Fiber and Saturated Fat Are Associated with Sleep Arousals and Slow Wave Sleep

Abstract

Sleep restriction alters food intake, but less is known about how dietary patterns affect sleep. Current goals were to determine: (1) whether sleep is different after consumption of a controlled diet vs. an ad libitum diet, and (2) whether dietary intake during ad libitum feeding is related to nocturnal sleep.
Randomized-crossover study.
Inpatient.
Twenty-six normal weight men and women (30-45 years), habitually sleeping 7-9 h/night.
Participants were studied for 5 nights under short (4 h in bed) or habitual (9 h in bed) sleep conditions. Only data from the habitual phase were used for the present analyses. During the first 4 days, participants consumed a controlled diet; on day 5, food intake was self-selected.
Linear regression was used to determine relationships between daytime food intake and nighttime sleep on day 5. Sleep duration did not differ after 3 days of controlled feeding vs. a day of ad libitum intake. However, sleep after ad libitum eating had less slow wave sleep (SWS, p = 0.0430) and longer onset latency (p = 0.0085). Greater fiber intake predicted less stage 1 sleep (p = 0.0198) and more SWS (p = 0.0286). Percent of energy consumed as saturated fat predicted less SWS (p = 0.0422). A higher percent of energy from sugar and other carbohydrates not considered sugar or fiber was associated with arousals (p = 0.0320 and 0.0481, respectively).
Low fiber and high saturated fat and sugar intake is associated with lighter, less restorative sleep with more arousals. Dietary changes could potentially be useful in the management of sleep disorders but this needs to be tested.
http://www.clinicaltrials.gov #NCT00935402.
Copyright © 2015 American Academy of Sleep Medicine. All rights reserved.

Full text

19 Journal of Clinical Sleep Medicine, Vol. 12, No. 1, 2016
Study Objectives: Sleep restriction alters food intake, but less is known about how dietary patterns affect sleep. Current goals were to determine whether:
(1) sleep is different after consumption of a controlled diet vs. an ad libitum diet, and (2) dietary intake during ad libitum feeding is related to nocturnal sleep.
Methods: Twenty-six normal weight adults (30–45 y), habitually sleeping 7-9 h/night, participated in a randomized-crossover inpatient study with 2 phases of
5 nights: short (4 h in bed) or habitual (9 h in bed) sleep. Only data from the habitual sleep phase were used for the present analyses. During the rst 4 days,
participants consumed a controlled diet; on day 5, food intake was self-selected. Linear regression was used to determine relations between daytime food
intake and nighttime sleep on day 5.
Results: Sleep duration did not differ after 3 days of controlled feeding vs. a day of ad libitum intake. However, sleep after ad libitum eating had less slow
wave sleep (SWS, P = 0.0430) and longer onset latency (P = 0.0085). Greater ber intake predicted less stage 1 (P = 0.0198) and more SWS (P = 0.0286).
Percent of energy from saturated fat predicted less SWS (P = 0.0422). Higher percent of energy from sugar and other carbohydrates not considered sugar or
ber was associated with arousals (P = 0.0320 and 0.0481, respectively).
Conclusions: Low ber and high saturated fat and sugar intake is associated with lighter, less restorative sleep with more arousals. Diet could be useful in
the management of sleep disorders but this needs to be tested.
Clinical Trial Registration: http://www.clinicaltrials.gov, #NCT00935402.
Keywords: sleep duration, sleep architecture, food intake, diet
Citation: St-Onge MP, Roberts A, Shechter A, Choudhury AR. Fiber and saturated fat are associated with sleep arousals and slow wave sleep. J Clin Sleep
Med 2016;12(1):19–24.
INTRODUCTION
It is now well established that short sleep duration is associated
with obesity and risk of future weight gain. Cross-sectional and
longitudinal studies alike have demonstrated this relationship
in both adults and children.1,2 Moreover, Grandner et al. have
shown that total sleep time (TST) was negatively associated
with fat intake in women.3 These associations, however, do not
equate causality, and studies assessing the effects of sleep re-
striction on energy balance have been undertaken to elucidate
the causation of the relationship. Clinical studies have shown
that sleep restriction leads to increased energy intake, energy
intake from snacks, and intake of energy-dense foods.4 –10 It
seems, therefore, that altering sleep can affect food choice and
macronutrient intake.
