour during foraging. These brain structures act in a concerted manner to regulate learning about the hedonic properties of food, to direct attention and effort towards obtaining food rewards, and to regulate the incentive value of environmental stimuli that predict the availability of food rewards. For the sake of clarity, not all links between these structures are shown. (Graphic Source: Horstmann & Villringer, 2013)
Overview of the remote patterns of brain activity observed in each of the comparisons relating to the effects of weight maintenance, weight loss and leptin replacement. (Graphic Source: Rosenbaum et al., 2008)
The orbitofrontal cortex and amygdala are thought to encode information related to the reward value of food. The insula processes information related to the taste of food and its hedonic valuation. The nucleus accumbens and dorsal striatum, which receive dopaminergic input from the ventral tegmental area and substantia nigra, regulate the motivational and incentive properties of food. The lateral hypothalamus may regulate rewarding responses to palatable food and drive food-seeking behaviors. These brain structures act in a concerted manner to regulate learning about the hedonic properties of food, shifting attention and effort toward obtaining food rewards and regulating the incentive value of environmental stimuli that predict availability of food rewards.
Disruption of the sleep-wake cycle, brain responses and food intake
Total sleep deprivation
Partial or total sleep deprivation can alter hormone levels and also neuronal activation in response to dietary intake. Circulating leptin levels rapidly decrease or increase in response to acute caloric shortage or surplus, respectively.
24-hour leptin profiles of individuals who were underfed, then overfed (A) or overfed and then underfed (B) in each of the four treatment periods: 1 = baseline (ο); 2 = underfed (▲); 3 = eucaloric (◊); 4 = overfed (■) Leptin levels decreased after subcaloric feeding, increased after supercaloric feeding and did not change significantly after eukaloric feeding compared to the previous treatment period. The data represent the mean value of the three persons who underwent the individual protocols of the treatment sequence. (Graphic Source: Chin-Chance et al., 2000)
One review proposed that leptin and ghrelin “represent the ‘yin–yang’ of one regulatory system that has developed to inform the brain about the current energy balance state”. 
The neural response to rewarding food cues is sensitive to the disruption of the sleep-wake cycle. Sleep deprivation has been found to stimulate appetite and actual food intake, which might imply that humans lacking sleep are more sensitive to rewarding food stimuli.
Relationship between the change in the rate of hunger and the change in the ghrelin/leptin ratio during the period from 12:00 to 21:00 when sleep is restricted (4 hours) compared to longer sleep (10 hours). (Graphic Source: Spiegel et al., 2004)
For example, in a small study (n=12) total sleep deprivation, compared to a full night of sleep in the same subjects after 2 weeks (crossover design), was associated with an increased activation in the right anterior cingulate cortex in response to food images, independent of calorie content and prescan hunger ratings. The activation in the anterior cingulate cortex evoked by foods correlated positively with postscan subjective appetite ratings. Even though there was no change in fasting plasma glucose concentration, subjects felt hungrier during the night.
Researchers concluded that “acute sleep loss enhances hedonic stimulus processing in the brain underlying the drive to consume food, independent of plasma glucose levels”. They also suggested that this activation could reflect a behavior to compensate for the significantly increased energy expenditure (∼7%) induced by total sleep deprivation, and may represent a mechanism subserving the brain’s energy restoration after sleep loss.
Total sleep deprivation (TSD) increases brain activation in the right anterior cingulate cortex in response to food images. A.) In the sleeping state, nightly sleep was allowed from 23:00 h (lights off) to 06:00 h (lights on); in the TSD state, subjects remained awake throughout the test. In the morning, the change in brain activation, measured by the BOLD signal by fMRI, was recorded in response to alternating blocks of either HC or LC food images. Brain activation was measured after ingestion of a liquid test meal (cup symbol). Secondary measures were appetite assessment, pre-scan hunger (before and after the liquid test meal) and morning fasting plasma glucose concentration (syringe symbol). B = BOLD signal in response to food images. Shown are rendered fMRI images of contrast TSD Vs. Sleep for images of food. The color bar shows t values. The peak value of the voxel is given as MNI coordinate. C = Relative change between conditions in the anterior cingulate cortex BOLD signal plotted against the relative change in the frequency of yes responses to the question “Do you find the food appetizing? For this correlation analysis, the region of interest (i.e. the right anterior cingulate cortex) was specified from an anatomical region as defined in the AALatlas. Pearson’s r=0.67; P0.02; n=12. (Graphic Source: Benedict et al., 2012)
In 2013 Greer et al. tested the effects of a single night of total sleep deprivation compared with a night of normal rested sleep (8.2 h) in the same subjects (n=23). During each fMRI session participants rated 80 different food items that varied in calorie. After the scan they received one food item based on their ratings (1 to 4). Results indicated that there was no difference between sleep conditions for the desire for low calorie items, however after the night of total sleep deprivation the total calorie content of all wanted items (summed together) was significantly greater by 600 kcal compared with a night of normal sleep.
