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


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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. #NCT00935402. Copyright © 2015 American Academy of Sleep Medicine. All rights reserved.
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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:, #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):1924.
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
Fiber and Saturated Fat Are Associated with Sleep Arousals and Slow Wave
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 /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
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.
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.
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 Briey, 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.
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 InterceptCoef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
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.
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).
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
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
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.
SOL, sleep onset latency
SWS, slow-wave sleep
TST, total sleep time
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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
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.
... Diets that are low in omega-3 acids may impair sleep at night because of an endogenous disturbance of the daily clock and a reduction in melatonin secretion. Studies in hamsters with omega-3 deficiency have shown a disturbance in the melatonin-secretion rhythm and chronic locomotor hyperactivity [59,60]. ...
... Animal fats contain, almost exclusively, saturated fatty acids. Foods that are fried in hydrogenated oil are also a rich source of saturated fatty acids [59]. Studies on the effect of saturated fatty acids on sleep have shown that the consumption of saturated fatty acids leads to a greater number of wakes at night and shortens the duration of slow-wave sleep, which is the stage of sleep during which the body can recover [59]. ...
... Foods that are fried in hydrogenated oil are also a rich source of saturated fatty acids [59]. Studies on the effect of saturated fatty acids on sleep have shown that the consumption of saturated fatty acids leads to a greater number of wakes at night and shortens the duration of slow-wave sleep, which is the stage of sleep during which the body can recover [59]. The regular consumption of saturated fatty acids contributes to the development of diabetes, which is often associated with sleep problems [61]. ...
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Sleep is a cyclically occurring, transient, and functional state that is controlled primarily by neurobiological processes. Sleep disorders and insomnia are increasingly being diagnosed at all ages. These are risk factors for depression, mental disorders, coronary heart disease, metabolic syndrome, and/or high blood pressure. A number of factors can negatively affect sleep quality, including the use of stimulants, stress, anxiety, and the use of electronic devices before sleep. A growing body of evidence suggests that nutrition, physical activity, and sleep hygiene can significantly affect the quality of sleep. The aim of this review was to discuss the factors that can affect sleep quality, such as nutrition, stimulants, and physical activity.
... Restricting sleep for up to 5 days can lead to short-term weight gain [18]. Several crosssectional studies have indicated that short sleep duration is associated with obesity and the risk of future weight gain in both adults and children [19]. There is evidence that eating and sleeping at unconventional times is associated with a higher risk of obesity and an unfavorable metabolic profile. ...
... It is associated with poor eating habits, including an increase in meals, snacks, and night-time eating, with the consumption of high energy foods, lower intake of fruits and vegetables, and a higher intake of fast foods, sugar, and fats, resulting in an overall higher energy intake and increased BMI [30][31][32][33][34][35]. St-Onge and colleagues [19] suggested that diet can influence night-time sleep propensity, depth, and architecture. They reported that a higher intake of saturated fat and a lower intake of fiber were associated with a lighter, less profound sleep profile, and that increased intake of both sugar and non-sugar carbohydrates was associated with more frequent nocturnal arousal during sleep [19]. ...
... St-Onge and colleagues [19] suggested that diet can influence night-time sleep propensity, depth, and architecture. They reported that a higher intake of saturated fat and a lower intake of fiber were associated with a lighter, less profound sleep profile, and that increased intake of both sugar and non-sugar carbohydrates was associated with more frequent nocturnal arousal during sleep [19]. Improvement in dietary quality may mitigate the disease risk associated with obesity and impaired sleep quality [12]. ...
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This narrative review presents the findings from intervention studies on the effects of sleep deprivation on eating habits, metabolic rate, and the hormones regulating metabolism, and discusses their relevance to weight loss efforts. Disturbed sleeping patterns lead to increased energy intake, partly from excessive snacking, mainly on foods high in fat and carbohydrates. The studies focused mainly on the effects of sleep duration, but also of sleep quality, on dietary intake during weight loss trials, and on weight loss maintenance. It is important to explore sleep routines that could enhance the efforts of obese and overweight people to lose weight, maintain their weight loss, and improve their overall health.