Interestingly, the reverse causation, whether food choice and
dietary patterns affect sleep, has received much less attention.
Severe energy restriction is known to disturb sleep.11 Karklin
et al. reported that 4 weeks of an 800-kcal diet in 9 overweight
women increased sleep onset latency (SOL) and decreased time
spent in slow wave sleep (SWS).12 Two days of a high-carbo-
hydrate, low-fat diet also decreased SWS and increased REM
sleep in 8 normal-weight men compared to a 2-day diet low in
carbohydrates, high in fat, and a balanced diet.13 More st ud-
ies have assessed the effects of single meals, differing either in
SCIENTIFIC INVESTIGATIONS
Fiber and Saturated Fat Are Associated with Sleep Arousals and Slow Wave
Sleep
Marie-Pierre St-Onge, PhD1; Amy Roberts, PhD2; Ari Shechter, PhD1; Arindam Roy Choudhury, PhD3
1New York Obesity Research Center and Institute of Human Nutrition, College of Physicians & Surgeons, Columbia University, New York, NY; 2New York Obesity Research Center,
St. Luke’s/Roosevelt Hospital, New York, NY; 3Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY
pii: jc-00089-15 http://dx.doi.org /10.5664/jcsm.5384
size or in macronutrient composition, on post-meal sleepiness
or nightly sleep. Such studies have shown that a high-carbohy-
drate meal increases sleepiness in women relative to a low-car-
bohydrate meal14 but does not affect SOL to a post-meal nap.15
However, a high-glycemic index meal reduced SOL relative
to a low-glycemic index meal, with no effect on TST or sleep
architecture.16 Similarly, high-energy meals did not affect TST
post-meal compared to low-energy meals.17,18
There is thus very little information on the role of diet on
sleep patterns. Studies have been small, of short duration, and
with no clear focus on the nighttime sleep episode. Addition-
ally, most work on the effects of diet on sleep has been based
on epidemiological ndings relying on self-report of food in-
take or on the acute effects of a single meal. It is therefore
BRIEF SUMMARY
Current Knowledge/Study Rationale: Research has established
a convincing link between short/disrupted sleep duration and food
intake. Here we aimed to investigate the effects of dietary intake on
subsequent sleep propensity, depth, and architecture.
Study Impact: Few studies have utilized controlled conditions to
determine how food intake affects sleep. Current ndings—that
daytime fat and sugar/ber content affect nocturnal sleep—imply
that diet-based recommendations might be used to improve sleep in
those with poor sleep quality.

20
Journal of Clinical Sleep Medicine, Vol. 12, No. 1, 2016
MP St-Onge, A Robers, A Shechter et al. Fat and Carbohydrate Intake Affect Sleep
important to have data based on direct observations of daily
dietary intake to determine how this can affect nocturnal sleep.
The purpose of this study, therefore, was two-fold: rst, to as-
sess whether sleep patterns differed after periods of controlled
feeding and ad libitum intake; and second, whether intake on
an ad libitum feeding day was related to sleep patterns at night.
We aimed to answer these questions by comparing nocturnal
sleep after a day of a strictly controlled and balanced weight-
maintenance diet compared to a day wherein the participant
could freely make their own food choices based on preference.