Self-reported hunger rate: Neither at arrival at the experimental evenings nor before the morning fMRI scan session were there significant differences between rested and deprived in self-reported hunger (measured by a visual analogue scale with a 10 cm line, the y-axis is in mm). Hunger levels were significantly higher in both groups before the scan compared to the study arrival (the evening before) (P=0.05; paired t-tests in 23 participants). Error bars are displayed as S.E.M. (Graphic Source: Greer et al., 2013)
Behavioral consequences of sleep deprivation on food cravings. a.) Behavioral responses for the percentage of desired high or low caloric items, and b.) The degree to which individual differences in sleepiness (after sleep deprivation) predict the desired high caloric choice (b. ) High-calorie or low-calorie items are based on the median distribution of calories per portion; the items sought were collapsed by the ratings “somewhat” and “strongly” desired (*P=0.05; t-test over 23 participants). Error bars are displayed as S.E.M. (Graphic Source: Greer et al., 2013)
Neuronal consequences of sleep deprivation on food cravings. a.) Sleep deprivation led to a significant decrease in anterior cingulate, left lateral orbital frontal cortex and anterior insular reactivity to food cravings. b.) In addition, sleep deprivation led to a significant increase in amygdala reactivity to food cravings but no significant difference in ventral striatum reactivity. All parameter estimates were obtained from a GLM with a parametric contrast of the individual “desire” ratings of 23 participants. Whole-brain analyses (see above) are evaluated with a threshold value of P=0.005 for presentation purposes. ROI analysis (represented by the bar charts) are mean parameter estimates with S.E.M. extracted from 5 mm spheres centered on focal points from the previous literature (see Methods; circles indicate general areas of interest, not specific focal points; *P=0.05 for paired t-tests over 23 participants and **P=0.05 with Bonferroni correction for five ROIs). (Graphic Source: Greer et al., 2013)
Results from fMRI showed that total sleep deprivation decreased activity in all three cortical areas of interest involved in appetitive evaluation regions within the human frontal cortex (the anterior cingulate cortex, lateral orbital frontal cortex and anterior insula cortex) combined with an increase in activity within one of the two subcortical areas of interest, namely the amygdala, in response to food desirability. These changes in neural reactivity were accompanied by a significant shift in preferences for food items carrying the highest caloric content, proportional to the subjective severity of sleep loss across participants.
Finally, the increased desire for food was not accompanied by increased ratings of hunger suggesting that the increased desire for food, was driven by the condition of sleep loss itself and not by a biological need. The authors hypothesized a progressive deterioration in the brain and body systems that regulate and maintain optimal energy balance, and the presence of a central nervous system dysfunction that together may result in improper valuation of food stimulus features, shifting behavioral choice-selection to high calorie desirable items.
A possible central nervous system disruption due to sleep loss for the evaluation of food was also suggested by another study published in the same year.
In another study in the morning after total sleep privation subjects had increased plasma ghrelin levels (+13%), and chose larger food portions (+14%), irrespective of the type of food, as compared to the sleep condition. Self-reported hunger was also enhanced. Additionally, following breakfast, sleep-deprived subjects chose larger portions of snacks (+16%). Results suggested that overeating in the morning after sleep loss is driven by both homeostatic and hedonic factors.
Finally, while sweet taste perception was not altered after acute sleep deprivation in healthy young men, feelings of hunger were significantly more intense under both fasted and sated conditions when subjects were sleep-deprived compared with the sleep condition. Plasma concentrations of ghrelin were significantly higher under conditions of total sleep deprivation, whereas plasma glucose did not differ between the conditions. No effects were found either on sweet taste intensity or on pleasantness after total sleep deprivation.
