Sleep and Obesity in Children and Adolescents

C H A P T E R

13 Sleep and Obesity in Children and Adolescents Erin C. Hanlon*, Magdalena Dumin†, and Silvana Pannain* *Department of Medicine, Section of Adult and Pediatrics, Endocrinology, Diabetes, and Metabolism, The University of Chicago, Chicago, IL, United States † Academic Endocrine, Metabolism & Nutrition, Wheaton, IL, United States

13.1 INTRODUCTION Over the past few decades, childhood and adolescent obesity have increased significantly worldwide [1]. Genetic factors alone cannot explain the rapid and alarming increase of excess weight in children. Such phenomena of the last 40 years likely reflect behavioral, environmental, and social factors such as increased screen time, decreased physical activity, increased energy intake [2–4], and the interaction of the gene pool with these factors. Important changes in lifestyle have taken place during the last few decades, which have affected both adults and children. Habitual sleep duration has declined amongst children and adolescents [5–9] and parallels the increase of overweight and obesity. The occurrence in parallel of these two phenomena has raised the question of the role of sleep in the expanding epidemic of obesity [10], and within the last two and half decades, epidemiological and laboratory studies have explored the link between sleep duration, sleep quality, sleep timing, and risk of obesity [11]. Additionally, the increased prevalence and severity of obesity in children has led to an increase in sleep disorders related to excess weight, such as obstructive sleep apnea syndrome (OSAS). Although compelling literature in both the adult and pediatric population demonstrates a causative role of obesity in obstructive sleep apnea (OSA), more recent studies have begun to suggest an inverse causative relationship between obesity and OSA, such that OSA may play a potentiating role in weight gain. In the following sections, we will first review the epidemiologic and intervention studies that have shown an association between short sleep and obesity in children and adolescents. We will later examine the laboratory studies in adults that have examined the effect of sleep restriction on neurohormonal control of appetite, and we will formulate hypotheses on the possible mechanisms linking short sleep and the risk of obesity. We will briefly review the pediatric sleep disorders related to obesity and propose that one of the common sleep disorders, OSA, may possibly contribute to the epidemic of obesity.

13.2 PREVALENCE OF OBESITY AND SHORT SLEEP IN CHILDREN AND ADOLESCENTS 13.2.1 Prevalence of Obesity Within the last few decades, the prevalence of obesity in children has increased significantly such that the World Health Organization has declared it a global epidemic. The National Health and Nutrition Examination Survey (NHANES) conducted in 2009–10 found that 16.9% of children 2–19 years old were obese (defined as body mass index [BMI]
95th percentile for-age growth chart) and 31.8% were either overweight or obese [12]. This is triple the 5%–6% estimation of the NHANES II (1976–80) of almost four decades ago. Twelve percent of children (12.3%, 95% CI 11.1%–13.5%) were considered more severely obese (
97% BMI)[12], outnumbering those affected by childhood cystic fibrosis, HIV, juvenile diabetes, and cancer combined. Similar trends have been observed throughout the world both in developed and developing countries [1].

Global Perspectives on Childhood Obesity https://doi.org/10.1016/B978-0-12-812840-4.00013-X

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Obese children and adolescents are likely to become obese adults [13, 14] and to develop a variety of comorbidities such as diabetes, cardiovascular disease, OSA, polycystic ovarian syndrome, nonalcoholic fatty liver disease, orthopedic disorders, pseudotumor cerebri, and psychological dysfunction [15, 16]. As obesity rates in children reached record levels, an interest in the role of behavioral risk factors, including sleep curtailment, has emerged as a topic of research studies in the last two and half decades. Many factors play a role in the development of obesity. It is assumed that polygenic factors, combined with poor behavioral and environmental factors, contribute to the epidemic of childhood obesity [17, 18]. Although twin and adoption studies have indicated that genetic factors have a significant role in obesity predisposition, the rapid increase in obesity over the past few decades cannot be explained by a change in the genetic pool alone but rather by the interaction of the preexisting genetic pool with new environmental, socioeconomic, and demographic factors [19, 20]. If genetic factors confer susceptibility to obesity, the environment may modulate the phenotypic expression [19]. The rapid behavioral and environmental changes that have occurred during the last few decades in modern society are likely to have contributed to the changes in the phenotypic expression of obesity [21–23]. Excess prepackaged and processed food, an overabundance of fast food, availability of sweetened drinks, increased portion sizes, decreased family meals, and an increasingly sedentary lifestyle may all be contributing factors [22–24]. It is estimated that 25% of children between the ages of 8–16 spend about 4 h a day watching television whereas physical activity and sports are on the decline at school [25–27]. Data published by the Kaiser Family Foundation in 2010 showed that children and adolescents ages 8–18 spent an average of more than 7 h per day engaging with entertainment media [28]. The 2011 Sleep in America Poll showed that 13–18-year-olds are heavy users of technology in the hour before trying to go to sleep, particularly with the usage of cellphone (72%), electronic music devices (64%), computers (60%), and/or video game consoles (23%) [29].

13.2.2 Prevalence of Short Sleep The hectic pace of the modern world has not only negatively affected the lives of children and adolescents through poor diet and exercise habits but also through poor sleep habits. Currently, there is increasing concern regarding diminished sleep duration in children [5–7, 30]. The National Sleep Foundation recommends sleep durations per age range as follows: toddlers 1–2 years old require 11–14 h, preschoolers 3–5 years old need 10–13 h, school-age children 6–13 years old should sleep 9–11 h per night, and teenagers 14–17 years old are recommended to sleep 8–10 h [31]. Similar recommendations were published recently in an American Academy of Sleep Medicine Consensus Statement [32]. However, there is a lack of consensus regarding what constitutes “adequate” sleep as there is almost no empirical evidence to validate the official recommendations of optimal sleep duration for children and adolescents [8]. During the last decades, reports indicate that children went to sleep later, but wake time remained unchanged. Data from 218 articles on 690,747 children from 20 countries, dating from 1905 to 2008, indicate a secular decline of 0.75 min per year in children’s sleep duration over the last 100 years [8]. The greatest rate of decline in sleep occurred for older children, boys, and on schooldays. Most recently the 2014 Sleep in America Poll found that 30% of school-age children aged 6–11 years old sleep under 9 h as reported by parents [33], less than the 9–11 h/night recommended. Sleep deprivation observed in preadolescents and adolescents is in part due to environmental and social factors including excessive homework or afterschool activities, active social lives, video or computer games, late night television, and early school starting times [34, 35]. Adolescents are more at risk of chronic sleep deprivation as the early school time conflicts with the natural delay of the circadian control of sleep propensity typical of this age [36, 37]. The 2011 US National Sleep Foundation “Sleep in Adolescents Poll” found that about 60% of adolescents in the United States receive less than 8 h of sleep on school nights [29], which has increased from an estimate of 45% adolescents in the 2006 Sleep in America Poll [38]. In addition, 77% of adolescents reported having sleep problems, with waking up feeling unrefreshed (59%) and difficulty falling asleep (42%) most commonly reported [29]. Keyes et al. examined the frequency of self-reported sleep being greater than 7 h in a sample of 272,077 US adolescents, aged 12–19 years, between 1991 and 2012[9]. Adolescents were consistently 30% less likely to report 7 or more hours of sleep in 2012 compared to 1991. In a large cohort of US adolescents, more than 40% in 2015 reported sleeping less than 7 h per night on most nights, which is significantly less than 9 h suggested as sleep requirement for that age range [39]. This estimate was increased by 16% compared to 2009. New media screen time (electronic device use, social media, and reading news online) increased over the same time period and was associated with increased odds of short sleep duration [40]. A review of 67 studies published from 1999 to early 2014 also found that screen time was adversely associated with shortened duration and delayed timing of sleep

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[41]. Additional studies have suggested that long screen time may be a contributor to short sleep and poor sleep quality in children and adolescents [42, 43]. Not surprisingly sleep duration mediates in part the relationship between screen media exposure and obesity in children and adolescents [44]. Overall there is a general concern that children and adolescents today could be chronically sleep deprived, which may have an impact on their physical, social, and mental well-being. Sleep, like physical activity and diet, plays an important role in the growth, maturation, and health of children and adolescents. It is well known that decreased sleep duration has immediate negative effects on the pediatric population including behavioral problems, poor concentration [45–47], and an increased susceptibility to illness [48]. Given the pediatric obesity epidemic and the concomitant shortened sleep duration, researchers have been investigating the correlation between recent trends in increased obesity and sleep problems [5, 6, 49, 50]. In adults, multiple epidemiological association studies and laboratory intervention studies have suggested that sleep curtailment may contribute to obesity through a variety of mechanisms, which include changes in appetite regulating hormones, eating patterns, caloric intake, physical activity, and energy expenditure [51, 52]. Although epidemiologic literature pertaining to this relationship is abundant in children, similar laboratory studies to those conducted in adults are scarce because enforced sleep curtailment on the pediatric population is not deemed appropriate. Conclusions regarding the possible impact of short sleep duration on the risk of obesity in the pediatric population are mostly derived from epidemiological and a few intervention studies in children and adolescents and, in part, by extrapolation from adult laboratory studies.

13.3 EPIDEMIOLOGIC EVIDENCE OF A LINK BETWEEN SLEEP LOSS AND OBESITY In adults, sleep restriction has been linked to the risk of weight gain in multiple epidemiologic and a few laboratory studies [51]. In children and adolescents, similar and often large-scale epidemiologic studies have been conducted and demonstrate an increased prevalence and incidence of obesity if sleep is restricted. A 2008 metaanalysis by Cappuccio et al. analyzed 17 epidemiologic studies of 604,509 adults and 30,002 children, and showed in short sleepers (<5 h per night for adults, <10 h per night for children) a pooled odds ratio (OR) of obesity of 1.55 (range 1.43–1.68) in adults and 1.89 (range 1.46–2.43) in children [53]. Another metaanalysis published in 2008 examined sleep duration and childhood obesity findings from 17 observational studies (12 cross-sectional, 3 cohort, and 2 case-control studies) in 9 countries [54]. Consistent with the previous metaanalysis, they found that children with shorter sleep duration had pooled OR of 1.58, or 58% higher risk for overweight/obesity. Further, children with the shortest sleep duration had 92% risk compared to those with longer sleep duration. Interestingly, this metaanalysis supports a significant gender difference in that boys had a stronger inverse association than girls (OR ¼ 2.50 vs 1.24)[54]. More recent metaanalyses have continued to find these associations. Hart and colleagues [55] reviewed the data of 29 epidemiological studies from 16 countries that suggest an association in children between short sleep and the risk of becoming overweight/obese or having increased body fat. The association, which persisted in most of the studies even after controlling for confounders, was found in both prospective and cross-sectional studies [55]. A 2015 metaanalysis identified 25 prospective cohort studies examining the relationship between sleep duration and obesity [56]. Eleven studies were conducted in the United States, four in Australia, two in Canada, two in Denmark, and additional studies were conducted in the United Kingdom, Germany, New Zealand, Portugal, Korea, and Belgium. Follow-up was in the range to 21 months to 5 years. Ten studies looked at the risk of overweight and obesity in relationship with sleep duration. Children with sleep duration of 10 h were 76% more likely to be overweight or obese compared to children sleeping 12.2 h on average (OR: 1.76, 95% CI: 1.39–2.23). For the dose-response relationship (which could be calculated for eight studies), with every hour increase of sleep duration the risk of overweight/obesity declined by 21% (OR: 0.79, 95% CI: 0.70–0.89). Data on sleep duration and annual BMI gain were available for six studies: for every hour increment per day in sleep duration, the annual BMI gain dropped by 0.05 kg/m2. These relationships were found to be independent of region, baseline age, or duration of follow-up. The recent metaanalysis by Felso et al. [57] included 33 studies (30 observational and 3 randomized-control studies) and examined the relationship between childhood obesity and sleep duration. Of the 23 studies assessed that were specifically about obesity and sleep duration as measured by accelerometer, 17 found a negative relationship between sleep time and adiposity (short sleep duration associated with increased risk of obesity) [57]. The latest metaanalysis, conducted by Miller et al. [58], included 42 studies in infants, children, and adolescents. These authors report that short sleep was associated with a greater risk of developing overweight/obesity in infants (RR: 1.40; 95% CI 1.19 to 1.65; P < .001), early childhood ages 3 to <9 years old (RR: 1.57; 95% CI 1.40 to 1.76; P < .001), middle childhood ages 9–12 (RR: 2.23; 95% CI 2.18 to 2.27; P < .001), and in adolescents (RR: 1.30; 95% CI 1.11 to 1.53; P < .002).

