PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Clin Nutr. Author manuscript; available in PMC Jan 1, 2010.
Published in final edited form as:
PMCID: PMC2615460
NIHMSID: NIHMS70094
Sleep curtailment is accompanied by increased intake of calories from snacks
Arlet V. Nedeltcheva, Jennifer M. Kilkus, Jacqueline Imperial, Kristen Kasza, Dale A. Schoeller, and Plamen D. Penev
Department of Medicine, MC 1027 (AVN., PDP.), General Clinical Research Center, MC 7100 (JMK., JI), and Department of Health Studies, MC 2007 (KK), The University of Chicago, 5841 S. Maryland Avenue, Chicago, IL 60637, USA; and Nutritional Sciences (DAS), University of Wisconsin, 1415 Linden Avenue, Madison, WI 53706, USA
Address correspondence and reprint requests to: Plamen Penev, M.D., Ph.D. Section of Adult and Pediatric Endocrinology, Diabetes, and Metabolism, Department of Medicine, The University of Chicago, 5841 S. Maryland Ave. MC-1027, Chicago, IL 60637, Tel: (773) 702-5125; Fax: (773) 702-9194, e-mail: ppenev/at/medicine.bsd.uchicago.edu
Background
Short sleep is associated with obesity and may alter the endocrine regulation of hunger and appetite.
Objective
To test the hypothesis that the curtailment of human sleep could promote excessive energy intake.
Design
Eleven healthy volunteers (5F/6M; mean ± SD age 39 ± 5y; BMI 26.5 ± 1.5 kg/m2) completed in random order two 14-day stays in the sleep laboratory with ad lib access to palatable food and 5.5- or 8.5-hour bedtimes. The primary endpoints were the calories from meals and snacks consumed during each bedtime condition. Additional measures included total energy expenditure and 24-hour profiles of serum leptin and ghrelin.
Results
Sleep was reduced by 122 ± 25 min/night during the 5.5-hour bedtime condition. While meal intake remained similar (P=0.51), sleep restriction was accompanied by increased consumption of calories from snacks (1087 ± 541 vs. 866 ± 365 kcal/day; P=0.026) with higher carbohydrate content (65 vs. 61%; P=0.04), particularly during the period from 19:00 to 7:00. These changes were not associated with a significant increase in energy expenditure during the 5.5-h (2526 ± 537 kcal/d) vs. 8.5-h bedtime period (2390 ± 369 kcal/d, P=0.58) and there were no significant differences in serum leptin and ghrelin between the two sleep conditions.
Conclusions
The results indicate that recurrent bedtime restriction can modify the amount, composition, and distribution of human food intake, and support the notion that sleeping short hours in an obesity-promoting environment may facilitate the excessive consumption of energy from snacks, but not meals.
Genetic factors are thought to underlie individual susceptibility to obesity, whereas non-genetic factors determine the phenotypic expression of the disease (1, 2). The rising rates of obesity in the US have been associated with considerable environmental, social and behavioral changes that promote overeating and inactivity (3). Driven by the demands and opportunities of modern life, a growing number of Americans report reduced sleep times (4). The potential importance of this trend for public health is highlighted by epidemiological data, which indicate that self-reported sleep of less than 6 hours per night is associated with increased adiposity (5, 6). Some, but not all, of the available prospective studies in adults have also linked short sleep duration with greater risk of weight gain and obesity (6, 7). An important question raised by these reports is whether reduced sleep duration contributes directly to the mechanisms of unhealthy weight gain, or reflects the presence of other relevant but unrecognized risk factors and pathways of reverse causation (5, 6).
Existing cross-sectional data suggest that the association of short sleep with obesity may be accompanied by changes in the peripheral levels of the orexigenic hormone, ghrelin, and the anorexigenic hormone, leptin (8, 9). In addition, lower leptin and higher ghrelin levels, and increased hunger and appetite have been reported following short-term sleep restriction of healthy men (10). If chronically activated by the lack of sufficient sleep, such hormone changes have been hypothesized to promote overeating and raise the risk of obesity. While an increase in food intake has been documented in rodents subjected to partial sleep deprivation, these experimental interventions are also accompanied by markedly enhanced energy expenditure and weight loss (1113). To date, no studies have measured the impact of recurrent sleep curtailment on the components of human energy intake and expenditure, and the validity of the increased energy intake hypothesis remains uncertain.
