Am J Physiol Regul Integr Comp Physiol 303: R883–R889, 2012.
First published September 12, 2012; doi:10.1152/ajpregu.00222.2012.
Integrative and Translational Physiology: Integrative Aspects
of Energy Homeostasis and Metabolic Diseases
CALL FOR PAPERS
Alterations in sleep architecture in response to experimental sleep curtailment
are associated with signs of positive energy balance
Ari Shechter,1 Majella O’Keeffe,1 Amy L. Roberts,1 Gary K. Zammit,2 Arindam RoyChoudhury,3
and Marie-Pierre St-Onge1
1
Submitted 14 May 2012; accepted in final form 5 September 2012
Shechter A, O’Keeffe M, Roberts AL, Zammit GK, RoyChoudhury
A, St-Onge MP. Alterations in sleep architecture in response to experimental
sleep curtailment are associated with signs of positive energy balance. Am J
Physiol Regul Integr Comp Physiol 303: R883–R889, 2012. First published
September 12, 2012; doi:10.1152/ajpregu.00222.2012.—Sleep reduction is
associated with increased energy intake and weight gain, though few
studies have explored the relationship between sleep architecture and
energy balance measures in the context of experimental sleep restriction. Fourteen males and 13 females (body mass index: 22–26 kg/m2)
participated in a crossover sleep curtailment study. Participants were
studied under two sleep conditions: short (4 h/night; 0100 – 0500 h)
and habitual (9 h/night; 2200 – 0700 h), for 5 nights each. Sleep was
polysomnographically recorded nightly. Outcome measures included
resting metabolic rate (RMR), feelings of appetite-satiety, and ad
libitum food intake. Short sleep resulted in reductions in stage 2 sleep
and rapid eye movement (REM) sleep duration (P ⬍ 0.001), as well
as decreased percentage of stage 2 sleep and REM sleep and increased
slow wave sleep (SWS) percentage (P ⬍ 0.05). Linear mixed model
analysis demonstrated a positive association between stage 2 sleep
duration and RMR (P ⫽ 0.051). Inverse associations were observed
between REM sleep duration and hunger (P ⫽ 0.031) and between
stage 2 sleep duration and appetite for sweet (P ⫽ 0.015) and salty
(P ⫽ 0.046) foods. Stage 2 sleep percentage was inversely related to
energy consumed (P ⫽ 0.024). Stage 2 sleep (P ⫽ 0.005), SWS (P ⫽
0.008), and REM sleep (P ⫽ 0.048) percentages were inversely
related to fat intake, and SWS (P ⫽ 0.040) and REM sleep (P ⫽
0.050) were inversely related to carbohydrate intake. This study
demonstrates that changes in sleep architecture are associated with
markers of positive energy balance and indicate a means by which
exposure to short sleep duration and/or an altered sleep architecture
profile may lead to excess weight gain over time.
sleep duration; sleep architecture; sleep deprivation; energy expenditure; appetite; food intake
THE PREVALENCE OF OBESITY has risen drastically over the last
few decades and has been mirrored by concomitant decreases
in sleep episode length. Indeed, results from epidemiological
studies indicate an association between short sleep duration
and weight gain (20). These cross-sectional studies have been
supported by laboratory-based research, which demonstrate
Address for reprint requests and other correspondence: M.-P. St-Onge,
1090 Amsterdam Ave., Suite 14D, New York, NY 10025 (e-mail:
[email protected]).
http://www.ajpregu.org
that experimental sleep length curtailment leads to decreased
physical activity (25), increased feelings of hunger (4, 27) and
food intake (4, 18, 29), and changes in appetite-regulating
hormones (24, 27).
While the aforementioned studies demonstrate an association between sleep duration per se and markers of energy
balance, none considered the role of specific sleep architecture
changes in response to experimental sleep restriction. This is
important since sleep is not a uniform process, and the amount
and presence of specific sleep stages throughout the night is not
constant. Sleep is regulated by an interaction between homeostatic and circadian mechanisms (3). Accordingly, in response
to the build-up of homeostatic sleep pressure during the waking
episode, the expression of slow wave sleep (SWS) is highest at
the start of the sleep episode, whereas rapid eye movement
(REM) sleep, under a circadian drive, shows maximal propensity during the early morning hours (5). Thus, under conditions
of experimental and/or real-life sleep restriction, the amount of
REM sleep may be disproportionately reduced compared with
SWS, which is expected to be conserved.
Various sources indicate a link between REM sleep and
energy balance-related parameters (8). An inverse relationship
was found between REM sleep and orexinergic neuronal activity (12), and REM sleep-deprived rats show increased and
decreased hypothalamic gene expression of neuropeptide Y
and pro-opiomelanocortin, respectively (14), which may account for the hyperphagia observed in rats under REM sleep
deprivation (15). Such an altered hormonal profile in response
to REM sleep loss may lead to overeating and weight gain in
humans as well. Compared with other sleep stages, only REM
sleep was found to be significantly and independently associated with overweight in a group of children and adolescents,
and a 1-h decrease in REM sleep was associated with an
approximately threefold increased odds of overweight (16).
Thus, when discussing the sleep-obesity link, sleep architecture
should be considered in addition to sleep duration.
The goal of the current study was to further elucidate the
mechanisms by which reductions in sleep duration lead to
obesity. We aimed to explore how specific alterations in sleep
architecture in response to experimental sleep restriction affect
energy balance-related parameters. We hypothesized that a
sleep architecture profile characterized by reductions in stage 2
sleep and REM sleep, which would likely occur in response to
0363-6119/12 Copyright © 2012 the American Physiological Society
R883
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New York Obesity Nutrition Research Center, St. Luke’s-Roosevelt Hospital, New York, New York; 2Clinilabs, and
Department of Psychiatry, Columbia University College of Physicians & Surgeons, New York, New York; 3Department
of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York
R884
SLEEP ARCHITECTURE AND ENERGY BALANCE
the experimental sleep restriction, would be associated with
indicators of positive energy balance, namely increased appetite and food intake, and lower energy expenditure, which can
lead to excess weight gain over time.