... Third, fast food also tends to contain more saturated fatty acids. Supporting this notion, a higher intake of saturated fats during the day has been linked to a shorter duration of slow-wave sleep (Grandner et al., 2010;St-Onge et al., 2016) and more arousal episodes at night (St-Onge et al., 2016). However, caution is required to interpret these results, since studies on the role of saturated fatty acids on sleep are relatively scarce (Zhao et al., 2020). ...
... Third, fast food also tends to contain more saturated fatty acids. Supporting this notion, a higher intake of saturated fats during the day has been linked to a shorter duration of slow-wave sleep (Grandner et al., 2010;St-Onge et al., 2016) and more arousal episodes at night (St-Onge et al., 2016). However, caution is required to interpret these results, since studies on the role of saturated fatty acids on sleep are relatively scarce (Zhao et al., 2020). ...
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Purpose The aim of the current study was twofold: first, to determine the prevalence of anxiety-induced sleep disturbances among Argentine adolescents according to sex, and second, to identify the association between these sleep disturbances and lifestyle behaviors in this population. Methods This is a cross-sectional study with data from the Global School-based Student Health Survey (GSHS) in Argentina (2018). A total of 32,393 adolescents (aged 12–17 years; 53.4% girls) were included in the final analysis. Anxiety-induced sleep disturbances were assessed with the question “During the past 12 months, how often have you been so worried about something that you could not sleep at night?” Results The prevalence of anxiety-induced sleep disturbances was higher in girls (17.4%) than in boys (7.9%) ( p < 0.001). In boys, results indicated that those who used marijuana (cannabis) (odds ratio [OR] = 1.46, 95% confidence interval [CI] 1.08–1.98), used amphetamine or methamphetamine (OR = 2.19, 95% CI 1.28–3.77), walked or biked to or from school (OR = 1.53, 95% CI 1.19–1.96), and spent 3 h or more in sedentary behaviors (OR = 1.35, 95% CI 1.05–1.74) were more likely to report anxiety-induced sleep disturbances. In girls, those who ate from a fast-food restaurant (OR = 1.24, 95% CI 1.05–1.47), consumed alcoholic beverages (OR = 1.45, 95% CI 1.19–1.75), smoked cigarettes (OR = 2.09, 95%CI 1.05–4.14), consumed any tobacco product (OR = 1.47, 95% CI 1.19–1.82), used amphetamine or methamphetamine (OR = 2.08, 95% CI 1.33–3.26), and those who spent 3 h or more in sedentary behaviors (OR = 1.32, 95% CI 1.11–1.57) were more likely to report frequent anxiety-induced sleep disturbances. Conclusion In conclusion, considerable sex differences were observed with respect to the prevalence of anxiety-related sleep disturbances and associated lifestyle aspects.
... On the one hand, it can decrease sleep latency [35] due to the elevation of tryptophan [36] and the suppression of orexin [37]. However, it also results in poorer sleep quality [38,39], as the circadian rhythm of core body temperature is delayed and nocturnal melatonin secretion is reduced [40]. In turn, this disruption of the circadian rhythm can lead to metabolic dysfunction [41], such as an increased risk of insulin resistance, predisposing to diabetes [42], as well as a higher chance of preterm birth [23,43]. ...