In this experiment, we examined the effects of recurrent bedtime restriction by 3 hours per night on food intake, energy expenditure, and the 24-hour levels of leptin and ghrelin in healthy middle-aged adults. Since habitual sleep curtailment is an increasingly common aspect of the “Western lifestyle” characterized by physical inactivity and overeating, these studies were carried out under controlled laboratory conditions of sedentary living with ad lib access to palatable food. We hypothesized that recurrent bedtime curtailment will be accompanied by an increased intake of energy from meals and snacks.
Participants
Sedentary men and women ages 34 to 49 with a body mass index between 24 and 29 kg/m2 and self-reported sleep duration of 6.5 to 8.5 hours/day were recruited through local newspaper advertisements and by word of mouth. Volunteers were excluded from participation if they had: self-reported sleep problems (Pittsburgh Sleep Quality Index, PSQI, score >10), night work, variable sleep habits, or habitual daytime naps; physically demanding occupations or regular exercise; depressed mood (Center for Epidemiologic Studies of Depression, CES-D, score >15); excessive intake of alcohol (>14 drinks/week for men; >7 for women) or caffeine (>300 mg/day); smoking; use of prescription medications or over-the-counter drugs affecting sleep or metabolism; and abnormal findings on medical history, physical exam, and laboratory screening tests (including a 75g oral glucose challenge and one night of full polysomnography). Only non-pregnant women were studied and data collection was scheduled during the first half of their menstrual cycle. Eleven subjects (5F/6M) including 5 Caucasian, 4 African American, and 2 Hispanic individuals completed the study. All research volunteers gave written informed consent and were paid for their participation.
Study protocol
The study protocol was approved by the Institutional Review Boards of the Universities of Chicago and Wisconsin. Each subject completed two 14-day study periods with scheduled bedtimes of 5.5 or 8.5 hours per night in random order at least 3 months apart. To achieve bedtimes of 5.5 and 8.5 hours without shifts in circadian phase, the usual lights-off and wakeup times of the subjects were moved proportionally closer together or further apart. Subjects were studied in the controlled environment of the University of Chicago sleep research laboratory, which offers individual accommodations similar to those of a comfortable hotel room with a queen-size bed and has built-in infrastructure for video monitoring and sleep recording. Six subjects started with the 5.5-hour bedtime condition and 5 subjects were studied under 8.5-hour bedtimes first. Participants spent most waking hours indoors engaged in leisure activities or home-office-type work and had free access to a telephone, desktop computer, TV, videos, reading material, and the internet. On average, subjects spent no more than 30 min/day outside of the laboratory on the university campus. No naps were allowed and individual safety and compliance were monitored continuously by our research staff. Before and after each 14-day study, participants remained at bed rest for 48 hours with identical caloric intake including oral and intravenous doses of glucose at 9:00 and identical carbohydrate-rich meals at 14:00 and 19:00 as previously described (14). During the last 24 hours of this period, blood was sampled every 30 min starting at 20:00. Bedtimes were 7 hours/night before each study, and 5.5 or 8.5 hours/night afterwards according to the assigned sleep condition.