METHODS
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Participants. Thirty participants were enrolled in the study. One
man was dismissed after phase 1 for previously undiagnosed periodic
leg movement (PLM) disorders, one woman was dismissed before the
start of the study for previously undisclosed use of antidepressant
medication (an exclusion factor for the study), and one woman
withdrew for personal reasons after completing phase 1. Twentyseven (14 men, 13 women) healthy participants, age 30 – 45 yr [mean
(SD): 35.3 (5.2) yr] and body mass index (BMI) 22–26 kg/m2 [23.5
(1.1) kg/m2], completed both experimental phases. Before entry into
the study, a history of habitual sleep duration between 7 and 9 h/night
was confirmed with the use of waist-worn actigraphy (19) and sleep
diary for 2 wk. Inclusion criteria required mean sleep duration during
the 2-wk screening period of 7–9 h/night, with at least 10 nights of
sleep with a duration ⱖ7 h and less than 4 nights of sleep with a
duration ⬍6 h. Exclusion criteria for the study included smoking,
Type 2 diabetes, history of alcohol or substance abuse, excessive
caffeine intake, shift work, and transmeridian travel within the last 4
wk. Other exclusion criteria included the presence of any eating,
sleeping, or neurological disorder and use of antidepressant medications, among others. Each participant provided written informed
consent before being enrolled in the study.
Experimental design. Experimental procedures for the current
study have been previously described (29). This was a laboratorybased randomized, crossover study composed of two separate phases,
including a short and a habitual sleep duration condition. Each
experimental phase lasted 6 days, and each phase was separated by a
3-wk washout period to ensure recuperation from sleep deprivation
and that women were studied in both conditions during the same
menstrual cycle phase. Both inpatient phases took place at Clinilabs,
a research sleep laboratory (New York, NY). Experimental procedures during each phase were identical except for the duration of the
nocturnal sleep episodes. Specifically, during the short sleep phase,
participants had the opportunity to sleep from 0100 to 0500 h. During
the habitual sleep phase, participants had the opportunity to sleep from
2200 to 0700 h. While the in-lab sleep length opportunity for this
phase was in fact longer than the actual mean duration spent asleep
(⬃7.5 h; see RESULTS), we chose to refer to the 9-h sleep opportunity
as “habitual” as opposed to “long” because this length ensured that all
participants would achieve their habitual sleep length. It has also been
recommended that sleep restriction studies utilize sleep opportunities
of ⬎8 h time in bed for baseline sleep periods (32). Daytime naps
were not permitted, and study personnel ensured that all participants
remained awake during the entire duration of scheduled wake episodes. Participants were inpatients but were allowed to leave the
laboratory under study personnel supervision.
During the first 4 days of each experimental phase, participants
were fed a controlled diet based on their weight maintenance energy
requirements, estimated by the Harris-Benedict equation with an
activity factor of 1.3. Meals were served at 0800, 1200, and 1900 h,
with a snack at 1600 h. Each meal provided 30% of daily energy
requirements, and the snack provided the remaining 10%. Starting on
the morning of day 5 and continuing until discharge at 2000 h on day
6, participants underwent the ad libitum feeding portion of the study
in which they self-selected their food intake (see Measures below).
All experimental procedures were approved by the Institutional
Review Boards of St. Luke’s-Roosevelt Hospital Center and Columbia University (New York, NY). Participants were given the opportunity to ask questions about the study protocol prior to providing
informed consent.
Measures. Sleep duration and composition for each sleep episode
was assessed via polysomnographic (PSG) recording (Aurora Recording Systems, Gamma, version 4.9; Grass Technologies, West Warwick, RI). PSG recordings, including electroencephalogram, electrooculogram, chin electromyogram (EMG), and electrocardiogram,
were visually scored in 30-s epochs according to standard criteria
(22). The amount of time in each sleep stage was determined and
expressed in minutes and as a percentage of total sleep time (TST).
TST was the sum of stage 1 and stage 2 sleep, SWS (stage 3 and stage
4 combined), and REM sleep. The number of REM sleep periods
during the sleep episode was also determined. PLM disorders and
sleep-disordered breathing were ruled out during the first two nights’
recordings. For PLM, EMGs of the left and right anterior tibialis were
recorded. Respiratory events were monitored with nasal and oral
airflow via thermistor and oxygen saturation via pulse oximetry, and
participants were excluded if an apnea/hypopnea index ⬎5 events/h
was observed.
Resting metabolic rate (RMR) and respiratory quotient (RQ) were
measured in the fasted state on the morning of day 5 at the Body
Composition Unit of St. Luke’s-Roosevelt Hospital. Each participant
rested for ⱖ30 min before beginning the test. RMR was measured
using a ventilated-hood indirect calorimetry system (Delta-Trac II
Metabolic Monitor; SensorMedics, Yorba Linda, CA). On a same day
test-retest, the coefficient of variation for the system was 2.3%.
Participants were asked to remain supine while the ventilated hood
was placed over their head and respiration gases were collected over
the 30-min recording period. Oxygen consumed and carbon dioxide
produced were analyzed to calculate RMR and RQ according to the
equations of Jequier et al. (10). All participants reached a steady state
when measured.
On day 4, during the last day of the controlled feeding period and
before the start of the ad libitum feeding portion of the study,
participants filled out Likert scales assessing their feelings of appetite
and satiety. The participants rated their feelings, on a scale of 0 to 10,
to the following questions: 1) How hungry do you feel right now?,
2) How satisfied do you feel right now?, 3) How full do you feel right
now?, 4) How much do you think you could eat right now?, 5) How
much would you like to eat something sweet right now?, 6) How
much would you like to eat something salty right now?, 7) How much
would you like to eat something savory right now?, and 8) How much
would you like to eat fruits and vegetables right now? A rating of 0
corresponded to “not at all” and a rating of 10 corresponded to “very
much so.” Appetite-satiety Likert scales were completed hourly from
0700 –2200 h, and a mean waking episode score spanning from 0800
to 2200 h was calculated for each participant.
