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Inadequate sleep as a risk factor for obesity: Analyses of the NHANES I

Article (PDF Available)inSleep 28(10):1289-96 · November 2005with3,111 Reads
DOI: 10.1093/sleep/28.10.1289 · Source: PubMed
Abstract
Sleep deprivation has been hypothesized to contribute toward obesity by decreasing leptin, increasing ghrelin, and compromising insulin sensitivity. This study examines cross-sectional and longitudinal data from a large United States sample to determine whether sleep duration is associated with obesity and weight gain. Longitudinal analyses of the 1982-1984, 1987, and 1992 NHANES I Followup Studies and cross-sectional analysis of the 1982-1984 study. Probability sample of the civilian noninstitutionalized population of the United States. Sample sizes of 9,588 for the cross-sectional analyses, 8,073 for the 1987, and 6,981 for the 1992 longitudinal analyses. Measured weight in 1982-1984 and self-reported weights in 1987 and 1992. Subjects between the ages of 32 and 49 years with self-reported sleep durations at baseline less than 7 hours had higher average body mass indexes and were more likely to be obese than subjects with sleep durations of 7 hours. Sleep durations over 7 hours were not consistently associated with either an increased or decreased likelihood of obesity in the cross-sectional and longitudinal results. Each additional hour of sleep at baseline was negatively associated with change in body mass index over the follow-up period, but this association was small and statistically insignificant. These findings support the hypothesis that sleep duration is associated with obesity in a large longitudinally monitored United States sample. These observations support earlier experimental sleep studies and provide a basis for future studies on weight control interventions that increase the quantity and quality of sleep.

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Available from: Dolores Malaspina, Aug 20, 2014
SLEEP, Vol. 28, No. 10, 2005
1289
INTRODUCTION
MODERN HUMANS ARE EXPERIENCING 2 PARALLEL
TRENDS, INCREASING BODY MASS INDEX (BMI)
1
AND
A DECLINE IN AVERAGE SLEEPING TIME.
2
OBESITY is
reaching epidemic proportions throughout the developed world
and is attributed largely to industrialization with reduced acute
and chronic disease,
3
increased food consumption, and lowered
levels of physical activity.
1
Early humans were likely to have got-
ten more sleep per night on average, since their circadian rhythms
were more closely synchronized to the rising and setting of the
sun. Today we have artificial light to extend our active phases
and many other distractions that prevent us from getting adequate
sleep.
Mounting evidence from laboratory studies with animals and
humans suggests a mechanistic link between lack of sleep and
increasing body weight. Prolonged sleep deprivation has been
shown to increase food intake in rats.
4
Circadian fluctuations in
blood leptin levels have been reported in humans, with peaks in
leptin secretion occurring during sleep.
5
A study conducted by
researchers at the University of Chicago found that short-term
sleep curtailment impacted the neuroendocrine control of appetite
in healthy young lean men.
6
Comparisons were made between
results obtained after the subjects were exposed to a sleep-debt
condition and after a sleep-recovery condition. The sleep-cur-
tailment condition resulted in decreased leptin levels, increased
ghrelin levels, and markedly elevated hunger and appetite ratings.
The subjects were found to particularly crave sweets, starch, and
salty snacks after being deprived of sleep. The metabolic regula-
tory system would be expected to initiate caloric intake to coun-
terbalance additional energy expenditures from increased wake
time, but, in this case, the increased energy expenditures were
presumed to be negligible, since the experimental protocol called
for the extra hours of wakefulness to be spent lying in bed or
sitting in a comfortable chair.
6
In light of evolutionary pressures
such as the thrifty genotype that favors weight gain and mainte-
nance, it is not surprising that the metabolic regulatory system
could overcompensate for additional energy expenditures and
lead to obesity over time. In an analysis of data from the Wiscon-
sin Sleep Cohort Study, a population-based longitudinal study of
sleep disorders, short sleep duration was found to be associated
with reduced leptin levels, elevated ghrelin levels, and increased
BMI in subjects between the ages of 30 and 60 years.
7
A U-shaped
curvilinear association between sleep duration and BMI was ob-
served, with persons sleeping less than 8 hours having increased
BMI proportional to decreased sleep.
7
Sleep loss has also been linked to decreased glucose tolerance,
a risk factor for obesity. Depriving normal subjects of sleep has
been shown to result in insulin responses to hyperglycemia char-
acteristic of insulin resistance and a prediabetic metabolic state.
8
Spiegel et al
9
found that healthy men whose sleep was restricted
to 4 hours per night for 6 nights experienced a 30% reduction in
insulin response to glucose.
Inadequate sleep and poor-quality sleep are associated with
obesity in children,
10-12
adolescents,
13
and adults
14,15
studied in
case-control and cross-sectional studies. Sleep duration was de-
Inadequate Sleep as a Risk Factor for Obesity: Analyses of the NHANES I
James E. Gangwisch, PhD
1
; Dolores Malaspina, MD, MPH
2
; Bernadette Boden-Albala, Dr.PH
3
; Steven B. Heymsfield, MD
4
1
Mailman School of Public Health, Department of Epidemiology;
2
Department of Psychiatry, Division of Clinical Neurobiology;
3
Department of Neurol-
ogy and Department of Sociomedical Sciences;
4
Obesity Research Center, St. Luke’s-Roosevelt Hospital Center; Columbia University, College of Physi-
cians and Surgeons, New York, NY
Inadequate Sleep as a Risk Factor for Obesity—Gangwisch et al
Disclosure Statement
This was not an industry supported study. Drs. Gangwisch, Heymsfield,
Malaspina, and Boden-Albala have indicated no financial conflicts of inter
-
est.
Submitted for publication September 2004
Accepted for publication June 2005
Address correspondence to: James Gangwisch, Columbia University, Mail
-
man School of Public Health, Department of Epidemiology, 722 West 168th
Street, Room R720E, New York, NY 10032; Tel: (212) 543 5572; Fax: (212)
568 3534; E-mail: jeg64@columbia.edu
Study Objectives:
Sleep deprivation has been hypothesized to contribute
toward obesity by decreasing leptin, increasing ghrelin, and compromising
insulin sensitivity. This study examines cross-sectional and longitudinal
data from a large United States sample to determine whether sleep dura-
tion is associated with obesity and weight gain.
