Impact of insufﬁcient sleep on total daily energy
expenditure, food intake, and weight gain
Rachel R. Markwalda,b,1, Edward L. Melansonb,c, Mark R. Smitha, Janine Higginsd, Leigh Perreaultb, Robert H. Eckelb,
and Kenneth P. Wright, Jr.a,b,2
aSleep and Chronobiology Laboratory, Department of Integrative Physiology, University of Colorado, Boulder, CO 80309; bDivision of Endocrinology,
Metabolism, and Diabetes, cDivision of Geriatric Medicine, and dDepartment of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora,
CO 80045

Edited by Joseph S. Takahashi, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, and approved February 6, 2013
(received for review September 28, 2012)

calorimetry| misalignment | dysregulated eating | deprivation | restrictionM

ore than 1.4 billion adults, 150 million school-aged children,and 43 million preschool children are estimated to be
overweight or obese worldwide (1–3), substantially raising risk for
cardiovascular diseases (4) hyperlipidemia (5), diabetes (5, 6),osteoarthritis (6), sleep apnea (7), depression (8), and cancer (9).
Excessive food consumption and inadequate physical activity are
primary factors contributing to the obesity epidemic. When dailyenergy intake is in excess of energy expenditure (EE) a state of
positive energy balance occurs. Over weeks, months, or years,
a small cumulative impact of sustained positive energy balance
results in weight gain and obesity (10). Alongside the rise in obesity
there has been a decline in the number of individuals who reportobtaining the recommended 7–9 h of sleep, with many obtaining
less than 6 h per night (11). Insufﬁcient sleep is a risk factor ofweight gain and obesity (11–13), yet how insufﬁcient sleep contributes to this risk is unclear. Sleep inﬂuences energy metabolism(14, 15), and one function of sleep is to conserve energy (16).
Proposed mechanisms that associate insufﬁcient sleep and higher
body mass index (BMI) include changes in satiety and hunger
hormones altering food intake and changes in EE (17). Insufﬁcient
sleep is associated with decreases in the satiety hormone leptin,increases in the hunger-stimulating hormone ghrelin, and increases
in appetite when food intake is controlled (18, 19). It has also been
hypothesized that chronic insufﬁcient sleep reduces EE, leading toweight gain (17). Understanding mechanisms by which insufﬁcient
sleep contributes to weight gain and obesity has public healthwww.pnas.org/cgi/doi/10.1073/pnas.1216951110

relevance for education on importance of adequate sleep for healthand has therapeutic implications for discovery of novel strategies toprevent weight gain and assist with weight loss and weight maintenance programs. Therefore, a primary aim of the current study
was to quantify the energy cost of insufﬁcient sleep and associatedchanges in food intake. Here, we report results from a 2-wk-long
Clinical Translational Research Center (CTRC) study at the
University of Colorado Hospital that used whole room calorimetry
to measure 24-h EE and measured ad libitum food intake to assess
both sides of the energy balance equation. We also examined how
insufﬁcient sleep affected consumption and oxidation of macronutrients. Lastly, we examined satiety and hunger hormones released by adipose tissue (leptin), stomach (ghrelin), and small
intestine [peptide YY (PYY)—PYY reduces food intake], feelingsof hunger and physical exhaustion, light exposure, and circadianphase. We hypothesized that insufﬁcient sleep would increase total
daily EE and alter satiety and hunger hormones [reduce leptin (18,19) and PYY and increase ghrelin (18, 19)], resulting in increased
hunger and food intake. Furthermore, we expected food intake
would be more than necessary to meet increased energy demandsof wakefulness during sleep loss, thus leading to positive energybalance and storage of carbohydrates and fats. We hypothesizedthat transitioning from sleep loss to an adequate sleep schedulewould reduce EE and food intake.
Results
Sleep, Light Exposure, and Circadian Phase. Average bed and waketimes and circadian melatonin onset and offset times are shownin Fig. 1. After baseline (BL), half the participants were provided
a 5-h per night sleep opportunity for 5 d (5-h sleep loss condition), whereas the other half remained on a 9-h per night sleep
opportunity for 5 d (9-h adequate sleep control condition) withad libitum food intake (Fig. S1). Participants then crossed overto the other condition for 5 d, counterbalanced with equal
numbers of men and women starting with 5 h or 9 h ﬁrst. As
designed, participants slept similar amounts in BL, 460.7 ± 29.0