Energy intake
A Registered Dietitian interviewed all participants to determine their food preferences and exclude the presence of any eating disorders, and developed nutritionally balanced individual meal plans consisting of breakfast, lunch, dinner, and a selection of palatable snacks and soft drinks. During each bedtime condition, subjects received the same customized 3-day meal sequence including typical Western foods on a rotating basis. Breakfast was served at 8:00 to 9:00 and included such items as eggs, bacon or sausage; toast, bagel, pancakes or waffles; jelly, peanut butter, or cream cheese; cereal, fruit, yogurt, juice, milk, coffee and tea. Lunch was served at 13:00 to 14:00 and could be a hot or cold entrée (e.g. hot or cold sandwich, pizza, pasta, or meat, a vegetable and starch) along with soup or salad, soft drink, and dessert. Dinner was served at 18:30 to 19:30 and was usually a hot meat, poultry or fish entrée with a vegetable and starch, in addition to salad, non-caffeinated beverage, and dessert. Subjects were allowed one caffeinated beverage with breakfast and one with lunch as needed to match their usual consumption of caffeine at home. Meals were served in excess to allow ad lib intake of energy. Food was weighed before and after each meal to determine actual consumption. In addition, study participants had unlimited access to a snack bar in their room, which was kept stocked with soft drinks and the same individually customized assortment of 10 snacks during each study period. The snacks included salty snacks (e.g. pretzels, chips and dip, cheese and crackers, popcorn, nuts), sweets (e.g. snack bars, muffins, cookies, pudding, ice cream, candy), fresh and dried fruits, yogurt, raw vegetables and dip, and non-caffeinated beverages (e.g. milk, juice, soda, water). Items consumed from the snack bar were weighed and disappearance from the inventory was recorded twice every 24 hours to determine the intake of calories during the day (7:00–19:00) and at night (19:00-7:00). Total snack consumption of one of the subjects was measured only once daily at 7:00. The caloric content and macronutrient composition of all meals and snacks, consumed between 7:00 on day 1 and 7:00 on day 14 of each bedtime condition, was calculated using Nutritionist IV software (Axxya Systems, Stafford, TX).
Energy expenditure
For the measurement of total energy expenditure, study participants ingested 18O and 2H-labeled water (2.0 and 0.14g/kg total body water, respectively) on the first morning of each bedtime condition and their next three urine voids were collected. Two more urine voids were collected in the morning of day 14. Samples were analyzed by isotope ratio mass spectrometry at the University of Wisconsin in Madison as previously described (15, 16). Resting metabolic rate was measured under basal conditions by indirect calorimetry (Vmax Encore 29, Sensormedics, Yorba Linda, CA) after awakening on day 14 of each bedtime condition using standard methods (16). Subsequently, subjects consumed a customized standard breakfast and their resting metabolic rate was measured for the next 4 hours. The area under the 4-hour postprandial curve minus the resting metabolic rate was divided by the caloric content of breakfast and multiplied by 100 to obtain an estimate of the thermic effect of food (in % of energy intake). Activity energy expenditure was determined by subtracting the resting metabolic rate and the thermic effect of food from the total energy expenditure of the subjects. The physical activity level of the subjects was calculated as the ratio between their total energy expenditure and resting metabolic rate.
Measurement of body weight and composition
Body composition was measured by dual energy X-ray absorptiometry (DXA) before and after each bedtime condition on the same instrument (Prodigy, Lunar, Madison, WI). Technical problems resulted in loss of data from one subject at the end of the short sleep condition. Body weight was measured with a digital medical scale (Scale-Tronix Inc., Wheaton, IL) in the morning prior to each DXA scan. Height was measured at screening with a Harpenden stadiometer. Body fat was determined by multiplying body weight by the percent body fat from the DXA scan. The bone mineral content by DXA and body fat were subtracted from body weight to determine fat-free soft tissue mass.
Sleep recording
Sleep was recorded using a Neurofax-1100 EEG Acquisition System (Nihon-Kohden). Before enrollment, subjects had a night of full laboratory polysomnography for habituation and to exclude the presence of primary sleep pathology. Records were scored in 30-second epochs of wake, movement, stage 1, 2, 3, 4, and rapid eye movement sleep. Respiratory events, periodic leg movements, and arousals were defined according to established clinical criteria and subjects with a respiratory disturbance index >10 or any sleep movement disorder were excluded from participation. Only electroencephalographic, electrooculographic, and electromyographic channels were recorded on subsequent study nights. The data from each subject during both study conditions were scored by the same sleep technician. Total sleep time was calculated as the sum of all epochs scored as sleep. Sleep efficiency was calculated as the percent of scheduled time in bed that was scored as sleep.
Hormone assays
Serum leptin was measured by radioimmunoassay (Linco Research, St. Charles, MO). We also measured total ghrelin by radioimmunoassay (Linco Research) in 9 of the subjects.