Design: Longitudinal analyses of the 1982-1984, 1987, and 1992 NHANES
I Followup Studies and cross-sectional analysis of the 1982-1984 study.
Setting: Probability sample of the civilian noninstitutionalized population
of the United States.
Participants: Sample sizes of 9,588 for the cross-sectional analyses,
8,073 for the 1987, and 6,981 for the 1992 longitudinal analyses.
Measurements and Results: Measured weight in 1982-1984 and self-
reported weights in 1987 and 1992. Subjects between the ages of 32
and 49 years with self-reported sleep durations at baseline less than 7
hours had higher average body mass indexes and were more likely to
be obese than subjects with sleep durations of 7 hours. Sleep durations
over 7 hours were not consistently associated with either an increased
or decreased likelihood of obesity in the cross-sectional and longitudinal
results. Each additional hour of sleep at baseline was negatively associ-
ated with change in body mass index over the follow-up period, but this
association was small and statistically insignificant.
Conclusions: These findings support the hypothesis that sleep duration
is associated with obesity in a large longitudinally monitored United States
sample. These observations support earlier experimental sleep studies
and provide a basis for future studies on weight control interventions that
increase the quantity and quality of sleep.
Keywords: Sleep, obesity, insulin resistance
Citation: Gangwisch JE; Malaspina D; Boden-Albala B et al. Inadequate
sleep as a risk factor for obesity: analyses of the NHANES I
. SLEEP
2005;28(10): 1289-1296.
SLEEP, Vol. 28, No. 10, 2005
1290
termined in these studies by parental report with children, by 24-
hour wrist actigraphy with adolescents, and by self-report with
adults. In 1 of the cross-sectional studies with adults, data from
1772 subjects over the age of 15 years were examined from the
Health and Nutritional Survey of Valencia, Spain, conducted in
1994.
14
The investigators found an inverse and statistically sig-
nificant association between self-reported sleep duration and obe-
sity. Subjects who reported sleeping 9 or more hours per night had
less than half the risk of obesity of those who reported sleeping
only 6 hours or less per night (prevalence odds ratio [OR] = 0.43;
95% confidence interval [CI]: 0.27-0.67).
We are aware of only 1 other study that explored the relation-
ship between sleep duration and obesity in adults using longitu-
dinal data. In that single-age cohort study, 496 young adults, two
thirds of whom had high scores on a psychological symptoms
questionnaire, were interviewed at the ages of 27, 29, 34, and 40
years.
16
Strong cross-sectional associations were found between
sleep durations less than 6 hours and obesity at ages 27, 29, and 34
years. They found a virtually monotonic trend toward lower BMI
and lower weight gain over the follow-up period among those
with longer sleep durations.
The present investigation, based upon both longitudinal and
cross-sectional data from a large United States sample, was con-
ducted to explore whether the number of hours of sleep reported
per night is associated with obesity and weight gain after control-
ling for the potential confounding variables of depression, physi-
cal activity, education, ethnicity, alcohol consumption, cigarette
smoking, sex, waking during the night, daytime sleepiness, and
age. We hypothesized that shorter sleep durations would be asso-
ciated with higher average BMIs, higher likelihoods of suffering
from obesity, and higher weight gain over the follow-up period.
METHODS
Subjects
Subjects for this study were participants in the 1982-1984,
17
1987,
18
and 1992
19
Epidemiologic Follow-up Studies of the first
National Health and Nutrition Examination Survey (NHANES
I). The NHANES I survey included a standardized medical
examination and questionnaires to obtain information on the
effects of clinical, environmental, and behavioral factors on
health conditions. The survey included a probability sample of
the civilian noninstitutionalized population of the United States
between 1971 and 1975. The NHANES I Epidemiologic Followup
Study conducted between 1982 and 1984 attempted to trace and
interview NHANES I subjects, or their proxies, who were aged
25 to 74 years at baseline (n = 14 407). Eighty-five percent of
all eligible subjects were successfully recontacted (n = 12 220).
Individuals who were deceased (n = 1697), whose body weight or
height were not measured (n = 542), or who did not answer the
question regarding the number of hours of sleep obtained per night
(n = 393) were excluded from the cross-sectional components
of the analyses, yielding a total final sample size of 9588. The
NHANES I Epidemiologic Followup Study conducted in 1987
also traced and interviewed subjects from NHANES I who were
aged 25 to 74 years at baseline. Sixty-nine percent of the eligible
subjects were successfully recontacted (n = 9998). Individuals
who were deceased (n = 1096), whose height or self-reported
body weight was missing (n = 529), or who did not answer
the question regarding the number of hours of sleep obtained
per night (n = 300) were excluded from the 1987 longitudinal
components of the analyses, yielding a final sample size of 8073.
The 1992 Followup Study also traced and interviewed subjects
from NHANES I who were aged 25 to 74 years at baseline. Sixty
percent of the eligible subjects were successfully recontacted
(n = 8634). Individuals who were deceased (n = 1046), whose
height or self-reported body weight was missing (n = 373), or
who did not answer the question regarding the number of hours of
sleep obtained per night (n = 234) were excluded from the 1992
longitudinal components of the analyses, yielding a final sample
size of 6981. Missing values for variables other than body weight,
height, and hours of sleep per night, which for most variables
represented less than 5% of the total sample size, were imputed
using mean and mode substitution.
To see whether the subjects (n = 9588) included in our study
in 1982-1984 differed substantially from the NHANES I cohort
(n = 14 407) in 1971-1975, we performed analyses on baseline
variables available in both groups to determine their effects upon
loss to follow-up. Age at baseline was categorized into ten 5-
year age groups, and rates of loss to follow-up were highest for
participants aged 60 years and older. Rates of loss to follow-up
were also higher among nonwhites (45% for nonwhites vs 31%
for whites), men (39% for men vs 30% for women), those who
had not graduated from high school (42% for < high-school grad
vs 33% for high-school grad), and the obese (36% for obese,
34% for overweight, and 33% for lean).