During the ad libitum feeding portion of the study, participants
were allowed to self-select meal times and food quantity and type.
Various foods were available at the laboratory, and participants were
also given a monetary allowance of $25 to purchase food and
beverages of their choice outside the lab. For purchased items,
nutrient information must have been available, and beverages were all
nonalcoholic. Caffeinated beverages were limited to 1 per day. Food
intake during this period was weighed and recorded by study personnel and entered into Diet Analysis Plus Software version 8.0 (Wadsworth, Florence, KY). Only ad libitum food intake from day 5 was
considered for the current report, as previously reported (29).
Statistical analyses. Data from 27 participants were included in
initial PSG analyses. A total of 26 participants were included in the
food intake analyses, the appetite-satiety scale analyses, and the RMR
and RQ analyses. One male participant was considered an outlier for
food intake measurements because his food intake during the ad
libitum feeding portion of the habitual phase was almost twice his
estimated energy requirements, and his consumption level was 3.6
times the SD of the intake of all other participants. Data from this
participant were accordingly also not included in appetite-satiety scale
analyses. Data from one woman were not included in the RMR and
RQ analyses because she exercised before the measurement in the
SLEEP ARCHITECTURE AND ENERGY BALANCE
short sleep phase. Data from the aforementioned male outlier for food
intake were included in the RMR and RQ analyses because his
measurements were taken before the ad libitum feeding began.
Paired-samples t-tests were used to compare PSG sleep parameters
obtained from the final night’s sleep recording (day 5) between sleep
duration conditions. Linear mixed model analysis was used to assess
the relationships between sleep stage parameters and RMR, RQ, food
intakes, and appetite-satiety ratings, after controlling for age, sex,
body weight, and sleep phase condition as covariates. For all linear
mixed model analyses, sleep stages (minutes and percentage of stage
2, SWS, and REM sleep) and number of REM periods were designated as independent predictor variables, with RMR, RQ, appetitesatiety ratings, and food intake designated as outcome variables.
Analyses were adjusted for age, sex, weight, and sleep phase condition. Subject was used as a grouping variable in the mixed model. To
align sleep data with energy balance-related parameters, PSG data for
linear mixed model analyses with RMR, RQ, and food intakes were
taken from the last night’s sleep recording (day 5). We used night 5
PSG data instead of night 4 data (which precede the RMR, RQ, and
food intake measures) because an in-dwelling antecubital vein catheter that was inserted to draw blood samples, including throughout the
night 4 sleep episode, could have affected sleep outcomes. PSG data
for linear mixed model analysis with appetite-satiety ratings were
taken from the third night’s sleep recording, which corresponded to
the night before the assessment of appetite-satiety. Data are expressed
as means (SD). A P value of ⬍0.05 was used to define statistical
significance. We have also tested for potential carryover effects by
testing the significance of phase X treatment in the linear mixed
model. It was not found to be significant and subsequently we dropped
the interaction term for further analyses.
min; P ⬍ 0.001] (Fig. 1). The amount of time spent in SWS
was not different in short sleep compared with habitual sleep
conditions [57.1 (30.5) min vs. 56.6 (31.6) min; P ⫽ 0.90]. The
number of REM sleep periods during the sleep period was
significantly decreased in short sleep compared with habitual
sleep [2.5 (0.7) vs. 5.3 (0.8); P ⬍ 0.001].
When data were expressed as a percentage of TST, significant reductions were observed for the percentage of time spent
in stage 1 sleep [6.8 (2.8)% vs. 12.4 (4.1)%; P ⬍ 0.001], stage
2 sleep [48.4 (10.5)% vs. 54.0 (6.3)%; P ⫽ 0.001], and REM
sleep [19.5 (4.9)% vs. 21.2 (3.7)%; P ⫽ 0.03] during short
sleep compared with habitual sleep (Fig. 2). The percentage of
time spent in SWS was significantly increased during short
compared with habitual sleep [25.3 (13.4)% vs. 12.5 (7.0)%;
P ⬍ 0.001].
Linear mixed model analysis. Our prior publication detailed
the absolute values and differences in RMR, RQ, appetitesatiety ratings, and ad libitum energy and macronutrient intakes between short and habitual sleep durations (29). In
general, there was no difference between sleep periods on
RMR, RQ, and appetite-satiety ratings but participants ate
more calories, particularly from fat, during the period of short
sleep relative to habitual sleep.
Time spent in stage 2 sleep was positively and marginally
significantly associated (P ⫽ 0.051) with RMR but not with
RQ (Table 1). Time spent in other sleep stages was not
associated with RMR or RQ. In the model considering percentage of each sleep parameter, none were significantly associated with RMR or RQ.
Minutes of REM sleep showed a significant inverse relationship with ratings on question 1 (“How hungry do you feel right
now?”; P ⫽ 0.031; Table 2) and a marginally significant
inverse relationship with question 4 (“How much do you think
you could eat right now?”; P ⫽ 0.052). Minutes of stage 2
sleep showed a significant inverse relationship with ratings on
question 5 (“How much would you like to eat something sweet
right now?”; P ⫽ 0.015) and question 6 (“How much would
you like to eat something salty right now?”; P ⫽ 0.046), and
percent TST in stage 2 showed a significant inverse relationship with question 5 ratings (P ⫽ 0.04; Table 2). A trend for a
RESULTS
PSG sleep. Sleep duration during the short sleep condition
was 225.8 (6.5) min (⬃3 h 46 min) compared with 455.1 (30.2)
min (⬃7 h 35 min) for the habitual sleep condition (P ⬍
0.001). During short sleep compared with habitual sleep duration, significant decreases were observed in the amount of time
spent in stage 1 sleep [15.3 (6.3) min vs. 56.2 (18.8) min; P ⬍
0.001], stage 2 sleep [109.1 (23.8) min vs. 245.8 (35.5) min;
P ⬍ 0.001], and REM sleep [43.9 (11.0) min vs. 96.4 (18.2)
Fig. 2. Distribution of sleep stages under short (4 h in bed; left column) and
habitual (9 h in bed; right column) sleep episodes. P values indicate level of
significance, by paired-samples t-test.