They concluded that an altered sweet taste perception is an unlikely mechanism by which total sleep deprivation enhances food intake.
Partial sleep deprivation
A previous study from the same group of researchers, using an inpatient crossover design for 5 days, showed that sleep restriction (4h/night) increased energy intake (+300kcal), mainly fat intake and notably saturated fat, compared to normal sleepers (9 h/ night), while others have also shown in 2004 that short sleep increased the desire for calorie-dense foods with high carbohydrate content, such as sweets, salty snacks, and starchy foods. In contrast, appetite for fruits, vegetables, and high-protein nutrients was less affected.
Energy and nutrient supply of normal-weight men and women during a period of short sleep (4h/night) or habitual sleep (9h/night). (Graphic Source: St-Onge, 2011)
In that study, 12 young, healthy, normal-weight men exhibited reductions in the satiety hormone leptin (-18%), increases in the hunger hormone ghrelin (+28%), and increases in hunger (+24%) and appetite (+23%), after 2 nights of only 4 hours of sleep compared with after 2 nights of 10 hours of sleep. The implications were that inadequate sleep seems to influence the hormones that regulate satiety and hunger in a way that could promote excess eating. The decrease in leptin levels as a result of only 2 nights of 4h of sleep were comparable to the 22% decrease in leptin levels after 3 days of underfeeding by approximately 900 calories per day in healthy lean volunteers.
Average appetite ratings after a 2-day sleep restriction or extended sleep phase. (Graphic Source: Spiegel et al., 2004)
In a subsequent study from these researchers, increased activation in areas involved in reward was observed in response to food stimuli in the sleep deprived condition. In this study normal weight men were submitted to either 4 h/night (restricted sleep) or 9 h/night (habitual sleep) in bed for 6 days. Functional magnetic resonance imaging performed in the fasted state on day 6 showed greater overall neuronal activity in response to food stimuli after restricted sleep than after habitual sleep, while a relative increase in brain activity in areas associated with reward, including the putamen, nucleus accumbens, thalamus, insula, and prefrontal cortex in response to food stimuli.
Short- term partial sleep deprivation (4 days) also led to increased energy intakes (+20%) and body weight (+0.4 kg) in a study with female subjects only, while no change in energy expenditure was detected to offset the increased energy intake. Specifically, sleep was progressively decreased over time: 2 nights of >8 h sleep/night were followed by 4 nights of consecutively increasing sleep curtailment (7 h sleep/night, 6 h sleep/night, 6 h sleep/night and 4 h sleep/night). Female subjects also had 2 nights of sleep recovery (>8 h sleep/night) after that.
Nutritional status parameters of energy intake, energy consumption, physical activity and endocrine profile at baseline (T0, 9h/night), during follow-up in maximum sleep deprivation (T1, 5.5h/night) and after sleep recovery (T2, 9.35h/night). (Graphic Source: Bosy-Westphal et al., 2008)
Relationships between a.) the increase in energy intake and body weight from T0 to T1 induced by sleep restriction and b.) the difference between energy intake and body weight during sleep recovery (T1-T2).● = normal weight women; ■ = overweight women,▲= obese women. (Graphic Source: Bosy-Westphal et al., 2008)
No changes in glucose tolerance and ghrelin levels were detected after increasing sleep deprivation.
Another study with men only also found greater energy intake after just one night of sleep deprivation (4h).