Author contributions: R.R.M., E.L.M., M.R.S., J.H., L.P., R.H.E., and K.P.W. designed research; R.R.M., E.L.M., M.R.S., J.H., L.P., and K.P.W. performed research; R.R.M., E.L.M.,
M.R.S., J.H., and K.P.W. analyzed data; and R.R.M., E.L.M., M.R.S., J.H., L.P., R.H.E., and K.P.W.
wrote the paper.
Conﬂict of interest statement: There are no conﬂicts of interest directly related to thisproject. L.P. has received speakers fees from Merck. R.H.E. has current funding througha Sanoﬁ research grant (fellowship–education grant), Diadexus, and GlaxoSmithKline,
and also serves as a consultant for the following: Amylin, GTC Nutrition, Genﬁt, Lilly,
Pﬁzer, Johnson & Johnson, and Esperion. Additionally, R.H.E. has ﬁnancial and/or material
support with the following: Cardiometabolic Health Congress, Vindico (honorarium),
Metabolic Syndrome Institute, CME Incite (honorarium), Voxmedia (honorarium). K.P.W.
serves as a consultant for Takeda Pharmaceuticals and Zeo, Inc. K.P.W. is chair of the
Scientiﬁc Advisory Board and has stock options at Zeo, Inc. and has received honoraria
from Potomac Center for Medical Education, the Associated Professional Sleep Societies,
and the National Institutes of Health.
This article is a PNAS Direct Submission.
1

Present address: Naval Health Research Center, San Diego, CA 92106.

2

To whom correspondence should be addressed. E-mail: kenneth.wright@colorado.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1216951110/-/DCSupplemental.

PNAS | April 2, 2013 | vol. 110 | no. 14 | 5695–5700

PHYSIOLOGY

Insufﬁcient sleep is associated with obesity, yet little is known
about how repeated nights of insufﬁcient sleep inﬂuence energy
expenditure and balance. We studied 16 adults in a 14- to 15-d-long
inpatient study and quantiﬁed effects of 5 d of insufﬁcient sleep,
equivalent to a work week, on energy expenditure and energy
intake compared with adequate sleep. We found that insufﬁcient
sleep increased total daily energy expenditure by ∼5%; however,energy intake—especially at night after dinner—was in excess ofenergy needed to maintain energy balance. Insufﬁcient sleep led to
0.82 ± 0.47 kg (±SD) weight gain despite changes in hunger andsatiety hormones ghrelin and leptin, and peptide YY, which signaled excess energy stores. Insufﬁcient sleep delayed circadianmelatonin phase and also led to an earlier circadian phase of waketime. Sex differences showed women, not men, maintained weight
during adequate sleep, whereas insufﬁcient sleep reduced dietary
restraint and led to weight gain in women. Our ﬁndings suggestthat increased food intake during insufﬁcient sleep is a physiological adaptation to provide energy needed to sustain additional
wakefulness; yet when food is easily accessible, intake surpasses
that needed. We also found that transitioning from an insufﬁcientto adequate/recovery sleep schedule decreased energy intake, especially of fats and carbohydrates, and led to −0.03 ± 0.50 kg
weight loss. These ﬁndings provide evidence that sleep plays
a key role in energy metabolism. Importantly, they demonstratephysiological and behavioral mechanisms by which insufﬁcient
sleep may contribute to overweight and obesity.

9h Sleep

22

24

02

04

06

08

10

Clock Time

Fig. 1. Sleep and circadian timing. Average timing of sleep episodes (boxes),
melatonin onset (black upward triangles), and melatonin offset (black downward triangles). Error bars are SEM. P values are calculated by mixed model
ANOVAs. Melatonin onset signiﬁcantly delayed in the 5-h condition (P < 0.01
versus BL and 9 h), whereas melatonin offset was similar for conditions (P =0.77). Durations between melatonin onset and bedtime and between melatonin onset and melatonin offset were similar for conditions (both P > 0.39),
whereas duration between melatonin offset and wake time was signiﬁcantly
longer in the 5-h condition (P < 0.001 versus BL and 9 h).

(±SD) and 9 h, 461.5 ± 42.6 min conditions, and slept less inthe 5-h condition, 280.0 ± 10.1 min as determined by polysomnography. As participants did not sleep for all of each sleep
opportunity, the difference in sleep time between 9-h and 5-h
conditions was ∼3 h, equivalent to ∼39% reduction in sleep
duration.
Average light exposure during scheduled wakefulness was
similar for the BL, 9-h, and 5-h conditions (230 ± 122, 247 ± 101,and 229 ± 102 lx, respectively; P = 0.47 for condition; note 1 lx
equals light from a candle 1 m away from the eye). Average lux
levels during the 4 h of additional wakefulness at the end of the
day in the 5-h condition were 91 ± 63 lx. Circadian melatoninphase and phase relationships to scheduled sleep and wake times
were similar for BL and 9-h conditions, whereas circadian melatonin onset phase signiﬁcantly delayed by ∼1.5 h and the duration between wake time and circadian melatonin offset phase
was signiﬁcantly longer in the 5-h condition (Fig. 1).