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Fig. 1. Duration of sleep stages and number of rapid eye movement sleep
(REM) periods (inset) under short (4 h in bed; filled bars) and habitual (9 h in
bed; open bars) sleep. St1, stage 1 sleep; St2, stage 2 sleep; SWS, slow wave
sleep. P values indicate level of significance, by paired-samples t-test. Values
are means ⫾ SD.
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SLEEP ARCHITECTURE AND ENERGY BALANCE
Table 1. Results of the linear mixed model analysis for sleep
stages with energy expenditure
Minutes
Coefficient
Standard
error
Percent
P value
Standard
error
P value
1.11
⫺2.06
⫺4.90
⫺26.30
5.53
4.98
6.47
25.749
0.84
0.68
0.45
0.31
⫺2.14E-03
⫺1.70E-03
⫺1.37E-03
5.46E-03
1.59E-03
1.44E-03
1.86E-03
7.42E-03
0.19
0.24
0.47
0.46
RMR
Stage 2
SWS
REM
REM periods
1.7
0.81
0.06
⫺29.87
0.85
0.99
1.29
25.77
0.051
0.42
0.96
0.25
RQ
Stage 2
SWS
REM
REM periods
1.06E-04
1.78E-04
3.21E-04
4.66E-03
2.48E-04
2.90E-04
3.78E-04
7.55E-03
0.67
0.54
0.40
0.54
Sleep stages were expressed as minutes and percent total sleep time.
Analyses were adjusted for age, sex, weight, and sleep phase condition. RMR,
resting metabolic rate; RQ, respiratory quotient; SWS, slow wave sleep; REM,
rapid eye movement sleep.
negative association was observed between percent TST in
SWS and question 3 (“How full do you feel right now?”; P ⫽
0.056).
A statistically significant inverse relationship was observed
between percentage of stage 2 sleep and calories consumed
(P ⫽ 0.024, Table 3). Percent time spent in stage 2 (P ⫽
0.005), SWS (P ⫽ 0.008), and REM sleep (P ⫽ 0.048) was
significantly and inversely associated with fat intake (Table 3).
Percent SWS showed a marginally significant inverse association with saturated fat intake (P ⫽ 0.054). Sleep stages and
protein were not associated, although REM sleep percentage
showed a tendency for a positive association with protein
intake (P ⫽ 0.093). Percent time spent in stage 2 (P ⫽ 0.057)
and REM sleep (P ⫽ 0.050) was marginally, and SWS (P ⫽
0.040) was significantly inversely related to carbohydrate intake (Table 3).
Table 2. Results of linear mixed model analysis for sleep
stages with appetite-satiety ratings
Minutes
Coefficient
Standard
error
Percent
P value
Coefficient
Standard
error
P value
0.06
0.05
0.07
0.22
0.55
0.82
0.32
0.58
0.06
0.05
0.07
0.22
0.29
0.27
0.91
0.89
0.05
0.05
0.06
0.19
0.15
0.056
0.75
0.67
Q1: How hungry do you feel right now?†
Stage 2
SWS
REM
REM periods
⫺0.02
⫺0.01
⫺0.03
0.10
0.01
0.01
0.01
0.24
0.069
0.27
0.031
0.66
⫺0.04
⫺0.01
⫺0.07
⫺0.13
Q2: How satisfied do you feel right now?
Stage 2
SWS
REM
REM periods
0.00
0.00
0.02
⫺0.06
0.01
0.01
0.01
0.25
0.77
0.79
0.25
0.81
⫺0.07
⫺0.06
⫺0.01
⫺0.03
Q3: How full do you feel right now?
Stage 2
SWS
REM
REM periods
⫺0.02
⫺0.02
0.02
0.16
0.01
0.01
0.01
0.22
0.095
0.12
0.079
0.46
⫺0.08
⫺0.09
0.02
0.08
Q4: How much do you think you could eat right now?
Stage 2
SWS
REM
REM periods
⫺0.01
0.00
⫺0.03
0.07
0.01
0.02
0.02
0.29
0.54
0.83
0.052
0.82
⫺0.03
⫺0.01
⫺0.10
⫺0.06
0.07
0.06
0.08
0.26
0.70
0.87
0.25
0.81
Q5: How much would you like to eat something sweet right now?†
DISCUSSION
Few studies have examined the association between sleep
architecture changes in response to experimental restriction of
sleep duration and resulting markers of energy balance, including energy expenditure, feelings of hunger, and food intake.
This line of investigation is important, since laboratory-based
research on the link between sleep and weight gain has frequently only considered short sleep duration, per se, and not
resultant alterations in sleep stage duration and distribution.
Furthermore, as was pointed out in a recent review (13),
methodological variations in prior sleep restriction studies,
which can differentially and uniquely alter the expression of
sleep stages, may account for discrepancies in the literature
regarding how sleep affects energy homeostasis. Our data
show a consistent pattern of lower RMR, increased subjective
appetite for sweet and salty foods, and increased energy and fat
intake with lower TST in stage in 2 sleep, specifically. Stage 2
sleep may be critical in the maintenance of energy balance
regulation. SWS and REM sleep, although not related to
energy expenditure, were also found to be related to food
choice: increased intakes of fat and carbohydrates were observed with lower SWS and REM sleep.