The NHANES I includes weights to account for the complex
sampling design and to allow approximations of the United States
population. We conducted nonweighted analyses using SAS
Software
20
for 3 reasons. First, our objective was not to provide
national estimates but, rather, to look at the relationship between
sleep duration, BMI, obesity, and weight gain. Second, our study’s
baseline measures were taken from the 1982-1984 Follow-up to
the NHANES I, so the weights created for baseline measures
taken from the 1971-1974 NHANES I did not account for subjects
who were lost to follow-up between the 2 waves. Third, there
have been differences of opinion regarding the appropriateness of
using the sample weights with the NHANES.
21
Measures
The dependent variables used in the study were the subject’s
BMI (ie, weight in kilograms divided by the square of height in
meters—kg/m
2
), obesity status based upon BMI, and change in
BMI over the follow-up period. The subjects’ adult heights were
obtained at the baseline NHANES I conducted between 1971 and
1975, and the presumption was made that their heights had not
changed during the follow-up period. Cross-sectional analyses
were conducted using the actual measured body weight obtained
from the 1982-1984 Follow-up, and longitudinal analyses were
performed using the self-reported body weight obtained from the
1987 and 1992 follow-ups. Change in BMI over the follow-up
period was computed by subtracting the subjects’ BMI in 1982-
1984 from their BMI at the end of the follow-up period in 1992.
BMI was dichotomized between obese (BMI ≥ 30) and nonobese
(BMI < 30) for logistical regression analyses and retained as a
continuous variable for linear regression analyses.
The independent variable used in the study was the subjects’
self-reported average number of hours of sleep obtained per night.
The question asked in the 1982-1984 NHANES was, “How many
Inadequate Sleep as a Risk Factor for Obesity—Gangwisch et al
SLEEP, Vol. 28, No. 10, 2005
hours of sleep do you usually get a night (or when you usually
sleep)?” This question was asked only at baseline, the time of
the 1982-1984 study, and was not asked again at the times of
the 1987 and 1992 follow-ups. Questions were also asked about
trouble waking up during the night and daytime sleepiness. If the
number of hours of sleep obtained per night was represented in the
regression models as a continuous variable, then the assumption
would have to be made that each additional hour of sleep per night
is associated with the same change in the dependent variable,
regardless of the number of hours of sleep. The validity of this
assumption was checked by running a logistic regression model
with obese and nonobese as the dependent variable and the
different numbers of hours of sleep per night as the independent
variables. We then plotted the β coefficients associated with each
hour of sleep reported per night and found that the plot was not
linear and, therefore, indicated that each additional hour of sleep
per night is not associated with the same change in the log odds
of obesity. We therefore chose to categorize the number of hours
of sleep per night rather than using sleep duration as a continuous
variable. Few subjects reported getting either extremely low (2
or 3, n = 64), or extremely high (11 or more, n = 63) numbers of
hours of sleep per night, so we created categories for subjects who
reported getting 2 to 4 hours and 10 or more hours per night.
Control variables in the study included depression, physical
activity, education, ethnicity, alcohol consumption, cigarette
smoking, sex, waking during the night, daytime sleepiness, and
age. Measures for all of the control variables were obtained at the
time of the 1982-1984 Follow-up.
The Center for Epidemiologic Studies Depression Scale, de-
signed to measure symptoms of depression in community stud-
ies, was used in the NHANES I Followup Study. The Center for
Epidemiologic Studies Depression Scale has 20 items that ask
the frequency of experiencing specific depressive symptoms dur-
ing the previous week. The scoring for positively worded items
is reversed, so high scores represent responses in the depressed
range. The standard cutoff score for the presence of depressive
symptoms is 16 out of a total possible score of 60.
22
We therefore
defined the presence of depression as a score of 16 or above on the
Center for Epidemiologic Studies Depression Scale.
The subjects’ level of physical activity was measured by adding
the scores from 2 questions that asked them to estimate how much
physical activity they obtained in recreational and nonrecreational
activities. They were given a score of 3 if they were very active, 2
if they were moderately active, and 1 if they were inactive. Scores
therefore ranged from 2 to 6, with increasing scores representing
increased levels of physical activity.
The subjects’ consumption of alcohol was determined by add-
ing their reported daily consumption of beer, wine, and hard li-
quor. Their reported daily intake of alcohol was then placed into
3 categories. The first category was made up of subjects who re-
ported complete abstinence from alcohol. The second category
was made up of those who reported drinking more than 0 and less
than or equal to 2 drinks per day, and the third category was made
up of those who reported drinking more than 2 drinks per day.
Smokers were defined as subjects who reported currently
smoking cigarettes or who reported smoking an average of at
least 1 cigarette per day. The waking-during-the-night and day-
time-sleepiness questions were answered on 5-point Likert scales
(never, rarely, sometimes, often, and almost always).
Statistical Analysis
After preliminary univariate and bivariate analyses, we used
logistic and linear regression analyses to examine the relationship
between the dependent variables of obesity status and BMI
and the number of hours of sleep per night while controlling
for depression, physical activity, education, ethnicity, alcohol
consumption, cigarette smoking, sex, waking during the night,
daytime sleepiness, and age. We performed 2 types of analyses to
test whether sleep duration was associated with weight gain. First,
we conducted a bivariate analysis of the subjects’ responses to 2
questions asked in 1982-1984. One question asked “Compared
to one year ago, do you have sleep problems much more now,
somewhat more now, somewhat less now, much less now, or is
your sleeping pattern about the same?” and the other question
asked “How does your weight now compare to your weight 6
months ago? Is it at least 10 pounds more, at least 10 pounds
less, or about the same?” Second, we performed linear regression
analyses with change in BMI over the follow-up period (BMI in
1992 minus BMI in 1982-1984) as the dependent variable. The
significance of individual coefficients in the logistic regression
models were determined by the 95% confidence limits for ORs,
while the significance of individual coefficients in the linear
regression models were determined by P values. The significance
of the overall logistic and linear regression models were
determined by Wald χ
2
tests.