Data analysis
T-tests accounting for the crossover design of the study (17) were used to compare the intake of energy from meals and snacks during each bedtime condition. To control for any difference in body weight at the beginning of each study (see Table 3), these comparisons were repeated using a generalized estimating equation (GEE) regression model (18) with baseline body weight and treatment period as time-varying covariates. Correlation analysis was used to explore the relationship between energy intake and body weight. Secondary measures, such as sleep parameters, macronutrient distribution, and metabolic hormone levels, were compared between the two study conditions using paired t-tests. Finally, a GEE regression model which included the consumption of calories from meals and snacks, physical activity related energy expenditure, bedtime condition, and treatment period (first vs. second study) as time-varying covariates was used to explore the factors that might explain the considerable variability in individual propensity for weight gain during these studies. All energy intake and expenditure variables in this model were expressed relative to the resting metabolic rate of the subjects. Analyses were performed using Stata (Version 10, StataCorp., College Station, TX) and SPSS (Version 11, SPSS Inc., Chicago, IL). Values in the text are reported as mean ± SD. Statistical significance was defined as P<0.05.
Table 3
Table 3
Energy balance, body weight and adiposity
Sleep duration
Subject characteristics at the time of enrollment are summarized in Table 1. The average amount of sleep during the 5.5-hour vs. the 8.5-hour bedtime condition was reduced by 122 ± 25 min/night (Table 2).
Table 1
Table 1
Participant characteristics
Table 2
Table 2
Average sleep duration and architecture during each study period
Energy consumption
There were no significant differences in the consumption (Table 3) and distribution of energy from meals between the two study periods: breakfast, lunch and dinner accounted for, respectively 31.7 ± 6.1, 32.8 ± 2.6, and 35.6 ± 5.4 % of meal calories during the 5.5-hour bedtime period, and for 30.5 ± 5.7, 33.6 ± 4.5, and 35.9 ± 5.4 % during the 8.5-hour bedtime condition. The macronutrient content of meals was also similar: carbohydrate, fat, and protein contributed respectively 52.4 ± 4.1, 33.6 ± 4.0, and 14.0 ± 2.0 % of meal calories during the 5.5-hour bedtime period, and 53.0 ± 5.8, 33.1 ± 4.3, and 13.9 ± 3.3 % during the 8.5-hour bedtime condition. In contrast, bedtime restriction was accompanied by increased consumption of energy from snacks (P=0.026, Table 3) and a shift towards more carbohydrate (64.5 ± 6.7 vs. 61.0 ± 6.3 % of energy, P=0.04) and relatively less fat (29.6 ± 5.6 vs. 32.2 ± 5.6 % of energy, P=0.08) and protein (5.9 ± 2.1 vs. 6.7 ± 2.8 % of energy, P=0.08) in the macronutrient content of ingested snacks. While the difference in the intake of calories from snacks between 7:00 and 19:00 was not statistically significant (P=0.18; Table 3), bedtime curtailment was accompanied by an increased consumption of snacks during the period from 19:00 to 7:00 (P<0.01).
Since body weight is an important determinant of energy needs, we examined the relationship between ingested calories and the initial body weight of the subjects during the 5.5 and 8.5-hour bedtime condition. Body weight correlated strongly with the intake of energy from meals, but was not related to the consumption of calories from snacks (Figure 1). Results remained unchanged when fat-free soft tissue mass was used instead of initial body weight (data not shown). Given these findings, the total caloric intake of the subjects (meals and snacks combined) during each sleep condition was compared in two different ways. Accounting only for the crossover design of the study, total energy intake was significantly higher during the period of bedtime curtailment (P=0.04). This difference was due primarily to the increased consumption of calories from snacks (P=0.04) and not meals (P=0.49). When study design and initial body weight were controlled for, the difference in total energy intake between the two bedtime conditions was no longer significant (P=0.46), while the difference in consumption of calories from snacks remained significant (P=0.026, Table 3).
Figure 1
Figure 1
Average daily energy intake from meals (top panels), snacks (middle panels), and meals plus snacks (bottom panels) as a function of the initial body weight of the 11 subjects during the 8.5-hour (left) and 5.5-hour bedtime condition (right). Each panel (more ...)