Satiety and Hunger Hormones. Average 24-h leptin levels increased
from 5.5 ± 5.2 ng/mL at BL by ∼22% to 6.7 ± 5.1 ng/mL in the 5-h
condition (P < 0.05); leptin levels were intermediate in the 9-h
condition at 5.9 ± 4.7 ng/mL [9-h nonsigniﬁcant difference from
BL (P = 0.16) and 5 h (P = 0.095)]. Average 24-h ghrelin levels
signiﬁcantly decreased from 794.6 ± 233.8 pg/mL at BL by ∼30%
to 660.2 ± 235.4 pg/mL in 5-h (P < 0.001) and by ∼21% to 655.6 ±229.3 pg/mL in 9-h (P < 0.01) conditions (no difference between
5 h and 9 h, P = 0.81). Average 24-h PYY levels signiﬁcantly
increased from 100.5 ± 35.1 pg/mL at BL by ∼32% to 136.1 ±44.8 pg/mL in 5-h (P < 0.01) and by ∼35% 133.2 ± 48.8 pg/mL
in 9-h (P < 0.001) conditions (no difference between 5 h and 9 h,
P = 0.68). Hourly hormone data are shown in Fig. S2.

A

Total Daily and Hourly EE, Food Consumption, Macronutrient Disappearance and Balance, Energy Balance, and Weight Gain. Par-

ticipants’ total daily EE was ∼9% higher during the 5-h condition
compared with BL and ∼5% higher during 5-h versus 9-h conditions (P < 0.01; Fig. 2A). Fig. 2B shows that regardless ofcondition order, 24-h EE was higher during the 5-h versus 9-h
condition. Furthermore, hourly EE was higher during wakefulness versus scheduled sleep regardless of condition (Fig. 3).
At BL, the average daily caloric need was estimated at 2,204 ±353 kcal. Participants consumed more calories than needed tomaintain weight when food was available ad libitum and 24-h
food intake was ∼6% greater during the 5-h than 9-h condition(P < 0.05; Fig. 2C). Table 1 shows participants consumed andused more carbohydrates in the 5-h condition, yet there were no
signiﬁcant differences in carbohydrate balance. Fig. 4 shows thatparticipants consumed a smaller breakfast but consumed 42%
more calories as after dinner snacks during sleep loss, whichcontained more carbohydrates, protein, and ﬁber (Table 1).
Furthermore, during sleep loss, more calories were consumed atnight after dinner than calories consumed for any individual
meal. Calories consumed as lunch, dinner, and predinner snacks
were similar between conditions (all P > 0.46). Food intake wasinﬂuenced by condition order such that participants maintained
elevated food intake after transitioning from the 9-h to 5-hcondition (Fig. 2D) and increased their consumption of carbohydrates from 360.2 ± 106.9 g in 9 h to 390.6 ± 114.1 g in the 5-h
condition (P < 0.05). In contrast, participants reduced their foodintake after transitioning from the 5-h to 9-h condition (Fig. 2D),
especially consumption of fats, 118.7 ± 32.9 g in 5 h to 106.9 ±44.3 g in 9 h (P < 0.05) and carbohydrates, 398.2 ± 131.7 g in 5 h
to 352.9 ± 118.2 g in 9 h (P < 0.001).
5696 | www.pnas.org/cgi/doi/10.1073/pnas.1216951110

C

E

G

B

3000

24h Energy expenditure(Kcals/d)

5h Sleep

2800
2600
2400

*

9h Condition
5h Condition
1 kcal = 4.184 kjoule

*

*

2200
2000
1800

5 Day average food intake(Kcals/d)

Baseline

Overeating led to positive energy balance (Fig. 2E) and weightgain (Fig. 2G) in both sleep conditions. Although energy balance
was not statistically different between conditions, participants on
average gained more weight in the 5-h versus 9-h condition (Fig.
2G). Participants maintained a state of greater positive energybalance when they transitioned from the 9-h to the 5-h condition(Fig. 2F), whereas participants were in a state of lower positiveenergy balance after they transitioned from the 5-h to 9-h condition (Fig. 2F). Related, participants who started in the 9-h
condition gained weight (not signiﬁcant from zero baseline, P =0.17) and subsequently gained additional weight (signiﬁcant fromzero baseline, P < 0.001) after they transitioned to the 5-h condition (Fig. 2H); whereas participants who started in the 5-h
condition initially gained weight (signiﬁcant from zero baseline,
P < 0.01) and subsequently lost a small amount of weight(Fig. 2H; not signiﬁcant from zero baseline, P = 0.88) after theytransitioned to the 9-h condition.

4000

5 Day average energy balance(Kcals/d)

Melatonin
OffsetSleep Episodes

1200

D

3800
3600
3400

*

*

3200
3000
2800

F

1100

*

1000

5 Day average weight gain(kg)

Melatonin
Onset

900
800
700
600
500

1.5
1.0

*

H

*

0.5
0.0
-0.5

Condition

5h first

9h first

Fig. 2. Effect of sleep loss on energy expenditure, intake, balance, andweight gain. P values calculated by mixed model ANOVAs for condition(Left, n = 16, two-tailed) and planned comparisons for condition by order(Right, n = 8 each order, one-tailed dependent t tests). Error bars are SEM.
*Signiﬁcant difference between 5-h and 9-h conditions (P < 0.05).

Markwald et al.

