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Timing and Intensity of Light Correlate with Body
Weight in Adults
Kathryn J. Reid, Giovanni Santostasi,
[...], and Phyllis C. Zee
Additional article information
Abstract
Light exposure can influence sleep and circadian
timing, both of which have been shown to influence weight regulation. The goal
of this study was to evaluate the relationship between ambient light, sleep and body mass index. Participants included 54
individuals (26 males, mean age 30.6, SD=
11.7
years). Light levels, sleep midpoint and duration were measured with wrist actigraphy (Actiwatch-L) for 7
days. BMI was derived from self-reported height and weight. Caloric intake was
determined from 7 days of food logs. For each participant, light and activity
data were output in 2 minute epochs, smoothed using a
5 point (10 minute) moving average and then aggregated over 24 hours. The mean
light timing above 500 lux (MLiT500) was defined as the average
clock time of all aggregated data points above 500 lux. MLiT500 was
positively correlated with BMI (r
=
0.51,
p<0.001), and midpoint of sleep (r
=
0.47,
p<0.01). In a multivariable linear regression model including MLiT500 and
midpoint of sleep, MLiT500 was a significant predictor of BMI (B
=
1.26
SE
=
0.34,
β
=
0.53
p
=
0.001,
r2Δ
=
0.22).
Adjusting for covariates, MLiT500 remained an independent
predictor of BMI (B
=
1.28
SE
=
0.36,
β
=
0.54,
p
=
0.002,
r2Δ
=
0.20).
The full model accounted for 34.7% of the variance in BMI (p
=
0.01).
Exposure to moderate levels of light at biologically appropriate times can
influence weight, independent of sleep timing and duration.
Introduction
Increased exposure to light late in the day and
less exposure to bright light in the morning is often a consequence of sleep
curtailment, and in particular with late sleep-wake timing [1], [2].
Several studies now indicate that morning light exposure influences body fat [3], [4] as
well as the level of appetite regulating hormones [5].
However no published studies have investigated the influence of both light
levels and sleep on body weight in humans.
Recent studies suggest that manipulating sleep
duration and/or light exposure in humans results in alterations in metabolic
function [5], appetite [3], and body fat [3],
[4]. Light exposure of different wavelengths
(i.e., 633 nm, 532 nm, 475 nm) in the morning for two hours immediately upon
waking in sleep restricted (5 hours/night) individuals altered the levels of
the satiety hormones, leptin and ghrelin [5]. Further support for the role of light in
weight regulation comes from two intervention studies in obese women. In a
study by Danilenko and colleagues, exposure to at
least 45 minutes of morning light (between 6–9 am at 1300 lux) for 3
weeks in obese women resulted in reduced body fat and appetite that was not
related to differences in photoperiod [3].
Similar findings were reported in a study by Dunai
and colleagues that combined both light and exercise compared to exercise alone
in obese women and found both groups had a significant difference in BMI but
there were greater reductions in body fat in the women in the light and
exercise group [4].
Evidence from animal studies also indicate that
alterations in the duration of light exposure and the timing of feeding in
relation to light exposure, can impair glucose metabolism and result in weight
gain [6], [7],
[8]. An important finding in these studies
was that the increased weight gain was not associated with changes in caloric
intake. Arble and colleagues [7]
observed a greater weight gain in mice fed only during the light phase (rest
period) compared to mice fed only during the dark phase (active period). This finding was further supported by Fonken
and colleagues [8] who found that when mice
were kept in constant light, they gained more weight than mice under a
light/dark cycle. However, this effect was no longer observed when
feeding was restricted to the clock time corresponding to the dark phase. Taken
together, data from both animals and human studies suggest that light exposure
may modulate metabolism and body weight/composition.
Short sleep duration and later sleep timing have
been linked to higher BMI in multiple studies [9],
[10], [11],
[12], [13],
[14]. These conditions increase the potential
for exposure to light at inappropriate biological times (i.e. light at night
and/or reduced morning light). Given the growing evidence for a role of light
in regulating body weight, the goal of this study was to evaluate the
relationship between the timing and duration of daily habitual ambient light
exposure, sleep timing and duration with BMI. We hypothesize that the timing
and intensity of light exposure (particularly in the morning) will be
associated with a lower BMI independent of sleep duration and timing.
