Chaput et al-2007-Obesity

Short Sleep Duration is Associated with
Reduced Leptin Levels and Increased
Adiposity: Results from the Québec Family
Study
Jean-Philippe Chaput,* Jean-Pierre Després,*† Claude Bouchard,‡ and Angelo Tremblay*
Abstract
CHAPUT, JEAN-PHILIPPE, JEAN-PIERRE DESPRÉS,
CLAUDE BOUCHARD, AND ANGELO TREMBLAY.
Short sleep duration is associated with reduced leptin levels
and increased adiposity: results from the Québec Family
Study. Obesity. 2007;15:253–261.
Objective: To explore cross-sectional associations between
short sleep duration and variations in body fat indices and
leptin levels during adulthood in a sample of men and
women involved in the Québec Family Study.
Research Methods and Procedures: Anthropometric measurements, plasma lipid-lipoprotein profile, plasma leptin
concentrations, and total sleep duration were determined in
a sample of 323 men and 417 women ages 21 to 64 years.
Results: When compared with adults reporting 7 to 8 hours
of sleep per day, the adjusted odds ratio for overweight/
obesity was 1.38 (95% confidence interval, 0.89 to 2.10) for
those with 9 to 10 hours of sleep and 1.69 (95% confidence
interval, 1.15 to 2.39) for those with 5 to 6 hours of sleep,
after adjustment for age, sex, and physical activity level. In
each sex, we observed lower adiposity indices in the 7- to
8-hour sleeping group than in the 5- to 6-hour sleeping
group. However, all of these significant differences disappeared after statistical adjustment for plasma leptin levels.
Finally, the well-documented regression of plasma leptin
Received for review May 9, 2005.
Accepted in final form August 29, 2006.
The costs of publication of this article were defrayed, in part, by the payment of page
charges. This article must, therefore, be hereby marked “advertisement” in accordance with
18 U.S.C. Section 1734 solely to indicate this fact.
*Division of Kinesiology, Department of Social and Preventive Medicine, Faculty of
Medicine, Laval University, Ste-Foy, Québec, Canada; †Quebec Heart Institute, Laval
Hospital Research Center, Ste-Foy, Québec, Canada.; and ‡Human Genomics Laboratory,
Pennington Biomedical Research Center, Baton Rouge, Louisiana.
Address correspondence to Angelo Tremblay, Division of Kinesiology (PEPS), Laval
University, Ste-Foy, Québec, Canada G1K 7P4.
E-mail: [email protected]
Copyright © 2007 NAASO
levels over body fat mass was used to predict leptin levels
of short-duration sleepers (5 and 6 hours of sleep), which
were then compared with their measured values. As expected, the measured leptin values were significantly lower
than predicted values.
Discussion: There may be optimal sleeping hours at which
body weight regulation is facilitated. Indeed, short sleep
duration predicts an increased risk of being overweight/
obese in adults and is related to a reduced circulating leptin
level relative to what is predicted by fat mass. Because sleep
duration is a potentially modifiable risk factor, these findings might have important clinical implications for the
prevention and treatment of obesity.
Key words: BMI, skinfold thickness, fat, lipid-lipoprotein profile, apolipoprotein B
Introduction
It is well established that the prevalence of obesity has
been increasing during recent decades, in both the United
States and the rest of the developed world. Interestingly,
over the past 40 years, daily sleep duration in the U.S.
population has decreased by 1 to 2 hours, and the proportion
of young adults sleeping less than 7 hours per night has
more than doubled between 1960 and 2001–2002 (from
15.6% to 37.1%) (1,2).
Sleep duration may be an important factor influencing the
regulation of body weight and metabolism. Indeed, body
weight is physiologically regulated, and this regulation involves complex physiological systems encoded by an array
of specific genes (3). These systems involve central and
peripheral components and interact with aspects of the
environment, including caloric and nutrient intake, exercise,
and other factors. The rapid increase in prevalence of obesity over recent decades is thought to be caused by changes
in our lifestyle rather than by changes in our genetic enOBESITY Vol. 15 No. 1 January 2007
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Sleep Duration and Leptin Levels, Chaput et al.
dowment. The attention has focused primarily on food intake and physical activity, but recent evidence suggests that
sleep may also deserve attention. For instance, Spiegel et al.
