Abstract
Study Objectives:
To assess the associations between sleep duration and cardiometabolic risk factors in Chinese school-aged children and to explore the possible mediating role of adipokines.
Methods:
Sleep duration was collected in 3166 children from the Beijing Child and Adolescent Metabolic Syndrome study. Glucose homeostasis and other cardiometabolic risk factors were assessed. Serum adipokines including leptin, total and high-molecular-weight (HMW) adiponectin, resistin, fibroblast growth factor 21 (FGF21), and retinol binding protein 4 (RBP4) were determined.
Results:
Among the 6- to 12-year-old children, after adjusting for covariates including puberty, short sleep duration was associated with increased body mass index (BMI), waist circumference, fasting glucose, insulin and homeostasis model assessment of insulin resistance (all p < .0001), higher triglyceride and lower high-density lipoprotein cholesterol (p < .05), along with increased leptin (p < .0001), FGF21 (p < .05) and decreased HMW-adiponectin (p ≤ .01); the association with leptin remained significant after further adjustment for BMI. However, these associations, except for glucose (p < .0001), disappeared after further adjusted for leptin. For the 13–18 years old group, short sleep duration was associated with higher BMI, waist circumference, and RBP4 (all p < .05), but the association with RBP4 was attenuated after adjusting for BMI (p = .067).
Conclusions:
Short sleep duration is strongly associated with obesity and hyperglycemia (in 6–12 years old), along with adverse adipokine secretion patterns among Chinese children. The associations with cardiometabolic risk factors appear to be more pronounced in younger children, and could be explained, at least partially, by leptin levels.
Keywords: Sleep duration, cardiometabolic risk factors, adipokines, children.
Statement of Significance
This is the first study to address the association of habitual sleep duration with adipokine levels, and their role in mediating cardiometabolic risk among Chinese children, where we leverage a large, well-characterized cohort. Second, we have adjusted for many potential confounders known to be associated with both cardiometabolic risk and sleep duration, which allow for our results to be particularly refined and robust. Furthermore, due to the different criteria for sleep insufficiency determination in children and adolescents, we separately analyzed 6- to 12-year-old children and 13- to 18-year-old adolescents. As a consequence, we found that the associations between sleep duration with cardiometabolic risk factors were more apparent in younger children, and could be at least partly explained by leptin. Our findings suggest reduced sleep may have detrimental effects on adipose tissue function early in child and highlight the importance of getting adequate amounts of sleep for young children.
INTRODUCTION
The alarming increase in the prevalence of childhood obesity and its related cardiometabolic disorders has become a global health problem.1 Parallel to the epidemic of obesity, there has been a similar decrease in the amount of time spent sleeping.2 A recent meta-analysis of longitudinal impact of sleep revealed that children with short sleep duration had twice the risk of being overweight or obese, compared with children sleeping for long duration.3 Moreover, previous studies in Western populations reported that short sleep duration could predict cardiometabolic risk both in adults4 and children.5
However, the mechanisms by which inadequate sleep increases the risk of obesity and cardiometabolic disorders are poorly understood. Currently, adipose tissue has been recognized as a dynamic endocrine organ, producing a range of biologically active substances, collectively called “adipokines”, that regulate energy homeostasis, inflammation, insulin resistance (IR), and cardiovascular function.6,7 One hypothesis is that sleep may impact adipose tissue function.8 It is possible that the short sleep may lead to the dysregulation of adipokine secretion and is thought to be the key events in promoting both systemic metabolic dysfunction and cardiovascular disease.8 To date, leptin, as a player in satiety regulation, is the most studied adipokine in relation to sleep both in adults9 and in children,10–12 but the results have been inconsistent. Other promising adipokines, such as adiponectin,13 retinol binding protein 4 (RBP4),8 and resistin,14 albeit with limited data, have also been implicated to correlate with sleep in adults; however, systematic study of these adipokine profiles is still lacking in children. Therefore, based on the large cohort of Beijing Children and Adolescents Metabolic Syndrome study (BCAMS), we aimed to: 1. assess the association between sleep duration and six functionally prominent adipokines: leptin, adiponectin (including total- and high-molecular-weight [HMW]-adiponectin), resistin, fibroblast growth factor 21 (FGF21), RBP4 and secreted protein acidic and cysteine rich (SPARC), as well as cardiometabolic risk factors, including central obesity, IR, high blood pressure, glucose, and lipids disorders; 2. evaluate whether adipokines are the mediator of the association between short sleep duration and adverse metabolic outcomes.
