Sleep Breath DOI 10.1007/s11325-013-0918-0
ORIGINAL ARTICLE
Sleep duration and body mass index in children and adolescents with and without obstructive sleep apnea Marta Moraleda-Cibrián & Louise M. O’Brien
Received: 20 June 2013 / Revised: 9 September 2013 / Accepted: 19 November 2013 # Springer-Verlag Berlin Heidelberg 2013
Abstract Purpose The prevalence of pediatric obesity and short sleep duration has simultaneously increased in recent decades. Sleep plays a critical role in metabolic and endocrine regulation and insufficient sleep has been shown to be associated with changes in metabolism. Obesity, a major risk factor for obstructive sleep apnea (OSA), has been also associated with metabolic dysregulation. Despite this, no study investigating short sleep and obesity has addressed the potential confounder of OSA. The aim of this study was to investigate the association between short sleep duration and obesity in children with and without OSA. Methods In this retrospective study, 306 children who underwent polysomnography between January and December 2010 were included. A diagnosis of OSA was made if the apnea/hypopnea index on polysomnography is ≥1. Typical sleep times were obtained by parental report. Short sleep duration was defined as a reduction of >1 h from the minimum total sleep time (TST) recommended for age from the National Sleep Foundation (NSF). Results Overall, 32 % were obese, 39.5 % had short sleep duration, and 78 % had OSA. Children with OSA had a similar frequency of short sleep duration than those without (39.6 vs. 42.4 %, p =0.950). In children with short sleep
duration, the odds ratio for obesity was 2.5 (95 % CI 1.3– 4.9; p =0.009) compared to children with TST within normal limits even after accounting for the presence of OSA. Conclusion A parental history of total sleep duration of only 1 h less than recommended per age by the NSF is associated with a higher risk for obesity in children independently of the presence of OSA.
M. Moraleda-Cibrián : L. M. O’Brien Sleep Disorders Center, Department of Neurology, University of Michigan, Ann Arbor, MI, USA
Introduction
M. Moraleda-Cibrián : L. M. O’Brien Department of Oral and Maxillofacial Surgery, University of Michigan, Ann Arbor, MI, USA L. M. O’Brien (*) Michael S. Aldrich Sleep Disorders Laboratory, C736 Med Inn, 1500 East Medical Center Drive, Box 5845, Ann Arbor, MI 48109-0845, USA e-mail:
[email protected]
Keywords Obesity . Childhood . Sleep duration . Obstructive sleep apnea Abbreviations BMI Body mass index CDC Center for Disease Control and prevention NHANES National Health and Nutrition Examination Survey NSF National Sleep Foundation OSA Obstructive sleep apnea PSG Polysomnography PSQ Pediatric Sleep Questionnaire SDB Sleep-disordered breathing TST Total sleep time
The prevalence of childhood obesity has dramatically increased in the last 30 years and has been recognized as one of the most important public health problems in developed countries [1]. Results of the NHANES indicated that from 1980 to 2007–2008 obesity in school-aged children and adolescents has tripled [2]. Of concern, childhood obesity is associated with short- and long-term health problems such as dyslipidemia, hypertension, cardiovascular disease, hyperinsulinemia, insulin resistance, obstructive sleep apnea
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(OSA), and substantial psychological problems [1, 3]. In addition, obese children are at high risk for remaining obese during adulthood [4], and consequently, for the first time, this generation might have a shorter life expectancy than the parents. Therefore, there is an urgent need to identify modifiable risk factors for childhood obesity [5, 6]. In parallel to the increased prevalence of obesity, the prevalence of short sleep duration has risen. Although the reasons for this are multifactorial, the 24-h lifestyle common in most Western countries plays a significant role. Later bedtimes, demanding after-school activities, television viewing, and use of electronic devices at bedtime are some of the factors that contribute to reduce sleep time [7]. This sleep curtailment could have a significant impact on daytime function in young and older children, for example, increased daytime sleepiness, fatigue, depressed mood, and cognitive impairment [8, 9]. Sleep plays a critical role in metabolic and endocrine regulation. Indeed, even