(4) and Taheri et al. (5) reported that increasing sleep
deficits, possibly as a result of our hectic lifestyles, bring
about physiological changes in hormonal signals that promote hunger and, perhaps thereby, obesity. They found that
short sleep duration was associated with decreased leptin
levels, increased ghrelin levels, and increased hunger and
appetite. These results are provocative and clearly show that
we need to consider other environmental variables of importance for the regulation of energy balance.
In population studies, a dose-response relationship between short sleep duration and high BMI has been reported
in different age groups (6 –13). In the largest study, elevated
BMI values were observed for habitual sleep lasting less
than 7 to 8 hours per day (7). A U-shaped curvilinear
relationship between sleep duration and BMI has been reported in women. However, in men, there seemed to be a
monotonic trend toward higher BMI with shorter sleep
duration. Importantly, a recent prospective study identified
a longitudinal association between short sleep duration and
subsequent weight gain (11).
With the limited availability of effective treatment of
weight management, the identification of potentially relevant modifiable risk factors may lead to the development of
better preventive approaches to obesity. Thus, the main aim
of this cross-sectional study was to explore associations
between short sleep duration and variations in body fat
indices and leptin levels during adulthood in a sample of
men and women of the Québec Family Study (QFS).1 To
assess this objective, we used objective assessments of
adiposity not generally found in previous studies pertaining
to the biology of sleep duration-obesity associations. Moreover, plasma leptin concentrations and physical activity
energy expenditure were taken into account because of their
potentially confounding effect and to further examine the
outcome of recent studies (4,5).
Research Methods and Procedures
Subjects
The QFS was initiated at Laval University in 1978 (14).
The primary objective of this study was to investigate the
role of genetics in the etiology of obesity and related cardiovascular risk factors. The 223 families (951 subjects)
involved in the entire study (Phases 1, 2, and 3) were
recruited through the media and were all French Canadians
from the greater Québec City area. The recruitment strategy
did not require a specific criterion regarding the BMI of
subjects included. Further details on QFS may be obtained
1
Nonstandard abbreviations: QFS, Québec Family Study; apoB, apolipoprotein B; OR, odds
ratio.
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OBESITY Vol. 15 No. 1 January 2007
in an article by Bouchard (14). The participation was voluntary, and all subjects signed an informed consent document. Individuals who participated in Phase 2 or Phase 3 of
the QFS and who were between 21 and 64 years of age were
selected for cross-sectional analyses (323 men and 417
women). Phases 2 and 3 of the QFS were conducted between 1989 and 2001. Additional inclusion criteria were: 1)
to be white; 2) to be a non-smoker; 3) not to be pregnant; 4)
to have had a stable body weight (⫾2 kg) during the 6
months preceding testing; 5) to be exempt of eating disorders; and 6) not to have a metabolic disease or be under
medication that could interfere with the outcome variables.
The number of hours of sleep was assessed through a
question inserted in a self-administered questionnaire on
physical activity participation. The question formulation
was: “On average, how many hours do you sleep a day?”.
The study was approved by the Medical Ethics Committee
of Laval University.
Anthropometric Measurements
Body weight, height, and waist and hip circumferences
were measured according to standardized procedures recommended at the Airlie Conference (15), and the waist-tohip ratio was calculated accordingly. BMI was calculated as
body weight divided by height squared (kg/m2). Skinfold
thicknesses were measured with a Harpenden caliper at six
sites (biceps, triceps, medial calf, subscapular, abdominal,
and suprailiac) according to the procedures described by the
International Biological Program (16). We also derived the
three following additional indicators of subcutaneous adiposity: the sum of the six above-referenced skinfolds, the
sum of trunk skinfolds (sum of scapular, abdominal, and
suprailiac), and the sum of extremity skinfolds (sum of
biceps, triceps, and medial calf).