Materials and Methods
Ethics Statement
All participants provided written informed
consent to participate in the study. This study was approved
by the Northwestern University Institutional Review Board.
Participants
Participants were adults recruited from the
community through advertisements for a study of circadian rhythms and sleep
patterns. The inclusion criteria from the initial telephone/email screening
were age >18 years and no major unstable health conditions. Participants who
were consented and completed wrist actigraphy and
food diaries were included in this analysis. Participants with elevated
depressive symptoms, as indicated by a score >20 on the Center for
Epidemiologic Studies Depression Scale (CESD) [15]
were excluded from these analyses. None of the participants reported employment
involving shift work.
Procedure
Participants underwent preliminary telephone or
email screening to determine eligibility and willingness to participate in the
study. Once informed consent was obtained, participants were provided with 7
days of diet logs, sleep logs, and a wrist actigraph (AW-L Actiwatch, Mini Mitter Co. Inc., Bend, OR) which was worn on the
non-dominant wrist for at least 7 days. Participants were instructed to wear
the Actiwatch on the outside of clothing at all
times. In the daily diet logs the participants were asked to list a description
of each food (quantity, preparation, name brand etc),
the time and location of the meal or snack. In the sleep logs participants were
asked to report sleep and wake timing, in combination with the actigraphy (Actiware-Sleep 5
software, Philips/Respironics) sleep and wake timing
and sleep duration were determined.
Measures
Participants were screened for depression with
CES-D. Body Mass Index (BMI) was calculated as kg/m2
based upon self-reported height and weight. Season was determined by the time
of year that the wrist actigraph was worn, Winter (December-February), Spring (March-May), Summer (June
–August) and Fall (September –November), there was fairly even
distribution of data collection during all four seasons.
Dietary Assessment
Dietary intake was assessed using a diet log in
which participants recorded all food and drinks for a 7 day period. We asked
participants to record the time the food or drink was consumed, meal
(breakfast, lunch, dinner, or snack), type of food with brand name if possible,
the location of the meal or snack (i.e. home or restaurant), portion size, and
whether it was a day they consumed less than a typical diet, more than a
typical diet, or a typical diet. Along with their diet logs, participants were
provided with two pages of instructions for completing diet logs. Instructions
asked participants to include portion size (cups, ounces, and pieces), brand, information on preparation method (e.g. boiled, fried in oil, eaten
with refuse), condiments and breaking foods into component parts (e.g.
sandwich is two pieces of wheat bread, 2 oz of turkey
breast). The second sheet was a portion size guide, and provided suggestions
for how to judge portions without measuring (e.g. the size of a deck of cards,
ping pong ball, your fist).
Diet logs were analyzed using publicly available
nutrition information (www.sparkpeople.com) as
well as restaurant and manufacturer websites. Caloric intake was computed for
each day then the mean was computed for the 7 day
period. Logs were considered valid if there were at least 2 weekdays and 1
weekend days completed. Dietary logs were excluded if total calories per day
were <500 (this was the case for one participant). If participants had fewer
than 7 days recorded, all of the available data was used; alternatively, if an
excess of 7 days were completed, the investigators used the first 7 consecutive
days that best coincided with actigraphy recordings.