partial chronic sleep restriction (sleep duration of 4 h per night) leads to changes in carbohydrate metabolism and homeostasis profiles in young adults [10]. These changes likely result in weight gain due to an upregulation of appetite and an increase in wake time [11]. In the last decade, multiple studies have investigated the association between sleep duration and obesity in children [12–17]. While the available data, in general, appear to be supportive of such an association, no study has addressed the potential confounder of sleep-disordered breathing (SDB). Sleep-disordered breathing describes a spectrum of breathing disturbances from habitual snoring at one end of the spectrum to OSA at the other. Habitual snoring occurs in approximately 10–12 % of children while objectively defined OSA affects approximately 1–4 % of children [18]. Robust relationships exist between SDB in children and neurobehavioral dysfunction [19, 20] and data suggest that SDB is also associated with cardiovascular dysfunction [21–23]. While obesity is a risk for SDB, SDB is also associated with the metabolic syndrome independently of obesity [24], suggesting a bidirectional relationship. In addition, SDB may be a risk factor for sleep disturbance and therefore contribute to sleep curtailment. Nevertheless, no study has investigated the association between short sleep duration, body mass index (BMI), and SDB. Therefore, the goal of this study was to investigate the relationship between short sleep duration, SDB, and overweight/obesity in children attending a pediatric sleep clinic.
Patients and methods Subjects In this retrospective study, consecutive subjects were eligible if they were aged between 3 and 17 years old and underwent
overnight polysomnography (PSG) at the University of Michigan between January and December 2010. Children with major medical problems, craniofacial malformations, and developmental delay were excluded. This study was approved by the Institutional Review Board at the University of Michigan.
Measurements Demographics and anthropometrics Demographic data collected from medical records included age, gender, and racial background. Weight and height were obtained at the time of the PSG and used to calculate BMI and BMI percentile (adjusted for age and sex). Obesity and overweight was defined according to BMI percentile thresholds for children over 2 years of age, as recommended by The American Academy of Pediatrics and CDC [25]. The sample was classified into four weight-groups: underweight (BMI <5th percentile), normal weight (BMI ≥5th percentile and <85th percentile), overweight (BMI ≥85th percentile and <95th percentile), and obese (≥95th percentile). Sleep duration All parents of children attending the University of Michigan pediatric sleep clinic complete the Pediatric Sleep Questionnaire (PSQ) [26]. The PSQ is a common wellvalidated scale to screen for sleep-related breathing disturbances in children aged 2–18 years. This questionnaire also queries bedtime and wake time on weekdays and weekends as well as any napping. Nap duration was extracted from the physician notes taken on the day that the PSQ was completed if there was a positive answer to the question: “Does your child usually take a nap during the day?” Time in bed on weekdays was calculated by the difference between bedtime and wake up time. Sleep latency, defined as time to fall sleep after “lights out,” was also collected from the PSQ. Finally, total sleep time (TST), the main sleep variable, was estimated with the equation: TST=[(time in bed−sleep latency)+nap duration]. Sleep duration was categorized into 15-min increments. Due to the differing sleep needs across the pediatric age groups, the study population was categorized into three age groups according to recommendations from the National Sleep Foundation (NSF): preschooler (3.0–5.0 years), school-aged (5.1–12.0 years), and adolescents (12.1– 18.0 years). Moreover, because it was not possible to find standardized age-appropriate sleep durations, the study population was grouped according to recommendations of the NSF: optimal sleep duration when TST was within limits recommended for age, borderline sleep duration when TST was within 1 h of recommendations for age, and short sleep duration when TST was more than 1 h less than the minimum recommended time for age. Table 1 shows the optimal and suboptimal TST.