METHODS
This investigation is a secondar y analysis of data from a previous
study aimed at assessing the effects of sleep restriction on en-
ergy balance in normal sleeping adults. Details of the study and
main results have been published.8,19, 20 Briey, 30- to 45-year-
old men and women, with body mass index 22–26 kg/m2, and
who reported sleeping 7–9 h/night with no daytime naps, were
recruited for this study. Some exclusion criteria included shift
work or any work that required frequent travel across time zones,
metabolic disorders such as type 2 diabetes, cardiovascular dis-
ease, and hypertension, smoking, and eating disorders, sleeping
disorders, or neurological disorders. Further exclusion criteria
included the use of medication, including benzodiazepines, an-
tidepressants, and other medications for insomnia. The pres-
ence of sleep disorders, excessive daytime sleepiness, and poor
sleep quality were also exclusionary, and were assessed with the
Sleep Disorders Inventory Questionnaire, Epworth Sleepiness
Scale, and Pittsburgh Sleep Quality Index, respectively. Finally,
recordings from the rst night’s polysomnographic (PSG) moni-
toring were analyzed to exclude participants with obstructive
sleep apnea or periodic leg movement disorder. One participant
was excluded for periodic limb movement disorder after the
rst phase of the study, and one participant was excluded for
use of antidepressant medication prior to starting the study. Par-
ticipants were screened by actigraphy over a 2-week period to
ensure normal sleep duration of 7–9 h/night. To exclude partici-
pants with habitual short sleep, the mean sleep duration over 14
nights was required to fall between 7 and 9 h/night, with ≥ 10
nights of sleep of ≥ 7 h and < 4 nights with < 6 h of sleep. The
study was approved by St. Luke’s/Roosevelt Hospital Institu-
tional Review Board and Columbia University Medical Center
Institutional Review Board. All participants provided informed
consent after being given information about the study and hav-
ing the opportunity to ask questions.
Once enrolled, participants were randomly assigned to one
of 2 intervention phases: restricted or habitual sleep. During the
short sleep phase, participants were limited to 4 h time in bed, at
01:00–05:00; during the habitual sleep phase, participants spent 9
h in bed, from 22:00 to 07:00. Participants were tested under both
conditions, in a crossover design, with a 3-week washout period
separating each phase. Each intervention phase was 6 days in du-
ration. During this time, participants were inpatients at Clinilabs,
a sleep research facility in midtown Manhattan (New York, NY).
The rst 4 days of each intervention were performed under
controlled feeding conditions; the last 2 days were done under ad
libitum feeding conditions. Meals for both phases were served at
08:00 (breakfast), noon (lunch), 16:30 (snack), and 19:00 (dinner).
Meal times and eating occasions were not controlled during the
ad libitum days. The controlled diet provided approximately 31%
of energy from fat (approximately 7.5% from saturated fat), 53%
of energy from carbohydrates, and 17% of energy from protein.
Energy requirements were calculated for each participant using
the Harris-Benedict equation.21 Each meal provided 30% of daily
energy requirements and the snack provided the remaining 10%.
Meals were assembled and prepared at the Bionutrition Unit of
the Columbia University Irving Institute for Clinical and Transla-
tional Research (New York, NY). For the ad libitum feeding days,
participants were given a monetary allowance ($25) to purchase
foods and beverages of their choice to consume in the lab on days
5 and 6. Participants were told that they could purchase any item
for which the nutrient content was readily available. Caffeine in-
take throughout the study was limited to one coffee beverage per
day, with breakfast. Food intake on days 5 and 6 was assessed
by weighing foods pre- and post-consumption. Macronutrient in-
take was determined using Diet Analysis Plus software, version
8.0 (Wadsworth, Florence, KY).8
Sleep was assessed every night in the lab using PSG, as pre-
viously described.22 Participants slept, on average, 3 h 46 min
during the short sleep phase and 7 h 35 min in the habitual
sleep phase. Because of the high sleep efciency in the short
sleep phase, only data from the habitual sleep phase were used
for the present analyses. Sleep data from night 3, after 3 days
of controlled feeding, and night 5, after one day of ad libitum
food intake, were analyzed. Data from night 4 were not used
because this was a night of nocturnal blood sampling, and the
presence of a catheter may have disturbed sleep. In addition,
food intake on day 4 was modied from previous days because
an oral glucose tolerance test was performed in the morning,
in lieu of the regular breakfast. Finally, participants were dis-
charged on the evening of day 6, and no sleep data are available
for that day. Therefore, day 5 is the only day when self-selected
food intake was measured before a night of PSG sleep monitor-
ing. Sleep scoring was conducted according to AASM 2007
criteria23 by a single certied benchmark scorer.