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The extent to which lifestyle practices at night influence sleep quality in pregnant women remains unknown. This study aimed to examine whether nocturnal behaviours were associated with poor sleep during pregnancy. We performed a cross-sectional analysis of a prospective cohort of pregnant women at 18–24 gestation weeks recruited from KK Women’s and Children’s Hospital, Singapore, between 2019 and 2021. Nocturnal behaviours were assessed with questionnaires, and sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) with a global score ≥5 indicative of poor sleep quality. Modified Poisson regression and linear regression were used to examine the association between nocturnal behaviour and sleep quality. Of 299 women, 117 (39.1%) experienced poor sleep. In the covariate-adjusted analysis, poor sleep was observed in women with nocturnal eating (risk ratio 1.51; 95% confidence interval [CI] 1.12, 2.04) and nocturnal artificial light exposure (1.63; 1.24, 2.13). Similarly, nocturnal eating (β 0.68; 95% CI 0.03, 1.32) and light exposure (1.99; 1.04, 2.94) were associated with higher PSQI score. Nocturnal physical activity and screen viewing before bedtime were not associated with sleep quality. In conclusion, reducing nocturnal eating and light exposure at night could potentially improve sleep in pregnancy.
... Omega-3 fatty acids found in fish, and flavonoids found in fruits and vegetables show promise for improving cognitive function (56,57), concentration, and memory (58). In addition, a low-fiber diet, high in sugar and saturated fat has been shown to impair sleep quality and is associated with lighter, less restorative sleep (59). Significant improvements in overall QoL were also reported in the MD group compared with the control. ...
Background: Depression is a common mental health condition which affects 1 in 8 males each year, especially young adults. Young adulthood offers an opportunity for early dietary interventions, with research suggesting that a Mediterranean diet (MD) could be beneficial in treating depression. Objective: This study aimed to determine if a MD can improve depressive symptoms in young males with clinical depression. Methods: A 12-week, parallel-group, open-label, randomized control trial was conducted to assess the effect of a MD intervention in the treatment of moderate to severe depression in young males (18-25 years). Befriending therapy was chosen for the control group. Assessments were taken at baseline, week 6 and week 12. MD adherence was measured with the Mediterranean Adherence Score (MEDAS). The primary outcome measure was the Beck Depression Inventory Scale (BDI-II) and secondary outcome was Quality of Life (QoL). Results: A total of 72 participants completed the study. After 12 weeks, the MEDAS scores were significantly higher in the MD group compared to the befriending group (Mean diff: 7.8, 95% CI: 7.23, 8.37, p<0.001). The mean change in BDI-II score was significantly higher in the MD group compared to the befriending group at week 12 (Mean diff: 14.4, 95% CI: 11.41, 17.39, p<0.001). The mean change in QoL score was also significantly higher in the MD group compared to the befriending group at week 12 (Mean diff: 12.7, 95% CI: 7.92, 17.48), p<0.001). Conclusion: Our results demonstrate that compared to befriending, a MD intervention leads to significant increases in MEDAS score, decreases in BDI-II score and increases in QoL scores. These results highlight the important role of nutrition for the treatment of depression and should inform advice given by clinicians to this specific demographic population.
... 35 Low fibre and high saturated fat and sugar intake are also positively associated with arousals from sleep. 36 Broadly, these findings are in line with our results of inverse associations between an overall healthy diet with sleep apnea. However, diet, foods and nutrients are clearly not consumed in isolation. ...
Study objectives: Most studies on diet and sleep apnea focus on calorie restriction. Here we investigate potential associations between dietary quality [healthy eating index (HEI), dietary inflammatory index (DII)] and overall healthy lifestyle with sleep apnea risk. Methods: National Health and Nutrition Examination Survey data (waves 2005-2008, and 2015-2018; N=14,210) were used to determine HEI, DII and their quintiles, with the fifth quintile indicating highest adherence to each dietary construct. A healthy lifestyle score was determined using diet, smoking, alcohol intake and physical activity level. STOP-BANG questionnaire was used to define sleep apnea risk. Generalized linear regression models with binomial family and logit link were used to investigate potential associations. The models were adjusted for socioeconomic status, lifestyle factors and chronic conditions. Results: Prevalence of high sleep apnea risk was 25.1%. Higher DII was positively associated with sleep apnea (odds ratio (OR)Q5 vs. Q1=1.55; 95% CI: 1.24-1.94; p for trend <0.001) whereas higher HEI was associated with reduced sleep apnea risk (ORQ5 VS. Q1=0.72: 0.59-0.88; p for trend=0.007). Higher healthy lifestyle score was also associated with decreased odds of sleep apnea (p for trend <0.001). There was a significant interaction between healthy lifestyle and sex with sleep apnea risk (p for interaction=0.049) whereby females with higher healthy lifestyle scores had lower risk of sleep apnea versus males. Conclusions: Higher quality and anti-inflammatory diets and a healthier overall lifestyle are associated with lower sleep apnea risk. These findings underline the importance of strategies to improve overall diet quality and promote healthy behavior, not just calorie restriction, to reduce sleep apnea risk.