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The authors further report that sleep duration was significantly associated with BMI (mean difference 0.03 kg/m2 for every hour of increased sleep, P ¼ .001) and BMI z-score (0.03 per hour of sleep, P ¼ .001)[58]. Overall, the findings of the pediatric studies are more robust then the adult studies, suggesting that the relationship between sleep duration and weight may weaken with age [59]. The consistent findings from studies spanning multiple countries and continents suggest that the association is independent of culture or ethnicity. A few studies in the pediatric population have attempted to identify the causal pathway linking sleep duration to obesity. In general, although cross-sectional studies strongly suggest an association, they do not inform on the direction of causality. More longitudinal and interventional studies are needed to uncover a causative role of short sleep duration on the risk of obesity. To that end, a metaanalysis assessed 11 longitudinal studies of 24,821 participants ages 0.5–18 years old and found a pooled OR of 2.15 (CI: 1.64–2.81)[60] of overweight/obesity, suggesting that short sleep in children and adolescents may be a contributing cause of overweight/obesity. A few studies have found a U-shaped relationship between sleep duration and obesity in studies in the adolescent population, and this is concordant to a similar finding in epidemiologic studies in adults [61–64]. However, this U-shaped relationship is not observed in children; one possible explanation is that in children who naturally exhibit prolonged sleep periods, school and other daytime activities constrain time allotted for excessive sleep duration. When trying to understand the significance of this U-shaped relationship, it must be considered that sleep duration is often based on self-report. Are self-reported long sleepers truly getting that much sleep or are they just spending longer hours in bed trying to sleep? The latter would reflect poor sleep quality, which may be caused by sleep disordered breathing, insomnia, and/or depression, conditions more frequent in adolescents and adults than in children. One alternative explanation is that long sleep leads to less time for physical activity, and therefore the association between long sleep and obesity would not persist when adjusting for physical activity. At this time there is no known physiological mechanism to explain how excessive sleep could lead to obesity, and a 2014 metaanalysis of 11 longitudinal studies of sleep duration and obesity in adults failed to find an impact of long sleep duration on future obesity [65]. Without more objective measures of sleep duration and quality, and without more mechanistic laboratory studies, it is premature to say that long sleep has unfavorable effects on weight. The following sections will address some of the individual studies examined in these metaanalyses. Most of the earlier studies were cross-sectional in design, and sleep duration was self-reported. Table 13.1 summarizes the studies adopting self-reported sleep duration, and Tables 13.2 and 13.3 summarize the study adopting objective measures of sleep duration and quality.

13.4 EPIDEMIOLOGY STUDIES ADOPTING SELF-REPORTED SLEEP DURATION Table 13.1 summarizes more than two decades of studies using self-reported sleep duration in children and adolescents.

13.4.1 Epidemiologic Studies in Children The earliest of these studies published in 1992 by Locard and colleagues [66] was a case-controlled study examining the relationship between sleep duration and risk of obesity in approximately 1000 French 5-year-old children (327 cases vs 704 controls)[66]. The authors looked at environmental factors related to lifestyle that could be associated with obesity at age 5. Obesity at this age predicts approximately 50 % of cases of obesity by age 10 and 12. The analysis found that short sleep duration had a dose effect relationship with obesity at age 5 even after adjusting for a highly predictive confounding factor such as parental overweight. In 2002, Von Kries et al. confirmed a dose-response effect in a larger German cohort of similar age in which the prevalence of overweight (BMI >90th percentile) and obesity (BMI >97th percentile) decreased with increased duration of sleep [68]. Specifically, after controlling for other lifestyle factors, the prevalence of obesity was 5.4% in the children who slept 10 h vs 2.1% in children who slept
11.5 h. Similarly a Japanese study of more than 8000 children aged 6–7 years found an inverse dose-response relationship between hours of sleep and obesity, after adjusting for age, sex, parental obesity, physical activity, TV watching, and snacking [67]. One weakness of this study is that the analysis was not controlled for low socioeconomic status, often associated with obesity [137] and possibly with decreased sleep hours [72]. The “Quebec en Forme” project on 422 Canadian children ages 5–10 years old also confirmed the dose-response effects of sleep duration on the risk of childhood weight and obesity after adjusting for age, sex, parental obesity, and other risk factors [73]. A Portuguese study of 4390 school

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TABLE 13.1

Subjective Sleep Duration and Obesity in Children and Adolescents Sample size

Study design Country

Locard et al. [66]

1031 ♂♀

Crosssectional

France

5

Sleep duration by parental report

Risk of OB: <10 h OR ¼ 2.8 11–12 h OR ¼ 2.0 >12 reference

Sekine et al. [67]

8941 ♂♀

Crosssectional

Japan

6–7

Sleep duration by parental report

Risk of OB: <8 h OR ¼ 2.9 8–9 h OR ¼ 1.9 9–10 h OR ¼ 1.49 >10 h reference

Von- Kries et al. [68] 6862 ♂♀

Crosssectional

Germany

5–6

Sleep duration by parental report

Prevalence of OB: <10 h ¼ 5.4% 1.5–11 h ¼ 2.8% >11.5 h ¼ 2.1%

Agras et al. [69]

Longitudinal (9 years)

United States

0–9

Sleep duration by parental report

Children OW at age 9 years slept 30 min less at age 3–5 years

Giugliano et al. [70] 97 ♂♀

Crosssectional

Brazil

6–10

Self-reported % BF inversely correlated to hours of sleep sleep duration (r ¼ .278, P < .02)

Knutson et al. [71]

4486 ♂♀

Crosssectional

United States

13–18

Self-reported ♂ sleep duration predicted OW (OR ¼ 0.9, sleep duration P ¼ 0.04) Girls: no effect

Padez et al. [72]

4411 ♂♀

Crosssectional

Portugal

7–9.7

Sleep duration by parental report

Risk of OW: 8 h reference 9–10 h OR ¼ 0.46 >11 h OR ¼ 0.44 Risk of OB 8 h reference 9–10 h OR ¼ 0.44 >11 h OR ¼ 0.39

Reilly et al. [3]

6426 ♂♀

Longitudinal (7 years)

United Kingdom 0–7

Sleep duration by parental report

Risk of OB at age 7 years with sleep duration at age 2.5 years <1.5 h OR ¼ 1.57 1.5–1.9 h OR ¼ 1.31 11–11.9 h OR ¼ 0.94 >12 h reference

Chaput et al. [73]

422 ♂♀

Crosssectional

Canada

5–10

Sleep duration by parental report

Risk of OW/OB 8–10 h OR ¼ 3.4 1.5–11.5 h OR ¼ 1.42 12–13 h reference

Chen et al. [74]

656 ♂♀

Crosssectional

Taiwan

13–18

Self-reported Higher frequency of adequate sleep (6–8 h on sleep duration >4 weekdays) OR ¼ 1.74 of normal BMI

Eisenmann et al. [75]

6324 ♂♀

Crosssectional

Australia

7–15

Self-reported Risk of OW/OB sleep duration ♂: <8 h OR ¼ 3 8–9 h OR ¼ 1.83 9–10 h OR ¼ 1.6 10 h reference ♀: No change

Dieu et al. [76]

670 ♂♀

Crosssectional

Vietnam

4–6

Sleep duration by parental report

Author

150 ♂♀

Age (years)/ Sleep school grade assessment

Summary of findings

Prevalence ratio OW/OB ¼ 0.85 for longer sleep duration

Continued

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13. SLEEP AND OBESITY IN CHILDREN AND ADOLESCENTS

Subjective Sleep Duration and Obesity in Children and Adolescents—cont’d

Author Lumeng et al. [77]

Sample size

Study design Country

785 ♂♀

Crosssectional

Age (years)/ Sleep school grade assessment

United States

9–12

Sleep duration by parental report

Summary of findings Risk of OW 1 h additional sleep at age 12 years: OR ¼ 0.8

Retrospective

1 h additional sleep at age 9 years: OR ¼ 0.6 at age 12 years

Longitudinal (3rd to 6th grade)

Tertile of change of sleep duration: T1
1.6 h OR ¼ 3.48 T2 1.5–0.3 h reference T3
0.3 h OR ¼ 0.75

Seicean et al. [78]

509 ♂♀

Crosssectional

United States

14–17

Self-reported Risk of OB: sleep duration <5 h OR ¼ 7.65 5–6 h OR ¼ 2.8 6–7 h OR ¼ 2.55 7–8 h OR ¼ 1.38 > 8 h reference

Snell et al. [79]

1441 ♂♀

Longitudinal (15 years)

United States

8–13.6

Sleep duration by parental and self-report

Yu et al. [80]

500 twins ♂♀

Crosssectional

China

10–20

Self-reported ♂: n.s. association between sleep duration and sleep duration measures of adiposity. ♀: U-shaped relationship, most notable for age
14 years

Taveras et al. [81]

915 ♂♀

Longitudinal (3 years)

United States

0.5–3

Sleep duration by parental report

Average sleep duration <12 h (vs >12 h) positively associated with BMI z-score and odds of OW

Touchette et al. [82] 1138 ♂♀

Longitudinal (4 years)

Canada

2.5–6

Sleep duration by parental report

Short persistent sleepers (<10 h) have higher risk of OW/OB (OR 4.2) vs. 11 h persistent sleepers

Hitze et al. [83]

414 ♂♀

Crosssectional

Germany

6–20

Self-reported Risk for OB/OW: sleep duration ♀ short sleep (<10 h at age < 10 years, <9 h at age
10 years) vs. long sleep associated with 5.5 fold higher risk OB and 2.3 fold higher risk OW

Jiang et al. [84]

1311 ♂♀

Crosssectional

China

3–4

Sleep duration by parental report

Risk of OB >11 h reference <9 h: OR ¼ 4.76 9–9.4 h: OR ¼ 3.42

Bell et al. [85]

1930 ♂♀

Longitudinal (4 years)

United States

0

Sleep duration by parental report

Change in BMI category (NW-OW, OW-OB).

1 h additional sleep at age 8 years: 5.3% decrease in likelihood of OW at age 13.6 years

Age 0–4 years (n ¼ 822): low nighttime sleep (<25%ile) OR ¼ 1.80, P < .01

Baseline sleep duration Longitudinal (8 years)

5

Baseline sleep Age 5–13 (n ¼ 1108): low nighttime sleep (<25% duration ile) OR ¼ 1.20, P ¼ n.s. Age 13 years low nighttime sleep (<25%ile) OR ¼ 1.80, P < .01 Sleep duration at follow-up (13 years)

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TABLE 13.1

153

Subjective Sleep Duration and Obesity in Children and Adolescents—cont’d

Author

Sample size

Age (years)/ Sleep school grade assessment

Study design Country

Summary of findings <6 h of sleep baseline OR of OB ¼ 1.91, 95% CI ¼ 1.27–2.9. n.s. when adjusted for obesity, age, gender, race and parental income.

Calamaro et al. [86] 13,568 ♂♀

Longitudinal (1 year)

United States

16

Student completed in-school surveys

Kong et al. [87]

2054 ♂♀

Crosssectional

Hong Kong

6–20

Self-reported in primary school children: sleep duration OR of OB >9 h reference 8–9.25 h: 2.36 <8 h: 2.88

Seegers et al. [88]

1916 ♂♀

Longitudinal (3 years)

Canada

10

Sleep duration by parental report

TIB at age 10 negatively associated with BMI at age 13 years (β ¼ 0.71, 95% CI: 1.18, 0.14). 1 h decrease in time in bed at age 10 years: OR of OW ¼ 1.51 (95% CI: 1.28–1.76) and OR of OB ¼ 2.07, (95% CI: 1.51–2.84) at age 13 years

Araujo et al. [89]

1171 ♂♀

Crosssectional

Portugal

13

Self-reported bedtimes and wake-up times

Age 13 years Association between sleep duration and BMI and BF%: ♂ BMI: β ¼ 0.155, (95% CI: 0.267–0.043) ♂s BF%: n.s ♀ BMI and BF%: n.s. Age 17 years Association between sleep duration and BMI and BF%: ♂BMI and BF%: n.s. ♀BMI: n.s. ♀ BF%: β ¼ 0.510, (95% CI: 0.061–0.958).