3.0

-12.6

2.5

-10.5
*****

2.0

- 8.4

1.5

- 6.3

1.0

▲

▲

▲

▲

▲

0.5
1

3

5

7

9 11 13 15 17 19 21 23

- 4.2

-2.1

Hours from scheduled waketime
Fig. 3. Hourly energy expenditure in the calorimetry room. Energy expenditure expressed as kilocalories per minute on the Left axis and kilojoules per
minute on the Right axis relative to scheduled wake time. Gray lines represent
low-intensity stepping sessions. Error bars are SEM. P values are calculated bydependent t test with modiﬁed Bonferonni correction P < 0.0159. *Signiﬁcant difference between the 5-h condition and baseline and 9-h conditions;▲ represents signiﬁcant difference between the baseline and 5-h and 9-h
conditions. In addition to signiﬁcant effects noted, there was a signiﬁcantdifference between baseline and 9-h conditions at hours awake 9.

Hunger and Physical Exhaustion Scales. Hunger decreased from44.7 ± 21.4 points at baseline by 39% and 37% to 27.1 ± 9.9 and28.3 ± 12.2 points during the 5-h (P < 0.01) and 9-h (P < 0.01)
conditions, respectively (no difference between 5 h and 9 h, P =0.51). Physical exhaustion increased to 54.9 ± 18.9 points during
the 5-h condition by 30% and 19% compared with 42.1 ± 15.5
and 46.0 ± 19.1 points during the BL and 9-h condition, respectively (both P < 0.001 versus 5 h; no difference between BL
and 9 h, P = 0.19). Order effects for scales are shown in Fig. S3.
Sex Differences. Total sleep time, circadian phase shift, and circadian phase relationships did not differ by sex (all P > 0.37).
Overall, men expended more energy (2,575.6 ± 64.6 kcal/d men
versus 2,045.2 ± 56.6 kcal/d women), consumed more calories(3,850.8 ± 118.9 versus 2,277.4 ± 92.4 kcal/d), were in greater
positive energy balance (1,275.2 ± 80.2 versus 232.2 ± 74.2 kcal/d),
and gained more weight (0.95 ± 0.14 versus 0.13 ± 0.16 kg)

than women during ad libitum food availability regardless of
sleep opportunity (all sex differences P < 0.0015). Hunger signiﬁcantly decreased in men from baseline to 9-h and 5-h conditions (both P < 0.01), whereas hunger did not change inwomen from baseline to either condition (both P > 0.69) (Fig.
S3). Compared with baseline, men consumed ∼68% and ∼62%
more in the 5-h and 9-h conditions than needed to maintainweight, whereas women consumed ∼19% and ∼10% more thannecessary to maintain weight in the 5-h and 9-h conditions, respectively. Men gained weight in the 5-h (1.11 ± 0.09 kg, P < 0.05
from zero baseline) and 9-h (0.78 ± 0.25 kg, P < 0.05 from zerobaseline) (no condition differences in weight gain for men, P =0.30) conditions, whereas women gained weight in the 5-h condition (0.52 ± 0.16 kg; P < 0.05 from zero baseline) and losta small amount of weight in the 9-h condition (−0.26 ± 0.20 kg;
not signiﬁcant from zero baseline P = 0.23) (condition differences in weight gain for women P < 0.05; sex differences forweight gain within conditions both P < 0.01).
Discussion
Insufﬁcient sleep is considered an independent risk factor forweight gain and obesity. We show that 5 d of insufﬁcient sleep
increases energy needs, but that sleep loss also increases foodintake such that intake is in excess of energy needed leading toweight gain. Food intake, especially of carbohydrates, was highdespite appropriate responses of satiety and hunger hormonesthat signaled food intake was in excess. During sleep loss, participants ate smaller breakfasts but ate more over the day, especially carbohydrates, proteins, and ﬁber at night after dinner.
Changes in circadian phase and the circadian timing of awakening may have contributed to the altered eating patterns during
insufﬁcient sleep. Speciﬁcally, participants may have eaten
smaller breakfasts because they awakened at an earlier circadianphase when the internal circadian clock was promoting sleep;i.e., wake time occurred during the biological night when melatonin levels were still high. Furthermore, a delay in melatonin
onset—the beginning of the biological night—may have led toa circadian drive for more food intake at night. Transitioning
from sleep loss to an adequate/recovery sleep schedule led toreduced food intake, especially fewer fats and carbohydrates,
and to weight loss. Sex differences are in agreement with previous research that women have more dietary restraint than men
during ad libitum food intake, selecting a diet that moreclosely matches their daily caloric needs (20). We uniquely show,

Table 1. Total daily macronutrient intake, disappearance, and balance
MeasureMacronutrient intake
Carbohydrate, gFat, gProtein, gMacronutrient disappearance
Carbohydrate, gFat, gProtein, gMacronutrient balance
Carbohydrate, gFat, gProtein, gMacronutrient intake of calories consumed after dinner
Carbohydrate, gFat, gProtein, gFiber, g

9-h condition (n = 16)

5-h condition (n = 16)

P value

356.5 (109.0)
119.6 (48.0)
123.3 (39.7)