Statistical Analyses
Linear mixed-model repeated measure analysis (with an inter-
cept/slope term, which was tested) was used to compare dif-
ferences in the amounts of PSG sleep architecture parameters
obtained on night 3 vs. night 5 during the habitual sleep du-
ration condition. In particular, sleep parameters investigated
included TST, SOL, number of arousals, and the amounts of
stage 1 sleep, stage 2 sleep, SWS, and REM sleep, expressed
in absolute minutes and as a percentage of TST. Night (5 vs. 3),
sex, and phase order were included as independent variables,
and participant ID as grouping variable. Linear model analysis
was also used to assess the relationship between food intake
parameters from day 5 and PSG-assessed sleep variables from
night 5; dietary variables including percent of energy from
protein, sugar, non-ber/non-sugar carbohydrates, unsaturated
fat, and saturated fat, and grams of ber were designated as in-
dependent predictor variables, with TST, sleep stages (minutes
and percentage of stage 1 sleep, stage 2 sleep, SWS, and REM

21 Journal of Clinical Sleep Medicine, Vol. 12, No. 1, 2016
MP St-Onge, A Robers, A Shechter et al. Fat and Carbohydrate Intake Affect Sleep
sleep), and arousals from PSG analyses of night 5 designated
as outcome variables. These models included sex and phase
order as covariates. Data are expressed as mean ± SD. A p
value < 0.05 was used to dene statistical signicance.
RE S ULTS
Data describing our research participants have been previously
published.8 Briey, 14 men and 13 women completed the study
and one man was excluded from these analyses since he was
considered an outlier based on his food intake data. A CON-
SORT diagram representing the ow of participants through-
out the study was previously published.24 Participants were
on average 35.1 ± 5.1 y of age and had a body mass index of
23.5 ± 1.3 kg/m2. All participants had at least some college
education; 12 were white, 5 were black, 6 were Hispanic, and 3
had other or mixed racial background.
Sleep Differences between Night 3 and Night 5
There were no differences in TST and absolute time spent
in stage 1, stage 2, and REM sleep between nights 3 and 5
(Tab l e 1). However, night 5 was associated with reduced ab-
solute and percent time spent in SWS (p = 0.0430 and 0.0565,
respectively) after adjusting for sex and phase order. Latency
to the rst 10 min of sleep was longer on night 5 than night 3
(p = 0.0085). Looking at individual data, a total of 9 partici-
pants (35% of sample) who initially had a SOL < 30 min on
night 3 demonstrated an increase in SOL to > 30 min on night
5. No differences in sleep duration and architecture, except for
arousals, were observed between men and women; men had
more arousals than women (p = 0.011).
Relationship between Diet and Sleep Parameters on
Night 5
Energy intake on day 5 has been previously reported.8 In
short, participants consumed signicantly more energy on
day 5 during the short vs. habitual sleep condition.24 Partici-
pants obtained approximately 14% of their energy intake from
protein, 54.6% from carbohydrates, and 32.7% from fat (10%
from saturated fat). Diet was not related to TST on night 5
(Tab l e 2). However, ber intake was associated with reduced
time spent in stage 1 sleep (absolute time: p = 0.023; percent of
TST: p = 0.020). Conversely, ber intake was associated with
greater time spent in SWS (absolute time: p = 0.039; percent
of TST: p = 0.029). Percent energy consumed from saturated
fat was associated with reduced time in SWS (absolute time:
p = 0.031; percent of TST: p = 0.042). Arousals on night 5 were
Tab l e 1—Sleep architecture on night 3, after a period of controlled feeding, and night 5, after a day of ad libitum food intake.
Sleep Parameter Night 3 Night 5 Intercept†Coefcient % Variance Explained p value
Total sleep time, min 453.5 ± 44.4 455.1 ± 30.2 471.16 1.61 ± 8.86 11.85 0.857
Stage 1, min 52.3 ± 21.8 56.2 ± 18.8 50.85 3.98 ± 2.86 1.98 0.176
Stage 2, min 240.3 ± 42.9 245.8 ± 35.5 247.82 5.56 ± 7.20 2.44 0.447
Slow wave sleep, min 29.3 ± 13.9 24.6 ± 12.8 68.52 −7.41 ± 3.48 2.27 0.0430
REM, min 91.6 ± 17.8 96.4 ± 18.2 93.43 4.80 ± 3.59 2.27 0.193
Sleep onset latency, min 16.9 ± 11.1 29.2 ± 23.1 15.10 12.23 ± 4.30 12.03 0.00851
Arousals 143.2 ± 52.1 143.4 ± 51.9 114.87 0.19 ± 6.73 22.94 0.978
Data are means ± SD, n = 26. Coefcients (effects) are estimates ± SE. p values presented in the table are for differences between nights 3 and 5,
assessed using linear mixed model repeated measure analyses with night, sex, and phase as independent variables and participant ID as grouping variable.