Body density was obtained from the mean of six valid
measurements derived from hydrostatic weighing (17). Before immersion in the hydrostatic tank, the helium dilution
method of Meneely and Kaltreider (18) was used to determine the pulmonary residual volume. The percentage of
total body fat was determined from body density with the
equation of Siri (19). Body fat mass was estimated from
body weight and the percentage of body fat.
Plasma Lipid-Lipoprotein Measurements
Blood samples were collected from an antecubital vein
into Vacutainer tubes (Becton Dickinson, Franklin Lakes,
NJ) containing EDTA after a 12-hour overnight fast. Plasma
was separated immediately after blood collection by centrifugation at 3000 rpm (850g) for 10 minutes at 4 °C for the
measurement of plasma lipid and lipoprotein levels. Cholesterol and triglyceride concentrations were determined
enzymatically in plasma lipoprotein fractions using a Technicon RA-500 automated analyzer (Bayer Corp., Tarrytown, NY), and enzymatic reagents were obtained from
Sleep Duration and Leptin Levels, Chaput et al.
Randox (Crumlin, United Kingdom). Plasma lipoprotein
fractions (low- and high-density lipoprotein) were isolated
using previously described procedures (20). Plasma apolipoprotein B (apoB) concentrations were measured by the
rocket immunoelectrophoretic method of Laurell (21).
Plasma Leptin Concentrations
Fasting plasma leptin concentrations were determined
with a highly sensitive commercial double-antibody radioimmunoassay (human leptin-specific radioimmunoassay
kit; Linco Research, St. Louis, MO), which detects relatively low leptin levels of 0.5 ng/mL and which does not
cross-react with human insulin, proinsulin, glucagon, pancreatic polypeptide, or somatostatin. Leptin levels were
determined in 166 men and 218 women. Our coefficients of
variation for the repeated assays ranged from 4.0% to 5.5%
for lower leptin concentrations and from 6.5% to 8.5% for
higher plasma leptin concentrations.
Physical Activity Energy Expenditure
Physical activity energy expenditure was assessed using a
physical activity record (22). Subjects had to complete a
physical activity diary for 3 days, including 2 weekdays and
1 weekend day. Each day was divided into 96 periods of 15
minutes each. For each 15-minute period, subjects had to
code the main activity performed on a scale from 1 to 9.
Participation in vigorous physical activity was estimated as
the number of periods graded 8 and 9 over the 3 days and
was used for statistical analyses. The reliability and the
validity of the record have been previously reported (22).
Statistical Analysis
Student’s t test was used to compare means of descriptive
characteristics between men and women. Logistic regression analysis was performed to evaluate the strength of the
relationship between sleeping hours and overweight/obesity
after adjustment for age, sex, and physical activity level.
Multivariate logistic regression analysis was performed separately by sex, and the odds ratios (ORs) were adjusted for
age and physical activity level. In multivariate analyses for
both sexes combined, ORs were adjusted for age, sex, and
physical activity level. An ANOVA was also performed to
assess the difference between means of anthropometric variables among sleep duration classes [short sleepers group (5
to 6 hours), normal sleepers group (7 to 8 hours), and good
sleepers group (9 to 10 hours)]. An analysis of covariance
was used to control for confounding factors such as age,
physical activity level, and plasma leptin level. In the presence of a significant effect, Tukey’s post hoc test was
performed to determine which groups were significantly
different. The regression between plasma leptin concentration and fat mass was also computed in each sex. These
regression lines excluded those reporting 5 or 6 hours of
sleep per day. In addition, these regression equations were
used to predict leptin levels of short-duration sleepers. The
predicted values of these short-duration sleepers were then
compared with their measured values. It must be mentioned
that, because the sample was derived from a family study
that was not collected for the specific purpose of our investigation, we adjusted for clustering in the analyses to avoid
the important underestimation of standard deviations. In this
respect, we used the generalized estimating equations statistical method. Data are given as mean ⫾ standard deviation unless otherwise noted. Statistical significance was set
at a p value of ⬍0.05. All statistical analyses were performed using the SAS statistical package (SAS Institute,
Inc., Cary, NC).