Background and aims Consumption of ultra-processed foods is negatively associated with health outcomes, however, the contribution to sleep quality is limited. Therefore, the objective of this study was to evaluate the association between food intake by frequency and degree of processing and sleep quality in adults during the covid-19 pandemic. Methods Population-based survey of adults from October to December 2020 in the Iron Quadrangle region, Brazil. The exposure variable was a food intake score that considered the frequency of consumption and food processing degree. The total score ranged from 0 (best) to 48 points (worst food quality), categorized into quartiles. Furthermore, we also evaluated whether individuals replaced their lunch and/or dinner based mostly on fresh/minimally processed foods for ultra-processed foods, for five or more days in the week. The outcome variable was sleep quality assessed with the Pittsburgh Sleep Quality Index. We constructed a contrasting directed acyclic graph (DAG) model to estimate the adjusted odds ratio of the association between score eating and sleep, by logistic regression. Results Most of the 1762 individuals evaluated had poor sleep quality (52.5%). The minimum and maximum food scores were 0 and 30 points (mean 9.16; 95% CI 8.50, 9.81). The higher values of the score corresponded to lower consumption of fresh and minimally processed foods and higher consumption of ultraprocessed foods. In multivariate analysis, individuals in the third food consumption score had 71% greater odds of poor sleep quality (OR=1.71; 95% CI: 1.03, 2.85) and in the fourth quartile 144% greater odds (OR=2.44; 95% CI: 1.32, 2.44). Besides, replacing the dinner meal with ultra-processed foods five days or more in the week was also associated with poor sleep quality (OR= 2.01; 95%CI: 1.14, 3.57). Conclusion Higher consumption of ultra-processed foods concomitant with lower consumption of fresh and minimally processed foods is associated with a higher chance of poor sleep quality.
Chronotype is the attitude of subjects to carry out their daily activities mainly in the morning ("lark") or in the evening ("owl"). The intermediate chronotype is located between these two categories. It has been demonstrated that chronotype can influence the incidence, course and response to treatments of tumors. In particular patients diagnosed with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) and evening chronotype are characterized by unhealthy lifestyle, obesity, metabolic syndrome, a worsen cardiometabolic profile, a poor prognosis with a progressive disease and the development of metastasis. In addition, evening chronotype has been associated with sleep disturbances, which in turn have been related to tumor development and progression of tumors. There is a strict connection between sleep disturbances and NENs because of the hyperactivation of proangiogenic factors that caused aberrant neoangiogenesis. A nutritional tailored approach could represent a tool to align subjects with evening chronotype to physiological biological rhythms based on the properties of some macro and micronutrients of being substrate for melatonin synthesis. Thus, we aimed to provide an overview on the association of chronotype categories and sleep disturbances with NENs and to provide nutritional advices to manage subjects with NENs and these disturbances of circadian rhythm.