METHODS
Subjects
Participants were recruited from a cross-sectional population-based survey: the BCAMS study.15 The BCAMS study was designed to evaluate the prevalence of obesity, hypertension, hyperglycemia, and dyslipidemia in Beijing school age children. A stratified, randomly cluster sampling design was used to select subjects from residential communities, elementary schools, middle schools, and high schools for different age groups. As such, four out of eight urban districts and three out of seven rural districts in the Beijing area were selected and the initial baseline survey was conducted within a representative sample of 19593 children (ages 6 to 18 years, 50.1% male). Within this cohort, 4500 subjects were identified as having one or more of the following disorders: being overweight, elevated blood pressure (systolic blood pressure and/or diastolic blood pressure ≥90th percentile), increased total cholesterol (TC) ≥5.2 (mmol/L), triglyceride (TG) ≥1.7 (mmol/L), or fasting glucose (FBG) ≥5.6 (mmol/L) based on initial capillary blood tests. Further, all subjects with risk for metabolism syndrome, together with a parallel reference population of 1095 children, were invited to undergo medical examinations for verification based on venipuncture blood samples. Amongst these, 3166 subjects, including 1024 normal controls, completed further examination, sleep time questionnaire, and provided a blood sample for measurement of adipokines, thus were included in the current analysis (Supplementary Figure S1). The comparison between the analytic sample and the total cohort is shown in Supplementary Table S1. Informed consents from all participants and/or their parents or guardians were obtained before entering in to the study. The BCAMS study was approved by the Ethics Committee at Capital Institute of Pediatrics in Beijing.
Anthropometric Parameters and Biochemical Analyses
The subjects’ height, weight, waist circumference, and blood pressures were measured according to our standard protocol, which has been described previously.15 Age- and sex-specific body mass index (BMI) percentiles, according to the Working Group for Obesity in China, were used to define overweight (85th) and obesity (95th).16 Pubertal status was evaluated by Tanner stage of breast development in girls and testicular volume in boys. Venous blood sample was collected by direct venipuncture after an overnight (≥10 h) fasting. Blood samples were analyzed for concentrations of FBG, insulin, TG, TC, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol plus adipokines, including leptin, total- and HMW-adiponectin, resistin, FGF 21, RBP4, and SPARC. Serum insulin, leptin, total- and HMW-adiponectin was measured by enzyme-linked immunosorbent assay (ELISA) which was developed and performed centrally in the Key Laboratory of Endocrinology in Peking Union Medical College Hospital. The insulin assay had a sensitivity of 0.5mU/L and an inter-assay coefficient of variation (CV) of <9.0%, and has no cross-reactivity to proinsulin (<0.05%).17 The intra- and inter-assay CVs for leptin were <7.4% and <9.3%,18 respectively, and <5.4% and <8.5% for total-adiponectin, and <4.8 % and <7.1% for HMW-adiponectin, respectively.19 Resistin and FGF21 were measured by using the ELISA kit (Phoenix Pharmaceuticals). The intra- and inter-assay CVs were <5.2% and <10.1% for resistin, and <6.0% and <8.6% for FGF21, respectively.15,20 RBP4 was measured by ELISA kits (R&D Systems) with intra- and inter-assay CVs of 6.2% and 8.5%, respectively. For measurement of SPARC, we have established a sandwich ELISA system with the intra- and inter-assay CVs of 5.2% and 9.1%, respectively.20 IR was assessed by using the homeostasis model assessment (HOMA-IR).