Sleep Timing and Duration
Sleep timing and duration were assessed using
sleep logs and wrist actigraphy [16], [17]. The
following variables were determined: sleep start, sleep end, and sleep
duration. Rest intervals (inclusive of bedtime and waketime) were set by the investigators using the sleep
logs as a guide [18], [19]. Sleep variables were
calculated by the Actiware 5 software (Philips/Respironics) using default settings. Sleep start was
defined as the first epoch, after the start of the rest interval, of the first
consecutive 10 minute period in which all but one
epoch was scored as immobile. Sleep end was defined as the last epoch, prior to
the end of the rest interval, of the last consecutive 10
minute period in which all but one epoch was scored as immobile. Immobile is defined by the software when the number of
activity counts recorded in that epoch is less than the epoch length in
15-second intervals. For example, there are four 15-second intervals for a
1-minute epoch length; hence, the activity value in an epoch must be less than
four, to be scored as immobile. Wake threshold, which is the number of activity
counts used to define wake, was set at medium (40 counts). Sleep duration was
defined as the amount of time between sleep start and sleep end that was scored
as sleep (an epoch is scored as sleep if the total activity counts ² wake
threshold value). We calculated midpoint of sleep based on the average of the
sleep onset and sleep offset for the 7 day period.
Light Levels and Timing
Light levels were determined at the wrist using
the AW-L Actiwatch (Mini Mitter
Co. Inc., Bend, OR) [20]. Data were cleaned
in Actiware 5 (Philips/Respironics),
this involved excluding periods where the actigraph
was taken off the wrist [21]. In order for a
day to be considered valid and therefore included in the analysis it could not
have more than four hours of excluded data in a 24 hour
period. For each participant light and activity data were exported from the Actiware 5 program at a time resolution of 2 minutes
(epoch). These exported data were first smoothed using a 5 point (10 minute)
moving average (Figure 1) and
then aggregated over 24 hours for each participant (Figure 2).
Representative log linear light plots of smoothed data
across 7 days from three individual participants. |
Representative log linear light plots from three
individual participants. |
The following variables were calculated from this
smoothed and aggregated data for each participant, time above threshold (TAT),
mean light timing above threshold (MLiT) and standard
deviation of the MLiT. TAT and MLiT
were also calculated separately for the day-time (6
am–8 pm) and the night-time (8 pm–6 am). TAT is defined as the
number of 2 minute epochs above a given threshold
multiplied by 2 minutes. Mean light timing (MLiT)
above threshold integrates information on the intensity (lux threshold),
duration (number of 2 minute epochs above the threshold) and timing (clock time
of each 2 minute epoch above the threshold) of light exposure. Individual level
MLiTC is
formulated with general threshold C of LUX as:
|
Where is the jth epoch and
is 1 if LUX>C on the kth day with
indicators: j
=
1,É,720; k
=
1,É,7;
and C
=
500,
1000, 1500. Here j reaches 720 because the light exposure in LUX is measured
every two minutes for 24 hours (720
=
24×60Ö2).
Thus, for example, MLiT500 of 720 minutes indicates that oneÕs light
exposure being greater than 500 lux is on average centered around 720 minutes
(or around 12 PM if the period starts at 12 AM) throughout 24 hours for the 7
days. MLiT500 was
only available in 51 participants due to threshold requirements. Representative
examples of individual profiles of light and the timing of MLiT500 are
provided in Figures 1 and
2. The
standard deviation of MLiT is defined as the standard
deviation of all the times of the 2 minutes bins above a given threshold, and
was determined to quantify the spread of the clock times above a given
threshold.
Statistical Analysis
Data were analyzed using SPSS v. 21.0 using
bivariate correlations and multivariable linear regression analyses. In
bivariate correlations, we tested correlations of BMI with sleep timing,
duration, caloric intake and light variables, including TAT and MLiT at 100, 500, and 1000 lux for 24 hours. For models in
which light was a significant predictor of BMI, the light variable was entered
into the model to predict BMI controlling for midpoint of sleep. In the second
model, we also controlled for relevant covariates including age, gender,
season, activity counts (24 hours) [22], sleep midpoint and total sleep time by entering them as
covariates in a regression equation. We also conducted sensitivity analyses on
TAT and MLiT to evaluate thresholds ranging from
1–1400 lux for the following time periods 1) the entire day (24 hours),
2) day-time (6 am–8 pm), 3) night-time (8 pm–6 am) and 4) morning
(8 am–12 pm). The times for day and night were selected to approximate
times when there may have been natural light year round, other time windows
were initially examined without significant changes to the results (for example
day-time 900
am–11
59
pm and night-time 12
00
am–8
59
am to approximate the average sleep-wake schedule). In addition, a sensitivity
analysis was conducted to evaluate the influence of measurement error on
correlations with BMI. Statistical significance was defined as p<.05 on two tailed tests. Data are available upon email
request to the senior author (Phyllis C. Zee).