Total energy intake and macronutrient composition (% energy) of food consumed before the 8 or 4 hour sleep session (day 1) and during the following day (day 2). (Graphic Source: Brondel et al., 2010)
Time course of the development of the feeling of drowsiness (A), the motivation for physical activity (PA; B) and the feeling of hunger (C) after the 8 or 4 hour sleep session (day 2). These are mean values ± SD, derived from a 10 cm visual analogue scale. The shaded bars (C) represent the meal times. In the 12 subjects ANOVA with 2-factor repeat measurements showed a significant difference for the feeling of sleepiness (P,0.001) and no difference for the motivation for physical activity between the two sessions, without significant interaction for either factor. Under laboratory conditions (i.e. 0800-1400), the pre-prandial sensation of hunger between the session with sleep restriction and the session with normal restriction was similar, but a significant interaction (F2.22= 5.98, P<0.01) was observed between sessions and time. Tukey’s post-hoc test showed a greater pre-prandial hunger before breakfast under sleep restriction conditions (*** P<0.001). During the “free-living” condition (i.e. 1400-2030), pre-prandial hunger before dinner was also greater in the sleep restriction group than in the normal restriction group (*P<0.05, pair test).Total energy intake and macronutrient composition (% energy) of food consumed before the 8 or 4 hour sleep session (day 1) and during the following day (day 2). (Graphic Source: Brondel et al., 2010)
Interestingly and contrary to researcher´s expectations, one study also in men only found no difference in total energy intake, feelings of hunger, appetite, ghrelin and leptin concentrations between 2 nights of short sleep and normal habitual sleep under free living conditions, even though physical activity registered by accelerometry was decreased during daytime.
(A) Quantification of the physical activity on day 1 and day 2 according to the evaluation of the accelerometers. (B) Low, moderate and high physical activity on day 1 (n= 12). (C) Self-reported activity level on day 2 (D) Feeling of fatigue on day 2 (n= 15). During the 2 x 2 nights of sleep manipulation performed in a crossover design, healthy men slept 8 hours (open bars/open circles) or only 4 hours (black bars/black circles). Day 1 = free living condition, day 2 = laboratory condition. These are average values (± SEM). tP<0.10, * = P<0.05, ** = P<0.01. (Graphic Source: Schmid et al., 2009)
Subjective evaluation of (A) appetite and concentration of serum levels of leptin (B) and ghrelin (C) after 2 nights of 8 hours sleep/night (white circles) and 2 nights of 4 hours sleep/night (black circles). (Graphic Source: Schmid et al., 2009)
This contradictory result could possibly be explained by unaltered activity in the ventral striatum, as discussed in a previous study by Greer et al. (2013).
In 2009 researchers had already observed that partial sleep deprivation by 3h over several days lead to increased stimulation of the anterior cingulate cortex. In this study by Nedeltcheva et al. (2009) eleven volunteers were submitted to two 14-d stays in a sleep laboratory with ad libitum access to palatable food in the form of meals and snacks and 5.5-h or 8.5-h bedtimes.
Energy balance, body weight and changes in body composition after a 14-day period with 8.5 hours of sleep per night and 5.5 hours of sleep per night in 11 healthy study participants. (Graphic Source. Nedeltcheva et al., 2008)
Calorie intake, calorie consumption and energy balance (kcal/day, left) and change in body weight (in kg, right) after a 14-day period with 8.5 hours of sleep (white bars/circles) per night and 5.5 hours of sleep per night (black bars/circles) in 11 healthy study participants. (Graphic Source. Nedeltcheva et al., 2008)
Results indicated that although meal intake remained similar, sleep restriction was accompanied by increased consumption of calories from snacks with higher carbohydrate content, particularly during the period from 19:00 to 07:00. Such changes were not associated with a significant increase in energy expenditure between conditions (5.5h vs 8.5h), and no differences in serum leptin and ghrelin between the 2 sleep conditions were observed.
The researchers concluded that partial sleep deprivation over several days (14 days) can alter the amount, composition and distribution of food intake, and that the preference for snacks but not meals could lead to excessive energy consumption and promote weight gain.
Another crossover study tested the hypothesis that 4 nights of sleep restriction (4.5h vs 8.5h) is associated with activation of the endocannabinoid (eCB) system, a key component of hedonic pathways involved in modulating appetite and food intake. For each sleep condition, caloric intake was rigorously controlled. When sleep deprived, participants reported increases in hunger and appetite concomitant with the afternoon elevation of 2-AG concentrations, and were less able to inhibit intake of palatable snacks.