†For all intercepts, p values were < 0.01.
Tab l e 2 —Results of the regression analysis for percent sleep time spent in stage 1, stage 2, and slow wave sleep after a day of
ad libitum food intake in men and women.
% TST in Stage 1 % TST in Stage 2 % TST in Stage SWS Arousals
Coefcient p value Coefcient p value Coefcient p value Coefcient p value
Intercept 4.46 ± 16.72 0.79 51.28 ± 28.11 0.086 21.27 ± 25.26 0.41 −249.8 ± 187.1 0.20
Sex, Male 2.10 ± 1.88 0.28 2.30 ± 3.16 0.48 −4.81 ± 2.84 0.11 14.66 ± 21.03 0.50
Protein, %En −0.22 ± 0.44 0.62 0.16 ± 0.74 0.83 0.31 ± 0.67 0.64 4.30 ± 4.95 0.40
Fiber, g −0.19 ± 0.07 0.020 −0.20 ± 0.12 0.11 0.26 ± 0.11 0.029 −0.11 ± 0.81 0.90
Sugar, %En 0.08 ± 0.17 0.62 0.21 ± 0.28 0.47 −0.18 ± 0.25 0.48 4.34 ± 1.86 0.032
Non-sugar/Non-ber
Carbohydrates, %En
0.04 ± 0.03 0.21 0.01 ± 0.05 0.77 −0.04 ± 0.04 0.39 0.66 ± 0.31 0.048
Unsaturated Fat, %En 0.34 ± 0.18 0.070 −0.27 ± 0.30 0.37 −0.03 ± 0.27 0.91 3.94 ± 1.98 0.062
Saturated fat, %En 0.03 ± 0.21 0.87 0.40 ± 0.36 0.30 −0.71 ± 0.32 0.042 2.17 ± 2.40 0.38
Phase, 2 −1.27 ± 1.60 0.44 0.52 ± 2.69 0.85 −0.86 ± 2.42 0.73 −0.11 ± 17.92 0.99
Coefcients (effects) are estimates ± SE, n = 26. Effects of sleep parameters on independent variables were assessed using linear model analyses. %En,
percent of energy intake; SWS, slow wave sleep; TST, total sleep time.

22
Journal of Clinical Sleep Medicine, Vol. 12, No. 1, 2016
MP St-Onge, A Robers, A Shechter et al. Fat and Carbohydrate Intake Affect Sleep
associated with male sex (p = 0.012) and percent of energy con-
sumed from sugar (p = 0.032) and non-sugar/non-ber carbo-
hydrates (p = 0.048).
DISCUSSION
This study shows that diet can inuence nighttime sleep pro-
pensity, depth, and architecture. First, we report differences in
nocturnal sleep after a 3-day controlled feeding period com-
pared to one day of ad libitum, self-selected food consump-
tion. Then, using regression analyses, we obser ved signicant
relationships between daytime intake of ber and saturated fat
on sleep depth. Ad libitum food intake was associated with a
decrease in SWS and an increase in SOL. Indeed, over a third
of participants increased their SOL to over 30 min after the ad
libitum feeding day. This nding is clinically relevant since
the 30-min SOL threshold is typically used as a cut-point to
indicate sleep onset insomnia.25 A greater intake of saturated
fat and lower intake of ber were associated with a lighter,
less deep sleep prole. Additionally, increased intake of both
sugar and non-sugar/non-ber carbohydrates was associated
with more nocturnal arousals during sleep. These results are
important since there is currently very little information on
the role of diet on sleep, and dietary recommendations for life-
style management of sleep disorders are lacking.
Our results show that higher saturated fat intake through-
out the day was associated with a lesser amount of SWS at
night. This is in contrast with a small study by Phillips et
al.13 that showed less SWS after a 2-day consumption pe-
riod of a high-carbohydrate/low-fat diet compared to a low-
carbohydrate/high-fat diet in 8 healthy, normal-weight men.