Results
Participant demographic, sleep, caloric intake
and light characteristics are listed in Table 1.
Average age of the participants was 30.6 years (SD=
11.7)
and half of the participants were female. Average BMI was in the normal range (M
=
24.0,
SD
=
4.2);
58% reported a BMI equal to or below 24. The average number of valid actigraphy days available was 6.2 (range 5–9 days).
Average sleep start time was 01
26
(02
03)
and average sleep end time was 08
49
AM (02
14).
Average midpoint of sleep was 05
12
AM (02
14),
and sleep duration was 6.2 hours (0.9). Average MLiT500 was
13
05
(01
46),
average time above a threshold of 500 lux was 1.4 (1.3) hours. The standard
deviation of MLiT was greater at the 100 lux threshold (4.4 hours) than at the higher thresholds
(1.4-1.2 hours).
Participant demographic, sleep, caloric, and light
characteristics. |
Correlations between Timing of Light
Exposure, BMI, Sleep and Calories
Correlations are listed in Table 2. Of
the light variables we tested, only MLiT500 was positively correlated
with BMI (r=
0.51,
p<.001; Figure 3B).
Later midpoint of sleep was associated with later light exposure (MLiT100 r
=
0.65,
p<.001, MLiT500, r
=
0.47,
p<.001 (Figure 3A),
MLiT1000 r
=
0.48,
p<.01) but was not associated with BMI or caloric intake. Sleep
duration was not associated with timing of light, BMI or total caloric intake. Caloric intake was not associated with BMI, light, sleep timing or duration.
Association between MLiT500 and sleep midpoint (A) and
between MLiT500 and BMI (B). |
Associations between body mass index and time above
threshold, mean light timing, caloric intake and sleep. |
Multivariable Analyses
In a multivariable linear regression model
including MLiT500, sleep midpoint and BMI, MLiT500
remained a significant predictor of BMI (B=
1.26
SE
=
0.34,
β
=
0.53
p
=
0.001,
r2Δ
=
0.22,
Figure 4). In
the fully adjusted model, which adjusted for covariates, including age, gender,
season, activity counts, sleep duration and sleep midpoint, MLiT500
remained a significant predictor of BMI (B
=
1.28
SE
=
0.36,
β
=
0.54,
p
=
0.001,
r2Δ
=
0.20).
The full model accounted for 34.7% of the variance in BMI (p
=
0.01).
Multiinear
association between BMI, sleep midpoint and MLiT500. |
Sensitivity Analyses
Several sensitivity analyses were conducted aimed
to assess the range of light level thresholds and times of day that were
associated with BMI. Sensitivity analysis was conducted for four different
windows of time: 1) the entire day (24 hours), 2) daytime (6 am–8 pm), 3)
night time (8 pm–6 am), and 4) morning (8 am–12 pm). We also
conducted sensitivity analyses to test measurement error in BMI.
Sensitivity Analysis 24 Hour Day
A sensitivity analysis was conducted for time
above threshold (TAT) and there were no associations between TAT at any light
threshold and BMI. Figure 5
depicts sensitivity analyses testing associations between BMI and MLiT of light at different thresholds ranging from 100 to 1400
lux. This analysis indicated that MLiT500 had the strongest
associations with BMI but there were statistically significant correlations
between 170 and 850 lux. We also assessed our novel measure of mean light
exposure time (MLiT) differently with a weight that
incorporates light intensity. This weight is defined as a normalized (log
transformed) light intensity, which serves as the distribution of light
intensity during a 24-hour day and above a certain lux threshold, for example,
500 lux. Multiplied by this light intensity weight, the MLiT
is expected to be closer to the period of major light exposure. Our assessment
of the MLiT with this weight was quite consistent
with the unweighted MLiT
because the interval of the major light exposure occurred during the day (6
AM–8 PM). Due to the consistent occurrence of major light exposure during
the day, the MLiT was robust either with or without
the light intensity weight.