Energy expenditure and balance
There were no statistically significant differences in total energy expenditure and its components, including activity energy expenditure, thermic effect of food, and resting metabolic rate, between the two bedtime conditions (Table 3). The physical activity level of the subjects (i.e. total energy expenditure divided by the resting metabolic rate) averaged 1.54 ± 0.28 with 5.5-hour and 1.49 ± 0.16 with 8.5-hour bedtimes (P=0.58), while the ratios of their overall energy intake relative to their resting metabolic rate were 2.23 ± 0.32 and 2.08 ± 0.34, respectively (P=0.13). The surplus of energy intake in the group as a whole during both sleep conditions (Figure 2A) was the result of markedly different contributions from the individual study participants: some subjects repeatedly showed a strong propensity to overeat and gain weight irrespective of the presence or absence of sleep loss, while others exhibited little, if any, change in body weight (Figure 2B). In an exploratory analysis, both the novelty of exposure to the experimental environment and the excessive consumption of calories from meals were significant predictors of individual weight gain during the study (Table 4). There was also a trend for the intake of energy from snacks to contribute to individual weight gain, whereas the opposing effects of daily physical activity and extended wakefulness were not statistically significant (Table 4).
Figure 2
Figure 2
A: Mean (+SE) daily energy balance of 11 subjects during the 8.5-hour (open bars) and 5.5-hour (solid bars) bedtime condition. B: Individual variability in weight gain during each 14-day study period. Data points connected with a line reflect the change (more ...)
Table 4
Table 4
Predictors of individual weight gain
Metabolic hormones
Mean 24-hour leptin levels before the 5.5 and 8.5-hour bedtime condition were 13.3 ± 10.3 and 13.0 ± 11.8 ng/ml (P=0.76; Figure 2C). The results were similar when the respective baseline values were expressed relative to the initial adiposity of the participants: 0.487 ± 0.337 vs. 0.481 ± 0.373 ng/ml per kg of body fat (P=0.66). Twenty-four-hour leptin concentrations increased in a similar fashion by 2.6 ± 3.7 and 3.1 ± 4.5 ng/ml at the end of the 5.5- and 8.5-hour bedtime intervention (P=0.66), and the corresponding mean levels (15.8 ± 12.3 vs. 16.1 ± 15.4 ng/ml, P=0.83; Figure 2D) were similarly matched to the final adiposity of the subjects both in the presence and absence of sleep loss (0.568 ± 0.404 vs. 0.577 ± 0.512 ng/ml per kg of body fat, P=0.87). The 24-hour levels of total ghrelin did not change significantly during the 5.5 or 8.5-hour bedtime intervention (P=0.45) and remained comparable both before (1295 ± 339 vs. 1262 ± 409 pg/ml, P=0.65; Figure 2E) and after (1242 ± 457 vs. 1311 ± 572 pg/ml, P=0.53; Figure 2F) each respective study period.
This study examined whether the curtailment of human sleep in an environment that promotes overeating and inactivity will be accompanied by an increased intake of energy from meals and snacks. Using a protocol of recurrent bedtime restriction, we were able to change the sleep duration of the study participants from over 7 hours/day, which in epidemiological studies corresponds to the sleep category with lowest prevalence of excess adiposity, to fewer than 5.5 hours/day, which falls in a category associated with an increased risk of obesity (5, 6). As intended, the physical activity levels of the participants during both sleep conditions were well within the range of sedentary humans (19). Our results show that recurrent bedtime restriction in the setting of ad lib access to palatable food is accompanied by an increased consumption of excess calories from snacks without a statistically significant change in the intake of energy from meals.
The concomitant monitoring of energy expenditure and peripheral leptin and ghrelin levels allowed us to assess the role of several factors that have been hypothesized to link the lack of sufficient sleep to increased energy intake. Previous studies in rodents have shown that partial sleep deprivation is associated with hyperphagia accompanied by markedly increased energy expenditure and weight loss (1113). In contrast, the direct measurements of energy expenditure during the short sleep condition of our study indicate that the increased consumption of snacks by the subjects was not related to a comparable rise in their energy needs (Table 3). The differences in the resting metabolic rate and the thermic effect of food between the two sleep conditions of this experiment were small and did not reach statistical significance. The existing studies of total sleep deprivation in humans further indicate that compared to relaxed wakefulness, a night of restful sleep saves relatively little energy (20, 21). The possibility that daily exposure to 3 extra hours of wakefulness may be accompanied by increased out-of-bed physical activity (22) also did not have a large impact on the energy budget of our sedentary subjects (Table 3). Overall, the small changes in energy expenditure during the 5.5-hour bedtime condition were not sufficient to offset the observed rise in consumption of excess calories derived mostly from snacks (Table 3). Clearly, such changes in the energy balance of susceptible individuals could exacerbate their risk of weight gain and obesity (3), however, the apparent modest size of any such effect indicates that much larger and longer studies will be needed to define the impact of sleep restriction on these clinical endpoints.