We tested for multiplicative interaction between age and
duration of sleep with the log likelihood ratio test and found that
age acted as an effect modifier between the number of hours of
sleep per night and obesity (P < .05). We then stratified the sample
by 10-year age increments and performed logistic regression
analyses with each. The OR for obesity of sleeping 7 hours per
night compared with the other sleep categories were similar for
subjects who were in their 30s and 40s at the time of the 1982-1984
Follow-up. We chose to divide the sample into 3 age groups, with
subjects who at the time of the 1982-1984 study were between
the ages of 32 and 49 in one group, subjects who were between
the ages of 50 and 67 in another group, and subjects who were
between the ages of 68 and 86 in the final group. Logistic and
linear regression analyses were completed with each age group
for the 1982-1984 data, 1987 data, and 1992 data. No significant
relationships were found between sleep and weight statuses in
the 2 older age groups, so multivariate results are shown for the
32-to-49 age group only. After excluding subjects who were
deceased, whose body weights or heights had not been measured,
and who did not answer the hours of sleep question, there were
3682 subjects between the ages of 32 and 49 years for the 1982-
1984 cross sectional analyses, 3355 for the 1987 longitudinal
analyses, and 3208 for the 1992 longitudinal analyses.
RESULTS
Results for the bivariate analyses at baseline are shown in Ta-
bles 1 and 2. There were significant differences between sleep-
duration categories and obesity-status categories for subjects be-
tween the ages of 32 and 49 years, while there were no significant
differences for subjects between the ages of 50 and 86 years. The
obese category had the highest percentage of subjects between
the ages of 32 and 49 years who reported getting less than 7 hours
of sleep per night, while the lean category had the lowest percent-
age of subjects who reported getting less than 7 hour of sleep per
Inadequate Sleep as a Risk Factor for Obesity—Gangwisch et al
1291
SLEEP, Vol. 28, No. 10, 2005
night (Table 1). There were also significant differences between
obesity status and the control variables of physical activity, educa-
tion, ethnicity, alcohol consumption, cigarette smoking, sex, wak-
ing during the night, and age (Table 2).
Figure 1 shows the average BMI at the times of the 1982-
1984, 1987, and 1992 surveys by sleep duration for subjects who
were between the ages of 32 and 49 years at baseline. BMI was
computed using actual measured body weights at the time of the
1982-1984 survey and self-reported body weights at the times of
the 1987 and 1992 follow-ups. Subjects who reported getting 2 to
4 hours, 5 hours, and 6 hours of sleep per night at baseline had the
first, second, and third highest average BMI, respectively, of the
sleep-duration categories in 1982-1984, 1987, and 1992.
Table 3 and Figure 2 show the results for the logistic regression
analyses, in which the dependent variable was dichotomized
between nonobese (BMI < 30) and obese (BMI ≥ 30), for subjects
who were between the ages of 32 and 49 years at baseline. Model
1 included only the number of hours of sleep obtained per night,
while Model 2 included the number of hours of sleep obtained per
night and was adjusted for the potential confounding variables. In
Table 3, we can see that women who slept less than 7 hours per
night were progressively more likely to be obese as their sleep
durations decreased. Men who slept 6 or fewer hours per night
were more likely to be obese that those who slept 7 hours per
night. The likelihood of being obese for subjects with 8- and
9-hour sleep durations, as compared with subjects with 7-hour
sleep durations, differed by sexes. In comparison with subjects
who slept 7 hours per night, women who slept 8 hours per night
1292
Inadequate Sleep as a Risk Factor for Obesity—Gangwisch et al
Table
1—Relationship Between the Number of Hours of Sleep per
Night and Obesity Status* by Age Group at Baseline (1982-1984)
Age, y Average Lean Overweight Obese X
2
Sleep (P value)
Per Night, h
32-49 (n = 3682)
2-4 22 (1) 20 (2) 27 (4) 55.34
(P < .0001)
5 72 (4) 76 (6) 57 (8)
6 349 (20) 286 (24) 175 (24)
7 639 (36) 391 (33) 206 (28)
8 592 (33) 340 (29) 233 (32)
9 75 (4) 43 (4) 28 (4)
≥ 10 24 (1) 16 (1) 11 (1)
50-67 (n = 3,324)
2-4 35 (3) 36 (3) 33 (4) 19.41
(P = .0792)
5 83 (6) 78 (6) 56 (8)
6 240 (18) 260 (20) 158 (21)
7 407 (31) 402 (31) 208 (28)
8 440 (34) 420 (33) 221 (30)
9 62 (5) 50 (4) 47 (6)
≥ 10 34 (3) 34 (3) 20 (3)
68-86 (n = 2,582)
2-4 36 (3) 32 (3) 18 (5) 11.96
(P = .4486)
5 91 (8) 87 (9) 24 (6)
6 217 (18) 157 (16) 73 (19)
7 251 (21) 228 (23) 74 (19)
8 417 (35) 327 (33) 137 (35)
9 108 (9) 93 (9) 34 (9)
≥ 10 77 (6) 69 (7) 33 (8)
*Lean is defined as a body mass index (BMI) ≤ 25, overweight as >
25 but < 30, and obese as ≥ 30.
(BMI is calculated as weight in kg divided by height in meters
squared [kg/m
2
].)
Data are presented as number (%)
23
24
25
26
27
28
29
30
31
Average BMI
2 to 4 5 6 7 8 9 => 10
Hours of Sleep Per Night
1982/1984
1987
1992
Figure 1—Average BMI by the number of hours of sleep reported per
night at baseline in 1982-1984. (BMI is calculated as the weight in kg
divided by height in meters squared.)
Figure 2—Odds ratios and 95% confidence intervals of obese (BMI
=> 30) versus non-obese (BMI < 30) for subjects between the ages
of 32 and 49 by sleep duration at baseline adjusted for depression,
physical activity, education, ethnicity, alcohol consumption, cigarette
smoking, gender, waking during the night, daytime sleepiness, and
age.
(BMI is calculated as weight in kg divided by height in meters
squared.)