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Background Obstructive Sleep Apnea (OSA) is considered a public health problem and its prevalence is increasing at an epidemic rate. The aim of this study was to examine whether individual nutrients (macronutrients, antioxidant vitamins) rather than energy restriction may potentially affect OSA severity in a representative population of Cyprus. Methods A total sample of 303 adults (>18 years old) with Cypriot citizenship and permanently residing in Cyprus were randomly selected. Selected patients have completed the food frequency questionnaire, and a physical activity questionnaire and underwent a sleep study to assess OSA severity. Results Overall, 303 patients were included in this study, 169 (55.8%) had mild OSA (apnea-hypopnea index—AHI <15) and the remaining 83 (27.4%) had moderate to severe OSA (AHI>15). The mean age of all patients was 55.7 years old. Patients with moderate to severe OSA had significant higher BMI levels, higher consumption of calories, higher hip circumference, waist circumference, waist-hip ratio and neck circumference and higher consumption of folic acid compared with the patients with mild OSA (p<0.05). Conclusions The findings suggest that increased energy intake regardless diet macronutrient composition is positively associated with OSA severity whereas higher folic acid intake seems to have a protective role.
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A significant body of research has investigated the effects of physical activity on sleep, yet this research has not been systematically aggregated in over a decade. As a result, the magnitude and moderators of these effects are unclear. This meta-analytical review examines the effects of acute and regular exercise on sleep, incorporating a range of outcome and moderator variables. PubMed and PsycINFO were used to identify 66 studies for inclusion in the analysis that were published through May 2013. Analyses reveal that acute exercise has small beneficial effects on total sleep time, sleep onset latency, sleep efficiency, stage 1 sleep, and slow wave sleep, a moderate beneficial effect on wake time after sleep onset, and a small effect on rapid eye movement sleep. Regular exercise has small beneficial effects on total sleep time and sleep efficiency, small-to-medium beneficial effects on sleep onset latency, and moderate beneficial effects on sleep quality. Effects were moderated by sex, age, baseline physical activity level of participants, as well as exercise type, time of day, duration, and adherence. Significant moderation was not found for exercise intensity, aerobic/anaerobic classification, or publication date. Results were discussed with regards to future avenues of research and clinical application to the treatment of insomnia.
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Energy intake (EI) and physical activity energy expenditure (PAEE) are key modifiable determinants of energy balance, traditionally assessed by self-report despite its repeated demonstration of considerable inaccuracies. We argue here that it is time to move from the common view that self-reports of EI and PAEE are imperfect, but nevertheless deserving of use, to a view commensurate with the evidence that self-reports of EI and PAEE are so poor that they are wholly unacceptable for scientific research on EI and PAEE. While new strategies for objectively determining energy balance are in their infancy, it is unacceptable to use decidedly inaccurate instruments, which may misguide health care policies, future research, and clinical judgment. The scientific and medical communities should discontinue reliance on self-reported EI and PAEE. Researchers and sponsors should develop objective measures of energy balance.International Journal of Obesity accepted article preview online, 13 November 2014. doi:10.1038/ijo.2014.199.
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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 of energy needed to maintain energy balance. Insufficient sleep led to 0.82 ± 0.47 kg (±SD) weight gain despite changes in hunger and satiety hormones ghrelin and leptin, and peptide YY, which signaled excess energy stores. Insufficient sleep delayed circadian melatonin phase and also led to an earlier circadian phase of wake time. 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 suggest that 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 insufficient to 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 demonstrate physiological and behavioral mechanisms by which insufficient sleep may contribute to overweight and obesity.
Background-Analytic guide lines were first created in 1996 to assist data users in analyzing data from the Third National Health and Nutrition Examination Survey (NHANES III),conducted from 1988 to 1994 by the Centers for Disease Control and Prevention's National Center for Health Statistics. NHANES became a continuous annual survey in 1999, with data released to the public in 2-year intervals. In 2002, 2004, and 2006, guidelines were created and posted on the NHANES website to assist analysts in understanding the key issues related to analyzing data from 1999 onward. This report builds on these previous guidelines and provides the first comprehensive summary of analytic guidelines for the 1999-2010 NHANES data. Objectives-This report provides general guidelines for researchers in analyzing 1999-2010 NHANES publicly released data. Information is presented on key issues related to NHANES data, including sample design, demographic variables, and combining survey cycles. Guidance is also provided on data analysis, including the use of appropriate survey weights, calculating variance estimations, determining the reliability of estimates, age adjustment, and computing population counts.