Sleep Breath Table 1 Classification of the study population based on the optimal total sleep time per age recommended by The National Sleep Foundation
Preschoolers (3–5 years) School-aged children (5.1–12 years) Adolescents (12.1–17 years)
Optimal TST (hours/night)
Borderline TST (hours/night)
Short TST (hours/night)
11.0–13.0
<11.0 and ≥10.0
<10.0
10.0–11.0
<10.0 and ≥9.0
<9.0
9.25
<9.25 and ≥8.25
<8.25
TST total sleep time
Obstructive sleep apnea Sleep architecture and respiratory events were scored by trained technologists using definitions of the American Academy of Sleep Medicine 2007 [27]. Obstructive apneas were defined as respiratory events that lasted at least two breaths and were associated with a fall in amplitude of the nasal pressure signal ≥90 %, accompanied by inspiratory effort. Obstructive hypopneas were defined as a fall in amplitude ≥50 % and the respiratory event was associated with arousal, awakening, or ≥3 % desaturation. Data from the baseline PSG was not used to estimate TST due to the potential for artificial sleep duration while in a sleep laboratory. Subjects were considered to have OSA if the apnea/ hypopnea index was ≥1. Statistical analysis Statistical analyses were performed using SPSS (version 19.0, IBM). Means and standard deviations were calculated for continuous variables and frequencies were calculated for dichotomous variables. First, bivariate relationships between BMI and other variables such as gender, age, racial background, and presence of OSA were calculated. The frequency of OSA in each age group was calculated. Differences in TST between age, racial groups, and BMI percentile groups were calculated using ANOVA. t tests were performed to investigate differences in sleep duration between boys and girls. The Pearson correlation coefficient was calculated to assess the relationship between BMI percentile and TST. Finally, multivariate logistic regression was performed to investigate the relationship between sleep duration and overweight/obesity after adjusting for potential covariates such as racial group and presence of OSA. Statistical difference was considered significant with a p value <0.05.
American, 16 % were overweight, and 32 % were obese. Both school-age children and adolescents were more likely to be obese compared to preschool children (36.3 vs. 15.3 %, p =0.0006 and 42.3 vs. 15.3 %, p <0.001, respectively). The mean BMI percentile between boys and girls did not quite reach statistical significance (69.2 vs. 75.5; p value=0.08). African-American children were more likely to be obese than Caucasians (70 vs. 27.1 %, respectively; p <0.001). Obstructive sleep apnea was diagnosed in 78 % of the sample population. Other demographic and anthropometric data for each age group are reported in Table 2. Distribution of TST according to demographic and anthropometric characteristics of the sample is shown in Table 3. Sleep duration in children and adolescents decreased significantly between age groups from 10.4±1.6 h in preschoolers to 9.6±1.3 and 8.4±1.6 h in school age and adolescent children, respectively (p <0.0001; Fig. 1). Short sleep duration was found in 39.5 % of the study population (42.5 % of preschoolers, 31.1 % of school-aged children, and 54.8 % of adolescents; p =0.025). There were no differences in TST between boys and girls (9.5±1.6 vs. 9.5±1.7 h; p =0.78) nor were there any differences between Caucasians and AfricanAmericans (9.5±1.6 vs. 9.7±1.7; p =0.21). Compared to children with normal weight, those who were obese had a significantly lower TST (9.8±1.5 vs. 8.9±1.8; p =0.006). Short TST was found in 54.2 % of obese children, 38.5 % of overweight children, and 30.8 % of normal weight children (p =0.027). A negative correlation was found between and TST and BMI percentile (r =−0.23; p =0.0007; Fig. 2). Children with OSA had a similar frequency of short sleep duration than those without OSA (39.6 vs. 42.4 %; p =0.950). Obese children with OSA had a significantly lower TST compared to normal weight children with OSA (8.9±1.9 vs. 9.9±1.5 h; p <0.001). Similarly, in children without OSA, those who were obese tended to have a lower TST than those with normal weight (8.6±1.8 vs. 9.6±1.5 h; p =0.15) although this did not quite reach statistical significance. Figures 3 and 4 show the distribution of TST in children with and without OSA, according to weight status. In a logistic regression, after adjusting for presence of OSA and race, the odds ratio for obesity in children with short sleep duration was 2.5 (95 % CI 1.3–4.9; p =0.009; Table 4). The multivariate logistic regression was not adjusted for age and gender since BMI percentile was already adjusted for these variables.