Stage 2
SWS
REM
REM periods
⫺0.04
⫺0.02
⫺0.03
⫺0.11
0.02
0.02
0.03
0.43
0.015
0.35
0.31
0.79
⫺0.24
⫺0.15
⫺0.17
⫺0.49
0.11
0.09
0.13
0.39
0.040
0.11
0.20
0.22
Q6: How much would you like to eat something salty right now?
Stage 2
SWS
REM
REM periods
⫺0.03
0.01
⫺0.01
0.23
0.02
0.02
0.02
0.37
0.046
0.74
0.71
0.53
⫺0.17
⫺0.10
⫺0.11
0.01
0.10
0.08
0.11
0.34
0.083
0.22
0.31
0.97
Q7: How much would you like to eat something savory right now?
Stage 2
SWS
REM
REM periods
⫺0.03
0.00
⫺0.04
0.23
0.02
0.02
0.02
0.38
0.11
0.84
0.095
0.55
⫺0.12
⫺0.06
⫺0.14
⫺0.03
0.10
0.08
0.11
0.35
0.23
0.52
0.22
0.93
Q8: How much would you like to eat fruits & vegetables right now?
Stage 2
SWS
REM
REM periods
⫺0.01
0.00
⫺0.04
0.03
0.02
0.02
0.02
0.39
0.37
0.94
0.065
0.94
⫺0.02
0.04
⫺0.08
⫺0.17
0.10
0.08
0.11
0.35
0.85
0.63
0.50
0.63
Sleep stages were expressed as minutes and percent total sleep time.
Analyses were adjusted for age, sex, weight, and sleep phase condition. Q,
question; †Under Minutes heading, significant effect of sleep phase covariate
(Q1: P ⫽ 0.009; Q5: P ⫽ 0.017).
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Coefficient
Our prior report (29), like others (18), found no change in
overall energy expenditure, including RMR, between short and
habitual sleep duration conditions, though we did observe a
tendency toward reduced RMR after short sleep (29). However, RMR was found to be significantly reduced in response to
a night of total sleep deprivation (2). Our current description of
a differential effect of sleep stages, and specifically a role of
stage 2 on RMR, may explain some of the discrepancies
between various studies. Stage 2 is the predominant sleep stage
constituting up to 50% of the sleep episode. Thus its elimina-
SLEEP ARCHITECTURE AND ENERGY BALANCE
Table 3. Results of linear mixed model analysis for sleep
stages with energy and macronutrient intakes
Minutes
Coefficient
Percent
Standard
error
P value
Standard
error
P value
⫺60.23
⫺43.43
⫺17.42
⫺149.30
25.78
23.87
28.55
123.27
0.024
0.076
0.54
0.23
⫺4.55
⫺3.93
⫺3.47
⫺0.27
1.54
1.42
1.70
7.36
0.005
0.008
0.048
0.97
⫺1.43
⫺1.50
⫺1.05
⫺5.25
0.81
0.75
0.89
3.85
0.084
0.054
0.25
0.18
⫺0.35
0.15
1.72
6.37
0.90
0.84
1.00
4.32
0.70
0.86
0.093
0.15
⫺8.87
⫺3.43
⫺1.81
⫺58.20
7.39
6.84
8.18
35.33
0.057
0.040
0.050
0.11
Calories
Stage 2
SWS
REM
REM periods
⫺4.27
⫺0.55
6.29
⫺154.73
4.51
5.16
6.81
132.01
0.35
0.92
0.36
0.25
Fat
Stage 2
SWS
REM
REM periods
⫺0.41
⫺0.26
⫺0.04
0.83
0.27
0.30
0.40
7.76
Stage 2
SWS
REM
REM periods
⫺0.13
⫺0.22
⫺0.03
⫺4.81
0.13
0.16
0.21
4.01
Stage 2
SWS
REM
REM periods
⫺0.20
⫺0.07
0.28
6.98
0.16
0.18
0.24
4.59
0.13
0.40
0.92
0.92
Saturated fat
0.35
0.17
0.89
0.24
Protein
0.21
0.69
0.25
0.14
Carbohydrate
Stage 2
SWS
REM
REM periods
0.17
1.46
1.51
⫺65.85
1.28
1.46
1.93
37.38
0.89
0.33
0.44
0.085
Sleep stages were expressed as minutes and percent total sleep time.
Analyses were adjusted for age, sex, weight, and sleep phase condition.
tion likely contributes to the reduction in RMR observed after
a full night spent awake. Furthermore, throughout a sleep
episode, energy expenditure is significantly reduced during
stage 2 sleep compared with wakefulness after sleep onset (11).
Together with the finding that energy expenditure was increased by ⬃32% during a night of total sleep deprivation
compared with baseline sleep (11), stage 2 sleep seems to play
a role in energy conservation during the sleep episode. Thus
periods of restricted sleep and the resulting substantial loss of
stage 2 sleep are likely to result in large increases in energy
expenditure during the night. It is theoretically possible that, in
an attempt to restore energy homeostasis, a compensatory
decrease in RMR upon awakening is observed, though this
speculative explanation needs experimental confirmation. The
current study did not record energy expenditure during the
sleep episode. However, future studies relating metabolic rate
during the sleep episode to daytime RMR under baseline sleep
conditions and comparing this to the relationship between
metabolic rate during the time spanning the habitual sleep
episode and daytime RMR under sleep restriction conditions
can explore the aforementioned hypothesis. In a study by
Hursel and colleagues (9), experimentally induced sleep fragmentation did not affect RMR or sleeping metabolic rate but
did result in increased RQ and carbohydrate oxidation as well
as reduced fat oxidation. A direct comparison with our present
report is difficult, however, since sleep fragmentation in the
prior study caused a simultaneous reduction in minutes of
SWS, REM sleep, and stage 2 sleep (9). Consistently reduced
energy expenditure combined with increased energy intake,
and possibly RQ, in response to reduced stage 2 sleep, may
therefore be a mechanism by which sleep restriction leads to
positive energy balance and weight gain over time.