Sleep Duration
Self-reported habitual sleep duration was obtained from the following question: “How many hours do you sleep on an average day over the past 7 days?” The response of the sleep time ranged from 4.5 to13 hours. Sleep duration was analyzed separately in children and adolescents according to the US National Sleep Foundation’s recommendation,3 and categorized into the following groups: ≤8 hours/day, 9–10 hours/day, and ≥10 hours/day for 6- to 12-year-old children and ≤7 hours/day, 8–9 hours/day, and ≥9 hours/day for 13- to 18-year-old adolescents. These cutoff points for the sleep groups were close to the 30th percentile and 70th percentiles, respectively, of the distribution of total sleep duration in our study.
Covariates
In order to control the effect of confounders, several factors were recorded as covariates, including family history, dietary habits, physical activities, and social-economic status.11,21 Family history included the parents’ obesity, diabetes, dyslipidemia, and hypertension status. Overall, diet scores were calculated according to their consumption frequency of dietary records including breakfast, bean, sea-food, milk, vegetables, fruits, red meat, and soft drinks, in which a lower score indicated poorer dietary quality, and vice versa. Activity (≥3 times/week) and inactivity (<3 times/week) were accessed by their frequency of participating in extracurricular physical activities for at least 30 minutes each time. The educational level and the vocation of parents were used as indicators of social-economic status.
Statistical Analysis
All skewed distributions were natural logarithm transformed for analysis. Results are expressed as mean ± standard deviation or percentage (%) as appropriate. Analysis of variance and chi-square test were used to analyze continuous variables and categorical variables, respectively. A Bonferroni post-hoc test was used for pairwise comparisons. Multiple linear regressions were applied to test the associations of sleep duration with adipokine and cardiometabolic parameters. We used two approaches to assess whether sleep duration affects cardiometabolic risk via adipokines. First, we compared analyses without and with adjustment for the potential adipokine. Second, we estimated mediation proportion by using R software (v3.3.1, http://cran.r-project.org/). We use bootstrapping methods to calculate 95% confidence intervals around this estimate. All analyses were initially performed with both splitting and merging the genders. Given our analyses yielded similar results, especially in the 6–12 years old group, we subsequently pooled all children in to the final analyses to increase statistical power. A test was deemed significant if two-tailed p < .05. All statistical analyses, except the causal mediation, were performed using SPSS version 17.0 software for windows (SPSS Inc., Chicago, IL).
RESULTS
Subjects
The characteristics of participants by age category and sleep duration groups are displayed in Table 1. The average sleep duration was 9.1 hours and 8.0 hours per day for 6- to 12-year-old children and 13- to 18-year-old adolescents, respectively. Subjects with shorter sleep duration were older, more mature in puberty development and a higher proportion of them lived in an urban area, with roughly an equal number of boys and girls. In the 6–12 aged group, children sleeping ≥10 hours/day had a lower dietary score, a higher frequency of physical activity, but less of them had highly educated parents, an unemployed father, or diabetic parents, compared to those sleeping <10 hours/day. In the 13–18 years old group, the adolescents who slept ≥9 hours/day had a lower proportion of having highly educated parents and an unemployed mother, compared to those who slept <9 hours/day.
Table 1.
Basic Characteristics Stratified by Sleep Duration and Age.