Cross-sectional observations of lower leptin and higher ghrelin levels in people with short sleep duration (8, 9) have led to the hypothesis that such hormone changes are likely to promote overeating and lead to increased risk of obesity. However, a recent clinical trial provided conflicting results (23), raising the possibility that single measurements of leptin and ghrelin in epidemiological studies (8, 9) may be influenced by systemic differences in the diurnal profiles of food intake and peripheral metabolic hormones in short sleepers (24, 25). The finding of increased energy intake from snacks, but not meals, and the lack of significant changes in ghrelin (26) during the short sleep condition of our study (Figure 2F) are also at odds with the existing hypothesis. Likewise, in the setting of ad lib energy intake, we did not find differences in the 24-hour leptin levels between the two bedtime conditions: final leptin concentrations increased along with the accumulation of a positive energy balance and reflected the adiposity of the subjects equally well irrespective of the presence or absence of sleep loss (Figure 2D). Two laboratory studies, designed to provide energy intake near weight maintenance levels, indicate that total sleep deprivation does not lower 24-hour leptin levels (25, 27). In contrast, lower leptin, higher ghrelin, and increased hunger and appetite were found in lean men exposed to short-term sleep curtailment and mild caloric restriction in the form of intravenous glucose (10). When combined, these observations raise the possibility that acute sleep loss may amplify the human neuroendocrine response to caloric restriction and enhance the defense of affected individuals against disruptions in their food supply.
What then are the factors that underlie the increased intake of energy from snacks in our experiment? The results of the study support the concept that the control of human energy balance can be easily compromised by the propensity of many individuals to overeat in the setting of sedentary living with unlimited food availability (28, 29). Under these circumstances, the excessive consumption of calories from meals and snacks was augmented by the novelty of the experimental environment and emerged as an important predictor of individual weight gain (Table 4). These observations support the concept that “non-homeostatic” factors could play a considerable role in determining human feeding behavior (30). Since bedtime curtailment resulted in more extended exposure (by 3 hours/day) to palatable food, this factor may have contributed to the observed increase in energy consumption. Such possibility is supported by the larger 54% relative increase in snack intake during the nighttime period of the 5.5-hour bedtime condition, which included most of the extra waking hours, compared to the 18% relative increase during the day, when the exposure to food between the two sleep conditions was more similar (Table 3). While these changes may share some similarity with certain aspects of the night eating syndrome (31), our subjects exhibited a much more consolidated pattern of sleep during the 5.5-hour compared to the 8.5-hour bedtime intervention (Table 2) and did not wake up to eat during the periods of restricted sleep.
Finally, the increase in the carbohydrate content of ingested snacks during the 5.5- hour bedtime condition suggests that sleep loss, itself, may have an effect on human energy intake. Enhanced carbohydrate consumption and preference for sweets have already been reported in psychologically demanding circumstances (32). More recently, the discovery of a new group of orexin-containing neurons in the hypothalamus has led to the description of an integrated network of pathways that regulate mammalian arousal, waking, and feeding behavior (33). These neurons respond to sleep loss and metabolic signals such as glucose, and can modulate the control of reward and motivation (34). Some animal data already raise the possibility that changes in this system may contribute to the association between chronic metabolic disorders and insufficient sleep (35). Our findings indicate that alterations in the balance between sleep and wakefulness can modify the amount, composition, and distribution of human food intake, and suggest that sleeping short hours in modern societies may aggravate the problem of excessive energy consumption.
In summary, bedtime restriction in an environment that promotes overeating and inactivity was accompanied by increased intake of calories from snacks with higher carbohydrate content without a statistically significant change in the consumption of energy from meals. In the absence of comparable changes in energy expenditure and differences in serum leptin and ghrelin, alternative mechanisms, such as more prolonged exposure to palatable food and sleep-loss-related changes in reward seeking and motivation, may underlie these changes in feeding behavior. The presence of considerable individual differences in the propensity to consume excess calories from snacks also raises the possibility that chronic bedtime curtailment may have more deleterious metabolic consequences in people with such preexisting susceptibility. While this hypothesis is consistent with epidemiological reports showing an association of self-reported short sleep with more frequent snacking and increased risk of obesity (36, 37), our discussion is based on the detailed evaluation of a small number of subjects over a limited period of time in the laboratory. Additional studies will be needed to examine the impact of habitual sleep curtailment on human food intake and energy metabolism under free-living conditions.