Background: Evidence indicates that men and African Americans may be more susceptible to weight gain resulting from sleep loss than women and whites, respectively. Increased daily caloric intake is a major behavioral mechanism that underlies the relation between sleep loss and weight gain. Objective: We sought to assess sex and race differences in caloric intake, macronutrient intake, and meal timing during sleep restriction. Design: Forty-four healthy adults aged 21-50 y (mean ± SD: 32.7 ± 8.7 y; n = 21 women, n = 16 whites) completed an in-laboratory protocol that included 2 consecutive baseline nights [10 or 12 h time in bed (TIB)/night; 2200-0800 or 2200-1000] followed by 5 consecutive sleep-restriction nights (4 h TIB/night; 0400-0800). Caloric intake and meal-timing data were collected during the 2 d after baseline sleep and the first 3 d after sleep restriction. Results: During sleep restriction, subjects increased daily caloric intake (P < 0.001) and fat intake (P = 0.024), including obtaining more calories from condiments, desserts, and salty snacks (Ps < 0.05) and consumed 532.6 ± 295.6 cal during late-night hours (2200-0359). Relative to women, men consumed more daily calories during baseline and sleep restriction, exhibited a greater increase in caloric intake during sleep restriction (d = 0.62), and consumed a higher percentage of daily calories during late-night hours (d = 0.78, Ps < 0.05). African Americans and whites did not significantly differ in daily caloric intake, increased caloric intake during sleep restriction, or meal timing. However, African Americans consumed more carbohydrates, less protein, and more caffeine-free soda and juice than whites did during the study (Ps < 0.05). Conclusions: Men may be more susceptible to weight gain during sleep loss than women due to a larger increase in daily caloric intake, particularly during late-night hours. These findings are relevant to the promotion of public health awareness by highlighting nutritional risk factors and modifiable behaviors for weight gain related to sleep-wake timing.
Sleep symptoms are associated with weight gain and cardiometabolic disease. The potential role of diet has been largely unexplored. Data from the 2007-2008 National Health and Nutrition Examination Survey (NHANES) were used (n = 4552) to determine which nutrients were associated with sleep symptoms in a nationally representative sample. Survey items assessed difficulty falling asleep, sleep maintenance difficulties, non-restorative sleep and daytime sleepiness. Analyses were adjusted for energy intake, other dietary factors, exercise, body mass index (BMI) and sociodemographics. Population-weighted, logistic regression, with backwards-stepwise selection, examined which nutrients were associated with sleep symptoms. Odds ratios (ORs) reflect the difference in odds of sleep symptoms associated with a doubling in nutrient. Nutrients that were associated independently with difficulty falling asleep included (in order): alpha-carotene (OR = 0.96), selenium (OR = 0.80), dodecanoic acid (OR = 0.91), calcium (OR = 0.83) and hexadecanoic acid (OR = 1.10). Nutrients that were associated independently with sleep maintenance difficulties included: salt (OR = 1.19), butanoic acid (0.81), carbohydrate (OR = 0.71), dodecanoic acid (OR = 0.90), vitamin D (OR = 0.84), lycopene (OR = 0.98), hexanoic acid (OR = 1.25) and moisture (OR = 1.27). Nutrients that were associated independently with non-restorative sleep included butanoic acid (OR = 1.09), calcium (OR = 0.81), vitamin C (OR = 0.92), water (OR = 0.98), moisture (OR = 1.41) and cholesterol (OR = 1.10). Nutrients that were associated independently with sleepiness included: moisture (OR = 1.20), theobromine (OR = 1.04), potassium (OR = 0.70) and water (OR = 0.97). These results suggest novel associations between sleep symptoms and diet/metabolism, potentially explaining associations between sleep and cardiometabolic diseases.