394.4 (119.1)
123.4 (39.8)
122.3 (106.6)<0.0010.32
0.82

298.0 (103.7)
66.1 (24.6)
87.0 (30.2)

319.2 (26.7)
72.5 (30.9)
81.9 (25.14)

<0.05
0.32
0.13

38.8 (52.4)
53.5 (58.0)
36.2 (16.8)

54.2 (57.6)
50.9 (56.1)
40.3 (19.2)

0.23
0.63
0.28

75.2 (43.6)
28.1 (20.6)
14.8 (9.6)
3.2 (1.6)

118.4 (60.0)
33.8 (15.9)
21.8 (11.1)
5.4 (1.9)

0.001
0.13
<0.001<0.001n, number of participants. Values in parentheses are SD. Comparison of condition effects for 9-h and 5-h sleep opportunities.
P values are two-tailed calculated by mixed model ANOVAs for main effect of condition.

Markwald et al.

PNAS | April 2, 2013 | vol. 110 | no. 14 | 5697

PHYSIOLOGY

Baseline
9h Condition
5h Condition

Energy expenditure (kJ/min)

Energy expenditure (kcals/min)

Stepping
Sessions

Fig. 4. Energy intake of meals. Energy intake for 9-h and 5-h sleep conditions
during ad libitum food availability expressed in kilocalories. Error bars are SEM.
P values are calculated by mixed model ANOVAs for main effect of condition(n = 16). *Signiﬁcant difference between 5 h and 9 h (P < 0.05).

however, that insufﬁcient sleep appears to reduce dietary restraint
in women, increasing their risk for weight gain.
Energy Expenditure and Energy Intake During Sleep Loss andAdequate Sleep Schedules. We observed that 5 d of insufﬁcient

sleep, equivalent to a work week, increases total daily EE bya physiologically meaningful amount. The average increase of ∼5%(∼111 kcal/d or ∼464 kJ/d) in 24-h EE observed during sleep loss
compared with 9-h control is similar to the energy cost of a 70-kg
adult performing water aerobics for ∼24 min. Increased total daily
EE during sleep loss was predominantly driven by the energy cost ofadditional wakefulness. This physiologically meaningful difference
was not detected in prior research (21, 22). Although factors such as
study design and population studied may contribute to this discrepancy, methodological differences associated with the measurement of EE likely explain the majority of the difference inﬁndings. Compared with the whole room calorimetry precision of(0.5–2%), the doubly labeled water technique used in prior research(21, 22) provides less sensitive and precise (∼6–8%) estimates of EE(23). Our ﬁnding that 5 d of sleep loss increases EE is unique, but
consistent with prior research from our investigative team on theenergetic costs of total sleep deprivation (16). Previously, we observed one night of total sleep deprivation under controlled bed rest
conditions increased 24-h EE by ∼7% (∼134 kcal/d or ∼562 kJ/d)(16). Taken together, these ﬁndings indicate that total sleep deprivation or insufﬁcient sleep both increase daily EE, thus providing
further support that one function of sleep in humans is to conservea small but physiologically meaningful amount of energy. Chronically, if increased energy demands during sleep loss were not met
with increased food intake, weight loss would ensue. Thus, insufﬁcient sleep per se may not directly lead to weight gain andﬁndings from nonhuman models support the latter. Speciﬁcally,
sleep-restricted rodents lost weight even though they ate more (24)and rats with sleep disturbance because of ventrolateral preoptic
area of the hypothalamus lesions (25) gained less weight eventhough they ate normal amounts of food. Although EE was notdirectly measured in the latter rodent studies, the ﬁndings are
consistent with the physiological principal that energy needs arehigher during sleep loss. Our ﬁndings also suggest that negative
energy balance and weight loss would ensue over time in studies of
sleep loss that provide energy balance diets designed to maintainweight at baseline. This may help to explain why leptin levels arelower and ghrelin levels are higher in prior sleep loss studies that
controlled food intake (18, 19). As discussed previously, sleep loss is
not a safe or effective means of losing weight (16) and our current
ﬁndings of dysregulated food intake and weight gain in humans
during sleep loss expounds this point.
Our ﬁndings indicate an important contributor to weight gain
during sleep loss in humans is dysregulated eating behavior. Total
daily food intake, especially of carbohydrates, was greater during
sleep loss beyond that necessary to meet increased energy needs
5698 | www.pnas.org/cgi/doi/10.1073/pnas.1216951110