0
0.
5
1
1.
5
2
2.
5
3
3.
5
4
4.
5
OR & 95% CI - 1987
n = 3,355
0
0.
5
1
1.
5
2
2.
5
3
3.
5
4
4.
5
OR & 95% CI - 1982 to 1984
n = 3,682
OR & 95% CI - 1992
n = 3,208
Average Sleep Duration in Hour at Baseline in 1982-1984
2 to 4 5 6
7 8
9
=> 10
SLEEP, Vol. 28, No. 10, 2005
and men who slept 9 hours per night were more likely to be obese,
while men who slept 8 hours per night and women who slept 9
hours per night were not more likely to be obese. In Figure 2, in
which the data for both sexes are pooled, subjects with 8- and 9-
hour sleep durations did not differ significantly from those with
7-hour sleep durations.
In the results from the cross-sectional analyses shown in Figure
2, subjects between the ages of 32 and 49 years who reported
getting 2 to 4, 5, and 6 hours of sleep per night were 235% (OR =
2.35, 95% CI 1.36-4.05), 60% (OR = 1.60, 95% CI 1.12-2.29), and
27% (OR = 1.27, 95% CI 1.01-1.60) more likely to be obese after
adjusting for the potential confounding variables than subjects
who reported getting 7 hours of sleep per night, respectively.
Subjects who reported getting 4 or fewer hours of sleep per night
at baseline continued to be significantly more likely than those
who reported getting 7 hours per night to be obese at the times
of the 1987 and 1992 follow-up studies. The likelihood of being
obese for subjects who reported getting more than 7 hours of sleep
per night was not significantly different than for subjects who
reported getting 7 hours per night in either the cross-sectional
or longitudinal results. All of the logistic regression models,
both before and after the inclusion of the potential confounding
1293
Inadequate Sleep as a Risk Factor for Obesity—Gangwisch et al
Table
2—Characteristics of Subjects Between the Ages of 32 and 49
Years by Obesity Status* at Baseline (1982-1984)
Lean Overweight Obese X
2
(P value)
Depression, CES-D
< 16 1,501 (85) 981 (84) 590 (80) 8.08
(P = .0176)
≥ 16 272 (15) 191 (16) 147 (20)
Physical Activity, score
2 - Low 115 (6) 90 (8) 87 (12) 56.63
(P < .0001)
3 293 (17) 231 (20) 172 (23)
4 756 (43) 490 (42) 285 (40)
5 374 (21) 243 (21) 144 (20)
6 - High 235 (13) 118 (10) 49 (7)
Highest Level of Education
< High- 334 (19) 276 (24) 237 (32) 62.41
school (P < .0001)
graduate
High-school 1,297 (73) 807 (69) 474 (64)
graduate
College 142 (8) 89 (8) 26 (4)
graduate
Ethnicity
Caucasian 1,580 (89) 995 (85) 554 (75) 79.33
(P < .0001)
Non- 193 (11) 177 (15) 183 (25)
Caucasian
Consumption of Alcohol, drinks/d
0 482 (27) 342 (29) 305 (41) 53.93
(P < .0001)
> 0 < 2 1124 (63) 707 (60) 382 (52)
≥ 2 167 (9) 123 (10) 50 (7)
Cigarette Smoker
Yes 706 (40) 390 (33) 232 (31) 21.51
(P < .0001)
No 1067(60) 782 (67) 505 (69)
Sex
Female 1374(77) 626 (53) 516 (70) 190.32
(P < .0001)
Male 399 (23) 546 (47) 221 (30)
Waking during the night
Never 437 (25) 327 (28) 221 (30) 23.30
(P = .0030)
Rarely 653 (37) 394 (34) 217 (29)
Sometimes 450 (25) 280 (24) 171 (23)
Often 149 (8) 104 (9) 77 (10)
Almost 84 (5) 67 (6) 51 (7)
Always
Daytime Sleepiness
Never 615 (35) 420 (36) 232 (31) 19.32
(P = .0132)
Rarely 613 (35) 364 (31) 224 (30)
Sometimes 401 (23) 272 (23) 200 (27)
Often 88 (5) 80 (6) 38 (5)
Almost 33 (2) 48 (4) 32 (4)
Always
Total 1696 (100) 1276 (100) 710 (100)
Sample
*Lean is defined as a body mass index (BMI) 25 kg/m
2
, over-
weight as > 25 kg/m
2
but < 30 kg/m
2
, and obese as ≥ 30 kg/m
2
. (BMI
is calculated as weight in kg divided by height in meters squared.)
Data are presented as number (%).
CES-D refers to the Center for Epidemiologic Studies Depression
Scale.
-3
-2
-1
0
1
2
3
4
5
Differences in BMI & 95% CI - 1987
**
**
*
n = 3,355
-3
-2
-1
0
1
2
3
4
5
Differences in BMI & 95% CI - 1982-84
**
**
*
n = 3,682
-3
-2
-1
0
1
2
3
4
5
Differences in BMI & 95% CI - 1992
n = 3,208
**
**
*
2 to 4 5 6 7 8 9 => 10
Average Sleep Duration in Hours at Baseline in 1982-1984
-3
-2
-1
0
1
2
3
4
5
**
**
*
-3
-2
-1
0
1
2
3
4
5
**
**
*
-3
-2
-1
0
1
2
3
4
5
**
**
*
2 to 4 5 6 7 8 9 => 10
Average Sleep Duration in Hour at Baseline in 1982-1984
Differences in BMI
& 95% CI - 1982-84
Differences in BMI
& 95% CI - 1987
Differences in BMI
& 95% CI - 1992
Figure 3—Differences in average BMI by sleep duration at baseline
for subjects between the ages of 32 and 49 adjusted for depression,
physical activity, education, ethnicity, alcohol consumption, cigarette
smoking, gender, waking during the night, daytime sleepiness, and
age.
(BMI is calculated as the weight in kg divided by height in meters
squared.) ** p < .01, * p < .05
n = 3,208
n = 3,355
n = 3,682
SLEEP, Vol. 28, No. 10, 2005
variables, were statistically significant (P ≤ .01).