Some prior studies have demonstrated an increase in hunger
ratings (4) and food intake (4, 18, 29) in response to experimental sleep restriction. Moreover, increased hunger was observed in connection with increased appetite for sweet and
salty foods (27). While sleep architecture was not reported in
the aforementioned study by Spiegel and colleagues (26, 27),
sleep restriction was achieved by reducing sleep from 10
h/night (2200 – 0800 h) to 4 h/night (0100 – 0500 h), which
would imply a significant reduction in stage 2 sleep and REM
sleep. Our current findings of an inverse association between
REM sleep and hunger, as well as inverse relationships between stage 2 sleep and appetite for both sweet and salty foods
may explain this. Similarly, the study by Nedeltcheva and
colleagues (18) reported significant reductions in stage 2 and
REM sleep when bedtimes were reduced to 5.5 h/night from
8.5 h/night. That investigation also reported increased energy
intake from snacks without concomitant changes in the leptin
and ghrelin concentrations (18), implying a mechanism of
increased energy intake that is more associated with altered
sleep architecture than altered appetite-regulating hormones. A
study by Gonnissen and colleagues (7) that utilized experimental sleep fragmentation to reduce REM sleep while maintaining
SWS and TST reported an increase in postdinner desire-to-eat
during fragmented compared with undisturbed sleep, which is
in line with our present findings. These and our findings
reinforce a role of sleep architecture in appetite and food intake
regulation.
Prior studies have demonstrated relationships between sleep
stages and overweight. An important epidemiological study
reported that REM sleep is the sleep stage most strongly
associated with overweight in children and adolescents and
attributed the short sleep-obesity association to reductions in
REM sleep (16). Our current data are consistent with this
hypothesis and support a pathway whereby reductions in REM
sleep are related to weight gain via increased hunger and
intakes of fat and carbohydrate.
Evidence for a relationship between sleep and body weight
and metabolism has also been demonstrated for SWS. An
innovative study demonstrating a role of SWS in metabolism
found that selective suppression of SWS without concomitant
reductions in TST resulted in decreased insulin sensitivity,
reduced glucose tolerance, and increased sympathovagal balance (30). SWS has also been found to be inversely related to
BMI (21), waist circumference (21), and hypertension (6) in
older men. Our data further show an inverse relationship
between SWS and intakes of fat and carbohydrates, presenting
a possible mechanism relating changes in this sleep stage with
weight gain and adverse metabolic outcomes. The inverse
association described here may be surprising since our sleep
manipulation did not reduce SWS duration and increased
percent time spent in SWS. However, others have described
significant reductions in both REM sleep and SWS in response
to reducing sleep time to 4 h/night from 8 h/night (26).
Interestingly, in the same study, sleep extension to 12 h/night
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SLEEP ARCHITECTURE AND ENERGY BALANCE
sleep stages as well as expressing them as percentage of TST,
we have gone some way into addressing these differential
effects. Specifically, data expressed as percent help to distinguish sleep duration from sleep stages by incorporating sleep
quantity: When expressed as a percentage, sleep duration is
taken into account, as the denominator is reduced sleep time.
Based on our findings, it may appear that sleep quantity plays
a larger role in influencing food intake, whereas it may be
playing a smaller role in subjective ratings of appetite and
satiety. A physiological underpinning of the aforementioned
discrepancy remains unknown. It is possible that the observed
inverse association between feelings of hunger and REM sleep
minutes is due to the increased signaling of orexin (12) and
neuropeptide Y (14) previously found to occur in response to
selective REM sleep deprivation. The observation in the current report that actual intakes of food were associated with
sleep stages when expressed as percent TST but not in absolute
minutes may indicate that sleep duration plays a role in reward
valuation of foods (31). Indeed, using functional brain imaging
techniques, we (28) and others (1) have recently presented
evidence that sleep restriction affects brain regions involved in
reward, decision making, and cognitive control. These explanations, however, are speculative and require further testing.
Our use of data from night 3 for some analyses and data
from night 5 for others could be viewed as a weakness, since
relationships may differ depending on the extent of sleep debt.
However, the difference between 3 and 5 days of restricted
sleep seems limited; there was no significant difference between sleep onset latency on night 3 short sleep versus night 5
short sleep (P ⫽ 0.67) or between SWS on night 3 short sleep
versus night 5 short sleep (both minutes and percent, P ⫽ 0.23
and P ⫽ 0.24, respectively).
Perspectives and Significance
We demonstrated an association between sleep architecture
and energy balance components. Specifically, changes in sleep
architecture including reductions in stage 2 sleep, REM sleep,
and SWS are associated with markers of positive energy
balance, such as reduced RMR, increased hunger, and increased intakes of energy, fat, and carbohydrates. Our results
give insight into the mechanisms underlying the association
between sleep and body weight and indicate a means by which
chronic exposure to short sleep duration or an altered sleep
architecture profile can lead to excess weight gain over time.
These findings also indicate that changes in sleep architecture
may affect energy balance components in a pathway independent of sleep duration. Sleep architecture likely plays a role in
the development and maintenance of adverse weight and energy balance outcomes, and this may be particularly relevant
for obese individuals and those with obstructive sleep apnea,
who often experience reductions in REM sleep and SWS (20,
29). Future studies should address the mechanisms by which
sleep affects energy homeostasis, and further clarify the relative contributions of sleep quantity and architecture on these
relationships.
ACKNOWLEDGMENTS
We thank Dr. Robert Basner for helpful discussions and comments on the
manuscript. Current affiliation of A. L. Roberts is School of Public Health,
University of North Carolina-Chapel Hill, Chapel Hill, NC.