| 6–12 years old | 13–18 years old | |||||||
|---|---|---|---|---|---|---|---|---|
| Covariates | ≤8 h/d (n = 565) | 9–10 h/d (n = 778) | ≥10 h/d (n = 498) | p | ≤7 h/d (n = 454) | 8–9 h/d (n = 559) | ≥9 h/d (n = 312) | p |
| Age (years) | 10.4 (1.7) | 9.5 (1.8) | 9.4 (1.8) | <.001 | 15.4 (1.5) | 14.8 (1.4) | 14.5 (1.3) | <.001 |
| Boys (Yes), N (%) | 309 (54.7%) | 411 (52.8%) | 268 (53.8%) | .793 | 193 (42.5%) | 259 (46.3%) | 150 (48.1%) | .269 |
| Tanner stage | 1.71 (0.97) | 1.51 (0.86) | 1.49 (0.81) | .001 | 3.99 (0.87) | 3.64 (1.03) | 3.37 (1.14) | <.001 |
| Urban (Yes), N (%) | 431 (76.3%) | 492 (63.2%) | 271 (54.4%) | <.001 | 300 (66.1%) | 348 (62.3%) | 157 (50.3%) | <.001 |
| Lifestyle factors | ||||||||
| Sleep time (hours) | 7.9 (0.3) | 9.0 (0.0) | 10.2 (0.6) | <.001 | 6.7 (0.6) | 8.0 (0.0) | 9.5 (0.6) | <.001 |
| Diet score | 36.2 (4.7) | 36.7 (4.4) | 36.1 (5.1) | .045 | 34.9 (5.0) | 35.0 (4.6) | 34.4 (4.5) | .207 |
| Activity (≥3 times/week), N (%) | 315 (56.7%) | 487 (63.5%) | 331 (68.1%) | .004 | 217 (48.6%) | 269 (48.8%) | 151 (47.2%) | .999 |
| Social-economic factors | ||||||||
| Father’s education ≥ University (Yes), N (%) | 165 (29.5%) | 156 (20.4%) | 89 (18.5%) | <.001 | 92 (21.2%) | 112 (20.8%) | 31 (10.2%) | <.001 |
| Mother’s education ≥ University (Yes), N (%) | 126 (22.5%) | 118 (15.4%) | 78 (16.1%) | .002 | 65 (14.8%) | 71 (13.0%) | 25 (8.3%) | .015 |
| Unemployment father (Yes), N (%) | 30 (5.4%) | 49 (6.5%) | 13 (2.7%) | .012 | 16 (3.8%) | 22 (4.2%) | 19 (6.3%) | .131 |
| Unemployment mother (Yes), N (%) | 52 (9.4%) | 68 (9.0%) | 40 (8.3%) | .815 | 40 (9.3%) | 52 (9.9%) | 32 (10.8%) | .045 |
| Family history | ||||||||
| Any parents has obesity (Yes), N (%) | 43 (11.3%) | 75 (11.2%) | 32 (7.3%) | .070 | 46 (9.3%) | 65 (9.0%) | 49 (9.8%) | .892 |
| Any parents has diabetes (Yes), N (%) | 15 (3.9%) | 32 (4.8%) | 4 (0.9%) | .002 | 24 (4.9%) | 27 (3.7%) | 17 (3.4%) | .455 |
| Any parents has hypertension (Yes), N (%) | 41 (10.8%) | 72 (10.7%) | 38 (8.6%) | .473 | 69 (14.0%) | 90 (12.4%) | 62 (12.4%) | .680 |
| Any parents has dyslipidemia (Yes), N (%) | 38 (10.0%) | 47 (7.0%) | 25 (5.7%) | .056 | 37 (7.5%) | 47 (6.5%) | 27 (5.4%) | .402 |
Values displayed as mean (standard deviation) or number (%).
Values in bold are significant at p < .05.
Comparison of Cardiometabolic Risk Factors and Adipokine Patterns Among Different Sleep Groups
The comparisons of cardiometabolic risk factors and adipokine levels among different sleep groups are listed in Table 2. In subjects of 6–12 years old, compared to short sleep groups, children with sleep duration was ≥10 hours/day had lower BMI, WC, FAT%, better metabolic profile, and a more favorable adipokine profile: lower leptin and higher HMW-adiponectin, FGF21 (all p < .017 in Bonferroni post-hoc test). In the 13–18 years old group, subjects with sleep duration ≥9 hours/day had lower BMI, WC, FAT%, and RBP4 levels compared to short sleep peers (all p < .017).
Table 2.
Cardiometabolic Risk Factors and Adipokines Stratified by Sleep Duration and Age.