The dependent variable of BMI was retained as a continuous
variable in the linear regression analyses. In figure 3, it can be seen
that at baseline, after controlling for the potential confounding
variables, the average BMI for subjects who slept 2 to 4, 5, and
6 hours per night was 2.25 (SEM 0.75), 1.33 (SEM 0.45), and
0.65 (SEM 0.27) points higher than the average BMI of subjects
who slept 7 hours per night, respectively. The average BMI
associated with sleep durations less than 7 hours continued to be
significantly elevated in comparison with sleep durations of 7
hours at follow-up in 1987 and in 1992. In comparison to getting
7 hours of sleep per night, getting more than 7 hours of sleep per
night was not significantly associated with either an increased or
decreased BMI. All of these linear regression models, both before
and after the inclusion of the potential confounding variables,
were statistically significant (P ≤ .01).
Table 4 shows the bivariate results comparing the subject’s re-
sponses to questions asked in the 1982-1984 survey about their
weight compared with 6 months earlier and their sleeping problems
compared with a year earlier. Among the subjects who reported
that their sleeping problems were much more than they had been
a year earlier, a higher percentage of them reported having gained
at least 10 pounds over the previous 6 months. Among the subjects
who reported that their sleeping problems were much less than
they had been a year earlier, a higher percentage of them reported
having lost at least 10 pounds over the previous 6 months.
Subjects between the ages of 32 and 49 years who remained
in the study through 1992 gained an average of 1.02 BMI points
(SD 2.91) over the 8- to 10-year follow-up period. The average
increase in BMI over the follow-up period was the highest for
subjects who reported getting 2 to 4 hours of sleep per night
(mean = 1.46, SD = 3.46) and was the lowest for subjects who
reported getting 10 or more hours of sleep per night (mean =
0.08, SD = 3.66). In linear regression analyses with change in
BMI as the dependent variable and sleep duration as a continuous
variable, each additional hour of sleep at baseline was negatively
associated with change in BMI over the follow-up period while
adjusting for the potential confounding variables. This association
was in the hypothesized direction but was small and statistically
insignificant (β = -.053, P = .27). Figure 4 shows the results from
the linear regression model with sleep duration categorized. In
comparison with getting 7 hours of sleep per night, getting 2 to
4 hours was associated with a higher increase in BMI over the
follow-up period and getting 10 or more hours was associated
with a lower increase in BMI, but these differences were not
statistically significant.
Thus, among the subjects between the ages of 32 and 49 years,
a higher percentage of obese subjects reported getting fewer than 7
hours of sleep per night. Subjects with 2- to 4-hour sleep durations
had the highest average BMI, while those with 5- and 6-hour
sleep durations had the second and third highest average BMI,
respectively. In comparison with subjects with sleep durations of
7 hours, those with sleep durations less than 7 hours were more
1294
Inadequate Sleep as a Risk Factor for Obesity—Gangwisch et al
-2
-1.5
-1
-0.5
0
0.
5
1
1.
5
2
Change in BMI & 95% CI
2 to 4 5 6 7 8 9 => 10
Average Sleep Duration in Hours at Baseline in 1982-1984
n = 3,208
Figure 4—Change in BMI and 95% confidence intervals between
baseline (1982-1984) and 1992 for subjects between the ages of 32
and 49 by sleep duration adjusted for depression, physical activity,
education, ethnicity, alcohol consumption, cigarette smoking, gender,
waking during the night, daytime sleepiness, and age.
(BMI is calculated as the weight in kg divided by height in meters
squared.)
Table
3—Cross-Sectional Logistic Regression Analyses by Sex for
3682 Subjects Who Were Between the Ages of 32 and 49 Years at
Baseline (1982-1984)
Sex Average Sleep Model 1* Model 2
Per Night, h Obese vs Obese vs
Nonobese Nonobese
Women (n = 2516)
2-4 3.34 (1.88-5.96) 2.34 (1.24-4.41)
5 2.50 (1.64-3.81) 1.93 (1.23-3.03)
6 1.44 (1.09-1.91) 1.25 (0.93-1.68)
7 1.00 1.00
8 1.48 (1.16-1.90) 1.39 (1.08-1.80)
9 0.97 (0.56-1.65) 0.84 (0.49-1.46)
≥ 10 1.41 (0.60-3.33) 1.06 (0.43-2.57)
Men (n = 1166)
2-4 3.04 (1.05-8.80) 2.51 (0.83-7.53)
5 1.21 (0.67-2.91) 1.07 (0.58-1.97)
6 1.26 (0.87-1.83) 1.24 (0.84-1.82)
7 1.00 1.00
8 0.77 (0.52-1.16) 0.78 (0.51-1.17)
9 2.28 (1.03-5.07) 1.93 (0.85-4.36)
≥ 10 1.30 (0.42-4.07) 1.06 (0.33-3.39)
Data are presented as odds ratios (95% confidence intervals).
*Model 1 includes only the number of hours of sleep obtained per
night.
†Model 2 includes the number of hours per night and was adjusted
for depression, physical activity, education, ethnicity, alcohol con
-
sumption, cigarette smoking, waking during the night, daytime
sleepiness, and age.
Obese is defined as a body mass index (BMI) of 30 kg/m
2
; non-
obese is < 30 kg/m
2
. (BMI is calculated as the weight in kg divided
by height in meters squared.)
Table 4—Self-Report of Weight Change Over the Past 6 Months by
Sleeping Problems Compared With 1 Year Ago
Sleeping problems Weight now X
2
(P value)
now compared compared with 6
with 1 year ago
months ago
At least About At least
10 lb less the same 10 lb more
Much less 31 (7) 79 (3) 25 (5) 83.12
Somewhat less 20 (4) 92 (3) 17 (3) (P < .0001)
About the same 339 (74) 2,297 (85) 365 (74)
Somewhat 48 (10) 181 (7) 55 (11)
more
Much more 22 (5) 55 (2) 34 (7)
Data are presented as number (%).