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was associated with a decrease in percent SWS (26), and some
epidemiological evidence links long sleep with obesity (17).
Whereas the current report focuses on different components
of energy balance, in our prior publication (29), energy balance
during short and habitual sleep was calculated by subtracting
total energy expenditure over 5 days (determined with doubly
labeled water) from total energy intake over the 5-day period
(i.e., the 4-day controlled feeding ⫹ ad libitum intake). After
controlling for phase order and sex, no significant effect of
sleep duration was observed (29). When calculated energy
balance values from day 1 to day 6 were included in the linear
mixed model analyses used in the current report, no statistically significant relationships with any sleep stages, both minutes or percent, were observed (data not shown). In contrast,
Rutters and colleagues (23) investigated interindividual relationships between sleep architecture during full sleep episodes
recorded in the laboratory and energy balance and observed an
inverse relationship between SWS and energy balance. It
should be pointed out that our current study was well designed
to explore how individual components of energy balance,
energy expenditure, and food intake relate to changes in sleep
architecture but less so in describing energy balance per se,
since controlled feeding contributed the majority of energy
intake, with only a short duration of time devoted to ad libitum
feeding. More studies should be conducted to further explore
the relationship between sleep architecture, independent of
sleep duration, and energy balance.
Since up to 50% of the sleep episode is composed of stage
2 sleep, it is possible that what we interpret as associations
between stage 2 sleep specifically and markers of energy
balance are really simply associations with reduced sleep
duration overall. Nevertheless, as is observed in this study,
different aspects of sleep architecture are associated with
different components of energy balance, such that whereas
stage 2 sleep alone seems to be related to energy expenditure,
REM sleep and SWS may be more related to hunger/satiety.
Moreover, if the relationships between stage 2 sleep and
energy balance components were due solely to reductions in
TST, we would expect stage 2 sleep percentage, as well as the
sleep phase covariate to be significant in all instances of
significant relationships with stage 2 sleep, though the latter is
only observed for the question of “How much would you like
to eat something sweet right now?” Future investigations
should utilize established techniques to selectively reduce the
expression of specific sleep stages and observe the metabolic
outcomes (30). Such an experimental approach would enable
researchers to determine whether a more direct causal link
exists between sleep architecture and energy balance outcomes, in addition to the relationships reported here.
The finding of significant relationships for appetite-satiety
ratings with sleep stages when expressed in minutes (but not
percentage) and for food intake with sleep stages when expressed as percent TST (but not minutes) was unexpected.
Nonetheless, we believe that these results are useful to further
delineate the differential effects of sleep quantity from sleep
architecture in a novel way, thus indicating more clearly the
specific roles of sleep architecture on energy balance-related
parameters and, by extension, weight regulation. Specifically,
it is possible that sleep stage as well as TST are related to
energy balance, and it is not known if these effects are
independent. By including comparisons of both minutes of
SLEEP ARCHITECTURE AND ENERGY BALANCE
GRANTS
This publication was supported by the New York Obesity Nutrition Research Center Grant P30 DK-26687, National Center for Research Resources
and the National Center for Advancing Translational Sciences, and National
Institutes of Health (NIH) Grants UL1 RR-024156, R01 HL-091352 (to M.-P.
St-Onge), and T32-DK-007559 (to A. Shecter). The content is solely the
responsibility of the authors and does not necessarily represent the official
views of the NIH.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
REFERENCES
1. Benedict C, Brooks SJ, O’Daly OG, Almen MS, Morell A, Aberg K,
Gingnell M, Schultes B, Hallschmid M, Broman JE, Larsson EM,
Schioth HB. Acute sleep deprivation enhances the brain’s response to
hedonic food stimuli: an fMRI study. J Clin Endocrinol Metab 97:
E443–E447, 2012.
2. Benedict C, Hallschmid M, Lassen A, Mahnke C, Schultes B, Schioth
HB, Born J, Lange T. Acute sleep deprivation reduces energy expenditure in healthy men. Am J Clin Nutr 93: 1229 –1236, 2011.
3. Borbely AA, Achermann P. Sleep homeostasis and models of sleep
regulation. J Biol Rhythms 14: 557–568, 1999.
4. Brondel L, Romer MA, Nougues PM, Touyarou P, Davenne D. Acute
partial sleep deprivation increases food intake in healthy men. Am J Clin
Nutr 91: 1550 –1559, 2010.
5. Dijk DJ, Czeisler CA. Contribution of the circadian pacemaker and the
sleep homeostat to sleep propensity, sleep structure, electroencephalographic slow waves, and sleep spindle activity in humans. J Neurosci 15:
3526 –3538, 1995.
6. Fung MM, Peters K, Redline S, Ziegler MG, Ancoli-Israel S, BarrettConnor E, Stone KL. Decreased slow wave sleep increases risk of
developing hypertension in elderly men. Hypertension 58: 596 –603, 2011.
7. Gonnissen HK, Hursel R, Rutters F, Martens EA, Westerterp-Plantenga MS. Effects of sleep fragmentation on appetite and related hormone
concentrations over 24 h in healthy men. Br J Nutr (Published ahead of
print June 8): 1–9, 2012.
8. Horne J. REM sleep, energy balance and “optimal foraging”. Neurosci
Biobehav Rev 33: 466 –474, 2009.
9. Hursel R, Rutters F, Gonnissen HK, Martens EA, Westerterp-Plantenga, MS. Effects of sleep fragmentation in healthy men on energy
expenditure, substrate oxidation, physical activity, and exhaustion measured over 48 h in a respiratory chamber. Am J Clin Nutr 94: 804 –808.
10. Jequier E, Acheson K, Schutz Y. Assessment of energy expenditure and
fuel utilization in man. Ann Rev Nutr 7: 187–208, 1987.