Pearson correlations with BMI by MLiT
at various thresholds in lux. |
Sensitivity Analysis for Day, Night
and Morning
A sensitivity analysis was also conducted for
light exposure only during the night-time (8
pm–6 am) and only during the day-time (6 am–8 pm) because different
thresholds may be relevant at these time periods. There were no associations
between TAT and BMI during the day-time or night-time.
There was also no association between BMI and MLiT at
night-time (1–100 lux) nor when only including
the morning hours. The average MLiT10 at night-time
was at 2248
(range 20
57–01
43).
This lack of association between MLiT and BMI may be
in part due to the very limited amount of TAT at higher light thresholds at night-time. The time above threshold at night-time
for 10 lux was 2.6 (±1.6) hours, 50 lux was 34 (±36) minutes and for 100 lux
was 14 (±22) minutes. During the day-time (6 am–8 pm) there was an
association between BMI and MLiT at similar lux
threshold levels as reported for the 24 hour day.
Sensitivity Analysis for
Self-reported BMI
In order to examine the influence of potential
measurement error of the self-reported BMI, we conducted an analysis to
determine the influence of random error in BMI on the strength of the
correlation between BMI and MLiT. The following
equation was used for this analysis: BMI2=
BMI1+0.1
¥BMI1¥ε
(where ε is a
random value from standard normal distribution and BMI1 is
self-reported BMI and BMI2 reflects an error in reporting BMI1 of 10 percent). BMI2 was
generated 1000 times. Even with adding 10% error, the average correlation
between BMI and MLIT500 was r
=
0.45
SD
=
0.14
(95% CI 0.17, 0.73).
Discussion
The results of this study demonstrate that the
timing of even moderate intensity light exposure is independently associated
with BMI. Specifically, having a majority of the average daily light exposure
above 500 lux (MLiT500) earlier in the day was associated with a lower
BMI. In practical terms, for every hour later of MLiT500 in
the day, there was a 1.28 unit increase in BMI. The complete regression model
(MLiT500,
age, gender, season, activity level, sleep duration and sleep midpoint)
accounted for 34.7% of the variance in BMI. Of the variables we explored, MLiT500
contributed the largest portion of the variance (20%).
Our results suggest that the relationship between
light and BMI is not simply a function of the accumulated minutes of light
during the day, but more importantly the temporal pattern of light exposure
above a biological threshold. The threshold/intensity of light is also an
important factor since the relationship between MLiT
(24 hours) and BMI was only significant for light intensities above 170 lux and
up to 850 lux in our sensitivity analyses. To put these light levels in
perspective, normal room light is typically between 150–500 lux [23]. In this sample on average, only 4 hours per
day was spent above 100 lux and 1 hour above 1000 lux suggesting that in
general light exposure is similar to that of indoor lighting for much of the
day. The time spent above 1000 lux in this sample was similar to that reported
by Staples et al. 2009 [2] and slightly
shorter than that reported by Goulet et al. 2007 [1]. Both biological and behavioral factors
related to ambient light exposure patterns may play a role in explaining why
these particular light levels are important for the association between light
and BMI. Since most participants had light above 100 lux for at least a few
hours each day, only light thresholds higher than this could discriminate
across the range of BMI. Whereas the cut off at the higher light levels was
most likely due to fewer individuals having ambient light exposure levels at or
above 1000 lux.
It is also possible that the natural changes in
the intensity and wavelength composition of light in the morning compared to
the afternoon/evening [24] may in part
explain our finding for a differential effect of earlier vs. a later daytime
light exposure pattern and BMI. For example, there is generally a higher amount
of blue light (shorter wavelength) in the morning. [25]
Blue light has been shown to have the strongest effect on the circadian system,
including the suppression of nocturnal melatonin secretion [26].