| 6–12 years old | 13–18 years old | |||||||
|---|---|---|---|---|---|---|---|---|
| ≤8 h/d | 9–10 h/d | ≥10 h/d | p | ≤7 h/d | 8–9 h/d | ≥9 h/d | p | |
| BMI (kg/m2) | 21.4 (4.6)a | 20.7 (4.3)a | 19.6 (4.5)b | <.001 | 23.8 (5.0)a | 23.3 (4.9)a | 22.8 (5.0)b | .011 |
| WC (cm) | 70.6 (12.2)a | 68.5 (11.9)a | 65.4 (12.0)b | <.001 | 77.5 (12.6)a | 76.5 (12.5)a | 75.2 (13.1)b | .018 |
| FAT% | 23.7 (8.2)a | 23.7 (8.1)a | 21.0 (8.1)b | <.001 | 26.9 (8.6)a | 26.1 (8.3)a | 25.3 (8.9)b | .009 |
| FBG (mmol/L) | 5.1 (0.5)a | 5.1 (0.5)a | 4.9 (0.5)b | <.001 | 5.1 (0.5) | 5.1 (0.5) | 5.1 (0.6) | .169 |
| ln insulin (mU/L) | 2.0 (0.7)a | 1.9 (0.8)a | 1.7 (0.8)b | <.001 | 2.2 (0.6) | 2.3 (0.7) | 2.3 (0.7) | .417 |
| ln HOMA-IR | 0.5 (0.8)a | 0.4 (0.8)a | 0.2 (0.8)b | <.001 | 0.8 (0.7) | 0.8 (0.7) | 0.8 (0.7) | .331 |
| TC (mmol/L) | 4.17 (1.0) | 4.22 (0.8) | 4.13 (0.7) | .195 | 4.10 (0.8) | 4.00 (0.8) | 3.99 (0.9) | .057 |
| TG (mmol/L) | 1.04 (0.6)a | 1.03 (0.5)a | 0.95 (0.5)b | .020 | 1.09 (0.6) | 1.03 (0.5) | 1.06 (0.5) | .232 |
| HDL-C (mmol/L) | 1.43 (0.3)a | 1.45 (0.3)ab | 1.50 (0.3)b | .008 | 1.33 (0.3) | 1.35 (0.3) | 1.35 (0.3) | .542 |
| LDL-C (mmol/L) | 2.57 (0.9)a | 2.67 (0.8)b | 2.53 (0.6)a | .014 | 2.58 (0.8) | 2.49 (0.7) | 2.49 (0.8) | .064 |
| SBP (mm Hg) | 104 (12)a | 104 (12)a | 102 (13)b | .014 | 113 (13) | 112 (14) | 111 (13) | .316 |
| DBP (mm Hg) | 65 (9)a | 66 (10)b | 65 (10)a | .005 | 71 (9) | 70 (9) | 70 (10) | .341 |
| ln leptin (ng/mL) | 1.78 (1.33)a | 1.62 (1.41)a | 1.17 (1.40)b | <.001 | 1.76 (1.16) | 1.74 (1.23) | 1.74 (1.23) | .914 |
| ln total-adiponectin (µg/mL) | 1.77 (0.54) | 1.81 (0.57) | 1.86 (0.63) | .087 | 1.55 (0.57) | 1.56 (0.59) | 1.57 (0.59) | .843 |
| ln HMW-adiponectin (µg/mL) | 1.00 (0.54)a | 1.12 (0.54)a | 1.19 (0.54)b | <.001 | 1.07 (0.57) | 1.10 (0.59) | 1.00 (0.64) | .053 |
| ln resistin (ng/mL) | 2.77 (0.56) | 2.76 (0.55) | 2.77 (0.56) | .957 | 2.70 (0.52) | 2.70 (0.49) | 2.68 (0.50) | .822 |
| ln FGF21 (pg/mL) | 6.37 (1.23)a | 6.52 (1.22)a | 6.61 (1.33)b | .024 | 6.37 (1.34) | 6.37 (1.33) | 6.34 (1.29) | .903 |
| ln RBP4 (µg/mL) | 3.41 (0.34) | 3.41 (0.34) | 3.38 (0.35) | .258 | 3.60 (0.33)a | 3.56 (0.33)a | 3.50 (0.32)b | <.001 |
| ln SPARC (µg/mL) | 0.10 (0.46) | 0.13 (0.48) | 0.13 (0.49) | .590 | 0.01 (0.56) | −0.01 (0.54) | −0.01 (0.50) | .817 |
BMI = body mass index; DBP = diastolic blood pressure; FAT% = fat mass percentage; FBG = fasting blood-glucose; FGF21 = fibroblast growth factor 21; HDL-C = high-density lipoprotein cholesterol; HMW-adiponectin = high molecular weight adiponectin; LDL-C = low-density lipoprotein cholesterol; RBP4 = retinol binding protein 4; SBP = systolic blood pressure; SPARC = secreted protein, acidic, cysteine-rich; TC = total cholesterol; TG = triglyceride; WC = waist circumference.