More recently, Crispim et al.26 reported that high fat intake,
specically at dinner and later in the evening, was related to
lower sleep efciency, greater time to the rst REM episode,
greater sleep time spent in stage 2 sleep with less time spent
in REM sleep, and greater time spent awake after having
fallen asleep (wake after sleep onset). Of note is that the study
by Crispim and colleagues included a relatively large num-
ber of men and women (25 and 27, respectively), and food
intake was assessed over a 3-day period via self-reported
food diaries prior to the sleep assessment. Moreover, partici-
pants reported a macronutrient intake prole similar to that
of the controlled diet in the present study. Participants were
younger than those enrolled in our study but had similar body
mass index. A limitation of those studies is their self-report
nature, leading to potentially erroneous data,27 and failure to
report on the type of fat consumed. Differences in fat type,
saturated or unsaturated, may explain some of the discrepan-
cies between studies. Our data, on the other hand, are based
on direct observations of dietary intake.
Our data also revealed an association between percent of
energy consumed from sugar and non-sugar/non-ber carbo-
hydrates throughout the day and nighttime arousals. A recent
epidemiological study by Yamaguchi et al.28 showed greater
odds of poor sleep-wake regularity in those with the highest
reported intake of carbohydrates compared to moderate car-
bohydrate consumption (≥ 70.7% of energy vs. 61% to 66%,
respectively). On the other hand, high carbohydrate intake
was associated with reduced odds of having difculty main-
taining sleep in the 2007–2008 NHANES dataset.29 Spring et
al.14 found that women reported feeling more sleepy, and men
more calm, after a high carbohydrate meal (86% of energy
from carbohydrates) compared to a high protein meal (85%
of energy from protein), although subsequent nocturnal sleep
was not reported. Along the same lines, Afaghi et al.16 found
that participants tended to feel sleepier and less awake af-
ter a high glycemic index compared to a low glycemic index
evening meal. In that study, sleep onset latency was 8.5 min
shorter after the high glycemic index meal compared to the
low glycemic index meal, but TST and sleep architecture and
quality, including arousal index, were not different between
conditions.
An effect on the circadian system may be one speculative
explanation for the current nding of an association between
carbohydrate intake and worsened nocturnal sleep. For exam-
ple, a carbohydrate-rich meal in the evening was found to de-
lay the circadian rhythm of core body temperature and reduce
nocturnal melatonin secretion.30 This is relevant, since sleep
propensity and quality are highest near the declining limb
of the core body temperature curve when melatonin levels
are increased.31, 32 Although body temperature and melatonin
were not recorded in the current study, these effects would
be consistent with an increase in SOL on night 5 and the as-
sociation with nocturnal arousals observed here. Conversely,
ber intake is associated with deeper, more restorative sleep.
Therefore, it is possible that a diet rich in ber, with reduced
intake of sugars and other non-ber carbohydrates, may be
a useful tool to improve sleep depth and architecture in in-
dividuals with poor sleep. This hypothesis requires further
investigation.
There is a large body of evidence showing a relationship
between sleep and food intake. For example, Grandner et
al.3 showed that sleep duration, assessed by actigraphy, was
negatively correlated with fat intake, and subjective nap-
ping, positively correlated with fat intake, in women from
the Women’s Health Initiative. On the other hand, a report
from the same group using National Health and Nutrition Ex-
amination Survey (NHANES) data showed lower intake of
fats and carbohydrates among very short and long sleepers
compared to normal sleepers.33 These results conict with
data from clinical intervention studies that report increased
carbohydrate and fat intake when participants are forced to
curtail their sleep by 2 to 4 h,5,7, 9,2 4 and the ndi ngs of another
epidemiological study which reported greater consumption
of carbohydrates and lower consumption of ber in short
sleepers than individuals obtaining sufcient sleep dura-
tion.34 The NHANES study only used one survey round of
data (2007–2008) rather than multiple survey years, which
may cause unreliable statistical estimates.35 Additionally,
the cross-sectional design, self-reported sleep, and one-day
self-reported 24-h recall of dietary data from one cycle pre-
vent conclusions on the direction of the relationship and the
implications of causality. We and others have reported that
alterations in sleep architecture may affect components of
energy balance and hunger.22,36 There is likely a feed-forward

23 Journal of Clinical Sleep Medicine, Vol. 12, No. 1, 2016
MP St-Onge, A Robers, A Shechter et al. Fat and Carbohydrate Intake Affect Sleep
mechanism whereby food intake patterns affect sleep archi-
tecture, which then further affects decision-making relative
to food intake and leads to alterations in dietary consump-
tion patterns. This, however, could not be tested using the
data obtained in the present study since we only had one day
of free-living energy intake and one night of PSG-assessed
sleep under ad libitum feeding conditions.