11. Jung CM, Melanson EL, Frydendall EJ, Perreault L, Eckel RH,
Wright KP. Energy expenditure during sleep, sleep deprivation and sleep
following sleep deprivation in adult humans. J Physiol 589: 235–244,
2011.
12. Kitka T, Adori C, Katai Z, Vas S, Molnar E, Papp RS, Toth ZE,
Bagdy G. Association between the activation of MCH and orexin immunorective neurons and REM sleep architecture during REM rebound after
a three day long REM deprivation. Neurochem Int 59: 686 –694, 2011.
13. Klingenberg L, Sjodin A, Holmback U, Astrup A, Chaput JP. Short
sleep duration and its association with energy metabolism. Obes Rev 13:
565–577.
14. Koban M, Le WW, Hoffman GE. Changes in hypothalamic corticotropin-releasing hormone, neuropeptide Y, and proopiomelanocortin gene
expression during chronic rapid eye movement sleep deprivation of rats.
Endocrinology 147: 421–431, 2006.
15. Koban M, Sita LV, Le WW, Hoffman GE. Sleep deprivation of rats: the
hyperphagic response is real. Sleep 31: 927–933, 2008.
16. Liu X, Forbes EE, Ryan ND, Rofey D, Hannon TS, Dahl RE. Rapid eye
movement sleep in relation to overweight in children and adolescents.
Arch Gen Psychiatry 65: 924 –932, 2008.
17. Marshall NS, Glozier N, Grunstein RR. Is sleep duration related to
obesity? A critical review of the epidemiological evidence. Sleep Med Rev
12: 289 –298, 2008.
18. Nedeltcheva AV, Kilkus JM, Imperial J, Kasza K, Schoeller DA,
Penev PD. Sleep curtailment is accompanied by increased intake of
calories from snacks. Am J Clin Nutr 89: 126 –133, 2009.
19. Paavonen EJ, Fjallberg M, Steenari MR, Aronen ET. Actigraph
placement and sleep estimation in children. Sleep 25: 235–237, 2002.
20. Patel SR, Hu FB. Short sleep duration and weight gain: a systematic
review. Obesity (Silver Spring) 16: 643–653, 2008.
21. Rao MN, Blackwell T, Redline S, Stefanick ML, Ancoli-Israel S, Stone
KL. Association between sleep architecture and measures of body composition. Sleep 32: 483–490, 2009.
22. Rechtschaffen A, Kales A. A Manual of Standardized Terminology,
Techniques and Scoring Systems for Sleep Stages af Human Subjects. Los
Angeles, CA: Brain Information Service, Brain Research Institute, UCLA,
1968.
23. Rutters F, Gonnissen HK, Hursel R, Lemmens SG, Martens EA,. and
Westerterp-Plantenga M.S.. Distinct associations between energy balance
and the sleep characteristics slow wave sleep and rapid eye movement
sleep. Int J Obes (Lond) 2012.
24. Schmid SM, Hallschmid M, Jauch-Chara K, Born J, Schultes B. A
single night of sleep deprivation increases ghrelin levels and feelings of
hunger in normal-weight healthy men. J Sleep Res 17: 331–334, 2008.
25. Schmid SM, Hallschmid M, Jauch-Chara K, Wilms B, Benedict C,
Lehnert H, Born J, Schultes B. Short-term sleep loss decreases physical
activity under free-living conditions but does not increase food intake
under time-deprived laboratory conditions in healthy men. Am J Clin Nutr
90: 1476 –1482, 2009.
26. Spiegel K, Leproult R, L’Hermite-Baleriaux M, Copinschi G, Penev
PD, Van Cauter E. Leptin levels are dependent on sleep duration:
relationships with sympathovagal balance, carbohydrate regulation, cortisol, and thyrotropin. J Clin Endocrinol Metab 89: 5762–5771, 2004.
27. Spiegel K, Tasali E, Penev P, Van Cauter E. Brief communication:
Sleep curtailment in healthy young men is associated with decreased leptin
levels, elevated ghrelin levels, and increased hunger and appetite. Annal
Intern Med 141: 846 –850, 2004.
28. St-Onge MP, McReynolds A, Trivedi ZB, Roberts AL, Sy M, Hirsch
J. Sleep restriction leads to increased activation of brain regions sensitive
to food stimuli. Am J Clin Nutr 95: 818 –824, 2012.
29. St-Onge MP, Roberts AL, Chen J, Kelleman M, O’Keeffe M, RoyChoudhury A, Jones PJ. Short sleep duration increases energy intakes
but does not change energy expenditure in normal-weight individuals. Am
J Clin Nutr 94: 410 –416, 2011.
30. Tasali E, Leproult R, Ehrmann DA, Van Cauter E. Slow-wave sleep
and the risk of type 2 diabetes in humans. Proc Natl Acad Sci USA 105:
1044 –1049, 2008.
31. Van der Zee EA, Boersma GJ, Hut RA. The neurobiology of circadian
rhythms. Curr Opin Pulm Med 15: 534 –539, 2009.
32. Van Dongen HP, Maislin G, Mullington JM, Dinges DF. The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and
total sleep deprivation. Sleep 26: 117–126, 2003.
AJP-Regul Integr Comp Physiol • doi:10.1152/ajpregu.00222.2012 • www.ajpregu.org
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Author contributions: A.S., A.R., and M.-P.S.-O. analyzed data; A.S., A.R.,
and M.-P.S.-O. interpreted results of experiments; A.S. prepared figures; A.S.
and M.-P.S.-O. drafted manuscript; A.S., M.O., A.L.R., G.K.Z., A.R., and
M.-P.S.-O. edited and revised manuscript; A.S., M.O., A.L.R., G.K.Z., A.R.,
and M.-P.S.-O. approved final version of manuscript; M.O., A.L.R., G.K.Z.,
and M.-P.S.-O. performed experiments; M.-P.S.-O. conception and design of
research.
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