An interesting and perhaps unexpected result was
the lack of correlation between BMI and TAT or MLiT
at night-time at any threshold, including very low
light levels of 10 lux or lower. One possible explanation for the lack of
association in this study between light at night and BMI may be due to the very
limited amount of even moderate levels of light at night in this sample. Thus,
our results do not address the issue of the potential relationship of brighter
light exposure during the night and BMI.
Although our data indicate that those with more
light exposure above key thresholds earlier in the day are more likely to have
a lower BMI, there was no association between MLiT
and BMI for the morning hours (8 am–12 pm) only. This finding supports
the hypothesis that the pattern of light exposure across the entire day is
important for weight regulation. This is complicated by the close link between MLiT and sleep timing, and for some individuals, ÒmorningÓ
as defined by clock time may be quite different than their Òbiological
morningÓ. In the studies that administered morning light and found reductions
in body fat [3], [4],
normalization of the timing of the sleep-wake cycle relative to light exposure,
may have played a role in the effect. Since in these studies the timing of
light exposure was always at a specific clock time (e.g. 45 minutes of exposure
between 6–9 am), participants needed to be awake, to be compliant with
treatment.
In our data, sleep timing was associated with the
timing of light exposure (24 hours) at all the thresholds examined (MLiT100–1000),
such that that a later sleep midpoint was associated with a later MLiT. However, unlike previous reports, the timing of sleep
was not directly correlated with BMI in this study. This lack of association
between sleep measures and BMI may be because of the larger range of sleep
times. In this sample participants with earlier sleep
and wake times (early chronotype) were included,
whereas, in our previous report [10] only
intermediate and evening (late) types were included. Unfortunately we are
underpowered (N=
8)
to investigate whether BMI is associated with sleep timing in morning chronotypes alone. There was also no association in this
study between sleep duration and BMI, nor between sleep duration and light
exposure. This may be due to a difference in the way that sleep data from
different studies has been collected (subjective vs
objective) and analyzed, since the association between sleep duration and BMI
has typically been reported when self-reported sleep duration was treated as
categorical variable [27]. Another factor
could be that the median BMI is this sample is in the normal range (23 kg/m2).
While there was no association between sleep timing and duration with BMI in
this sample, the relationship between sleep, light exposure and body weight is
likely to still be important. There is evidence from a recent experimental
study for an interaction between sleep duration and light exposure with
metabolic hormones. Sleep restricted to 5 hours per night paired with light
exposure in the morning resulted in an increase in leptin
and a decrease in ghrelin compared to the dim light condition [5].
Our findings, similar to those from two different
animal models [7], [8]
found that changes in the timing of light exposure were associated with body
weight independent of caloric intake. One possible mechanism linking light
directly to BMI, rather than caloric intake may be the influence of light on
the expression and secretion of hormones, such as melatonin. In addition to its
circadian timing effects, light exposure history during the day can alter
nocturnal levels of melatonin [28] and
sensitivity of the circadian clock to light [29],
[30], [31].
These effects of light may play a role in metabolism and weight regulation. It
has been shown, for example, that in middle-aged rats daily nocturnal melatonin
administration for 3 weeks reduced weight gain in response to a high-fat diet
and decreased nighttime plasma leptin concentrations,
independent of total food consumption [32].
Alteration in melatonin level has also been shown to affect insulin sensitivity
[33], [34],
and recent studies in humans suggest that a low melatonin level is a risk
factor for type 2 diabetes [35]. Future
studies are needed to determine whether the influence of
light on BMI is mediated by its effects on melatonin and/or circadian timing
and amplitude [34], [35], [36]. Other
potential mechanisms include the impact of light on sleep quality and autonomic
function, [37], [38],
[39] which can directly or indirectly affect
metabolism and energy balance. For example, light exposure in the blue range
(460 nm) in the evening may alter the dynamics of slow wave and rapid eye
movement sleep [40], and such changes in
sleep have been shown to affect metabolic function [41].