Our study had several strengths, including the controlled
nature of the study and therefore lack of bias due to self-re-
port of dietary intake and sleep. On the other hand, data were
obtained in the articial setting of the laboratory. This limita-
tion was somewhat mitigated by providing participants with a
monetary allowance to purchase foods that they wanted to eat
during the ad libitum feeding day. Another important aspect
of this study is that ad libitum intake measurements were ob-
tained after a 4-day period of controlled feeding, such that all
participants had similar prior food exposure. However, our
ad libitum measurement period was only for a single day. It is
therefore unknown whether the associations observed in the
current study represent transient acute effects of a change in
dietary intake or whether they would persist with continued
exposure and consumption of this dietary pattern. Moreover,
the inpatient design of the study necessarily restricted physi-
cal activity and exercise opportunities in participants. This
could have potentially affected some sleep parameters within
the current report, as exercise is known to improve SWS and
SOL in particular.37 However, the current reported differ-
ences in SWS and SOL occurred between night 3 and night
5 on the habitual sleep condition, when physical activity was
relatively constant. This implies that changes in food intake,
as opposed to changes in exercise or fatigue associated with
the sleep intervention, are likely to be driving the results on
SWS and SOL. Finally, the study would be strengthened by
the inclusion of a morning sleep diary to evaluate subjective
sleep quality of the preceding sleep episode. This could bet-
ter contextualize the observations and ramications of de-
creased SWS and increased SOL observed after ad libitum
feeding compared to controlled feeding.
Future studies are needed to evaluate the role of diet on
sleep. Emerging epidemiological evidence, along with the
results of the present analysis, suggest that dietary patterns
with differing fat and sugar/ber content in particular, may
affect nocturnal sleep depth, propensity, and architecture.
However, further testing is needed to determine causality.
If this is the case, then diet-based recommendations may be
warranted for those who suffer from sleep disorders, includ-
ing insomnia, short sleep duration, and poor overall sleep
quality. Current ndings also have clinical applications for
patients undergoing dietary-based therapies. Specically, a
high-fat, low-carbohydrate ketogenic diet has been promoted
as a therapeutic option for several neurological disorders in-
cluding Alzheimer disease, Parkinson disease, and epilepsy.38
These dietary alterations may be associated with changes in
nocturnal sleep, and indeed, insomnia has been reported
in response to a ketogenic diet.39 Therefore, increasing our
understanding of the impact of dietary intake on nocturnal
sleep will have many important and practical ramications
for public health.
ABBREVIATIONS
SOL, sleep onset latency
SWS, slow-wave sleep
TST, total sleep time
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SUBMISSION & CORRESPONDENCE INFORMATION
Submitted for publication March, 2015
Submitted in nal revised form June, 2015
Accepted for publication June, 2015
Address correspondence to: Marie-Pierre St-Onge, PhD, FAHA, New York Obesity
Research Center, 1150 St. Nicholas Avenue, Room 121H, New York, NY 10032; Tel:
(212) 851-5578; Fax: (212) 851-5579; Email ms2554@cumc.columbia.edu
DISCLOSURE STATEMENT
This study was funded by the National Institutes of Health grants #1R01HL091352
(St-Onge) and P30 DK26687, and by the National Center for Advancing
Translational Sciences, National Institutes of Health, through Grant Number UL1
TR000 040, formerly the National Center for Research Resources, Grant Number
UL1 RR024156. The content is solely the responsibility of the authors and does
not necessarily represent the ofcial views of the NIH. The Almond Board of
California provided almonds and Cabot Cheese provided cheese for the study. The
funding agencies played no role in the design and conduct of the study; collection,
management, analysis, and interpretation of the data; and preparation, review, or
approval of the manuscript. Dr. Roberts receives salary from and owns intellectual
property rights with Healthy Bytes, Inc. The other authors have indicated no nancial
conicts of interest.