Discussion Results A total of 306 children were included in this study. The mean participant age was 9.1±3.9 years. Sixty-two percent of children were boys, 75 % were Caucasian, 10 % were African-
Our findings, from a pediatric sleep clinic population, demonstrate a correlation between short sleep duration (at least 1 h fewer than national recommendations) and obesity independently of the presence of OSA. These findings are consistent
Sleep Breath Table 2 Characteristics of the study population by age
OSA obstructive sleep apnea, TST total sleep time
Group age
No.
Male (%)
Caucasian (%)
Overweight/ obese (%)
OSA (%)
Short TST (%)
Preschool (3.0–5.0 years) School age (5.1–12.0 years) Adolescent (12.1–18.0 years)
66 167 73
66.7 65.3 49.3
78.1 75.9 84.3
30.5 54.4 57.7
86.2 76.2 72.9
42.5 31.1 54.8
with the vast majority of studies performed in children without apparent OSA. In a cross-sectional Canadian study of short sleep and childhood overweight/obesity in 5–10-year-old children, children sleeping between 8 and 10 h per night, compared to those sleeping 12–13 h per night, had an odds ratio of 3.4 for overweight/obesity [16]. Furthermore, in agreement with these results, several other studies in preschool and school-aged children found that children with short sleep had a higher risk for overweight/obesity compared with those Table 3 Association between demographic and anthropometric data and TST Characteristics Age - Preschool (3.0–5.0 years) - School-age (5.1–12.0 years) - Adolescents (12.1–18.0 years) Gender - Boys - Girls Race - Caucasian - African - Others Four BMI groups - Underweight - Normal weight - Overweight - Obese Two BMI groups - Normal weight - Overweight/obese OSA - Underweight - Normal weight - Overweight - Obese Non-OSA - Underweight - Normal weight - Overweight - Obese
TST (mean±SD)
p value
10.39 h±1.64 9.61 h±1.34 8.41 h±1.55
<0.0001
9.53 h±1.56 9.47 h±1.70
0.782
9.52 h±1.62 9.74 h±1.70 8.99 h±1.43
0.214
9.53 9.81 9.49 8.93
h±0.91 h±1.48 h±1.57 h±1.82
0.006
9.81 h±1.48 9.13 h±1.74
0.002
9.95 9.88 9.44 8.89
h±0.89 h±1.49 h±1.66 h±1.75
0.005
9.00 9.55 9.62 8.59
h±0.35 h±1.49 h±1.17 h±1.84
0.419
TST total sleep time, h hours, BMI body mass index, OSA obstructive sleep apnea
who slept longer, with odds ratios between 2.9 and 4.8 [11–14]. Despite similar findings, direct comparison between these studies and the current study is limited as only the current study accounted for the age-appropriate reference range for sleep duration. This study is, to our knowledge, the first to investigate the relationship between short sleep duration and childhood obesity after accounting for the presence of objectively diagnosed OSA. This cross-sectional study in a pediatric sleep clinic sample found that older children have a higher frequency of obesity, short sleep duration, and SDB compared with previous epidemiological studies. These findings are consistent with the results of National Health and Nutrition Examination Survey [2]. Moreover, as expected, we did not find differences in the frequency of OSA in the three age groups due to the clinical sample population. Of concern, the increased prevalence of childhood obesity in the last few decades could be associated with a higher risk for OSA especially in older children. Indeed, in a clinical sample of 91 overweight children, polysomnography demonstrated that 11 % had primary snoring, 19 % had OSA (11 % mild and 8 % moderate-tosevere), and 17 % had central sleep apnea [28]. It is known that risk factors for adulthood obesity such as decreased physical activity, more TV viewing, and poor dietary choices also contribute to childhood obesity [5, 10, 13]. Nevertheless, data from the limited longitudinal pediatric studies to date highlight that childhood obesity is a complex puzzle where
Fig. 1 Association between total sleep time and age groups (mean±95 % confidence intervals)
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Fig. 2 Correlation between BMI percentile and total sleep time Fig. 4 Association between total sleep time and BMI groups in non-OSA children