thus leading to weight gain. In addition, nighttime consumption of
postdinner carbohydrate, protein, and ﬁber calories was 42%
higher during sleep loss. Sleep loss has been shown by others toincrease consumption of carbohydrate-rich after dinner snacks, but
not overall daily intake (21). Thus, nighttime eating after dinner
appears to consistently increase during sleep loss, although macronutrients consumed differs between studies, likely reﬂecting
differences in snack options or populations studied (e.g., leanparticipants in current study versus overweight participants studied
in ref. 21). We also found participants ate smaller breakfasts
during sleep loss. Our ﬁndings add to the growing body of evidence
from epidemiological (26) and nonhuman models (27, 28) thatindicate that overeating at night may contribute to weight gain.
We show insufﬁcient sleep leads to a delay in circadian timing
and thus a change in the circadian timing of meals, especiallybreakfast. Sleep and circadian systems are highly integrated andsleep loss as a consequence of circadian misalignment is well
documented (29, 30). Our ﬁnding showing that circadian misalignment may result from maintaining insufﬁcient sleep is
unique and indicates that altered circadian timing may contribute to negative health outcomes associated with short sleep
schedules. How the circadian timing of meal intake inﬂuences
metabolic physiology cannot be determined in the current ad
libitum protocol. Detailed evaluation of circadian misalignment
in future studies of sleep loss is warranted, especially because
curtailing sleep by advancing only bed or wake times could havedifferent effects on circadian timing, food intake, and energy
metabolism.
Satiety and Hunger Hormones. Findings from prior studies suggestthat changes in satiety and hunger hormones during sleep loss,
when food intake is controlled, initiate increases in hunger thatwould augment food intake (18, 19). Our ﬁndings indicate
however that mechanisms by which sleep loss contributes toweight gain are likely to be more complex as overeating occurred
despite increases in leptin and PYY and decreases in ghrelin thatsignaled food intake was in excess (31–33). Although altered byovereating, leptin, ghrelin, and PYY were still in the range observed in healthy lean individuals. Differences in eating behavior
occurred in the 5-h and 9-h conditions despite similar patterns ofcirculating hormones, and this may be indicative of decreased
responsivity to gut fullness and satiety hormones during sleep
loss. Our ﬁndings showing increased food intake despite changes
in hormones that promote satiety/reduce hunger are consistent
with ﬁndings from studies of clock mutant mice (34) and sleeprestricted ad libitum fed humans (21). The controlled CTRC
study limited the duration of sleep loss and thus it is possible thatgiven a longer time course of overeating, participants would have
responded to changes in satiety and hunger hormones (35).
Changes in other hormones not examined may also promote
food intake during sleep loss (e.g., cholecystokinin, glucagon-like
peptide-1). Furthermore, why overeating during ad libitum foodavailability occurred in the 9-h condition is unclear, but perhaps
not unexpected based on the availability and palatability of foodprovided (36), and comparisons of other feeding models during
adequate versus insufﬁcient sleep are needed.
Physical Exhaustion Ratings. It is unknown whether higher physicalexhaustion observed during sleep loss in our study will translate
to lower physical activity levels and more positive energy balancein the social–behavioral environment, as suggested by otherlaboratory ﬁndings (37, 38). As sleep loss induces signiﬁcantsafety impairments in cognitive performance equivalent to alcohol intoxication, we do not believe that experimentally induced
sleep loss outside of a controlled laboratory environment is safe.
Effects of Sleep History in an Ad Libitum Feeding Environment. Being
awake longer permits a greater opportunity to eat. However, wefound that overeating was not simply due to more time to eat, as
our counterbalanced-crossover design showed that sleep history
inﬂuenced overeating. Speciﬁcally, participants continued toMarkwald et al.

How Does Sleep Loss Promote Weight Gain? Increased food intake
during sleep loss appears to be a physiological adaptation toprovide the body with the energy needed to sustain extended
wakefulness. However, when exposed to the modern obesogenic
environment of readily accessible food, weight gain occurs because food intake is more than necessary to offset the energy costof sleep loss. This weight gain would be exacerbated if physicalexhaustion from sleep loss leads to reduced physical activity inthe work–home environment. Changes in peripheral satiety andhunger hormones do not explain the overeating we observed.
Thus, a central mediated drive to increase food intake to meetthe energy demands of sleep loss may have contributed to
overfeeding. For example, orexin/hypocretin levels increase during sleep loss (39) and orexin/hypocretin neurons are an importantcomponent of sleep–wakefulness and feeding neural systems (40,41). It is also possible that sleep loss alters brain mechanisms involved in nonhomeostatic food intake (e.g., mood, comfort, reduced eating restraint). As discussed earlier, sleep loss also
appears to consistently increase food intake at night. This, pluschanges in circadian timing implicate time of day or circadian
metabolic pathways in sleep-loss–induced weight gain.
Mounting evidence, including ﬁndings from the current study,
suggests that public health interventions should include sleep
education programs as part of strategies to prevent weight gain.
Furthermore, our ﬁnding that obtaining an adequate sleep duration reduced overeating and consumption of fats and carbohydrates, combined with ﬁndings of reduced fat loss during a caloric
restriction regimen when obtaining insufﬁcient sleep (42) indicates clinical trials are needed to determine whether sleep isa modiﬁable risk factor that can assist weight loss and maintenance programs to improve dietary habits and metabolic health.