SLEEP, Vol. 28, No. 10, 2005
likely to be obese in the logistic regression models and had higher
average BMIs in the linear regression models. In the 1982-1984,
1987, and 1992 logistic regression models, among those with
sleep durations less 7 hours, as their sleep durations decreased,
their likelihoods of being obese progressively increased. Subjects
who got 2 to 4 hours of sleep per night at baseline gained the
most weight over the follow-up period, while subjects who got
10 or more hours of sleep gained the least weight. In multivariate
models, increasing sleep duration had a negative association with
change in BMI over the follow-up period, but this association was
small and statistically insignificant.
DISCUSSION
This study showed an association between self-reported sleep
duration and obesity cross-sectionally at baseline while control-
ling for potentially confounding variables in a large United States
sample. Significant differences in sleep duration by obesity status
were found only for subjects who were between the ages of 32
and 49 years at baseline. Increased mortality associated with obe-
sity, age-related sleep changes, and a cohort effect represent pos-
sible explanations for the different relationships found between
sleep duration and obesity in the younger and older age groups.
First, obese subjects would be less likely to survive into their later
years, since they are at an increased risk for serious and potential-
ly fatal conditions such as diabetes mellitus, hypertension, dys-
lipidemia, coronary artery disease, and some cancers.
23
Second,
advanced age is associated with changes in sleep characteristics
and structure, with increased difficulties in sleep initiation and
maintenance.
24
Third, the older and younger cohorts lived through
distinct historical time periods with different stressors and soci-
etal norms for health practices, such as physical activity and diet.
The relationship between sleep duration and BMI in our study
differed from those found in studies with different age groups. In
an analysis of data from the Cancer Prevention Study II with 1.1
million subjects between the ages of 30 and 102 years, investi-
gators found a U-shaped relationship between BMI and self-re-
ported sleep duration in women and “a virtually monotonic trend
toward lower body mass indexes among those with longer sleep
durations” in men.
15
A U-shaped relationship between BMI and
sleep duration was also observed in men and women between the
ages of 30 and 60 years in the Wisconsin Sleep Cohort Study.
7
A cohort study of young adults with both sexes pooled found a
virtually monotonic trend toward lower BMI among those with
longer sleep durations.
16
We found a significant relationship be-
tween sleep duration and obesity only in the 32- to 49-year age
group, and this relationship neither precisely trended monotoni-
cally toward lower BMI with longer sleep duration, nor was it
U-shaped. We found the likelihood of being obese for subjects
who reported averaging 8 and 9 hours of sleep, as compared with
subjects who reported averaging 7 hours of sleep, to differ by sex,
with men who reported 9 hours and women who reported 8 hours
being more likely to be obese. None of the sleep durations longer
than 7 hours per night were statistically significant in the adjusted
cross-sectional models with data for both sexes pooled.
Self-reported sleep duration at baseline continued to be associ-
ated with obesity status and higher BMI at follow-up in 1987 and
in 1992. The small negative associations that we found between
increasing sleep duration at baseline and change in BMI over the
follow-up period were suggestive of a link between sleep duration
and weight gain, but these results was statistically insignificant
and therefore inconclusive. Stronger associations between sleep
duration and subsequent weight gain were found previously, but
that study had a younger age cohort and included repeated mea-
sures of sleep duration over the follow-up period.
16
Our study
lacked repeated measures of sleep duration, so we were unable to
determine how representative the baseline sleep measure was of
the sleep durations over the follow-up period. The association be-
tween sleep duration reported at baseline and subsequent weight
gain could have been weakened by changes in sleeping patterns
over the follow-up period.
When interpreting the results from this study, we must keep
in mind that the baseline measures of sleep duration and BMI
were obtained about 20 years ago. The prevalence of obesity has
increased since that time. Approximately 19% of the subjects in-
cluded in our study between the ages of 32 and 49 years were
obese at the time of the 1982-1984 NHANES Followup Study,
while approximately 30% of the subjects between the ages of 30
and 49 years were obese at the time of the 1999-2000 NHANES.
25
The average number of hours of sleep per night is likely to have
decreased since 1982-1984 due to societal and technologic chang-
es, including increases in shift work, cable television, use of the
Internet, 24-hour stores, and dual-income families. It is therefore
possible that an even stronger association now exists between
short sleep duration and obesity.
While the results from this epidemiologic study lend support
to the hypothesis that short sleep duration could lead to obesity,
an important consideration is whether reverse causation contrib-
uted toward this finding. One possibility is that the presence of
specific sleep disorders that are more prevalent among the obese
could have played a part in this association. The NHANES I Fol-
lowup Study did not include questions on sleep disorders, such
as sleep apnea, but it did include questions about trouble wak-
ing during the night and daytime sleepiness, which are associated
with sleep apnea.
26
Our multivariate models included variables
for trouble waking during the night and daytime sleepiness, there-
fore, at least partially controlling for sleep disorders. We would
also expect that individuals with sleep apnea would be more like-
ly to self-report higher average sleep times, since they are often
unaware of their disrupted sleep patterns and require longer sleep
durations to compensate for poor sleep quality.
Inadequate sleep could also influence body weight by making
it more difficult to maintain a healthy lifestyle. In results from
the National Sleep Foundation’s 2002 “Sleep in America” Poll,
not getting enough sleep was associated with irritability, impa-
tience, pessimism, and feeling tired and stressed.
2
It would seem
that these feelings and emotional states would function to lessen
one’s resolve and willpower to follow a diet or exercise routine.
This study has a number of limitations. The use of self-re-
ported weights to compute BMI in 1987 and 1992 represents a
limitation of the longitudinal analyses, since obesity prevalence
estimates based on self-reported data tend to be lower than those
based on measured data.
27
Both actual and self-reported weight
data were obtained at baseline in 1982-1984. The actual and self-
reported weights obtained in 1982-1984 had a Pearson correlation
coefficient of .975, indicating a reasonable level of potential ac-
curacy for the self-reported weights obtained from those followed
up with from the same cohort in 1987 and 1992. Another limita-
tion of the study was the use of self-reported sleep durations, as
opposed to measured sleep durations. Good agreement has been
1295
Inadequate Sleep as a Risk Factor for Obesity—Gangwisch et al
SLEEP, Vol. 28, No. 10, 2005
1296
Inadequate Sleep as a Risk Factor for Obesity—Gangwisch et al
found, though, in previous studies between self-reported sleep
durations and those obtained through actigraphic monitoring.