Results
Table 1 shows descriptive characteristics of the subjects.
As expected, men had higher body weight, waist circumference, and waist-to-hip ratio than women, whereas women
had greater body fat mass, percentage of body fat, skinfold
thicknesses, and leptin levels than men. In addition, men
had a higher physical activity level than women. However,
age and total sleep duration were not significantly different
between sexes.
Table 2 presents the relationship between short sleeping hours and overweight/obesity after adjustment for
age, sex, and physical activity level. In the model using
adults with 7 to 8 hours of sleep as a reference, the
adjusted OR was 1.38 (95% confidence interval, 0.89 to
2.10) for those with 9 to 10 hours of sleep and 1.69 (95%
confidence interval, 1.15 to 2.39) for those with 5 to 6
hours of sleep. Although age and sex are known to have
the potential to interact with several variables affecting
overweight/obesity, multiplicative interactions among
age, sex, and physical activity level did not add to the
relationship between short sleeping hours and overweight/obesity in the model.
Table 3 shows the adiposity differences between the
short sleepers group (5 to 6 hours), normal sleepers group
(7 to 8 hours), and good sleepers group (9 to 10 hours) in
men. Interestingly, the detrimental effects of short sleep
duration were observed on the majority of these variables. Indeed, we observed lower body weight and adiposity indices in the normal sleepers group (7 to 8 hours)
as compared with the short sleepers group (5 to 6 hours):
body weight (⫺8.5%), BMI (⫺8.4%), body fat mass
(⫺16.4%), percentage of body fat (⫺11.2%), waist circumference (⫺7.0%), waist-to-hip ratio (⫺4.2%), sum of
three extremity skinfolds (⫺15.4%), suprailiac thickness
(⫺18.3%), abdomen thickness (⫺21.9%), sum of three
trunk skinfolds (⫺25.1%), and sum of six skinfolds
(⫺18.3%) were significantly lower.
Table 4 presents the differences between means of anthropometric variables for the short sleepers group (5 to 6
hours), normal sleepers group (7 to 8 hours), and good
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Table 1. Descriptive characteristics of men and women in the study
Characteristic
Age (years)
Weight (kg)
BMI (kg/m2)
Body fat mass (kg)
Body fat (%)
Waist circumference (cm)
Waist-to-hip ratio
Skinfold thickness (mm)
Extremities
Biceps
Triceps
Calf
Sum of 3 extremity skinfolds
Trunk
Subscapular
Suprailiac
Abdomen
Sum of 3 trunk skinfolds
Sum of 6 skinfolds
Total sleep duration (hours)
Physical activity level (min)‡
Leptin concentrations (ng/mL)
Men
(n ⴝ 323)
Women
(n ⴝ 417)
41.5 ⫾ 13.7
82.7 ⫾ 19.7
27.5 ⫾ 6.5
20.6 ⫾ 13.3
23.4 ⫾ 9.1
94.4 ⫾ 16.6
0.93 ⫾ 0.08
41.2 ⫾ 12.9
71.9 ⫾ 22.4*
27.9 ⫾ 8.8
24.5 ⫾ 15.3*
32.3 ⫾ 10.5*
84.7 ⫾ 19.0*
0.79 ⫾ 0.08†
7.3 ⫾ 5.7
12.4 ⫾ 7.1
10.8 ⫾ 6.5
30.4 ⫾ 17.8
16.3 ⫾ 11.9†
26.1 ⫾ 12.2†
24.3 ⫾ 12.4†
66.5 ⫾ 34.7†
21.9 ⫾ 13.6
21.3 ⫾ 10.5
25.7 ⫾ 14.4
68.7 ⫾ 36.0
98.6 ⫾ 51.8
7.6 ⫾ 1.0
28.1 ⫾ 53.7
10.8 ⫾ 9.6
25.2 ⫾ 14.8*
23.7 ⫾ 12.4*
34.3 ⫾ 17.6†
82.9 ⫾ 42.1†
148.8 ⫾ 73.9†
7.8 ⫾ 1.1
15.2 ⫾ 27.2†
28.5 ⫾ 19.9†
Values are mean ⫾ standard deviation.