Total calorie intake (A), as well as macronutrient intake (C, E, G) under normal conditions (8.5 hours sleep per night, black bars / NS) as well as under sleep deprivation (4.5 hours sleep per night, red bars / RS). (Graphic Source: Hanlon et al., 2016)
Sleep restriction resulted in a trend for an increase in total caloric intake during the snack period, with consumption of nearly twice as much fat and protein. When sleep restricted, despite ingesting approximately more 50% kcal as snacks, the participants did not reduce significantly their caloric intake during the 19:30 meal. In contrast, under normal sleep conditions, the trend for lower snack intake was compensated with a significant increase in caloric intake at dinner. Thus, the temporal pattern of caloric intake was modified by sleep loss. The study concluded that activation of the eCB system may be involved in excessive food intake in a state of sleep debt and contribute to the increased risk of obesity associated with insufficient sleep.
A recent study showed that sleep deprivation resulted in increased energy intake and body weight during the 5 days of sleep restriction (5h). Sleep restriction was followed by ad libitum weekend recovery sleep, and again followed by 2 more nights of insufficient sleep. The circadian phase was delayed when they resumed their sleep deprivation schedule, and after-dinner energy intake and body weight increased versus baseline.
Total energy intake (A) and post-dinner snack intake (B) in the control group (CON), the sleep restriction group (SR) and the weekend recreational flu group (WR) The solid lines represent significant results to the value at the end of the line (p<0.05). (Graphic Source: Depner et al., 2019)
Finally, 3 weeks of chronic sleep restriction (6.5h) during adolescence appears to cause increased consumption of foods with a high glycemic index, particularly desserts/sweets.
Daily energy intake and macronutrient distribution under normal conditions (10 hours of sleep per night aka “healthy sleep”) and sleep restriction (6.5 hours of sleep per night aka “sleep restriction”) in adolescents. (Graphic Source: Beebe et al., 2013)
Daily number of portions of different food categories under normal conditions (10 hours of sleep per night aka “healthy sleep”) as well as with sleep restriction (6.5 hours of sleep per night aka “sleep restriction”) in adolescents. (Graphic Source: Beebe et al., 2013)
Controlling appetite & increasing satiety
In a pilot study under free-living conditions, sleep extension lead to a reduction in free sugar intake and may be a viable strategy to facilitate limiting excessive consumption of free sugars in an obesity-promoting environment (72). There was also trend for a reduction in reported fat intake in the plausible reporters, which became significant when analyzed as a percentage of total energy. Sleep extension may thus lead to a tendency to select foods with lower fat and higher protein content.
Sleep extension may also be useful during dietary restriction, at least in obese adolescents. This compared the effect of dietary restriction with or without prescription of sleep extension on weight loss in 52 adolescents with obesity. Sleep extension improved weight loss and waist girth reduction with diminution of insulin and interleukin 6 levels.
Sleep extension, in sleep deprived overweight young adults reporting average habitual sleep duration of less than 6.5 h, was associated with a 14% decrease in overall appetite and a 62% decrease in desire for sweet and salty foods. Desire for fruits, vegetables and protein-rich nutrients was not affected by added sleep.
Effect of extended bedtime (2 weeks from 6.5 hours to 8.5 hours) on the evaluation of vitality and appetite. (Graphic Source: Tasali et al., 2014)
A 2013 review indicated that a good sleep hygiene, together with circadian alignment of food intake, a regular meal frequency, and attention for protein intake or diets, contributes in curing sleep abnormalities and overweight/obesity features by preventing overeating.
Another review suggested that prolonging sleep, in short sleepers, may have the potential to lead to improved body weight and food choices, and may also improve cardiometabolic risk.
Exercise and physical activity
A review of studies suggests that exercise can help control appetite and satiety, although effects vary from person to person (according to individual physiological characteristics) and with the intensity and duration of exercise. The same review also presents data from a previous study indicating that increasing physical activity improves satiety signaling.
A modified version of the original graph by Mayer et al. (1954) on workers in Bengali jute factories, showing the relationship between energy expenditure (corresponding to the physical demands of the work) and food intake. It is postulated that appetite control is homeostatically regulated when energy expenditure is high, but is dysregulated in the sedentary “unregulated” zone where homeostatic control of appetite is weak, allowing excessive consumption (Graphic Source: Blundell et al., 2015)
A systematic review concluded that habitually active individuals showed improved compensation for the energy density of foods, but no consistent differences in appetite or absolute energy intake, in comparison with inactive individuals.