The purpose of this study was to evaluate the associations between dietary factors and sleep-wake regularity in the Japanese population. We analyzed 1368 eligible subjects (931 men and 437 women) aged 35-69 years who had participated in the baseline survey of a cohort study in Tokushima Prefecture, Japan. Information on individual lifestyle characteristics, including dietary habits and sleep-wake regularity, was obtained by a selfadministrated questionnaire. Logistic regression analyses were performed to evaluate adjusted associations of the intake energy ratios of macronutrients, as well as intake frequency, and the amount of staple foods with sleepwake regularity. The lowest quartile of protein intake as well as the highest quartile of carbohydrates showed significantly higher multivariable-adjusted odds ratios of 2.1 (95% confidence interval, 1.3-3.3) and 2.1 (1.3-3.5), respectively, for poor sleep-wake regularity compared with the respective second quartile that is thought to be moderate intake. Regarding intake of staple foods, low weekly intake frequency at breakfast (<5 times/week), the lowest intake amount (<1 bowl or slice/roll) at breakfast, and the highest intake amount (>=2 bowls or slices/ rolls) at lunch and dinner exhibited significantly high adjusted odds ratios for poor sleep-wake regularity. Additionally adjusting for sleep duration, these results did not substantially alter. Our results suggested that low intake energy ratio of proteins and high intake energy ratio of carbohydrates, skipping intake of the staple foods at breakfast, and excessive intake amount of the staple foods at lunch and dinner may be associated with poor sleep-wake regularity.
Examine sleep restriction's effects on weight gain, daily caloric intake, and meal timing. Repeated-measures experiments assessing body weight at admittance and discharge in all subjects (N = 225) and caloric intake and meal timing across days following 2 baseline nights, 5 sleep restriction nights and 2 recovery nights or across days following control condition nights in a subset of subjects (n = 37). Controlled laboratory environment. Two hundred twenty-five healthy adults aged 22-50 y (n = 198 sleep-restricted subjects; n = 31 with caloric intake data; n = 27 control subjects; n = 6 with caloric intake data). Approximately 8-to-1 randomization to an experimental condition (including five consecutive nights of 4 h time in bed [TIB]/night, 04:00-08:00) or to a control condition (all nights 10 h TIB/night, 22:00-08:00). Sleep-restricted subjects gained more weight (0.97 ± 1.4 kg) than control subjects (0.11 ± 1.9 kg; d = 0.51, P = 0.007). Among sleep-restricted subjects, African Americans gained more weight than Caucasians (d = 0.37, P = 0.003) and males gained more weight than females (d = 0.38, P = 0.004). Sleep-restricted subjects consumed extra calories (130.0 ± 43.0% of daily caloric requirement) during days with a delayed bedtime (04:00) compared with control subjects who did not consume extra calories (100.6 ± 11.4%; d = 0.94, P = 0.003) during corresponding days. In sleep-restricted subjects, increased daily caloric intake was due to more meals and the consumption of 552.9 ± 265.8 additional calories between 22:00-03:59. The percentage of calories derived from fat was greater during late-night hours (22:00-03:59, 33.0 ± 0.08%) compared to daytime (08:00-14:59, 28.2 ± 0.05%) and evening hours (15:00-21:59, 29.4 ± 0.06%; Ps < 0.05). In the largest, most diverse healthy sample studied to date under controlled laboratory conditions, sleep restriction promoted weight gain. Chronically sleep-restricted adults with late bedtimes may be more susceptible to weight gain due to greater daily caloric intake and the consumption of calories during late-night hours. Spaeth AM; Dinges DF; Goel N. Effects of experimental sleep restriction on weight gain, caloric intake, and meal timing in healthy adults. SLEEP 2013;36(7):981-990.