17

Longitudinal (4 years)

13–17

Association between sleep duration at age 13 years with BMI and BF% at age 17 years ♂ BMI: β ¼ 0.123, (95% CI: 0.233 to 0.012) ♂ BF%: β ¼ 0.731, (95% CI: 1.380 to 0.081) ♀ BMI: β ¼ 0.050, (95% CI: 0.002–0.097). All n.s. when adjusted for baseline adiposity

Lowry et al. [90]

30,451 ♂♀

Crosssectional

United States

9th, 10th, 11th, 12th grade

Self-reported ♀OR of OB: sleep duration Short sleep < 4 h: 1.50 Prolonged sleep >9: 1.54

Ochiai et al. [91]

3433 ♂♀

Crosssectional

Japan

9–10

Sleep duration by parental report

Adjusted OR for OW: ♂ (P for trend ¼ .014): >10 h: 1.0 9.0–9.9 h: 1.5 8.0–8.9 h: 1.65 <8.0 h: 2.38 ♀ (P for trend ¼ .149): >1.0 h: 1.0 9.0–9.9 h: .83 8.–8.9 h: 1.14 <8.0 h: 1.52

Magee et al. [92]

1079 ♂♀

Longitudinal

Australia

4–11

Sleep duration by parental report

Early onset (OW/OB at every age studied), significant concurrent relationship between sleep and BMI at age 6–7 years, β ¼ 0.61, P ¼ .006. Sleep duration age 6–7 years inversely associated with BMI at age 8–9 years, β ¼ 0.68, P ¼ .017 Sleep duration at age 8–9 years inversely associated with BMI at age 10 to 11 years, β ¼ 1.21, P ¼ .003. Continued

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Subjective Sleep Duration and Obesity in Children and Adolescents—cont’d Sample size

Study design Country

Mitchell et al. [93]

1390 ♂♀

Crosssectional

United States

14–18

Self-reported sleep times

Lee et al. [94]

1187 ♂♀

Crosssectional

South Korea

12–18

Self-reported Risk of OW: sleep duration <5 h: OR ¼ 2.04 6–7 h: OR ¼ 1.00 8–9 h: reference >10 h: OR ¼ 1.18 Risk of OB: <5 h: OR ¼ 1.04 6–7 h: OR ¼ 0.80 8–9 h: reference >10 h: OR ¼ 0.55

Taveras et al. [95]

1046 ♂♀

Longitudinal (7 years)

United States

0.5–7

Sleep duration by parental report

Cao et al. [96]

11,830 ♂♀

Crosssectional

China

6–18

Self-reported Comparing short (<7 h) vs long (>9 h) sleep, OR sleep duration for OB: ♂: 0.70 ♀: 1.73

Scharf et al. [97]

8950 ♂♀

Crosssectional

United States

4–5 years

Sleep duration by parental report

Author

Age (years)/ Sleep school grade assessment

Summary of findings Each additional hour of sleep associated with 0.07 kg/m2 decrease in BMI at 10th BMI %ile, 0.17 kg/m2 reduction in BMI at 50th BMI %ile 0.28 kg/m2 reduction in BMI at 90th BMI %ile

Sleep curtailment Score (lowest score indicating the most curtailment) and BMI z-score: 0–4: 0.48 (95% CI: 0.13 to 0.83) 5–7: 0.22 (95% CI: 0.00 to 0.44) 8–9: 0.13 (95% CI: 0.08 to 0.33) 10–11: 0.08 (95% CI: 0.07 to 0.23) 12–13: reference

Sleep duration in OB less than normal-weight (NW) children at age 4 years (1.5 h NW vs. 1.37 h OB, P < .01) and age 5 years (1.4 h NW vs. 1.27 h OB, P < .05). P < .01). Similarly, bedtime was later among obese children as compared with normal-weight children Each additional hour of sleep associated with a lower BMI z-score at age 4 years (0.06) and age 5 years (0.09). At age 4 years, sleep duration < 9.44 h vs > 9.44 h OB OR 1.36 (1.03–1.8), P ¼ .03. At age 5 years, bedtime at/or after 9 p.m. vs before 9 p.m., OR of OB 1.49 (1.16–1.91), P ¼ .002. At age 5, wake time before 6:30 a.m. vs at/or after 6:30 a.m. OR of OB 1.23 (1.01–1.51), P ¼ .04.

Longitudinal (1 year)

Wu et al. [98]

66,817 ♂♀

Crosssectional

Sleep duration at age 4 years inversely associated with BMI z-score at age 5 years, β ¼ 0.0287, P ¼ .026. Later bedtime at age 4 years associated with a higher BMI z-score at age 5 years, β ¼ 0.0637 (P ¼ .003). China

10–18

Self-reported OR of OW: sleep duration <5 h: 1.26 5–6.9 h: 1.06 7.0–8.9 h: reference >9 h: 1.27 OR of OB: <5 h: 1.24 5–6.9 h: .94 7.0–8.9 h: reference >9 h: 1.42

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TABLE 13.1

155

Subjective Sleep Duration and Obesity in Children and Adolescents—cont’d Sample size

Study design Country

Zhang et al. [99]

3086 ♂♀

Crosssectional

China

7–14

Sleep duration by parental report

OR of OW/OB (P < .05): Weekends: >10 h: 1.0 8.01–9.0 h: 1.697 <8 h: 2.691 Holidays: >10 h: 1.0 8.01–9.0 h: 1.856 <8 h: 2.921

Aguero et al. [100]

1810 ♂♀

Crosssectional

Chile

6–11

Sleep duration by parental report

Short sleep (<10 h) associated with OR of OW/ OB: 1.85 (95% CI: 1.3–2.62)

Ferranti et al. [101]

1586 ♂♀

Crosssectional

Italy

11–14

Self-reported TST and: sleep duration BMI: β ¼ 0.829, P ¼ .021 Fat mass: β ¼ 0.526, P ¼ .025 Waist circumference (WC): β ¼ 0.426, P ¼ .045

Wang et al. [102]

48,922 ♂♀

Crosssectional

China

3

Sleep duration by parental report

Author

Age (years)/ Sleep school grade assessment

Longitudinal (2 years)

Summary of findings

Prevalence ratio of OW/OB, cross-sectional analysis: <10 h: 1.13/1.25 >13 h: 1.16/1.25

3

Prevalence ratio of OW/OB, longitudinal analysis: <10 h: 1.48/1.77 >13 h: 1.48/1.19

Hager et al. [103]

240 ♂♀ Crosssectional

United States

12–32 months Sleep duration by parental report

Nighttime sleep duration and physical activity: β ¼ 0.332, P ¼ .017 Obese status: β ¼ 0.687, P ¼ .014

Rosi et al. [104]

690 ♂♀ Crosssectional

Italy

9–11

Sleep duration by parental report

Prevalence of OW/OB significantly higher in short sleepers (P ¼ .008)

Wang et al. [105]

5518 ♂♀

Crosssectional

China

6–12

Sleep duration by parental report

Longer sleepers and BMI z-score: β ¼ 0.16, P < .05 WC: β ¼ 1.11, P < .05 Later bedtime BMI z-score: β ¼ 0.03, P < .05 WC: β ¼ 1.72, P < .001 % BF: β ¼ 0.15, P < .05

Collings et al. [106]

1338 ♂♀

Crosssectional

United Kingdom 12, 18, 24, 36 months

Sleep duration by parental report

Sleep duration in South Asian cohort (P < .05) and Weight: β ¼  0.024 BMI: β ¼ 0.031 BF%: β ¼ 0.029

Summary of the findings from studies examining the association between subjective sleep duration and obesity in children and adolescents. In each study, the risk of obesity in relationship to sleep duration is expressed as odds ratio or prevalence of overweight or obesity or as coefficient of the association between sleep duration and measures of adiposity, unless those data were not available. Studies are listed by year of publication. Abbreviations: %BF, percent body fat; h, hour/hours; n.s., nonsignificant; NW, normal weight; OB, obesity; OR, odds ratio; OW, overweight; TIB, time in bed; WC, waist circumference; ♂ male; ♀ female; %ile, percentile.

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TABLE 13.2

Objectively Measured Sleep Duration and Obesity in Children

Author

Sample size

Study design

Country

Nixon et al. [107]

519 ♂♀

Crosssectional

Tikotzky et al. [107a]

96 ♂♀

Carter et al. [108]

Age (years)

Sleep assessment

New Zealand

7

Actigraphy

Sleep duration and risk of OW/OB <9 h: OR of 3.32 (95% CI ¼ 1.40, 7.87) (P ¼ .0064), increased BF 3.34% ( P ¼ .03)

Crosssectional

Israel

0.5

Actigraphy, brief infant sleep questionnaire

Sleep duration negatively associated with weightto-length ratio

244 ♂♀

Prospective 2–4 years

New Zealand

3–5

Accelerometry

Each additional hour of sleep at ages 3–5 years associated with decrease in BMI of 0.48 (95% CI: 0.01–0.96) and reduced risk of overweight 0.39 (95% CI: 0.24–0.63) at age 7 years

Chaput et al. [109]

550 ♂♀

Crosssectional

Canada

8–10

Accelerometry

Short sleepers (<10 h): OW/OB OR: 2.08 (95% CI: 1.16–3.67)

Spruyt et al. [110]

308 ♂♀

Crosssectional

United States

4–10

Actipraphy

School days TST did not vary between normal weight, overweight and obese. Weekend TST shorter for the obese children (P ¼ .03)

Klingenberg et al. [111]

311 ♂♀

Crosssectional

Denmark

3

Accelerometry

No association between sleep duration and adiposity (BMI z-score, sum of skin-folds, % BF, fat mass)

Bagley et al. [112]

228 ♂♀

Crosssectional

United States

9.08–12.25

Actigraphy

Shorter sleep duration predicted higher BMIz (β ¼ 0.01, P < .01) Both sleep minutes and efficiency interacted with family risk in the prediction of BMIz (P < .01)

Ekstedt et al. [113]

1231 ♂♀

Crosssectional

Sweden

6–10

Accelerometry

Later bedtime positively correlated with higher BMI (β ¼ 0.17, P < .05).

Wong et al. [114]

483♂♀

Crosssectional

United States

9–12

Accelerometry

OB children slept .2 h/day less than NW children (P < .02)

Burt et al. [115]

56 ♂♀

Crosssectional

Canada

5–12

Actigraphy, selfreport

Sleep duration neg. correlated with external eating score (β ¼ 0.35, P < .05) Emotional eating score pos. associated with the number of wake bouts (blocks of wakefulness) (β ¼ 0.40, P < .005). Emotional eating score neg. associated with mean sleep bout time (continuous blocks of sleep) (β ¼ 0.44, P < .05). Sleep start and bedtime neg. associated with restrained eating score (β ¼ 0.41, P < .05; β ¼ 0.40, P < .05)

Chaput et al. [116]

507 ♂♀

Crosssectional

Canada

9–11

Actigraph GT3X + acceleromter

Sleep duration not significantly associated with % BF or waist/hip ratio

Gomez et al. [117]

686 ♂♀

Crosssectional

Portugal

9–11

Actigraph + accelerometers

No difference in sleep duration between NW and OW/OB children

Hjorth et al. [118]

723 ♂♀

Crosssectional

Denmark

8–11

Accelerometer

WC inversely associated with sleep duration: 2.21 cm (3.79; 0.62) P < .05) Changes in sleep duration neg. associated with changes in HOMA-IR only after adjusting for physical activity and sedentary time (P ¼ .02).

Kjeldsen et al. [119]

676 ♂♀

Crosssectional

Denmark

8–11

Actigraphy

Short sleepers (8.41 h) had a higher BMI (P ¼ .05), higher % BF (P ¼ .03) vs. medium and longer sleepers. Sleep duration neg. associated with energy density of diet: β ¼ 0.32, P ¼ .003; added sugar in diet:

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Summary of findings

13.4 EPIDEMIOLOGY STUDIES ADOPTING SELF-REPORTED SLEEP DURATION

TABLE 13.2

Objectively Measured Sleep Duration and Obesity in Children—cont’d

Author

Sample size

Study design

Country

Age (years)

Sleep assessment

157

Summary of findings β ¼ 1.50, P < .001; Sugar-sweetened beverages: β ¼ 1.07, P < .001

Martinez et al. [120]

304 ♂♀

Crosssectional

United States

8–10

Accelerometry

Longer sleep duration related to lower child BMIz (β ¼ 0.17; 95% CI: 0.39, 0.08; P < .01).