Standardised energy intake after physical activity, based on ten cross-sectional studies that provided information on energy intake (n = 25 data points). The trend analysis confirmed a significant linear (P<0.05) and quadratic (P<0.01) relationship between gradual physical activity and energy intake. The thick, black lines imply the mean value of the Z-scores. SEM standard error of the mean value. (Graphic Source: Beaulieu et al., 2016)
Previous research using a psychobiological systems approach took into account realistic and fully supervised levels of physical activity, medium-term (not acute) interventions, measurement of body composition, energy metabolism (indirect calorimetry), satiety physiology (gut peptides), homeostatic and hedonic processes of appetite control, non-exercise activity, in obese adult participants and both genders. Results indicated that the impact of physical activity on appetite control is characterised by large individual differences.
Individual changes in body weight (BW) and fat mass (FM) after 12 weeks supervised aerobic exercise in a cohort of fifty-eight overweight and obese individuals (data from King et al., 2009). (Graphic Source: Caudwell et al., 2011)
They also concluded that changes in body composition, waist circumference and health benefits are more meaningful than changes in weight.
A review concluded that impulsive eating drive may be counteracted by physical activity due to its enhancement of neurocognitive resources for executive functions and goal‐oriented behaviour. By enhancing the resources that facilitate ‘top‐down’ inhibitory control, increased physical activity may help compensate and suppress the hedonic drive to over‐eat.
A diagrammatic representation of the hypothesis that connects physical activity with eating behaviour via the executive function. In the proposed model, physical activity can improve executive function by generating, strengthening and refining neural circuits in the prefrontal cortex. These executive functions are a prerequisite for inhibitory control, the ability to suppress impulsive behaviour and responses to external stimuli. Due to the abundance of negative food stimuli in the environment that trigger unhealthy eating habits, the exercise of healthy eating habits requires a high degree of inhibitory control. Inhibitory control acts as a brake and thus enables the execution of goal-directed behaviour and self-control. Thus, physical activity indirectly changes eating behaviour by strengthening the executive function. Over time, physical activity and healthy eating habits lead to behavioural adaptations that are linked by their goal-oriented character. (Graphic Source: Joseph et al., 2011)
Finally increases in cardiovascular fitness results in greater task-related activity in regions of the prefrontal and parietal cortices that are involved in spatial selection and inhibitory functioning.
Another strategy to improve satiety and control hunger is dietary fiber. A review from 2001 already concluded that under conditions of fixed energy intake, the majority of studies indicated that an increase in either soluble or insoluble fiber intake increases postmeal satiety and decreases subsequent hunger.
Effects of dietary fibre in the gastrointestinal tract on parameters related to energy regulation (Graphic Source: Howarth et al., 2009)
The consumption of an additional 14 g/day fiber for >2 days was also associated with a 10% decrease in energy intake and body weight loss of 1.9 kg over 3.8 months when energy intake was ad libitum. Obese individuals may exhibit a greater suppression of energy intake and body weight loss. Results were also comparable to reductions in dietary fat from 38% to 24% of energy intake in controlled studies of nonobese and obese subjects. Results were positive for both naturally high-fiber foods and fiber supplements.
Percentage decrease in energy intake in the course of diets with higher fibre content, compared to a control diet with lower fibre content. (Graphic Source: Howarth et al., 2009)
In a review of 107 studies, 39% of treatments significantly reduced subjective appetite rating compared with the control, and 22% reduced food or energy intake. The satiety-enhancing effects of β-glucan, lupin kernel fiber, rye bran, whole grain rye, or a mixed high-fiber diet were supported in more than one publication (83). However most fibers did not reduce appetite or energy intake in most acute study designs (78%).
Another review of ten satiety trials provided evidence that whole oats, barley, and rye can increase satiety, whereas the evidence for whole wheat and maize was not compelling.
When the effect of β-glucan, the viscous soluble fiber in oats, on satiety was analyzed, the majority of the evidence suggested that oat β-glucan has a positive effect on perceptions of satiety.
There is convincing evidence that a higher protein intake increases thermogenesis and satiety compared to diets of lower protein content. The weight of evidence also suggests that high protein meals lead to a reduced subsequent energy intake.