Methods
Subjects. Sixteen healthy participants (eight women) aged 22.4 ± 4.8 y (mean ±SD) with self-reported habitual sleep schedules of 8.26 h ± 0.69 h, BMI 22.9 ±2.4 kg/m2 and percent body fat 21.8 ± 8.3% as determined by dual energy
X-ray absorptiometry (DEXA) (DPX-IQ; Lunar) were studied. Study procedures were approved by the Scientiﬁc Advisory and Review Committee of
the Colorado Clinical and Translational Sciences Institute, by the Colorado
Multiple Institutional Review Board (IRB), and by the University of Colorado,
Boulder IRB. Written informed consent was obtained from participants whothen underwent health screening including: medical, psychological andsleep history, semistructured clinical psychiatric interview, physical examination, complete blood cell count and comprehensive metabolic panel, urine
toxicology, 12-lead electrocardiogram, and polysomnographic sleep disorders screen. Based on these tests, participants were deemed free of
medical and psychological disorders. Inclusion criteria were: 18–35 y old; BMI
18.5–24.9 kg/m2; habitual sleep time >7 and <9.25 h; low–moderate caffeine(<500 mg/d); alcohol use (average fewer than two standard drinks per day
per week and ﬁve or fewer drinks per day); no drug dependence; andnonsmokers. Low physically active participants were studied to control for
detraining during sedentary laboratory procedures on EE. Also see SI
Methods. Exclusion criteria were: current or chronic medical/psychiatricconditions; pregnancy; shift work or dwelling below Denver altitude (1,600m) the year prior; travel across more than one time zone 3 wk before CTRC
study; maximal lifetime BMI >27.5 kg/m2; recent self-reported weight loss;
and abnormal eating patterns identiﬁed by dietitian interview and threeitem eating questionnaire (43). Participants self-reported being medication
free and urine toxicology for illicit drugs veriﬁed drug-free status atscreening and CTRC study. All participants who met inclusion criteria andstarted CTRC procedures completed the protocol.
Study Protocol. One week before study, participants discontinued caffeine
use and maintained consistent ∼9-h-per-night sleep schedules. Compliance
was veriﬁed by daily time-stamped call-ins of bed and wake times, sleep
diaries, and wrist activity and light-exposure recordings (Actiwatch-L; Phillips
Respironics). These procedures ensured participants were not sleep restricted
before the CTRC protocol. Three days before the study, participants were

Markwald et al.

provided a diet that met their predicted individual daily caloric needs (DXAor resting metabolic rate with a 1.5 activity factor). The CTRC Nutrition Core
prepared meals with daily macronutrient ratios of 30% fat, 55% carbohydrate, and 15% protein reﬂecting average US daily intake. Participants were
instructed to eat all food provided and nothing else except water and exercise was proscribed to ensure they entered the CTRC in energy balance.
The CTRC protocol began with a 3-d BL segment of 9-h-per-night sleep opportunities at the participant’s habitual bedtime, as determined by prestudy
monitoring. This procedure permitted participants to initially sleep at theirhabitual entrained circadian phase. Sleep was restricted by delaying bedtime
and advancing wake time each by 2 h. A 5-h sleep opportunity was chosenbecause: (i) on average it does not reduce deep slow wave sleep as does
more severe sleep restriction, (ii) it is a level of sleep restriction that occurs
across a 5-d work week in many occupations (e.g., military and security
operations, emergency responders, and shift workers), and (iii) it is a level ofsleep restriction that is consistent with that used to examine the inﬂuence ofsleep loss on metabolism (18, 19, 21, 42). We chose a 9-h sleep opportunity
for our control condition to ensure individuals were provided with a sufﬁcient opportunity for sleep. Weight maintenance diets continued for BL
days. During ad libitum feeding of the 5-h and 9-h conditions, the CTRC
Nutrition Core prepared meals (∼1,500 kcal each for breakfast, lunch, dinner, and ∼200 kcal each for snack one and snack two at ∼2.17 h, 6 h, 10 h,
12 h, and 14 h from scheduled wake time, respectively) designed to provide
participants ∼130–150% more calories than BL. Additional snack choices
were freely available during scheduled wakefulness of the 5-h and 9-h
conditions (Table S1). Participants ate as much of scheduled meals andsnacks as desired during the 5-h and 9-h conditions. Participants performed
20-min low-intensity stepping sessions twice per day to mimic daily physicalactivity outside the CTRC. The specially designed sleep research suite permitted exposure of participants to indoor lighting and sunlight through thewindow during scheduled wakefulness and darkness during scheduled sleep(0 lx achieved by a lockable blackout shade with tracks to prevent lightleakage). We chose this procedure to approximate changes in light exposure patterns that occur during insufﬁcient sleep schedules in the home–work–social environment. During calorimeter room days, participants weremaintained in dim lighting during scheduled wakefulness (<8 lx maximum)
to permit assessment of melatonin levels.
Measures. A whole room calorimeter quantiﬁed changes in EE and macronutrient disappearance (16) on the last BL day (day 3) and last day of eachsleep condition (days 8 and 13; Fig. S1). EE and respiratory quotient (RQ)
were assessed from oxygen (O2) consumption and carbon dioxide (CO2)
production (16, 44). Gas concentrations were determined from differences in
CO2 and O2 between entering and exiting air with a fuel-cell–based dual
channel O2 analyzer (FC-2 Oxzilla; Sable Systems International) and two infrared CO2 analyzers (CA-10 CO2 analyzers; Sable Systems International) (45).
Accuracy and precision of the system is tested monthly using propane
combustion tests and average O2 and CO2 recoveries during this study were≥98.0%. Protein disappearance was calculated based on urine total nitrogen(46). EE, carbohydrate, and fat disappearance were calculated from O2
consumption and RQ (47). Food intake was determined by the CTRC Nutrition Core using ProNutra software (48). We used a 5-d average for foodintake for each condition to most accurately represent global changes infood intake that occurred during each condition. Morning body weight wasrecorded with participants wearing identical clothing. Ghrelin, leptin, PYY,
and melatonin were assessed from hourly blood samples during calorimetry
room days via an indwelling venous catheter with heparinized saline drip
and 12-foot extension tubing through the calorimetry room porthole. Thispermitted blood sampling without entering the room during scheduled
wakefulness and sleep. Visual analog scales were used to assess hunger andphysical exhaustion ratings (0 = not hungry at all and 100 = as hungry as I’ve
ever felt; 0 = energetic and 100 = physically exhausted) starting 1.5 h afterscheduled wake time and every 2 h thereafter during scheduled wakefulness. Sleep recordings were obtained (Siesta; Compumedics) on day 1 (sleep
disorders screen) and the last 2 d of BL, 5-h and 9-h segments of the protocol. Recordings consisted of monopolar EEGs referenced to contralateral
mastoids (C3xA2, C4xA1, O1xA2, and F3xA2), right and left electrooculograms, chin electromyogram, electrocardiogram, and respiration. Scheduled
wakefulness was veriﬁed by research staff via continuous monitoring andwith the addition of EEG on calorimetry days.
Data Analyses. EE was calculated hourly and for total daily EE. Macronutrient
oxidation was calculated for each 24-h day. Total daily food intake and thecaloric intake for meals, as well as scheduled and unscheduled snacks pre- andpostdinner time, were calculated. Energy and macronutrient balance was