Data are expressed as the mean (SD).
a and b mean the significant difference between the two group after pairwise comparison (all p < .017 in Bonferroni post-hoc test).
Values in bold are significant at P < .05.
Sleep Duration in Relation to Adipokines With and Without Adjustment for BMI
When treating sleep duration as a continuous variable, multiple linear regressions were used to evaluate the association between sleep duration and adipokines (Table 3). Among the 6–12 years old, after adjusting for age, gender, and Tanner stage, for every hour reduction of sleep time, ln(leptin) increased by 0.222 (p < .001), ln(HMW-Adiponectin) decreased by .057 (p < .001), and ln(FGF21) decreased by 0.074 (p = .048); these associations were also significant when further adjusting for family history, dietary habits, physical activities, and social-economic status. However, after further adjusting for BMI, the associations of sleep duration with HMW-Adiponectin (p = .097) and FGF21 (p = .104) were no longer significant, whereas the association with leptin remained (p = .039). In contrast, sleep duration was not associated with levels of resistin, RBP4, and SPARC (all p > .05). In the 13–18 years old group, sleep duration was only associated with RBP4 that each 1 hour reduction in sleep duration was associated with a 0.016 unit increase in ln(RBP4) (p = .027) when controlling for age, gender, and Tanner stage; however, this association was attenuated with further adjustment for BMI (p = .067).
Table 3.
Sleep Duration in Relation to Adipokines Levels Adjusted With or Without BMI.
| 6–12 years old | 13–18 years old | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| βa | p | βb | p | βc | p | βa | p | βb | p | βc | p | |
| ln leptin (ng/mL) | −0.222 | <.001 | −0.188 | <.001 | −0.054 | .039 | −0.017 | .492 | −0.044 | .102 | 0.004 | .821 |
| ln total-adiponectin (µg/mL) | 0.019 | .231 | 0.009 | .622 | −0.013 | .446 | 0.010 | .416 | 0.015 | .267 | 0.006 | .646 |
| ln HMW-adiponectin (µg/mL) | 0.057 | <.001 | 0.045 | .006 | 0.030 | .097 | 0.006 | .669 | 0.004 | .779 | −0.005 | .739 |
| ln resistin (ng/mL) | −0.003 | .833 | −3.90E−04 | .982 | 0.008 | .661 | −0.012 | .281 | −0.009 | .469 | −0.006 | .617 |
| ln FGF21 (pg/mL) | 0.074 | .048 | 0.082 | .041 | 0.066 | .104 | −0.002 | .940 | −0.005 | .874 | −0.011 | .748 |
| ln RBP4 (µg/mL) | −0.010 | .335 | −0.002 | .845 | 0.007 | .491 | −0.016 | .027 | −0.017 | .032 | −0.014 | .069 |
| ln SPARC (µg/mL) | 0.013 | .342 | 0.018 | .234 | 0.020 | .187 | −9.75E−04 | .938 | −0.004 | .744 | −7.10E−04 | .957 |
FGF21 = fibroblast growth factor 21; HMW-adiponectin = high-molecular-weight adiponectin; RBP4 = retinol binding protein 4; SPARC = secreted protein, acidic, cysteine-rich.
β means unstandard coefficients.
βa was adjusted for age, gender, and Tanner stage.
βb was adjusted for model 1 and family history, diet, physical activity, parental education and vocation, and residence.
βc was adjusted for model 2 and BMI.
Values in bold are significant at p < .05.