Martinez et al. [121]

229 ♂♀

Prospective 2 yrs

United States

8–10

Accelerometry

Short sleep (<10 h) at baseline associated at 2 yrs with: Higher BMI: β ¼ 0.07, P ¼ .01 Higher WHR: β ¼ 0.11, P < .01 Higher weight gain: β ¼ 0.14, P ¼ .02

Michels et al. [122]

239 ♂♀

Crosssectional

Belgium

6–12

Sleep diary and actigraphy

Measured sleep duration neg. associated with waist size at 2 years (β ¼ 1.083, P < .05)

193♂♀

Prospective 2 years

Katzmarzyk et al. [123]

6025 ♂♀

Crosssectional

Multicenter12 countries

Mcneil et al. [124]

515 ♂♀

Crosssectional

Canada

Butte et al. [125]

111 ♂♀

Prospective 1 year

United States

Morrissey et al. [126]

298 ♂♀

Crosssectional

Hjorth et al. [127]

530 ♂♀

Wilkie et al. [128]

374 ♂♀

Borderline association with % BF and BMI No associations with sleep efficiency, sleep latency and WASO 9–11

An Actigraph GT3X1 accelerometer

OB OR and sleep duration 0.75, 0.69–0.82 P <.05

Actigraphy

Sleep efficiency, not sleep duration, was negatively associated with weight, WC, % BF, BMI z-score and waist to-height ratio with small effect size (P  .05)

3–5

Accelerometry

Baseline TST inversely related to Fat mass: (β ¼ 0.006, P ¼ .005) and % FM (β ¼ 0.022, P ¼ .006) at 1 year

Victoria, Australia

1.2–12.2

Accelerometry, sleep duration by parental report

Insufficient sleep (<10 h): OW OR: 1.97 (95% CI: 1.11–3.48) P <.05; OB OR: 2.43 (95% CI: 1.26–4.71) P< .01

Crosssectional

Denmark

8–11

Accelerometry

Long sleepers (9.20–1.47 h/night) reference Short sleepers (7.45–9.00 h/night) increase of: fat mass by 0.21 kg (95% confidence interval 0.03–0.38); android fat mass by 0.02 kg (0.001–0.04); WC by 0.73 (0.23–1.24) cm; all (P < .04)

Crosssectional

United Kingdom

9–11

Actigraph GT3X + accelerometer

Sleep duration and OW/OB OR 0.65, P ¼ .011 in adjusted model

Summary of the findings from studies examining the association between objective sleep duration and quality and obesity in children. In each study the risk of obesity in relationship to sleep duration is expressed as odds ratio of overweight or obesity or as coefficient of the association between sleep duration and measures of adiposity, unless those data were not available. Studies are listed by year of publication. Abbreviations: BF, body fat; BMI, body mass index; BMIz, body mass index standard deviation (adjusted for child age and sex); h, hour/hours; HOMA-IR, homeostatic model assessment of insulin resistance; n.s., nonsignificant; NW, normal weight; OB, obesity; OR, odds ratio; OW, overweight; TST, total sleep time; WC, waist circumference; WASO, wake after sleep onset; ♂ male; ♀ female; %ile, percentile.

children aged 7–9 was the first one to examine gender differences [72]. Overall, children who slept more had a decreased risk of overweight and obesity; however, when analyzed by gender, a significant association with short sleep duration was only found for the obese boys and the overweight girls. A few prospective studies utilizing subjective sleep duration have been conducted in children. These studies examined the risk factors early in life that were associated with obesity later in childhood. The largest study conducted by Reilly et al. [3] analyzed a data set from the Avon Longitudinal Study of Parents and Children conducted in United Kingdom [138]. More than 8000 children of both genders were followed prospectively from birth to age 7. The authors examined 25 putative early life risk factors for obesity and, using multivariate analysis, found that only birth weight, parental obesity, sleep duration, and television viewing were independently associated with the risk of obesity in the entire cohort. Snell and colleagues published data on a sample of approximately 1400 children from a larger American longitudinal database [79]. The authors showed that children who get less sleep tend to weigh more 5 years later. When the

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13. SLEEP AND OBESITY IN CHILDREN AND ADOLESCENTS

Objectively Measured Sleep Duration and Obesity in Adolescents Study design

Country

Age (years)

Sleep assessment

Summary of findings

383 ♂♀

Crosssectional

United States

11–16

Wrist actigraphy

For every hour of increased sleep, OR of OB decreases by 80% (P<.001)

Benefice et al. [130]

40 ♀

Crosssectional

Senegal

13

Accelerometer

1 kg/m2 increase in BMI associated with 6.85 min decrease in sleep duration

Weiss et al. [131]

240 ♂♀

Crosssectional

United States

16–19

Wrist actigraphy

Higher prevalence of shorter sleep duration (<8 h/ night) in obese (80%) vs. nonobese (63%), P ¼ .04 Short sleep duration (<8 h/night) associated with different macronutrient composition

Javaheri et al. [132]

471 observations on 387 ♂♀ individuals

Crosssectional

United States

8–19

Actigraphy

BMI %ile and WC highest in short sleepers ( 6.5 h) and lowest in long sleepers (
8.75 h). Subjects sleeping 5 h or 1.5 h had 25% higher HOMA-IR than those sleeping 7.75 h. The association between short sleep and HOMA-IR was in part explained by WC

IglayReger et al. [133]

37 ♂♀

Crosssectional

United States

11–17

Physical activity sensor (SWA, SenseWear Pro3)

Cardiometabolic risk score and: Total sleep: r ¼ .535, P <.001 Sleep session length: r ¼ .365, P ¼ .026 TST as best independent predictor of cardiometabolic risk score (P ¼ .025)

He et al. [134]

324 ♂♀

Crosssectional

United States

16–17

Actigraphy

With 1-h increase in habitual sleep Variability: 170 kcal increase in daily total energy intake 65% and 94% higher odds of consuming more after dinner snacks during school/work days and weekends/vacation days, respectively

He et al. [135]

305 ♂♀

Crosssectional

United States

16–17

Actigraphy

With 1-h increase in habitual sleep variability: android/gynoid fat ratio increased by 0.02 cm2, SE ¼ 0.01, (P ¼ .03) visceral fat area increased by 6.86 cm2, SE ¼ 2.82 (P ¼ .02)

Valrie et al. [136]

25 ♂♀

Crosssectional

United States

12–16

Actigraphy in 6 participants

Shorter weekday sleep and more sleep debt were associated with higher WC (r ¼ .54, P ¼ .01) and (r ¼ .56, P ¼ .01) respectively. Lower sleep quality associated with higher BMIz (r ¼ .49, P ¼ .02)

Author

Sample size

Gupta et al. [129]

Summary of the findings from studies examining the association between objective sleep duration and quality and obesity in adolescents. In each study, the risk of obesity in relationship to sleep duration is expressed as odds ratio of overweight or obesity or as coefficient of the association between sleep duration and measures of adiposity, unless those data were not available. Studies are listed by year of publication. Abbreviations: BMI, body mass index; BMIz, body mass index standard deviation (adjusted for child age and sex); h, hour/hours; HOMA-IR, homeostatic model assessment of insulin resistance; n.s., nonsignificant; OB, obesity; OR, odds ratio; OW, overweight; TST, total sleep time; WC, waist circumference; ♂, male; ♀, female; % ile, percentile.

association between sleep duration and BMI was examined by age groups, an extra hour of sleep decreased the likelihood of being overweight from 36% to 30% in children ages 3–8, and from 34% to 30% in those ages 8–13[79]. More recently, Taveras et al. analyzed exposure to sleep curtailment in 1046 children from age 6 months until 7 years, whereby sleep scores ranged from 0 to 13, with children with a score of 0–4 experiencing the most sleep curtailment [95]. Participants who obtained a sleep score of 0–4 had a higher BMI, a higher total and trunk fat mass index, higher waist and hip circumference, and higher risk of obesity than those with a sleep score of 12–13, indicating the least sleep curtailment. These longitudinal and cross-sectional studies conducted in the United States and abroad further suggest that the association between short sleep and obesity spans different countries, cultures, and ethnicities [70, 75, 76, 84, 107, 120, 139, 140]. To that end, in Japan, sleep habits of 3433 fourth-grade school children were analyzed by questionnaire, and an increased risk of overweight was found in boys with shorter sleep duration but not in girls [91]. However, this study did not collect data on dietary intake or socioeconomic status, potential confounders of this relationship. Another study of Chinese children by Zhang et al. found that only weekend and holiday sleep duration was significantly

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159

associated with risk of overweight/obesity [99]. Furthermore, children with compensated sleep on holidays and weekends were less likely to be overweight/obese. Similarly, a positive association between insufficient sleep duration and increased risk of overweight/obesity has been found in other studies of children from Portugal [89], United Kingdom [106], South Korea [94], Australia [92], Chile [100], China [102], United States [79, 95], and Italy [104]. Few studies have adopted additional measures of adiposity other than BMI. Wang et al. [105] analyzed the sleep habits of 5518 9–12-year-old Chinese children via sleep questionnaire in relation to BMI z-score (BMI adjusted for age and gender) and percentage of body fat as measured by bioelectrical impedance. The authors found that longer sleep duration was negatively associated with BMI z-score and waist circumference. Additionally, later bedtime was positively associated with higher BMI z-score, waist circumference, and percentage of body fat after adjustment for confounders. Another study reported the link between sleep duration and indices of total and abdominal adiposity [106] in a group of disadvantaged South Asian and Caucasian UK children at 12, 18, 24, and 36 months of age. The authors observed that sleep duration was negatively and independently associated with body weight, BMI, percent body fat, and waist circumference in the South Asian cohort. Further, inverse independent associations were found between weight, BMI, and percent body fat and sleep duration with sleep duration as the outcome. No such associations were found in the Caucasian cohort. The authors concluded that a bidirectional, inverse, and independent relationship exists between sleep duration and measures of adiposity in South Asian children.

13.4.2 Epidemiologic Studies in Adolescents Although adolescents are a population that may be at particular risk of chronic partial sleep loss [141], several studies are available at this time that look at the relationship between sleep duration and risk of overweight and obesity in this age group. The studies are included in Table 13.1. Knutson et al. examined a data set of 4486 American teenagers from the National Longitudinal Study of Adolescent Health, mean age 16.6 years [71]. When analyzed by gender, each hour increase in self-reported sleep duration was associated with a 10% reduction in risk of being overweight in boys, but the same effect was not found in girls. In contrast, Yu and colleagues, in a study of 500 twins, showed that short sleep duration was associated with higher adiposity in females but not in males [80]. Both groups of researchers comment that these sex differences may be explained by gender-specific physiology changes that accompany puberty. The study by Yu et al. introduced DEXA scan and waist circumference as additional measures of adiposity and showed that these measures have a stronger inverse relationship with short sleep than BMI [80]. Furthermore, this is one of the studies showing a U-shaped relationship between sleep duration and adiposity in adolescents where both short (<8 h) and long sleep duration (
9 h) in girls tended to have higher adiposity measures than durations of 8–8.9 h. Similar U-shaped associations between sleep duration and overweight/obesity were observed in American high school girls who slept less than 4 or more than 9 h [90] and Chinese adolescents who slept less or more than 7–8 h [90, 98]. Chen and colleagues found in 656 Taiwanese school teenagers (mean age 15.0 years) that overall 54% reported obtaining less than adequate sleep on school days, defined in this study as 6–8 h per night on more than 4 weekdays per week [74]. Middle-school adolescents had a higher frequency of adequate sleep compared to high-school students. Furthermore, after controlling for gender and school grade, the frequency of obtaining adequate sleep was directly associated with normal weight (P < .001), healthy behaviors, including eating a healthy diet and regular exercise, and a lower incidence of doctor and hospital visits [74]. A cross-sectional study assessed lifestyle and sleep behaviors in 529 students from Ohio, United States (mean age 15.6  1.23)[78]. As much as 90% of students reported less than 8 h average sleep time and 19% reported less than 6 h of sleep per night on school nights. Sleep duration appeared to have a dose effect on the likelihood of being overweight. Compared with students sleeping over 8 h, the age- and genderadjusted OR of being overweight was 8.53 (95% CI: 2.26, 32.14) for those who slept less than 5 h (P ¼ .0036), 2.79 (95% CI :1.03, 7.55) for those who slept 5–6 h, 2.81 (95% CI 1.14, 6.91) for those with 6–7 h of sleep, and 1.29 (95% CI 0.52, 3.26) for those getting 7–8 h of sleep. Further, Hitze and colleagues examined 414 adolescents and found an inverse relationship between sleep duration and BMI standard deviation score (SDS) (girls: r ¼  .27, P < .001; boys: r ¼  .25, P < .01), strengthened after adjusting for socioeconomic status, physical activity, media consumption, and nutrition quality score [83]. More recent studies provide further evidence for the association between short sleep and obesity in adolescents [87, 94, 101]. However, not all studies have reported a significant association between sleep duration and overweight/obesity. In a study that utilized data from the National Longitudinal Study of Adolescent Health (ADD Health), sleep and BMI characteristics were assessed in 13,568 adolescents (mean age 15.96 years) at two time points spanning a year (Wave I and Wave II)[86]. Unadjusted analysis revealed a cross-sectional association between short sleep and obesity at Wave I. Further, in prospective analysis adolescents with short sleep at Wave I (<6 h) were twice as likely to be obese

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in Wave II compared to those with normal sleep. However, once adjustments were made for obesity at Wave I as well as confounders including race, gender, age, and parental income, there was no significant association between sleep duration at Wave I and obesity at Wave II. Lastly, sleep duration was not associated with obesity cross-sectionally, when considering Wave II only [86].