* Significantly different from men (p ⬍ 0.01).
† Significantly different from men (p ⬍ 0.0001).
‡ Mean time spent in vigorous physical activity participation estimated as the number of periods graded 8 and 9 over the 3 days.
sleepers group (9 to 10 hours) in women. As observed in
men, results also show the trend for negative outcomes in
regard to lowest time of sleeping. Thus, body weight
(⫺6.9%), BMI (⫺8.4%), body fat mass (⫺20.2%), percentage of body fat (⫺12.5%), waist circumference (⫺5.6%),
calf thickness (⫺16.4%), sum of three extremity skinfolds
(⫺15.1%), abdomen thickness (⫺17.8%), sum of three
trunk skinfolds (⫺15.4%), and sum of six skinfolds
(⫺14.6%) were significantly lower for the normal sleepers
group (7 to 8 hours) as compared with the short sleepers
group (5 to 6 hours).
In regard to the lipid-lipoprotein profile for these three
classes of sleepers (data not shown), the only significant
effect was observed in men between means of apoB concentrations, wherein the good sleepers group (9 to 10 hours)
exhibited lower apoB levels (⫺14.3%) as compared with
the short sleepers group (5 to 6 hours).
However, all of the differences observed were no longer
significant after statistical adjustment for plasma leptin lev256
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els. In this regard, Figures 1 and 2 present plasma leptin
levels among short sleepers (5 and 6 hours of sleep per
night) in comparison to the regression curve which put into
relation plasma leptin concentration and fat mass. The majority (88%) of short sleepers had lower leptin levels and
were, therefore, under the regression curve obtained in the
whole sample of normal and good sleepers. In addition, the
regression equations were used to predict leptin levels in the
5- to 6-hours sleeping group, and the predicted values of
these short sleepers were then compared with their measured values. As expected, the measured leptin values were
significantly lower than the predicted values (10.1 ⫾ 1.2 vs.
11.9 ⫾ 0.8 ng/mL for men and 22.7 ⫾ 2.4 vs. 27.4 ⫾ 1.6
ng/mL for women, p ⬍ 0.01).
Discussion
Reduction in sleeping hours has become a hallmark of
modern society. In fact, the proportion of young adults
Sleep Duration and Leptin Levels, Chaput et al.
Table 2. Relationship between short sleep duration and overweight/obesity
Men (n ⴝ 323)
Sleep duration (hours)
5 to 6
7 to 8
9 to 10
p
n
% OW/
OB
Multivariate* OR
(95% CI)
42
239
42
83.3
55.2
57.1
1.72 (1.09 to 2.71)
1.00
1.18 (0.98 to 1.47)
⬍0.05
Women (n ⴝ 417)
n
% OW/
OB
42
292
83
77.1
49.6
71.8
Multivariate* OR Multivariate† OR
(95% CI)
(95% CI)
1.63 (1.11 to 2.59)
1.00
1.51 (1.02 to 2.46)
⬍0.05
1.69 (1.15 to 2.39)
1.00
1.38 (0.89 to 2.10)
⬍0.05
OW/OB, overweight/obese; 95% CI, 95% confidence interval.
* The adjusted ORs calculated separately by sex. ORs were adjusted for age and physical activity level.
† The adjusted ORs for both sexes combined. ORs were adjusted for age, sex, and physical activity level.
sleeping 8 to 8.9 hours per night has decreased from 40.8%
in 1960 to 23.5% in 2001–2002 in the United States (1,2).