early risk factors, even during the newborn period, and family environmental factors are also involved. The Avon longitudinal study of parents and children [5] in the UK found eight potential risk factors and a longitudinal study in the USA found five risk factors for childhood obesity [29]. Notably, a common risk factor identified in both studies was sleep duration. Experimental studies in young adults show that sleep deprivation induces changes in different endocrine pathways that are involved in appetite regulation [10]. These adjustments include reduction of leptin levels, elevation of ghrelin levels [30], and a decrease in acute insulin response to glucose [31]. These changes, independent of caloric intake, increase appetite and likely lead to an increased BMI. At the same time,
reduced sleep time results in longer periods of wake and therefore increases opportunities for food intake. Thus, there are several mechanisms by which short sleep duration is believed to play a key role in the development of obesity. There are two major strengths to our study. Firstly, the classification of sleep duration into optimal and suboptimal times based on age group as recommended by the NSF [32]. Previous studies have not addressed different sleep needs across the age spectrum and therefore misclassification into an age-appropriate sleep duration group is likely. For example, the recommended sleep duration for an adolescent would be considered short sleep for a preschool child. Second, unlike most published studies, we have accounted for a common condition in childhood; the presence of OSA. The possibility that underdiagnosed OSA could influence sleep duration, especially in obese children, suggests that pediatric OSA is a risk factor that should be taken into account in studies of sleep duration. Finally, the presence of OSA in the current study was based on a diagnosis from the gold standard polysomnography rather than by parental report. The main limitation of our study is that it is cross-sectional from a sleep clinic population. As such, we were unable to investigate the longitudinal association between short sleep Table 4 Results of the multivariate logistic regression for childhood obesity against short sleep duration and other variables
Short TST OSA African-American Fig. 3 Association between total sleep time and BMI groups in OSA children
OR
95 % CI
p value
2.5 1.1 6.7
1.3–4.9 0.5–2.3 2.8–15.9
0.009 0.793 <0.001
OR odds ratio, 95 % CI 95 % confidence interval
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duration and overweight/obesity. Nonetheless, the amount of sleep children have is typically determined by lifestyle so it is unlikely that the short sleep duration observed in the current study was a recent phenomenon. Although sleep duration was reported by parents and not based on objective measurements such as actigraphy, it is important to take into account that parents frequently overestimate sleep duration particularly in adolescents and therefore findings from this study may be even stronger if objective measures of sleep duration were employed [33, 34]. Another limitation is that socioeconomic risk factors for short sleep duration were not available so we were unable to account for specific environmental, lifestyle, or family characteristics. Finally, the non-OSA group was small, as would be expected from a sleep clinic sample, and this was likely the reason why differences in TST according to BMI groups were not statistically significant. Nonetheless, despite these limitations, our data support those in the literature and further extend previous findings by accounting for the impact of OSA on obesity. In summary, our findings demonstrate that even parent report of a 1-h reduction in recommended sleep duration is associated with overweight/obesity in children after accounting for the presence of OSA. Given the high prevalence of short sleep duration and the accumulating evidence of an association with childhood obesity, sleep duration appears to be an important factor to include in obesity education programs. Furthermore, it is a key modifiable risk factor that could be targeted in obesity interventional trials. Acknowledgments This study and MMC were supported by the Fundació Universitària Agustí Pedro i Pons, University of Barcelona, Spain. LMO was supported in part by National Heart, Lung, and Blood Institute R21 HL089918). Dr. O’Brien was also supported by a career grant from the National Heart, Lung, and Blood Institute (K23 HL095739) and in part by R21 HL087819. Conflict of interest The authors do not have any conflicts of interest to declare.
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