The limitations of this study include lack of
random selection from a nationally representative sample and use of self-reported
diet and BMI which may have resulted in measurement
error [42]. After accounting for a 10%
variation in BMI, the association between MLiT and
BMI remained significant in this study. In addition, since young and normal
weight participants (such as in this study) typically demonstrate a higher
correlation between self-reported and objective BMI [43],
one would expect a lower level of reporting bias. Ambient light levels were
measured at the wrist and thus may not be representative of light intensity
reaching the eyes, limiting our ability to determine the absolute light values
for the biological effect of light on BMI. However, a study comparing light
levels measured near the eye, to levels at the wrist, indicates that at light
levels less that 5000 lux the readings from each
device were fairly similar [44]. Another
potential concern is the effect of clothing obscuring the light sensor. To
limit this all participants were instructed to wear the device on the outside
of clothing. In addition, controlling for season, which may impact the
likelihood of the device being covered by clothing, did not significantly
impact the relationship between MLiT and BMI. Even
with these potential limitations, the ambient light values measured at the
wrist do represent relative changes in light levels between individuals and
within an individual across the day, and therefore do not limit the assessment
of the timing of light exposure. The cross sectional study design does not
allow us to directly infer directionality of the relationship between light and
body weight.
In conclusion, the findings of this study
indicate that the temporal pattern of light exposure during the daytime can
influence body weight independent of sleep timing and duration. Further studies
are needed to understand the causal relationship and mechanisms linking
biologically appropriate and inappropriate light timing with weight.
Nevertheless, light is a powerful biological signal and appropriate timing,
intensity and duration of exposure may represent a potentially modifiable risk
factor for the prevention and management of obesity in modern societies.
Acknowledgments
The authors would like to acknowledge the
contribution of Gregory Kodesh, Ashley Jaksa, Tiffany St James, Brandon Lu, Andrew Kern, Brittany Fondel, and Erin McGorry for
their assistance with data collection and entry and to Nicholas Cekosh for his aid in formatting the figures.
Funding Statement
The work on this project was funded through
Grants R01HL069988, P01 AG11412, 5K12 HD055884, and 1K23HL109110. The funders
had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript.
Article information
PLoS One.
2014; 9(4): e92251.
Published online Apr 2, 2014. doi: 10.1371/journal.pone.0092251
PMCID: PMC3973603
Kathryn J. Reid,#1 Giovanni Santostasi,#1 Kelly G. Baron,1 John Wilson,1 Joseph Kang,2 and Phyllis C. Zee1,*
Ralph E. Mistlberger,
Editor
1Department
of Neurology, Northwestern University, Feinberg School of Medicine, Chicago,
Illinois, United States of America
2Department
of Preventive Medicine, Northwestern University, Feinberg School of Medicine,
Chicago, Illinois, United States of America
Simon Fraser University, Canada
#Contributed
equally.
* E-mail: p-zee/at/northwestern.edu
Competing Interests: The
authors have read the journalÕs policy and have the following to report, Dr.
Reid reports receiving a grant from Philips. Dr. Santostasi,
Mr. Wilson and Dr. Kang report no competing interest. Dr.
Baron reports receiving grants from the National Institute of Health.
Dr. Zee has served as a consultant to Purdue Pharma,
Merck, Vanda, Jazz and Philips/Respironics, stock
ownership in Teva, member of the Board of Directors
of the Sleep Research Society. None of these potential competing interests
relate to the work presented in this manuscript. This does not alter the
authorsÕ adherence to all the PLOS ONE polices on sharing data and materials.
Conceived and designed the experiments: KJR GS PCZ.
Performed the experiments: KJR. Analyzed the data: GS KB JK JW. Contributed
reagents/materials/analysis tools: PCZ. Wrote the paper: KJR GS PCZ KB.
Prepared data for analysis: JW KJR.
Received November 4, 2013;
Accepted February 19, 2014.
This is an open-access article distributed under
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