PNAS | April 2, 2013 | vol. 110 | no. 14 | 5699

PHYSIOLOGY

overeat a similar amount and gained weight after transitioning
from the 9-h to 5-h condition. Conversely, fat and carbohydrate
intake was signiﬁcantly reduced and a small amount of weightloss ensued after transitioning from the 5-h to 9-h condition.

calculated as the difference between the 5-d average food intake for eachcondition and total daily EE and macronutrient oxidation measured in theroom calorimeter. Carbohydrate balance was calculated factoring ﬁber intake. Hormones were analyzed as a 24-h mean and hourly. Total sleep time
was averaged for sleep recordings the last 2 d of each condition. Circadianphase was determined for the dim-light melatonin onset (DLMO 25%) anddim-light melatonin offset (DLMOff 25%) and phase relationships with bed
and wake times and each other were calculated (49, 50). Average outcomes
were analyzed with mixed model ANOVAs with condition and condition
order as ﬁxed factors and subject as a random factor, also with and without
sex as a ﬁxed factor using Statistica (version 10.0; Statsoft). Analyses of sex
differences were not planned and are considered exploratory. Hourly outcomes for EE and hormones were analyzed with condition and hours fromscheduled wake time as ﬁxed factors and modiﬁed Bonferroni correction
factors for multiple comparisons. Single sample t tests were used to comparechanges in weight from a zero baseline. Analyses focused on conditiondifferences with two-tailed tests. Assessment of order effects were of in-terest as they permitted examination of prior sleep history on ad libitum
food intake and energy metabolism (i.e., continuous adequate 9-h sleep
opportunities followed by 5-d sleep restriction and 5-d sleep restriction
followed by 5-d, 9-h adequate/recovery sleep opportunities). Planned comparisons for condition by order effects were performed using one-tailed
dependent t tests to test directional hypotheses for primary outcome
measures: 24-h EE, food intake, energy balance, and carbohydrate and fat
intake, all predicted to be higher during the 5-h condition.

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5700 | www.pnas.org/cgi/doi/10.1073/pnas.1216951110

ACKNOWLEDGMENTS. We thank the Clinical Translational Research Center
physicians, nurses, dieticians, and technicians; and M. Weissburg, B. Grifﬁn,
B. Ball, U. Mohapatra, S. Peralta, B. Perry, A. W. McHill, and G. Wright forassistance with this study. This work was supported by National Institutes of
Health (NIH) R01 HL109706 and Colorado Clinical and Translational Sciences
Institute Grant UL1 TR000154 from NIH/National Center for Advancing Translational Sciences and Howard Hughes Medical Institute in collaboration with
the Biological Sciences Initiative and Undergraduate Research Opportunities
Program at the University of Colorado, Boulder, CO.

Markwald et al.