13.5 EPIDEMIOLOGIC STUDIES ADOPTING OBJECTIVELY MEASURED SLEEP DURATION There is increasing recognition that sleep disturbances, including not only chronic sleep restriction but also alterations in sleep architecture or sleep efficiency (sleep quality), are also risk factors for obesity. In recent years researchers have moved from traditional questionnaire-based measures of sleep duration toward objective measures of sleep allowing capture of multiple aspects of sleep (duration, timing, quality, and variability), all of which may contribute to and influence optimal sleep. Moreover, when examining the sleep-adiposity relationship, prospective studies may help elucidate a possible causative role. To that end, the more recent epidemiology work on the relationship between sleep and obesity is often prospective in design.

13.5.1 Epidemiologic Studies in Children Twenty-three studies summarized in Table 13.2 examine the relationship between objectively measured sleep duration and measures of adiposity. Depending on the study, either actigraphy or accelerometry were used to assess sleep characteristics. Both actigraphy and accelerometry are noninvasive methods of monitoring human sleep/wake activity, which measure gross motor activity and utilize an algorithm to estimate sleep time and quality. Four of the studies examine the relationship between sleep quality and measures of adiposity [112, 115, 122, 124]. Four studies have a prospective design investigating the longitudinal effect of short sleep on adiposity [108, 120, 122, 125]. The majority of the studies adopting objective sleep measures demonstrated an inverse correlation between sleep and adiposity such that shorter sleep duration and/or lower sleep quality are associated to increased adiposity, after controlling for cofounders. All prospective studies report a meaningful longitudinal linear relationship between measures of sleep and adiposity. The FLAME study from New Zealand examined sleep duration by accelerometry in 244 children aged 3–7 years and found that an additional hour of sleep at ages 3–5 was associated with a BMI reduction of 0.48 kg/m2 and a reduced risk of overweight of 0.39 at age 7 [108]. Spruyt et al. studied the sleep patterns of 308 children 4–10 years old utilizing 1 week of actigraphy and found that sleep duration did not differ in the normal weight, overweight, and obese cohorts [110], with all groups sleeping 8 h per night. Although total sleep time did not differ between groups, the obese children were less likely to experience catch-up sleep on weekends, indicating that short sleep duration and high variability in sleep duration may contribute to obesity [110]. A large study utilizing accelerometry in 1231 Swedish children, 6–10 years old, did find a small but statistically significant association between sleep duration and BMI SDS [113], such that short sleep was associated with a higher BMI SDS. Two of the 23 studies are unique as they focus on African-American and Hispanic children in the United States Wong et al. studied 483 African-American and Hispanic 9–12-year-olds and reported that this group had a mean sleep duration of 8.8  0.6 h/night, which is below the 10–11 h of recommended sleep by the National Sleep Foundation [114]. Further, in this cohort, obese children slept 0.2 h less per day than normal-weight children (P < .02). Socioeconomic status had no effect on sleep duration. Martinez et al. measured sleep duration by maternal report and accelerometry in 304 8–10-year-old Mexican American children [121]. Interestingly, mother-reported sleep and accelerometer-estimated sleep were correlated (r ¼ .33, P < .001) and BMI z-score was negatively associated with both mother-reported and accelerometer-estimated sleep duration, suggesting that sleep duration reported by the mother may be a reliable measure of sleep duration with a reasonable concordance with sleep-measured objectively, when the latter methodology is not feasible. Martinez et al. also looked prospectively at the same cohort [120] and found that children who slept less were more likely to have a higher BMI z-score, waist-to-height ratio and weight gain at 24-month follow-up (β ¼  0.07, P ¼ .01; β ¼  0.11, P < .01; β ¼  0.14, P ¼ .02, respectively) after controlling for baseline weight status, child gender, maternal BMI, and occupation. Chaput et al. examined the data from the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE), a large multinational cross-sectional study. Their analysis included 5777 9–11-year-old children from

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Australia, China, Colombia, Brazil, India, Finland, United Kingdom, Kenya, Portugal, South Africa, Canada, and the United States. Consistent with the findings by Wong et al., the average sleep duration was 8.8  0.9 h, lower than recommended sleep, suggesting that chronic sleep deprivation is common across countries and cultures [142]. From the same multinational study, Katzmarzyk et al. reported that sleep duration was inversely related to the odds of obesity with a OR of 0.75 (0.69–0.82; 95 % CI, P < .05)[123]. Gomez et al. used actigraph and accelerometers to measure physical activity, sedentary time, and sleep for at least 7 days including 2 weekend days in 9–11-year-old Portuguese children. They obtained complete sleep data on 686 children, 417 normal weight and 269 overweight/obese [117]. In contrast to other studies, these authors report no difference in sleep time between the two groups as well no difference in sedentary time, level of moderate to vigorous physical activity, and no difference in sociodemographics. Further, the two groups did differ in parental BMI. One possible explanation of the negative finding in this study is that overweight and obese children were grouped together. The results of the analysis may have been different if it had focused on obese children alone. More recently, a few studies have examined the association between sleep duration and measures of adiposity other than BMI. Chaput et al. in a cohort of 507 Canadian 9–11-year-old children found that sleep duration was not significantly associated with percent body fat or waist to hip ratio after controlling for age, sex, ethnicity, maturity offset, fast food consumption, annual household income, and highest level of parental education [116]. In the adjusted model, only moderate to vigorous physical activity (MVPA) was inversely associated with adiposity indicators such as BMI, percent body fat, and waist circumference, yet sedentary time was positively associated. Hjorth et al. also looked at the independent and combined association between movement, cardiometabolic risk factors (including waist circumference), and sleep duration measured by accelerometer in a sample of 723 Danish children who were 8–11 years old [118]. The authors found that only waist circumference and insulin resistance (HOMA-IR) were inversely associated with effective sleep duration after adjusting for physical activity and sedentary time, whereas self-reported sleep disturbances were associated with several cardiometabolic risk factors. In this sample, 45% of the children slept at least 10 h per night suggesting that, independently of the sleep duration, sleep quality (including sleep efficiency) may be a better measure of effective sleep duration [118]. Michels et al. adopted actigraphy to assess sleep duration and BMI z-score, fat on plethysmography, and waist circumference to measure adiposity [122]. They also derived sleep quality using measures of sleep latency, wake-up time after sleep onset, and sleep efficiency. The cross-sectional and 2-year longitudinal assessment of the relationships were explored after adjusting for age, gender, parental education, measured physical activity, and weekly snacking frequency. In this sample, only a small portion of the children had less than recommended hours of sleep (14.2%) and were overweight/obese (7%). Both in cross-sectional and longitudinal analysis reported, sleep duration was negatively associated with all three measures of adiposity yet actual sleep duration only had an effect on waist circumference over a period of 2 years. No associations were observed with sleep quality parameters such as sleep efficiency, sleep latency, and wake after sleep onset (WASO). The findings by Hjorth et al. [118] and Michels et al.[122] indicate that sleep quality, not only sleep duration, need to be accounted for as low sleep quality leads to lower effective sleep duration and could, by itself, induce hormonal changes that affect obesity risk. Therefore caution needs to be adopted when interpreting studies that do not utilize objective measures of sleep duration and quality. Additionally, the results of the study by Michels et al.[122] point to the importance of assessing waist circumference as a measure of adiposity when examining cohorts with a lower prevalence of obesity. Butte et al.[125] used accelerometry to analyze the longitudinal relationship between sleep duration and body composition and observed that sleep duration inversely predicted fat mass and percentage of fat mass at 1-year followup in a cohort of preschool-aged children. Also McNeil et al. examined data in 515 Canadian children from the ISCOLE study and found that sleep efficiency via actigraphy, not sleep duration, was significantly and inversely associated with body weight, waist circumference, BMI z-score, percentage body fat, and waist-to-height ratio [124]. In summary, the majority of studies that utilized objective measures of sleep duration and quality indicate that diminished sleep time and/or quality were associated with increased BMI and/or risk of overweight and obesity.

13.5.2 Epidemiologic Studies in Adolescents Table 13.3 lists the limited number of studies available that adopt objectively measured sleep duration in adolescents. Of note, a subset of these studies examined the impact of sleep duration/sleep quality on cardiometabolic risk, insulin resistance, or dietary patterns, variables related to BMI. In these studies, BMI is treated as confounding factor rather than a dependent variable and is controlled for in the analysis. He et al. analyzed the sleep and dietary patterns of 324 adolescents from the Penn State Child Cohort [134]. The authors observed that 1 h increased habitual sleep variability (HSV, calculated as the individual standard deviation

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of the 7-night sleep duration) was associated with a 170-kcal increase in daily total energy intake and 65% and 94% higher odds of consuming more after-dinner snacks during school/work days and weekends/vacation days, respectively. In a subset of 305 adolescents from the same cohort, increased HSV was associated with abdominal adiposity, as measured by dual-energy X-ray absorptiometry [135]. Each 1-h increase in HSV resulted in higher android/gynoid fat ratio and visceral fat area by 0.02 cm2 (P ¼ .03) and 6.86 cm2 (P ¼ .02), respectively. This relationship was in part mediated by total caloric intake. Neither study demonstrated significant associations between sleep duration and energy intake and abdominal obesity, respectively. Weiss et al. observed that the prevalence of shorter mean sleep duration was significantly higher in obese compared with nonobese adolescents (80% vs. 63%, P ¼ .04) and short sleep duration affected the macronutrient intake [131]. The Heartfelt study [129] used 24-h actigraphy to estimate sleep duration and sleep disturbance in 383 11–16-year-old boys and girls. Sleep duration was negatively associated with obesity, and each hour of sleep loss resulted in 80% increased risk of obesity [129]. Sleep disturbance, but not sleep duration, appeared to be weakly associated with decreased physical activity, suggesting that sleep disturbance could indirectly affect the risk of obesity by a reduction in daytime physical activity. In a study of 387 individuals 8–19 years old, BMI percentile and waist circumference were highest in short sleepers (6.5 h) and lowest in long sleepers (
8.75 h)[132]. Subjects sleeping 5 or 1.5 h had 20% higher HOMA-IR than those sleeping 7.75 h. The association between short sleep and HOMA was in part explained by waist size. A study by Benefice et al.[130] is unique as it is run in a rural African area rather than in an industrialized country. The authors objectively measured sleep duration for 3 days and 4 consecutive nights in 40 13–14-year-old Senegalese girls and found that sleep duration was reduced by 6.85 min for every 1 kg/m2 increase in BMI [130]. In summary, the studies noted earlier demonstrate that, in adolescents, low objectively measured sleep duration is associated with increases in energy intake and adiposity. Thus the association between sleep duration and quality and risk for overweight/obesity persists in adolescents.

