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Keywords:

  • BMI;
  • skinfold thickness;
  • fat;
  • lipid-lipoprotein profile;
  • apolipoprotein B

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

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 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.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

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 endowment. 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, 7, 8, 9, 10, 11, 12, 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

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

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 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-to-hip 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 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

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

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 1.  Descriptive characteristics of men and women in the study
CharacteristicMen (n = 323)Women (n = 417)
  • 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.

Age (years)41.5 ± 13.741.2 ± 12.9
Weight (kg)82.7 ± 19.771.9 ± 22.4*
BMI (kg/m2)27.5 ± 6.527.9 ± 8.8
Body fat mass (kg)20.6 ± 13.324.5 ± 15.3*
Body fat (%)23.4 ± 9.132.3 ± 10.5*
Waist circumference (cm)94.4 ± 16.684.7 ± 19.0*
Waist-to-hip ratio0.93 ± 0.080.79 ± 0.08
Skinfold thickness (mm)  
 Extremities  
  Biceps7.3 ± 5.716.3 ± 11.9
  Triceps12.4 ± 7.126.1 ± 12.2
  Calf10.8 ± 6.524.3 ± 12.4
  Sum of 3 extremity skinfolds30.4 ± 17.866.5 ± 34.7
 Trunk  
  Subscapular21.9 ± 13.625.2 ± 14.8*
  Suprailiac21.3 ± 10.523.7 ± 12.4*
  Abdomen25.7 ± 14.434.3 ± 17.6
  Sum of 3 trunk skinfolds68.7 ± 36.082.9 ± 42.1
 Sum of 6 skinfolds98.6 ± 51.8148.8 ± 73.9
Total sleep duration (hours)7.6 ± 1.07.8 ± 1.1
Physical activity level (min)28.1 ± 53.715.2 ± 27.2
Leptin concentrations (ng/mL)10.8 ± 9.628.5 ± 19.9

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 2.  Relationship between short sleep duration and overweight/obesity
 Men (n = 323)Women (n = 417)
 n% OW/OBMultivariate* OR (95% CI)n% OW/OBMultivariate* OR (95% CI)Multivariate OR (95% CI)
  • 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.

Sleep duration (hours)       
5 to 64283.31.72 (1.09 to 2.71)4277.11.63 (1.11 to 2.59)1.69 (1.15 to 2.39)
7 to 823955.21.0029249.61.001.00
9 to 104257.11.18 (0.98 to 1.47)8371.81.51 (1.02 to 2.46)1.38 (0.89 to 2.10)
p  <0.05  <0.05<0.05

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 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
 5 to 6 hours (n = 42)7 to 8 hours (n = 239)9 to 10 hours (n = 42)
  • 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).

Weight (kg)88.7 ± 18.581.2 ± 19.3*84.6 ± 22.6
BMI (kg/m2)29.6 ± 6.227.1 ± 6.2*27.0 ± 7.9
Body fat mass (kg)23.8 ± 13.019.9 ± 13.2*20.8 ± 13.8
Body fat (%)25.8 ± 8.022.9 ± 9.1*23.1 ± 9.5
Waist circumference (cm)100.0 ± 16.093.0 ± 16.1*95.0 ± 19.2
Waist-to-hip ratio0.96 ± 0.070.92 ± 0.07*0.92 ± 0.08
Skinfold thickness (mm)   
 Extremities   
  Biceps8.3 ± 5.77.0 ± 5.77.0 ± 4.5
  Triceps14.2 ± 8.411.9 ± 6.812.7 ± 7.1
  Calf12.0 ± 7.110.4 ± 6.311.7 ± 6.7
  Sum of 3 extremity skinfolds34.5 ± 20.429.2 ± 17.2*31.4 ± 17.0
 Trunk   
  Subscapular24.7 ± 13.421.3 ± 13.521.6 ± 13.3
  Suprailiac25.2 ± 11.020.6 ± 10.4*20.7 ± 9.9
  Abdomen31.5 ± 17.224.6 ± 13.725.1 ± 13.8
  Sum of 3 trunk skinfolds88.4 ± 38.566.2 ± 35.3*67.4 ± 33.8
Sum of 6 skinfolds116.0 ± 57.294.8 ± 50.4*98.8 ± 48.4

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 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).

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
 5 to 6 hours (n = 42)7 to 8 hours (n = 292)9 to 10 hours (n = 83)
  • Values are mean ± standard deviation.

  • *

    Comparison significantly different from 5- to 6-hour group (p < 0.05).

Weight (kg)75.7 ± 22.570.5 ± 21.5*74.8 ± 24.9
BMI (kg/m2)29.8 ± 8.727.3 ± 8.3*29.0 ± 10.3
Body fat mass (kg)29.2 ± 16.123.3 ± 14.3*25.9 ± 17.9
Body fat (%)36.1 ± 10.231.6 ± 10.2*32.1 ± 11.6
Waist circumference (cm)88.1 ± 19.383.2 ± 17.7*87.7 ± 22.2
Waist-to-hip ratio0.81 ± 0.070.79 ± 0.070.80 ± 0.08
Skinfold thickness (mm)   
 Extremities   
  Biceps18.7 ± 12.715.5 ± 11.317.4 ± 13.8
  Triceps28.8 ± 12.425.3 ± 11.827.5 ± 13.6
  Calf28.1 ± 13.223.5 ± 12.1*24.8 ± 13.0
  Sum of 3 extremities75.6 ± 36.364.2 ± 33.4*68.8 ± 37.7
 Trunk   
  Subscapular29.3 ± 15.124.0 ± 14.1*26.9 ± 16.8
  Suprailiac26.3 ± 12.923.1 ± 12.223.9 ± 12.9
  Abdomen39.9 ± 18.732.8 ± 16.6*35.9 ± 19.3
  Sum of 3 trunk skinfolds94.4 ± 42.679.9 ± 40.6*85.8 ± 45.5
 Sum of 6 skinfolds168.9 ± 75.9144.2 ± 71.9*152.5 ± 79.2

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 levels. 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).

image

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).

Download figure to PowerPoint

image

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).

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Reduction in sleeping hours has become a hallmark of modern society. In fact, the proportion of young adults 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 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.

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, 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, 32, 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 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

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

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.

Footnotes
  • 1

    Nonstandard abbreviations: QFS, Québec Family Study; apoB, apolipoprotein B; OR, odds ratio.

  • 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.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
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