Acknowledgments
Sources of support: This research was supported by a program project grant PO1- AG11412 from the National Institute on Aging, a General Clinical Research Center grant MO1-RR00055 from the National Institutes of Health, and a Diabetes Research and Training Center grant P60-DK020595 from the National Institute of Diabetes and Digestive and Kidney Diseases.
Author contributions: study concept and design - PDP, DAS; data acquisition, analysis or interpretation - AVN, JMK, JI, KK, DAS, PDP; drafting of the manuscript – PDP; critical revision of the text - AVN, JMK, JI, KK, DAS. The authors (AVN, JMK, JI, KK, DAS, PDP) have no conflict of interest related to this work. The study involved over 300 inpatient days in the laboratory of Dr. Eve Van Cauter at the University of Chicago. Dr. Van Cauter’s contribution to the conceptual design of the study and her comments on the results and an initial draft of this manuscript are gratefully acknowledged. We thank our volunteers for their participation and the staff of the University of Chicago General Clinical Research Center, Sleep Research Laboratory, Endocrinology clinic bone densitometry facility, and Diabetes Research and Training Center for their skilled technical assistance.
1. Friedman JM. Modern science versus the stigma of obesity. Nature Medicine. 2004;10:563–9. [PubMed]
2. Bouchard C. The biological predisposition to obesity: beyond the thrifty genotype scenario. Int J Obes. 2007;31:1337–9. [PubMed]
3. Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: where do we go from here? Science. 2003;299:853–5. [PubMed]
4. Basner M, Fomberstein KM, Razavi FM, et al. American time use survey: sleep time and its relationship to waking activities. Sleep. 2007;30:1085–95. [PubMed]
5. Cappuccio FP, Taggart FM, Kandala NB, et al. Meta-analysis of short sleep duration and obesity in children and adults. Sleep. 2008;31:619–626. [PubMed]
6. Patel SR, Hu FB. Short sleep duration and weight gain: a systematic review. Obesity. 2008;16:643–53. [PMC free article] [PubMed]
7. Stranges S, Cappuccio FP, Kandala NB, et al. Cross-sectional versus prospective associations of sleep duration with changes in relative weight and body fat distribution: the Whitehall II Study. Am J Epidemiol. 2008;167:321–9. [PMC free article] [PubMed]
8. Taheri S, Lin E, Austin D, Young T, Mignot E. Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Medicine. 2004;1:e62. [PMC free article] [PubMed]
9. Chaput JP, Despres JP, Bouchard C, Tremblay A. Short sleep duration is associated with reduced leptin levels and increased adiposity: Results from the Quebec family study. Obesity. 2007;15:253–61. [PubMed]
10. Spiegel K, Tasali E, Penev P, Van Cauter E. Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann Intern Med. 2004;141:846–50. [PubMed]
11. Rechtschaffen A, Bergmann BM, Everson CA, Kushida CA, Gilliland MA. Sleep deprivation in the rat: X. Integration and discussion of the findings 1989. Sleep. 2002;25:68–87. [PubMed]
12. Everson CA, Crowley WR. Reductions in circulating anabolic hormones induced by sustained sleep deprivation in rats. Am J Physiol. 2004;286:E1060–70. [PubMed]
13. Koban M, Swinson KL. Chronic REM-sleep deprivation of rats elevates metabolic rate and increases UCP1 gene expression in brown adipose tissue. Am J Physiol. 2005;289:E68–74. [PubMed]
14. Spiegel K, Leproult R, Van Cauter E. Impact of sleep debt on metabolic and endocrine function. Lancet. 1999;354:1435–9. [PubMed]
15. Schoeller DA, Ravussin E, Schutz Y, Acheson KJ, Baertschi P, Jequier E. Energy expenditure by doubly labeled water: validation in humans and proposed calculation. Am J Physiol. 1986;250:R823–30. [PubMed]