Comparison of the VAS values of the feeling of satiety (or fullness) of the postprandial effects of 7 different proteins – casein, soy, whey, whey GMP, alpha-lactalbumin, gelatin, gelatin plus tryptophan – about 3 hours after an iso-energetic, iso-volumetric, semi-solid breakfast, with identical appearance, taste, aroma, energy density and viscosity, at 20% of the individual energy requirement of the subject and with a single protein dosage of 25% of the energy. N = 24, according to Veldhorstet al., 2009 (Graphic Source: Westerterp-Plantenga et al., 2012)
Dietary protein also contributes to the treatment of obesity and the metabolic syndrome, by acting on the relevant metabolic targets of satiety and energy expenditure in negative energy balance, thereby preventing a weight cycling effect.
Dairy protein consists of 80% casein and 20% whey. Casein and whey differ in absorption and digestion rates, with casein being a “slow” protein and whey being a “fast” protein, and also differ in amino acid composition (88).
Macronutrient and amino acid composition of whey and casein at a glance. (Graphic Source: Hall et al., 2003)
Another review concluded that whey is more satiating in the short term, whereas casein is more satiating in the long term.
Effects of a 1,700 kJ (406 kcal) pre-load (48g casein or whey) on energy intake (including the proportions of total energy intake as protein, fat and carbohydrates) at an ad libitum buffet taken 90 minutes later. Grey = carbohydrate intake; white = fat intake; black = protein intake. These are average values for 16 test subjects (+ standard errors), indicated by vertical bars. The energy intake after a pre-load with whey compared to the pre-load with casein showed a significantly reduced energy intake (P<0.05). (Graphic Source: Hall et al., 2003)
Energy intake from a buffet meal ad libitum can also be significantly less 90 min after a liquid preload containing 48 g whey, compared with an equivalent casein preload. A 12 week treatment in subjects with well-controlled type 2 diabetes showed that a low-dose whey/guar preload, taken twice daily before meals, had sustained effects of slowing gastric emptying and reducing postprandial blood glucose, which were associated with a modest reduction in HbA1c, without causing weight gain.
Whey protein (55g) before a carbohydrate meal can stimulate insulin and incretin hormone secretion and slow gastric emptying, leading to marked reduction in postprandial glycemia in type 2 diabetes.
Gastric emptying (A), concentration of blood sugar (B), plasma insulin (C), plasma GLP-1 (D), plasma GIP (E) and plasma CCK(F) in response to a mashed potato meal in 8 type 2 diabetics. On each study day, subjects consumed 350 ml of beef-flavoured soup 30 minutes before a radiolabeled mashed potato meal; 55 g of whey protein was added to either the soup (Whey preload) or the mashed potatoes (Whey in meal) or no whey was administered (No Whey). These are mean values +/- SE. * = P0.05, Whey preload vs. Whey in meal; # = P0.05, Whey in meal vs. no whey; § = P0.05, Whey preload vs. no whey. (Graphic Source: Stevens et al., 2009)
A systematic review of studies of individuals dieting for weight loss or maintenance suggested a weight-reducing effect of increased water consumption, especially before meals, whereas studies in general mixed-weight populations not primarily dieting for weight loss or maintenance, yielded inconsistent results. The evidence for the association was still low, mostly because of the lack of good-quality studies.
Consumption of a 568 ml water preload immediately (<1 min before eating) before a meal increased fullness and satisfaction and decreased hunger, and also reduced energy intake in non-obese young males.
Energy intake (kJ) at an ad libitum breakfast for each participant immediately after consumption of 568 ml of water (pre-load) or without water (control). (Graphic Source: Corney et al., 2015)
When compared to high-fat snacks, eating less energy dense, high-protein snacks like yogurt (14 g protein/25 g CHO/0 g fat) improves appetite control, satiety, and reduces subsequent food intake in healthy women.
Perceived hunger (A) and feeling of satiety (B), evaluated from the time of snack consumption (▲) to the voluntary desire for dinner in 20 healthy women. The bar charts show the net increase area under the curve (AUC) after snacking. These are mean values ± SEM; the different letters indicate the significance (p<0.05). (Graphic Source: Ortinau et al, 2014)
Ad libitum dinner after consumption of each afternoon snack in 20 healthy women; these are mean values ± SEM; the different letters indicate significance (p<0.05), except for *yoghurt vs. chocolate (p=0.08), (Graphic Source: Ortinau et al, 2014)
In another study an afternoon snack of Greek yogurt, containing 24 g protein, led to reduced hunger, increased fullness, and delayed subsequent eating compared to lower protein snacks in healthy women.