During the same time period, the incidence of obesity has
nearly doubled (23). Four epidemiological studies have
found that a higher BMI is associated with shorter sleep
duration (5,7,8,24). This epidemiological evidence, together
with the experimental findings of Spiegel et al. (4), who
found that sleep restriction affects plasma leptin and ghrelin
Table 3. Difference between means of anthropometric variables for the short sleepers group (5 to 6 hours),
normal sleepers group (7 to 8 hours), and good sleepers group (9 to 10 hours) in men
Weight (kg)
BMI (kg/m2)
Body fat mass (kg)
Body fat (%)
Waist circumference (cm)
Waist-to-hip ratio
Skinfold thickness (mm)
Extremities
Biceps
Triceps
Calf
Sum of 3 extremity skinfolds
Trunk
Subscapular
Suprailiac
Abdomen
Sum of 3 trunk skinfolds
Sum of 6 skinfolds
5 to 6 hours
(n ⴝ 42)
7 to 8 hours
(n ⴝ 239)
9 to 10 hours
(n ⴝ 42)
88.7 ⫾ 18.5
29.6 ⫾ 6.2
23.8 ⫾ 13.0
25.8 ⫾ 8.0
100.0 ⫾ 16.0
0.96 ⫾ 0.07
81.2 ⫾ 19.3*
27.1 ⫾ 6.2*
19.9 ⫾ 13.2*
22.9 ⫾ 9.1*
93.0 ⫾ 16.1*
0.92 ⫾ 0.07*
84.6 ⫾ 22.6
27.0 ⫾ 7.9
20.8 ⫾ 13.8
23.1 ⫾ 9.5
95.0 ⫾ 19.2
0.92 ⫾ 0.08
8.3 ⫾ 5.7
14.2 ⫾ 8.4
12.0 ⫾ 7.1
34.5 ⫾ 20.4
7.0 ⫾ 5.7
11.9 ⫾ 6.8
10.4 ⫾ 6.3
29.2 ⫾ 17.2*
7.0 ⫾ 4.5
12.7 ⫾ 7.1
11.7 ⫾ 6.7
31.4 ⫾ 17.0
24.7 ⫾ 13.4
25.2 ⫾ 11.0
31.5 ⫾ 17.2
88.4 ⫾ 38.5
116.0 ⫾ 57.2
21.3 ⫾ 13.5
20.6 ⫾ 10.4*
24.6 ⫾ 13.7†
66.2 ⫾ 35.3*
94.8 ⫾ 50.4*
21.6 ⫾ 13.3
20.7 ⫾ 9.9
25.1 ⫾ 13.8
67.4 ⫾ 33.8
98.8 ⫾ 48.4
Values are mean ⫾ standard deviation.
* Comparison significantly different from 5 to 6 hours group (p ⬍ 0.05).
† Comparison significantly different from 5 to 6 hours group (p ⬍ 0.01).