28,29
Other limitations include possible bias arising from loss to fol-
low-up, missing data on baseline risk variables, and missing data
at follow-up.
If metabolic changes resulting from sleep deprivation contrib-
ute toward weight gain, then interventions designed to increase the
amount and quality of sleep could potentially augment the most
common clinical interventions of increasing physical activity and
improving nutrition. These interventions could include educating
patients about healthier sleep-hygiene practices and helping them
to modify maladaptive sleep habits.
The results from this study suggest that sleep deprivation could
play a significant role in the etiology of obesity in some individu-
als. Further research is needed to further explicate the biologic
mechanisms behind this relationship and to see whether interven-
tions addressing inadequate sleep or poor sleep quality could treat
or prevent obesity.
ACKNOWLEDGEMENTS
Financial support for this study was provided by National Re
-
search Service Award (NRSA) 5 T32 MH 013043 by the National
Institute of Mental Health to Columbia University’s Psychiatric
Epidemiology Research Training Program. We are grateful to
Sharon B. Schwartz, PhD, Fangfang Zhang, MD, and Xiaodong
Luo, M.Phil, for helpful comments for this manuscript.
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    • "This may due to the fact that drinkers usually substitute alcohol for other foods [53, 56, 57] potentially leading to a negative energy balance. Some studies suggested that number of sleeping hours have positive relationship with obesity [19, 58] while others suggest that less sleeping hours increase the incidences of obesity [59, 60]. However, this study revealed that sleeping hours did not have any relationship with overweight or obesity which is reported similarly in a clinical review done by Marshall et al., where they have found out that neither long nor short sleep was associated with obesity [61]. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Obesity has become a global epidemic. The prevalence of obesity has also increased in the South Asian region in the last decade. However, dietary and lifestyle factors associated with obesity in Sri Lankan adults are unclear. The objective of the current study was to investigate the association of dietary and lifestyle patterns with overweight and obesity in a cohort of males from the Central Province of Sri Lanka. Methods A total of 2469 males aged between 16 and 72 years (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{x}=31 $$\end{document}x¯=31) were included in the study. The sample comprised individuals who presented for a routine medical examination at the National Transport Medical Institute, Kandy, Sri Lanka. The Body Mass Index (BMI) cutoff values for Asians were used to categorize the participants into four groups as underweight, normal weight, overweight or obese. The data on dietary and lifestyle patterns such as level of physical activity, smoking, alcohol consumption, sleeping hours and other socio demographic data were obtained using validated self-administered questionnaires. Multinomial logistic regression model was fitted to assess the associations of individual lifestyle patterns with overweight and obesity. Results The mean BMI of the study group was 22.7 kg m⁻² and prevalence rates of overweight and obesity were 31.8 and 12.3%, respectively. Mean waist circumference of the participants was 78.6 cm with 17.1% of them being centrally obese. After adjusting for potential confounders, weight status was associated with older age (P < 0.0001), ethnicity (P = 0.0033) and higher income (P = 0.0006). While higher physical activity showed a trend for being associated with lower odds of being obese (odds ratio: 0.898 – confidence interval: 0.744–1.084), alcohol intake, consumption of fruits, level of education, sleeping hours, smoking, consumption of fish, meat, dairy, sweets or fried snacks were not significantly associated with the weight status. Conclusion The high prevalence rates of overweight and obesity in working-age males is a threatening sign for Sri Lanka. Since the prevalence rate is higher in certain ethnic groups and higher-income groups, targeted interventions for these groups may be necessary.
    Full-text · Article · Dec 2017
    • "Many people eat in response to negative emotions such as boredom, sadness and/or anger. Getting too little sleep can also increase body weight [45][46][47]. "
    [Show abstract] [Hide abstract] ABSTRACT: Due to the advancement in science, enhanced knowledge on the physiological aspects of almost all the tissues and the organs of the human body is gained. One of the most important prevalent topics needed for discussion is obesity and its effect on the metabolic changes leading to disorders in the human body such as diabetes, hypertension, cardiovascular diseases in addition to chronic diseases such as stroke, osteoarthritis, sleep apnea, some cancers, and inflammation-based pathologies. In recent years, obesity is a serious socioeconomic issue, which has become one of the major health problems all over the world, affecting people of all ages, sex, ethnicities and races. Obesity is a complex and multifactorial disease caused by the interaction of a myriad genetics, dietary, lifestyle and environmental factors and it is characterised by an excessive weight for height due to an enlarged fat deposition in the adipose tissue, which is due to a higher calorie intake than the energy expenditure. The pharmaceutical drugs are currently available to treat obesity but generally they have unpleasant side effects. Recent researches demonstrated the potential of natural products to counteract on obesity. Now the novel promising approach is the usage of dietary supplements and plant products and their bioactive compounds that could interfere on pancreatic lipase activity, food intake, lipid metabolism and adipocyte differentiation. In a similar way, hundreds of extracts are currently being isolated from plants, fungi, algae or bacteria and are screened for their potential inhibitions of activity against obesity. Natural products may have a synergistic activity that increases their bioavailability and action on multiple molecular targets.
    Article · Feb 2017
    • "In the National Health Interview Survey 2004 to 2007, more than one third of the North American adult population was noted to have an abnormal duration of sleep, defined as too short (<7 to 8 hours per night) or too long (>8 hours per night) [2] . Previous laboratory and epidemiologic studies showed that inadequate sleep patterns, in terms of both quality and quantity, are associated with an increased frequency of cardiovascular risk factors such as hypertension, diabetes mellitus, and obesity [3,4], as well as independently associated with an increased risk of adverse cardiovascular outcomes, such as stroke and myocardial infarction [5,6]. A meta-analysis of 16 prospective cohort studies reported a significant association between sleep of s