In a comparison between yogurt, cheese and milk snacks, yogurt had the greatest effect on suppressing subjective appetite ratings, but did not affect subsequent food intake compared with milk or cheese.
A) Average energy intake at an ad libitum midday meal after each of the four snacks (milk, cheese, yoghurt, water, n=40) and (B) average energy intake at midday including the energy content of the snack (milk snack or water). These are mean values whose standard errors are represented by vertical bars. a,b = mean values that differ significantly from the letters (P<0.05). Graphic S ource: Dougkas et al., 2012)
In another experiment, two yogurts (semisolid and liquid) led to lower hunger and higher fullness ratings as compared with the fruit drink or dairy fruit drink. There was no difference in satiety profiles between the yogurt that was eaten with a spoon and the drinkable version.
Average energy intake (breakfast, snack and lunch), average water consumption (lunch) and average weight of food consumed (lunch) for each of the four beverage conditions of a comparative study of the satiety power of semi-solid/liquid yoghurt, fruit drinks and milk-fruit drinks. (Graphic Source: Tsuchiya et al., 2006)
Chewing & chewing gum
A systematic review with meta-analysis presented preliminary evidence that chewing may decrease self-reported hunger and food intake, possibly through alterations in gut hormone responses related to satiety.
Some of the included studies showed that increasing the number of chews per bite increased relevant gut hormones and subjective satiety. Another study showed that by chewing slowly healthy women can reduce calorie intake.
Among healthy men in a fasting state, chewing sugarless gum can increase satiety with no effect on blood glucose and can decrease the decline of GLP-1 concentration. After chewing for 5, 15, and 30 min, the chewing group’s satiety was significantly higher than that of the control group.
Evaluation of satiety (A.) and hunger (B.) in the chewing gum group (Chewing) and the chewing gum free group (Control). Saturation was significantly higher in the chewing gum group after 5, 15 and 30 minutes than in the chewing gum free group. * p<0.05. No significantly different effect was observed in the feeling of hunger. (Graphic Source: Xu et al., 2015)
Short-term studies have shown that gum can reduce appetite and food intake  and that chewing gum for at least 45 min significantly suppressed self-reported hunger, appetite, and snack cravings and promoted satiety.
Change in self-reported hunger ratings over time for conditions with and without chewing gum (salty and sweet snacks combined). (Graphic Source: Hetherington & Regan, 2011)
In another short term study, gum chewing suppressed hunger, desire to eat and prospective consumption. Overall carbohydrate intake was reduced by GUM, as well as snacks characterized as high carbohydrate, low fat. They concluded that chewing gum intermittently post-lunch enhances perceptions of satiety and may have important implications in reducing afternoon high carbohydrate-snack intake.
Effect of chewing gum on the snack intake of participants, 3 hours after lunch. (Graphic Source: Park et al., 2016)
It must be noted that chewing gum may not decrease food intake in all people. Results may depend on different methods of chewing gum. For example, no effects have been observed when chewing was set at a fixed time (2 h after a meal) or chewing when hungry.
In addition, chewing sweet gum can increase hunger, depending also on the sex of the subject and the time after chewing.
- Short sleep duration is associated with increased body weight.
- Energy balance is tightly regulated by the hormonal system.
- Hormones such as leptin and ghrelin can be altered by sleep deprivation, in a manner that reduces satiety and increases hunger.
- The hypothalamus, especially the arcuate nucleus, is the control center for energy homeostasis. Other brain structures act to process the hedonic value of food.
- Total and partial sleep deprivation reduce satiety, increase hungers, enhance the activation of brain regions associated with caloric consumption, and shift food preferences from healthy to unhealthy foods.
- Sleep deprivation increases body weigh in experimental studies.
- Strategies to control appetite and increase satiety include sleep extension, exercise and high physical activity, higher fiber intake, higher protein intake, increased water consumption, protein and water pre-loads, yogurt, and chewing gum.
Title Image Source: depositphotos / kapustin_igor
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