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Table 4. Difference between means of anthropometric variables for the short sleepers group (5 to 6 hours),
normal sleepers group (7 to 8 hours), and good sleepers group (9 to 10 hours) in women
Weight (kg)
BMI (kg/m2)
Body fat mass (kg)
Body fat (%)
Waist circumference (cm)
Waist-to-hip ratio
Skinfold thickness (mm)
Extremities
Biceps
Triceps
Calf
Sum of 3 extremities
Trunk
Subscapular
Suprailiac
Abdomen
Sum of 3 trunk skinfolds
Sum of 6 skinfolds
5 to 6 hours
(n ⴝ 42)
7 to 8 hours
(n ⴝ 292)
9 to 10 hours
(n ⴝ 83)
75.7 ⫾ 22.5
29.8 ⫾ 8.7
29.2 ⫾ 16.1
36.1 ⫾ 10.2
88.1 ⫾ 19.3
0.81 ⫾ 0.07
70.5 ⫾ 21.5*
27.3 ⫾ 8.3*
23.3 ⫾ 14.3*
31.6 ⫾ 10.2*
83.2 ⫾ 17.7*
0.79 ⫾ 0.07
74.8 ⫾ 24.9
29.0 ⫾ 10.3
25.9 ⫾ 17.9
32.1 ⫾ 11.6
87.7 ⫾ 22.2
0.80 ⫾ 0.08
18.7 ⫾ 12.7
28.8 ⫾ 12.4
28.1 ⫾ 13.2
75.6 ⫾ 36.3
15.5 ⫾ 11.3
25.3 ⫾ 11.8
23.5 ⫾ 12.1*
64.2 ⫾ 33.4*
17.4 ⫾ 13.8
27.5 ⫾ 13.6
24.8 ⫾ 13.0
68.8 ⫾ 37.7
29.3 ⫾ 15.1
26.3 ⫾ 12.9
39.9 ⫾ 18.7
94.4 ⫾ 42.6
168.9 ⫾ 75.9
24.0 ⫾ 14.1*
23.1 ⫾ 12.2
32.8 ⫾ 16.6*
79.9 ⫾ 40.6*
144.2 ⫾ 71.9*
26.9 ⫾ 16.8
23.9 ⫾ 12.9
35.9 ⫾ 19.3
85.8 ⫾ 45.5
152.5 ⫾ 79.2
Values are mean ⫾ standard deviation.
* Comparison significantly different from 5- to 6-hour group (p ⬍ 0.05).
concentrations as well as hunger and appetite levels, suggests that chronic sleep curtailment may be an unrecognized
risk factor for obesity.
Animal studies have suggested a link between sleep and
metabolism (25,26). In rats, prolonged sleep deprivation
increased food intake and energy expenditure. The net effect
was weight loss and, ultimately, death (27). However, these
data were based on a stressful procedure producing intense
sleep deprivation (28,29), which may interfere with adequate food intake. Nevertheless, in societies in which food
is freely available, milder chronic sleep restriction may
favor elevated food intake in relation to energy expenditure,
leading to obesity.
Taheri et al. (5) found that less sleep from a mean of 7.7
hours was associated with a dose-dependent increase in
BMI but that sleep time of more than 7.7 hours was also
associated with increased BMI. Our data seem to be concordant with these results, even though it was the lack rather
than the excess of sleep time that affected body weight and
adiposity indices in greater proportions. Thus, our data
suggest that there may be an “ideal zone” of sleep duration,
7 to 8 hours, outside of which detrimental effects of sleeping
deviations could perturb energy balance.
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The adjustments of our data for plasma leptin levels
support the observations of Spiegel et al. (4) and Taheri et
al. (5), as correlations or between-group differences became
non-significant when leptin levels were taken into account.
Thus, sleep may influence the energy balance-regulatory
hormone leptin and obesity-related variables. However,
more direct studies of the changes in leptin levels and their
relationship to sleep duration are needed. In addition, future
studies need to examine the effect of regular short sleeping
hours on appetite, food intake, and obesity. These studies
could help answer the question of whether the rise in obesity
in many societies is partly due to the fact that people are
sleeping less. Moreover, future studies should address
whether increasing sleep to 7 or 8 hours per night could help
people to lose weight or prevent weight gain.
It is paradoxical that sleeping, the most sedentary of all
activities, may be associated with leanness. Physical activity
is a potential confounder, even if we have controlled for
physical activity level. One could argue that obese people,
usually less active, sleep less because they need less time to
recover or, alternatively, that obese people tend to stay up
late watching television, which, in turn, leaves them too
tired to exercise the next day (8). On the other hand,
Sleep Duration and Leptin Levels, Chaput et al.
Figure 1: Plasma leptin levels among men who are short sleepers
(n ⫽ 25) (5 and 6 hours of sleep per night) in comparison to the
regression curve relating plasma leptin concentration and fat mass
in normal and good sleepers (A) and comparison between measured and predicted leptin levels (B). Data are given as mean ⫾
standard error of the mean. * Significantly different from predicted
leptin levels (p ⬍ 0.01).