16. Schoeller DA, Hnilicka JM. Reliability of the doubly labeled water method for the measurement of total daily energy expenditure in free-living subjects. Journal of Nutrition. 1996;126:348S–354S. [PubMed]
17. Hills M, Armitage P. The two-period cross-over clinical trial. Br J Clin Pharmacol. 1979;8:7–20. [PubMed]
18. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–30. [PubMed]
19. Hayes M, Chustek M, Heshka S, Wang Z, Pietrobelli A, Heymsfield SB. Low physical activity levels of modern Homo sapiens among free-ranging mammals. International Journal of Obesity & Related Metabolic Disorders: Journal of the International Association for the Study of Obesity. 2005;29:151–6. [PubMed]
20. Fontvieille AM, Rising R, Spraul M, Larson DE, Ravussin E. Relationship between sleep stages and metabolic rate in humans. Am J Physiol. 1994;267:E732–7. [PubMed]
21. Zhang K, Sun M, Werner P, et al. Sleeping metabolic rate in relation to body mass index and body composition. Int J Obes Relat Metab Disord. 2002;26:376–83. [PubMed]
22. Zepelin H, Rechtschaffen A. Mammalian sleep, longevity, and energy metabolism. Brain, Behavior & Evolution. 1974;10:425–70. [PubMed]
23. Littman AJ, Vitiello MV, Foster-Schubert K, et al. Sleep, ghrelin, leptin and changes in body weight during a 1-year moderate-intensity physical activity intervention. Int J Obes. 2007;31:466–75. [PubMed]
24. Birketvedt GS, Florholmen J, Sundsfjord J, et al. Behavioral and neuroendocrine characteristics of the night-eating syndrome. JAMA. 1999;282:657–63. [PubMed]
25. Mullington JM, Chan JL, Van Dongen HP, et al. Sleep loss reduces diurnal rhythm amplitude of leptin in healthy men. J Neuroendocrinol. 2003;15:851–4. [PubMed]
26. Ravussin E, Tschop M, Morales S, Bouchard C, Heiman ML. Plasma ghrelin concentration and energy balance: overfeeding and negative energy balance studies in twins. J Clin Endocrinol Metab. 2001;86:4547–51. [PubMed]
27. Shea SA, Hilton MF, Orlova C, Ayers RT, Mantzoros CS. Independent circadian and sleep/wake regulation of adipokines and glucose in humans. J Clin Endocrinol Metab. 2005;90:2537–44. [PMC free article] [PubMed]
28. Schoeller DA. Balancing energy expenditure and body weight. Am J Clin Nutrition. 1998;68:956S–961S. [PubMed]
29. Hill JO, Wyatt HR. Role of physical activity in preventing and treating obesity. J Appl Physiol. 2005;99:765–70. [PubMed]
30. Saper CB, Chou TC, Elmquist JK. The need to feed: homeostatic and hedonic control of eating. Neuron. 2002;36:199–211. [PubMed]
31. O’Reardon JP, Peshek A, Allison KC. Night eating syndrome: diagnosis, epidemiology and management. CNS Drugs. 2005;19:997–1008. [PubMed]
32. Christensen L. The effect of carbohydrates on affect. Nutrition. 1997;13:503–14. [PubMed]
33. Sakurai T. The neural circuit of orexin (hypocretin): maintaining sleep and wakefulness. Nat Rev Neurosci. 2007;8:171–81. [PubMed]
34. Harris GC, Wimmer M, Aston-Jones G. A role for lateral hypothalamic orexin neurons in reward seeking. Nature. 2005;437:556–9. [PubMed]
35. Horvath TL, Gao XB. Input organization and plasticity of hypocretin neurons: possible clues to obesity’s association with insomnia. Cell Metabolism. 2005;1:279–86. [PubMed]
36. Imaki M, Hatanaka Y, Ogawa Y, Yoshida Y, Tanada S. An epidemiological study on relationship between the hours of sleep and life style factors in Japanese factory workers. J Physiol Anthropol & Appl Hum Sci. 2002;21:115–20. [PubMed]
37. Patel SR, Malhotra A, White DP, Gottlieb DJ, Hu FB. Association between reduced sleep and weight gain in women. Am J Epidemiol. 2006;164:947–54. [PMC free article] [PubMed]