13.6 SLEEP QUALITY AND OBESITY RISK As discussed earlier in this chapter, multiple studies have validated the important role of sleep as a modulator of metabolic homeostasis and sleep quality has also emerged as potential risk factor in obesity. A metaanalysis of 26,553 subjects by Fatima et al. found an OR of 1.27 (95% CI: 1.05–1.53) of overweight/obesity in those who had inadequate sleep, including short sleep duration and poor sleep quality [143]. Measures of sleep quality included higher latency, recurrent awakenings, and poor efficiency. When subjects were subanalyzed, the OR grew to 1.46 (95% CI: 1.24–1.72) for those with poor sleep quality independently of sleep duration. Together with the data described in the previous sections these findings suggest that sleep variation and quality may play a significant role in obesity independent of sleep duration.

13.7 LABORATORY EVIDENCE FOR A LINK BETWEEN SLEEP LOSS AND OBESITY Although multiple epidemiologic studies have pointed to an association between sleep loss and the increased risk of obesity in the pediatric population, the direction of causality and the underlying mechanisms are still unclear. The effect of sleep on metabolism and hormonal circadian rhythms in adults and children has been known for several years [144]. In this section, we will review the results of these studies. Although the seminal studies were conducted in adults, emerging data shows similar results in the pediatric population. Thus we assume mechanisms act similarly in the adult and in the pediatric population, however we recognize that physiology in these two populations can differ. The pioneer laboratory studies by Spiegel et al. looked at the impact of recurrent partial sleep deprivation in healthy young men [145, 146] and demonstrated a disruption of the neuroendocrine regulation of appetite as the levels of the anorexigenic hormone leptin were markedly decreased throughout the 24-h cycle [145, 146], yet the levels of the orexigenic factor ghrelin were found to be increased [146]. The changes in appetite regulation in the sleep debt condition were paralleled by an increase in the peripheral sympathetic nervous activity [146]. Scores of hunger, global appetite, and food preferences revealed increased hunger and appetite when the subjects were sleep deprived [145], particularly for calorie-dense foods with high carbohydrate content such as sweets, salty snacks, and starchy foods. Since this seminal publication by Spiegel et al. [146], subsequent laboratory studies of total or partial sleep restriction have been reproduced exclusively in adults and have reported somewhat variable results on hunger, food intake, and anorexigenic leptin and orexigenic ghrelin hormone levels [147–151]. For the most part, these studies demonstrate that sleep deficiency produces alterations in leptin and ghrelin, both in the direction to promote food intake [145, 151–155].

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Specifically, studies show lower leptin and higher ghrelin levels following sleep restriction [150], with higher levels of ghrelin in men but not not in women [156]. However, a few studies have reported higher, not lower, leptin levels, perhaps indicating a state of leptin resistance [157, 158]. Interestingly, no changes in hunger or appetite were observed following sleep restriction in one of these studies that reported higher leptin levels following a period of sleep restriction [158]. The varying results may be attributed to difference in the study design such as the duration of the sleep restriction protocol (few vs several days) and the calorie intake during the leptin and ghrelin measurements (controlled calorie restriction in the early studies [146, 159] vs ad libitum intake in more recent studies [148, 150]). The endocannabinoid (eCB) system, involved in the modulation of hedonic eating, seems to also be altered following sleep restriction. A recent study in adults reported that sleep restriction to 4.5 h/night for 3 nights was sufficient to increase the peak and amplitude of the 24-h profile of the eCB 2-arachidonoylglycerol (2-AG)[155]. Moreover, the peak of the 24-h profile was delayed by approximately 2 h in the sleep restriction condition. In this study, the participants had higher hunger and appetite ratings, coinciding with the same time of day in which 2-AG was elevated. Taken together, these studies suggest that peptides involved in regulating food intake (leptin, ghrelin, eCBs) are altered following sleep deficiency, most often in the direction to promote food intake. Thus these findings suggest that, if exposed to ad libitum food in these studies, the subjects, under sleep restriction, would have increased their food intake and possibly gained weight overtime. To this point, a handful of studies have demonstrated that experimental sleep curtailment results in actual increased energy intake, particularly for snack foods high in carbohydrates and fat [155, 160–162]. A study in healthy young adults estimated that, after 4 nights of restricted sleep, participants ate on average 460  196 more kcal from an ad libitum buffet as compared to baseline sleep [147]. A sleep restriction study of 14 nights of 5.5 vs 8.5 h demonstrated an increased consumption of carbohydrates and calories mostly from snacks, particularly in the evening and overnight, however did not show changes in serum leptin and ghrelin levels [148]. A few studies report that sleep restriction not only leads to increased caloric intake but also weight gain [163, 164]. These carefully controlled laboratory studies in adults have revealed that sleep deficiency can alter neuroendocrine factors involved in regulating food intake in favor of increased consumption, increased ratings of feelings of hunger and appetite, actually leading to increased caloric intake, and in some cases, weight gain. Although child and adolescent physiology is different to that observed in adults, it is reasonable to hypothesize that similar relationships amongst sleep deficiency, neuroendocrine modulators, and food intake behavior are also present in a younger population thus helping to explain the association between deficient sleep and obesity in the pediatric population. A few studies in this group have attempted to examine the link between short sleep and obesity under experimental conditions. Hart et al. utilized a within-subject, counterbalanced, crossover design to assess the effect of short sleep compared to sleep extension (from baseline) in 37 children, ages 8–11 years old. The children were evaluated during a week of habitual sleep, and then were randomized to either decreased or increased sleep by 1.5 h/night for 1 week. In the third week, the children were assessed on the other schedule [165]. Sleep was measured by actigraphy; weight change and fasting leptin and ghrelin were also assessed. Further, participants provided 24-h dietary recall. The children slept over 2 h more in the sleep extension vs the sleep restriction condition. The authors report that sleep extension was accompanied by a mean .22 kg lower weight and the children reported to have consumed 134 kcal/day less (P < .05) in sleep extension vs. sleep restriction [165]. Moreover, morning fasting leptin levels were lower following sleep extension than sleep restriction (P < .05). Other studies have reported similar results. Nine obese adolescents were restricted by 1 h of sleep and were found to have mild elevations in insulin, glucose, and leptin levels, suggesting leptin resistance, and participants gained 0.8 kg on average [166]. More recently, Mullins et al. analyzed the sleep and dietary habits of 10 toddlers age 32–47 months over 1 day of baseline sleep, 1 day of sleep restriction whereby participants did not nap, had a 2.3-h bedtime delay, and 1 day of ad libitum sleep recovery [167]. The authors observed that the children consumed 21% more kilocalories, 25% more sugar, and 26% more carbohydrates during the sleep restriction phase and also had increased caloric and fat intake during sleep recovery [167]. The authors therefore suggest that sleep restriction in toddlers not only precipitates increased caloric intake immediately preceding a period of sleep restriction but persists the following day. Interestingly, Simon et al. showed that teens aged 14–17 years, when sleep restricted to 6.5 vs 10 h, rated pictures of sweet/dessert foods to be more appealing after sleep restriction than after habitual sleep [168]. Further, they consumed 11% more total calories, with an increase of 52% of sweet/dessert servings, following sleep restriction as compared to the 10-h sleep duration [168]. These studies suggests that, similar to studies conducted in adults, sleep restriction in children and adolescents also produces changes in food intake and weight, perhaps mediated by the alteration in hormone levels known to regulate food intake. However, there is scarce literature regarding sleep deficiency and hormonal regulation in the pediatric population and, further, no studies examining the relationship between eCBs and sleep in children.

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It is important to note that there is also an association between sleep, glycemic control, and insulin resistance. A review examining the effect of sleep on glycemic control and insulin resistance in adolescents, children, and adult populations revealed an inverse association between sleep duration and the homeostasis model assessment of insulin resistance (HOMA-IR), indicating shorter sleep duration correlated with higher levels of insulin resistance [169]. Additionally, another more recent review of multiple adult and pediatric studies by Koren et al. showed that chronic sleep deprivation, as well as other sleep disturbances including sleep fragmentation and OSA, increase the risk of obesity, insulin resistance, and metabolic syndrome [170]. Interestingly, in a study by Beebe et al.[171], 41 healthy adolescents were found to consume foods with a higher glycemic index and glycemic load when they were restricted to 6.5 h/night for 5 nights vs 10 h/night. In summary, data emerging from the laboratory studies, mostly in adults, indicate that reduced sleep duration may increase the risk of weight gain and obesity via a decrease in leptin, an increase in ghrelin, and an overall increase in hunger and caloric intake. Thus it is reasonable to postulate that similar mechanisms by which sleep loss affects metabolism and the increased risk of obesity in adults may play a role in children and adolescents. A review of the regulation of body fat and the relationship amongst sleep, food intake, and metabolism is provided later as a background to formulate some hypotheses.

13.8 PUTATIVE MECHANISMS LINKING SLEEP LOSS AND THE RISK OF WEIGHT GAIN AND OBESITY 13.8.1 Neuroendocrine Regulation of Energy Balance and the Potential Impact of Sleep Loss Body fat stores are regulated by a complex physiologic process involving a cross-talk between the periphery and the brain, a process that ultimately affects the balance between energy intake and expenditure. Fig. 13.1 provides a schematic representation of the cross-talk between brain and periphery that regulates energy homoeostasis. Fig.13.2 summarizes some of the main pathways connecting sleep-wake and energy homeostasis, and identifies putative targets for the adverse impact of sleep deficiency. There are various redundant mechanisms that can lead to the initiation and cessation of a feeding bout. Similarly, these mechanisms can be involved in the utilization or storage of fat. There are extensive and divergent projection system innervating numerous structures in the central nervous system (CNS) including all the components of the ascending arousal system and the entire cortex [172]. One potential mechanism of action linking sleep loss, appetite regulation, and metabolism may lie within the hypothalamus and/or within hypothalamic communication with peripheral systems. Hypothalamic nuclei have a well-established role in modulating energy homeostasis and feeding regulation; lesions of ventromedial, paraventricular, and dorsomedial nuclei or stimulation of the lateral hypothalamus (LH) produce hyperphagia whereas lesions of the LH inhibit feeding [173]. Most specifically, the arcuate nucleus of the hypothalamus (ARC) directly interacts with circulating signals that indicate hunger or satiety, considering its location near the third ventricle. Neurons within the Arc synthesize and respond to appetite-promoting neuropeptide Y (NPY) and Agouti-Related Peptide (AgRP) as well as appetite-inhibiting Pro-opiomelanocortin (POMC) and Cocaine- and Amphetamine-Related Transcript (CART) (Fig. 13.1). These neurons within the ARC represent the “first order” neurons in the hypothalamus where afferent signals from the periphery are integrated with central stimuli in a neuronal response. Thus some circulating factors modulate feeding and energy homeostasis via direct interaction with neurons within this nucleus. These “first order” neurons project then to “second order” neurons, which comprise neurons in the hypothalamic paraventricular nucleus (PVN), the lateral hypothalamus area (LH), and perifornical area (PFA) that also influence regulation of feeding [174]. Among the afferent peripheral signals that act within the Arc include leptin, a 142-aminoacid peptide secreted by adipocytes, and ghrelin, released mainly by the stomach. Leptin, encoded by the obesity gene (ob) [175], is secreted by white adipose tissue into the circulatory system and is thought to indicate energy sufficiency [176]. It is thought that low leptin is a critical signal to the CNS indicating depleted energy stores and thus triggering a decrease in energy expenditure and increase in appetite. Specifically, leptin acts within the Arc to activate anorexigenic POMC/CART neurons and simultaneously inhibit orexigenic NPY/AgRP neuronal activity [177, 178]. Most striking is the clinical data demonstrating that individuals lacking leptin or the leptin receptor become obese [179], whereas leptin replacement has been reported to decrease weight in some obese subjects [180]. In the prevalent form of obesity not related to a genetic defect in the leptin system, there is indication of leptin-signaling resistance as leptin levels are increased, rather than decreased [181].