Figure 2: Plasma leptin levels among women who are short
sleepers (n ⫽ 25) (5 and 6 hours of sleep per night) in comparison
to the regression curve relating plasma leptin concentration and fat
mass in normal and good sleepers (A) and comparison between
measured and predicted leptin levels (B). Data are given as
mean ⫾ standard error of the mean. * Significantly different from
predicted leptin levels (p ⬍ 0.01).
although insomnia is highly prevalent, much of the reduction in sleep time reflects voluntary sleep restriction, with
43% of adults reporting that they often stay up later than
they should, watching television or using the Internet, and
45% reporting that they sleep less to get more work done
(1,2). In this regard, there seems to exist a difference between insomnia and short sleep duration, because subjects
with insomnia have, on average, a rather low BMI (7,30). In
addition, survey data from the United States and United
Kingdom have found a clear association between obesity
and obstructive sleep apnea (31–33). It has also been suggested that those who are awakened in the middle of the
night tend to snack, which implies an increase in energy
intake. Finally, a further alternative is that shorter duration
of sleep results from hormonal and neuroendocrine alterations related to obesity (4,5). Be that as it may, although
recommendations to get both a better night’s sleep and more
exercise might superficially seem to be at odds with each
other from the perspective of energy expenditure and energy
balance, these simple goals may well become a part of our
future approach to combating obesity.
One of the limitations of this study is its sample size,
which limits the generalizability of our results to the whole
population. Furthermore, because this was a cross-sectional
study, the temporal relationship between sleep time and
body fat indices is unknown. It is also to be noted that the
sample was derived from a family study that was not collected for the specific purpose of this investigation, and
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Sleep Duration and Leptin Levels, Chaput et al.
there was a lack of community-based recruitment strategy,
reducing the generalizability of the results. However, statistical adjustments for clustering were realized to minimize
the underestimation of standard errors. In addition, the
number of individuals in whom the plasma leptin levels
were assessed was lower in comparison to the initial sample
size, thus reducing statistical power. Moreover, we have to
keep in mind that the sleep duration was assessed from a
questionnaire and was not measured. Differential reporting
of sleeping time may have occurred in the obese if they
tended to systematically underestimate the number of hours
of sleep owing to fatigue or sleepiness when awake. However, Taheri et al. (5) found that self-reported sleep duration
and polysomnographic measurement are both stable and
highly correlated. Finally, this was a study of adults ages 21
to 64 years. Caution must, therefore, be exercised with
regard to the heterogeneity in age, because some published
and unpublished studies have observed an important influence by age. Indeed, the association of sleep time with
obesity is reported to diminish with age (11).
In conclusion, our results show that short sleep duration
is associated with increased body weight and adiposity. This
study supports previous observations and suggests that sleep
variation seems to be associated with variation in leptinemia. Because sleep duration is a potentially modifiable risk
factor, these findings may have important clinical implications for the prevention and treatment of obesity. In this
respect, these observations emphasize the relevance of conducting a randomized trial on sleep prolongation as a treatment for obesity.
Acknowledgments
J-P.C. is supported by a studentship from the Canadian
Institutes of Health Research, J-P.D. is Chair of Nutrition
and Lipidology supported by Pfizer, Provigo, and the Foundation of the Québec Heart Institute, C.B. is partly supported by the George A. Bray Chair in Nutrition, and A.T.
is partly funded by the Canada Research Chair in Physical
Activity, Nutrition, and Energy Balance. We express our
gratitude to the subjects for their excellent collaboration,
and to the staff of the Physical Activity Sciences Laboratory
for their contribution to this study. We especially thank
Germain Thériault, Guy Fournier, Monique Chagnon, Lucie
Allard, and Claude Leblanc for their help in the collection
and analysis of the data.
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