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13.8 PUTATIVE MECHANISMS LINKING SLEEP LOSS AND THE RISK OF WEIGHT GAIN AND OBESITY Cerebral cortex Ascending arousal system Motor brain stem Medulla and spinal cord (autonomous system)

Feeding

Feeding

Energy expenditure

Energy expenditure

Hypothalamus Second order neurons

Area postrema Feeding

Feeding Gastric emptying metabolic rate

Hindbrain MCH orexin

PVN

LH PFA

Sympathetic spinal cord

First order neurons

Arcuate nucleus (Arc)

165

POMC/ CART

Nucleus tractus solitarius (NTS)

NPY/ AgRP

Leptin, insulin

Ghrelin, eCB

Adiposity signals

Hunger signals

PYY, GLP-1, CCK

Satiety signals

Vagal afferents

FIG. 13.1

Schematic representation of the cross-talk between the periphery and the brain, which regulate energy homeostasis. The arcuate nucleus of the hypothalamus (ARC) is a key region in the central homeostatic control of appetite and contains the “first order” neurons that synthesize and respond to appetite-promoting neuropeptide Y (NPY) and Agouti-Related Peptide (AgRP) as well as appetite-inhibiting Proopiomelanocortin (POMC) and Cocaine- and Amphetamine-Related Transcript (CART). Within the ARC, afferent signals from the periphery are integrated with central stimuli in a neuronal response. Among the afferent peripheral signals are leptin and ghrelin. Leptin acts in the ARC to stimulate anorexigenic and simultaneously inhibit orexigenic neurons. In contrast, ghrelin stimulates appetite, partly by activating NPY neurons in the ARC. The first order neurons project then to “second order” neurons, which comprises neurons in the PVN and the orexin producing neurons in the lateral hypothalamus (LH) and perifornical area (PFA). The orexins neurons have direct appetite-stimulating effects in part mediated by stimulation of neuropeptide Y (NPY) neurons in the ARC. Other hunger signals such as endocannabinoids (eCB) and satiety signals such as PYY act via neurons within the ARC. Lastly, these peripheral metabolic cues, including glucose, leptin, cholecystokinin, and ghrelin may also influence activity of the orexin neurons in the LH via vagal afferent activity to the nucleus of the solitary tract (NST).

In contrast, ghrelin stimulates appetite, partly by activating NPY neurons in the ARC [179]. Ghrelin, a hormone produced by endocrine cells within the stomach, is thought to signal energy insufficiency and thus plays a role in stimulating appetite, as well as promoting energy storage [179]. Ghrelin exerts these effects via pathways that partly overlap leptin-sensitive pathways [176]. For instance, NPY/AgRP cells within the Arc express ghrelin receptors, and ghrelin activates these NPY/AgRP neurons while simultaneously increasing inhibitory input to POMC/CART neurons [176, 182]. There is evidence that ghrelin is involved in initiating food consumption, as there is a premeal rise in plasma ghrelin in humans [183]. It should be noted, that the system is redundant as there is also evidence that leptin and ghrelin directly affect the orexin system, with leptin inhibiting and ghrelin stimulating orexigenic activity, respectively [184, 185] (Fig. 13.2). The orexin system likely plays a key role in the interaction between sleep and feeding as orexin-containing neurons play a central role in the maintenance of arousal. This system is involved in the regulation of many functions such as sleep-wakefulness, locomotor activity, feeding, thermoregulation, and neuroendocrine and cardiovascular control [186]. The orexins (or hypocretins) are two distinct neuropeptides (orexin A and B) synthesized mainly by neurons in LH and PFA (Fig. 13.1). This notion, stimulated in vivo studies in rodents, demonstrated the role of the orexins in the link between sleep-wake cycle and feeding. Orexins induce and support arousal and promote feeding, particularly at a time when normal food intake is low [186] (Fig. 13.1). Proper maintenance of wakefulness is crucial for food search and intake, and the orexins represent the molecular basis of this vital interaction. Orexigenic neurons fire during the wake period and are inactive during non-REM (slow wave) sleep due to the direct inhibition by GABA-ergic hypothalamic neurons [187]. As represented in Fig. 13.2, the activity of the orexigenic neurons in the control of energy homeostasis is regulated by peripheral metabolic cues. During starvation, orexin neurons may be disinhibited by low levels of the anorexigenic

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FIG. 13.2 Schematic representation of the potential mechanistic pathways linking sleep deficiency (diminished quantity or quality) to obesity in the pediatric population. As described, there are multiple pathways in which sleep deficiency may influence obesity. Sleep deficiency may decrease energy expenditure and physical activity, which would cause weight gain and ultimately lead to obesity. Sleep deficiency could modulate other factors that may promote food intake or increase appetite, which then lends to weight gain and subsequent obesity. These factors include alterations in behavioral elements such as parent-child dynamics; alterations in central reward systems; central energy-balance regulating systems (hypothalamic nuclei); lateral hypothalamus (LH) orexin neuron activation; peripheral energy-balance regulating systems (gut, adipose tissue, pancreas, etc.); and sympathetic nervous system activity. These factors also interact, exhibit cross-talk, and impinge on each other (dotted lines). Sleep deficiency may also act on those interactions to facilitate increases in appetite, weight gain, and thus obesity.

hormone leptin and low glucose levels [187], and are excited by the orexigenic hormone ghrelin [188]. Peripheral metabolic cues, including glucose, leptin, cholecystokinin, and ghrelin, might also influence the activity of the orexin neurons via vagal afferent activity to the nucleus of the solitary tract (NST) (Fig. 13.1). The orexins conversely have direct appetite-stimulating effects in part mediated by an increase in the activity of NPY neurons in the Arc. Consistent with the fact that sympathetic nervous activity is higher during wake than during sleep, orexin activity is associated with increased sympathetic tone, an effect mediated in part through the stimulation of neurons in the NTS and the PVN [184]. An orexin effect on sympathetic activity could explain the changes in sympathovagal balance reported in laboratory studies of sleep restriction in humans [145, 159]. The NTS integrates peripheral vagal afferent signals with satiety signals from the area postrema and directly modulates the activity of “first order” neurons in the Arc and “second order” neurons in the PVN, zona incerta, the PFA, and the LH [179] (Fig. 13.1). Further, increased peripheral sympathetic tone in sleep deprivation may further inhibit leptin release and stimulate ghrelin release, consistent with the effects of short sleep on the peripheral levels of both hormones observed in adults [62, 145, 146, 189]. Increased catecholamine levels in sleep deprivation also inhibit insulin secretion and promote glycogen breakdown, increasing the risk of hyperglycemia and insulin resistance seen in obesity [169]. Additionally, both acute total and partial sleep deprivation in healthy individuals are associated to an increase in serum C-reactive protein (CRP) concentrations [190]. It has been suggested that CRP is a leptin-binding protein [191], and increased CRP levels, as seen in obesity, have been proposed as a possible mechanism of leptin resistance. In a similar fashion, CRP increase seen in sleep loss may reduce the amount of free leptin available to penetrate the blood-brain barrier and promote satiety.

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In sleep deprivation, the combination of reduced leptin levels possibly secondary to increased sympathetic activity with the increased leptin-binding capacity secondary to higher CRP levels might lead to a larger negative impact on energy balance than that of the leptin reduction alone. There may also be a role for the reward system in modulating food intake and energy storage following a state of sleep loss. The orexin system may be involved in reward processing; orexin-producing neurons in the LH send dense projections to the dopaminergic ventrotegmental area (VTA) and nucleus accumbens (NA) [192, 193], regions important in the hedonic control of food intake. Moreover, as previously stated in this chapter, the eCB system may play a role in linking sleep restriction, appetite control, and energy homeostasis (Figs. 13.1 and 13.2). It is well established that eCB-dependent CB1 receptor activation is a potent orexigenic signal; agonists of CB1 receptors stimulate feeding whereas antagonists result in appetite suppression [194]. These CB1 receptors are found in hedonic as well as homeostatic pathways, including the hypothalamic nuclei known to modulate energy homeostasis like the LH and PVN, and interact with leptin and ghrelin [195–197]. Most interestingly, activation of the eCB system affects hedonic (motivation and reward) circuits in the mesolimbic system, including the nucleus accumbens (Acb) and ventral tegmental area [198] (reviewed in [199]). Further, data suggest that the eCB system interacts with the opioid system, which has a well-established role in mediating the hedonic value of food reward [200, 201]. Activation of the eCB system within reward pathways elicits a preference for highly palatable rewarding food [198, 202–204]. Thus in this way, the eCB system may influence the increased food intake observed following sleep loss.

13.8.2 Energy Expenditure and the Potential Impact of Sleep Loss Sleep loss may also have an adverse effect on energy expenditure, which has an important role in the control of body weight and adiposity. The amount of total daily energy expenditure (TEE) is divided into three components: (1) resting metabolic rate (RMR, 60% of TEE), defined as the energy expenditure of an individual under basal conditions (at rest, fasting the morning after sufficient sleep); (2) thermic effects of meal (TEM, 10% of TEE), which includes the energy expenditure involved in digestion, absorption metabolism, and storage of food; and (3) activity-related energy expenditure (AEE, 30% of TEE), which involves all volitional and nonvolitional activities. For most individuals, AEE is not accounted by physical exercise but rather by low-moderate intensity activities of daily living such as sitting, standing, walking, and other occupational, volitional, and spontaneous activities, all together referred to as nonexercise activity thermogenesis (NEAT)[205]. AEE is the most variable component of TEE, carries major weight in the energy balance equation, and is critical for long-term weight. Theoretically, sleep loss could have either a positive or a negative effect on TEE, either as a simple consequence of increased time awake, an altered metabolism, and/or as behavioral changes. Whether sleep loss has an impact on TEE possibly mediated by a decrease in reduced voluntary activity and/or other component of NEAT has not been directly studied. Adults with sleep disturbances and/or excessive daytime sleepiness have reported significant reduction in their energy and the level of physical activity [206, 207], which could decrease overall AEE. Subjective sleepiness and fatigue increase immediately and significantly with sleep deprivation [208]; however, it is not clear if these would affect volitional or nonvolitional daily activities, other components of TEE, or neither if the effect was to be different in children and adults. There is an overall paucity of studies both in adults and children to answer these questions. Data from the Nurses’ Health Study showed a different body weight but no difference in voluntary activity levels measured in the women sleeping 6 h/day vs those sleeping 7 h/day [209]. Both physical activity and BMI were not independently associated with sleep duration or sleep efficiency in approximately 700 early to middle-aged adults participating in the CARDIA study [210]. In contrast, in participants of the Third National Health and Nutrition Examination Survey, self-reported fatigue was associated with a higher BMI, higher waist circumference, and a reduced likelihood of getting recommended levels of physical activity [211]. Further, there are a limited number of observational studies in the pediatric population. Gupta et al. and Benefice et al. estimated activity levels in adolescents and found no relationship between sleep duration and physical activity [129, 130], whereas Hager et al.[103] observed a positive association between sleep duration and physical activity in toddlers. Hjorth et al. found that 8–11-year-old children who were short sleepers (7.45–9 h/night) had decreased total physical activity by 7.2% (CI: 1.6–12.7) compared to long sleepers (9.20–1.47 h/night), (P  .04)[127]. Ekstedt et al. observed that neither sleep duration nor sleep efficiency affected mean physical activity level the subsequent day in 1231 Swedish children aged 6–10 years [113]. Morning tiredness reduced the odds of participating in any leisure-time physicalsporting activity in 2179 Spanish adolescents [212]. Data from the national Youth Risk Behavior Survey 2011–13 indicates that adolescents with the highest levels of physical activity and lowest level of sedentary behavior generally have greater odds of having sufficient sleep (
8 h/night)[213]. Overall the evidence on the relationship between sleep duration and physical activity in children is limited and somewhat conflicting.

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When it comes to examining the effect of sleep restriction strictly on energy expenditure, intervention studies are available only in adults and overall show that sleep deficit does affect RMR or TEE [214]. In the pediatric population, a recent observational study in 6–19-year-old children reported a decrease in RMR with short sleep in boys but not in girls [83]. Hitze et al.[83] measured RMR in a group of 312 boys and girls aged 13 years and did not show a difference in RMR between short and long sleepers. Evidently, the available literature on the effects of short sleep on energy expenditure is unclear and limited, particularly in children. One challenge is that, although it is conceivable that small changes in energy expenditure may affect one individual’s weight over time, the current methodology does not allow for detection of very small changes in energy expenditure. One additional complicating factor is that sleep duration could simultaneously, but differentially, affect the multiple component of TEE and would be challenging to separate those effects with the currently methodology. Overall the data so far on the relationship between sleep and physical activity are conflicting. More research is needed to examine if behavioral changes are mediating such relationships and furthermore if hormonal changes (such as changes in ghrelin and leptin) determine the behavior.

13.8.3 Behavior and the Potential Impact of Sleep Loss Although hormonal and neuroendocrine changes associated with sleep restriction in children may contribute to increased food intake and/or decreased energy expenditure, it is also possible that behavioral changes and poor parent-child dynamics may influence sleep-deprived children, which may contribute to weight gain. From a behavioral standpoint, there are a few hypotheses as to why sleep-deprived children may overeat. It is possible that parents may use food to pacify sleep-deprived, irritable, and behaviorally unregulated children. These children may also request food more often and overeat to quell feelings of anxiety and depression, which can accompany sleep deprivation. These children may simply have more time available to eat and may consume an additional meal or snack every day, which they ordinarily would not if they were sleeping an appropriate amount. The same parents who report less control over their child’s intake may also be less strict about their child’s bedtime. It is well known that children with sleep loss in early childhood show tiredness, hyperactivity, attention difficulties, cognitive disruptions, and poor impulse and emotional control [215, 216]. Insufficient sleep, self-imposed due to excessive television or video games, early school wake times, or overinvolvement in social or school-related activities or sports, may induce similar behavioral, cognitive, and mood impairments in children and adolescents [217]. Several studies have demonstrated a relationship between increased screen time or video game activity and eating habits, increased caloric intake, and/or risk of obesity [44, 119, 123, 218–226]. These relationships could, in part, be mediated by the effect of screen time and video games on sleep duration and quality. Specifically, increased use of technology prior to bedtime was shown to decrease sleep quality and increase odds of obesity [227] as well as adiposity [228]. Sleep deprivation has been repetitively associated with increase food intake and/or unhealthy diet in children and adolescents [229–231]. Cespedes et al. observed a positive association between mean sleep duration and diet via the Youth Healthy Eating Index (YHEI), and higher YHEI scores were associated with lower BMI [232]. However, adjustment for YHEI score did not attenuate the sleep-BMI relationship. Similarly, Ferranti et al. found a positive association between short sleep duration and higher BMI, fat mass, and unhealthy eating habits [101]. Further, as mentioned in the previous section, sleep deprivation could promote weight gain via decreased physical activity and energy expenditure.

13.9 SLEEP DISORDERS AND OBESITY IN CHILDREN The increase in the prevalence of obesity in the pediatric population and its severity has translated into a corresponding increase in the prevalence of the obesity-associated morbidities. Among these, childhood OSA has become widely recognized as a common disorder with potential serious clinical implications. Although compelling evidence points to obesity as a major risk factor for OSA in children and adults, a possible reverse causation has emerged from more recent studies suggesting that OSA could contribute to obesity.

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Although OSA, the main component of obstructive types of sleep disordered breathing (SDB), is characterized by recurrent events of partial and/or complete upper airway obstruction resulting in a disruption of normal ventilation and sleep, obstructive SDB is considered an entire continuum that also encompasses upper airway resistance syndrome (sleep fragmentation in the absence of blunt apneas and abnormalities in gas exchange) and primary snoring, a relatively more benign expression of abnormal upper airway resistance [233]. True OSA manifests with noisy breathing, paradoxical chest and abdominal motion, retractions, witnessed apnea, or cyanosis. Daytime symptoms can include mouth breathing, difficulty in waking up, daytime sleepiness, moodiness, hyperactivity, and cognitive problems [234]. It is suggested that, due to excess neck fat, obesity decreases the size and increases the collapsibility of the pharyngeal airway [235]. Also, increased adiposity in the abdominal wall and cavity reduces intrathoracic volume, which leads to poor oxygen reserves and increased work of breathing while asleep [236]. The most severe cases of OSA may lead to systemic hypertension, pulmonary hypertension and cor pulmonale, developmental delay, and even sudden death [237]. The prevalence of the OSA is estimated to be 7.5% in all children [238] and 55% or higher in obese children [239]. Obesity is an important predisposing factor for OSA. A study conducted in China that directly compared obese and lean children aged 7–11 years showed that OSA was prevalent in 32.6% of children who were obese compared to 4.5% of the normal-weight children [240]. Furthermore, in the United States, the Cleveland Family study of 4–18-year-olds found that obese children are at 4.6-fold increased risk for OSA than normal-weight children [241]. An increased risk of OSA in obese and, interestingly, underweight children vs. normal-weight children was further confirmed by Kang et al. in a study of 197 Taiwanese children [242]. Interestingly, it has been reported that obese African-American children are at higher risk than obese Caucasian children [243]. Additionally, OSA risk has been found to be increased more so in pubertal adolescents vs. prepubertal children [244]. The increased risk in adolescents vs. younger children may also relate to OSA type, as pediatric OSA can manifest phenotypically as two variants [245]. OSA Type I effects younger children with equal prevalence in girls and boys and is linked to lymphadenoidal hypertrophy with subsequent airway obstruction as opposed to obesity. OSA Type II, seen in obese children and adolescents, is similar to the adult phenotype and has a greater prevalence in males vs. females. Although Type II relates primarily to the presence of obesity, lymphadenoidal hypertrophy may also be seen. More recently, studies have looked at the relationship between OSA, obesity, and the metabolic syndrome, and have suggested a potentiating role of OSA for obesity. If sleep deprivation (short sleep duration) appears to be a risk factor for obesity, OSA, which is associated with sleep fragmentation, overall sleep loss, and daytime sleepiness, could also represent an independent risk factor for weight gain, which then subsequently further worsens OSA. Specifically, children with sleep-disordered breathing have been observed to have an increased risk of overweight independently of short sleep duration [246]. Similarly to obesity, OSA also appears to activate certain inflammatory pathways, which suggests that the two entities may intensify each other and increase the severity of their respective metabolic consequences [247]. In fact, a bidirectional relationship between obesity and OSA has been suggested [248], whereby obesity is a significant risk factor for OSA via the aforementioned mechanisms, and OSA subsequently aggravates obesity. Although the exact mechanism of how OSA may contribute to obesity is not fully understood, emerging studies suggest that OSA causes a complex interaction of behavioral changes, leptin resistance, and possibly increased ghrelin levels leading to increased appetite [249]. Many of the behaviors (low self-esteem, externalization disorders) and diseases (insulin resistance and systemic inflammation) associated with childhood OSA [249] have also been implicated in the risk of obesity. Children who manifest high levels of anger/frustration or clinically meaningful behavior problems are at increased risk of becoming overweight [69, 250]. Although there are no studies at this time that have linked behavioral problems in a causal association between OSA and overweight, it is plausible that a vicious cycle exists where increased externalizing behavior and low self-esteem lead to overweight, which results in OSA, which may worsen behavioral problems and consequently weight gain. OSA may promote further weight gain in overweight obese children by a decrease in physical activity and/or an increase in unhealthy eating habits. Reduced physical activity has been demonstrated in one study of adults with OSA [251] and is also present in children with obesity and/or OSA [252]. OSA could directly affect appetite regulation via alterations in hormones known to modulate food intake and subsequently result in increased caloric intake. A prospective study by Phillips et al. in obese adult males with newly diagnosed SDB and obese controls showed that subjects with SDB gained weight in the year preceding the diagnosis and had higher leptin levels than expected by their percent of body fat [253]. More recent studies confirmed similar higher leptin levels in adult OSA patients [254–256]. It is known that obesity leads to ineffective elevation of circulating leptin levels due to peripheral and central leptin resistance [257, 258]. Adults with OSA have high levels of leptin and sympathetic activity [237, 259], which points to OSA as a condition of leptin resistance and a tendency for weight gain and cardiovascular dysfunction.

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A study of adults at high altitude suggested that hypoxia leads to increased leptin levels, and thus hypoxia during SDB could be a possible mechanism by which increased leptin levels are observed in SDB [260]. Management of OSA in children includes positive airway pressure (PAP) for short-term management and weight loss for long-term management. PAP can be used as continuous PAP (CPAP) and bilevel PAP. If hypoxemia persists despite adequate resolution of the obstructive respiratory events with CPAP, noninvasive ventilation in form of bilevel PAP with or without supplemental oxygen is necessary [261, 262]. Tracheostomy with or without nocturnal ventilation may be necessary in cases of PAP failure or poor adherence to PAP therapy [261]. Tonsillectomy and adenoidectomy exist as a surgical treatment specifically for the management of airway obstruction. Weight loss, although it is a difficult process, may be ultimately the most effective management for OSA. Recent studies show that weight loss, both spontaneous [263] as well as from bariatric surgery [264], has a high success rate in relieving OSA in the pediatric population, but at this time there are no large studies in children that examine the role of weight loss and weight reduction surgery in the treatment of OHS and determining the amount of weight loss necessary to improve OSA. Multiple intervention studies in adults demonstrated a decrease in leptin levels in patients treated with CPAP [254, 265–270]. One study showed that 2 days of CPAP treatment were sufficient to significantly decrease ghrelin levels in patients with OSA [269]. Additionally, 3-month CPAP therapy was shown to significantly decrease both subcutaneous and visceral fat and lower BMI in adults [271]. To date the studies in children on the relationship between OSA and leptin have conflicting results. Four studies found elevated leptin levels with sleep disordered breathing [272–275]. Additionally, one study demonstrated that leptin levels significantly decreased after treatment with CPAP [273]. Conversely van Eyck et al. found that leptin levels were influenced by central obesity but not by OSA in an obese pediatric population [276]. Two additional studies found no relationship between leptin and measures of sleepdisordered breathing [272, 277]. These conflicting results may depend on the variability of the obesity phenotype in the cohorts studied or on the measure of adiposity adopted, BMI vs central obesity vs percent fat. In summary, if OSA contributes to the leptin resistance observed in obesity, OSA could affect energy homeostasis by decreasing leptin signaling, which would result in increased food intake and decreased energy expenditure. In parallel, higher ghrelin levels would lead to increased hunger and caloric intake. The studies detailed earlier did not systematically measure ratings of hunger, food preferences, or caloric intake. One of the abnormalities of sleep architecture seen in OSA is reduced non-REM or “deep” sleep. Preliminary data showed that experimental suppression of nonREM sleep without affecting sleep duration in young healthy adults leads to increased hunger for calorie-dense foods with high carbohydrate content particularly in the afternoon and evening hours [278]. In parallel to these early findings in adults, a study in 5–9-year-old obese children with and without OSA demonstrated that children with OSA ate 2.2 times more fast food, less healthy food such as fruits and vegetables, and were 4.2 times less likely to be involved in organized sports [279]. Furthermore, OSA severity positively correlated with plasma ghrelin levels. In summary, it appears that in sleep deprivation, OSA and obesity may interact in a complex relationship and ultimately exacerbate the severity and consequences of one another.

13.10 CONCLUSION Although it is ironic that a reduction in the most sedentary activity of all—sleep—should be associated with weight gain, strong evidence exists for an association between short sleep and obesity in children and adolescents. The association between increased BMI and short sleep has important implications for those concerned with the current pediatric obesity epidemic. Overweight children suffer from a poor quality of life and are more likely to become overweight adults with a wide range of physical and social health problems [280]. Additionally, there is compelling literature that excessive weight is associated with an increased risk of sleep problems and also suggests that sleep disorders, specifically OSA, may worsen weight problems. Despite these associations and the increasing prevalence of long-term sleep deprivation in children, the 2004 Sleep in America Poll [281] found that only 38% of parents of school-aged children reported being asked about their children’s sleep habits by the child’s doctor. Currently, no univocal direct causal link has been established. Further, prospective studies are required to confirm an effect of sleep loss on the risk of obesity. To better elucidate the mechanisms involved, more interventional laboratory studies in the pediatric population should be performed to analyze the hormonal changes that occur in sleep deprivation and confirm that the findings are similar to that observed in the adult population. Subsequently, field intervention studies would then be necessary to observe whether the same alterations persist in free living conditions. Studies should also aim to investigate the role of age, gender, and genetic differences in the interaction between sleep loss and obesity risk. II. PATHOPHYSIOLOGY

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Although there is a need for further research in these areas, the evidence to date warrants that clinicians who work with overweight children evaluate their sleep and sleep habits and initiate discussion about appropriate bedtimes, wake times, and sleep hygiene. Additionally, public health officials and members of the medical community should institute policies that promote healthier lifestyles, including urging school districts to avoid very early school start times and urging parents to put their children to bed earlier. A recent survey in high school children found that school start time after 8:30 a.m. was associated with a sleep extension by 25–46 min, primarily due to later wake time [282]. Similarly, earlier data from a national sample highlight an association between school start time and sleep duration in adolescents [283]. The 2014 Sleep in America Poll [33] improved on previous surveys and provided parents with sleep tips to improve their children’s sleep hygiene. The effects of additional sleep may prove to be a relatively low-cost strategy to reduce childhood obesity and the related cardiometabolic risk.

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II. PATHOPHYSIOLOGY