Sleep Duration in Relation to Cardiometabolic Risk Factors, With and Without Adjustment for Adipokines
To explore a possible mediating role of adipokines in the association between sleep duration and cardiometabolic risk factors, a series of linear regression analysis models were performed with or without adjustment for levels of related adipokines (Table 4). In the 6- to 12-year-old children, after adjustment of covariates, sleep was inversely associated with BMI (p < .0001), WC (p < .0001), FAT% (p < .001), FBG (p < .0001), ln(insulin) (p < .0001), ln(HOMA-IR) (p < .0001) and TG (p = .039), and positively associated with HDL-C (p = .008). However, these associations were disappeared after further adjusted for leptin levels, but were not changed when adjusting for adiponectin. Notably, the association between sleep and glucose was not affected by adjusting for either adipokine levels or BMI (p < .0001). Further, mediation analysis revealed that leptin accounted for 81.6%, 85.8%, 71.5%, 63.0%, 69.2%, and 65.0% of the total effect of sleep duration on BMI, WC, ln(insulin), ln(HOMA-IR), TG, and HDL-C, respectively, indicating that leptin is a significant mediator in the association between sleep duration and such cardiometabolic risk factors. In contrast, the contribution of leptin on the total effect of sleep duration on FBG was remarkably low, accounting for only 10.0% (Table 5).
Table 4.
Sleep Duration in Relation to Cardiometabolic Risk Factors Adjusted With or Without Adipokine.
| Metabolic factors | βa | p | βb | p | βc | p | βd | p | βe | p |
|---|---|---|---|---|---|---|---|---|---|---|
| 6–12 years old | ||||||||||
| BMI | −0.599 | <.001 | −0.607 | <.001 | / | / | −0.097 | .223 | −0.481 | <.001 |
| WC (cm) | −1.534 | <.001 | −1.342 | <.001 | −0.006 | .962 | −0.179 | .388 | −1.208 | <.001 |
| FAT% | −0.976 | <.001 | −0.848 | <.001 | 0.199 | .119 | 0.081 | .622 | −0.739 | .004 |
| FBG (mmol/L) | −0.064 | <.001 | −0.058 | <.001 | −0.057 | <.001 | −0.055 | <.001 | −0.065 | <.001 |
| ln insulin(mU/L) | −0.098 | <.001 | −0.084 | <.001 | −0.021 | .257 | −0.022 | .221 | −0.097 | <.001 |
| ln HOMA-IR | −0.113 | <.001 | −0.094 | <.001 | −0.042 | .028 | −0.034 | .078 | −0.108 | <.001 |
| TC (mmol/L) | −0.015 | .509 | 0.007 | .786 | 0.010 | .671 | 0.011 | .658 | 0.004 | .885 |
| TG (mmol/L) | −0.036 | .022 | −0.034 | .039 | −0.008 | .630 | −1.36E−04 | .993 | −0.024 | .191 |
| HDL-C (mmol/L) | 0.022 | .018 | 0.025 | .008 | 0.009 | .325 | 0.008 | .409 | 0.015 | .136 |
| LDL-C (mmol/L) | −0.017 | .399 | −0.003 | .906 | 0.010 | .646 | 0.009 | .695 | 0.001 | .975 |
| SBP (mm Hg) | −0.564 | .118 | −0.549 | .147 | 0.267 | .422 | 0.098 | .779 | −0.304 | .443 |
| DBP (mm Hg) | −0.013 | .963 | −0.209 | .480 | 0.386 | .147 | 0.397 | .151 | 0.021 | .944 |
| 13–18 years old | ||||||||||
| BMI | −0.092 | .371 | −0.251 | .014 | / | / | −0.134 | .069 | −0.223 | .038 |
| WC (cm) | −0.505 | .038 | −0.680 | .007 | −0.144 | .119 | −0.418 | .021 | −0.707 | .007 |
| FAT% | −0.046 | .798 | −0.455 | .011 | −0.027 | .782 | −0.200 | .134 | −0.445 | .017 |
| Fasting glucose (mmol/L) | 0.011 | .358 | 0.018 | .160 | 0.021 | .109 | 0.018 | .152 | 0.020 | .137 |
| ln insulin (mU/L) | −0.009 | .533 | −0.018 | .256 | −0.001 | .947 | −0.003 | .793 | −0.018 | .278 |
| ln HOMA-IR | −0.006 | .708 | −0.015 | .355 | 0.004 | .782 | −4.02E−04 | .977 | −0.014 | .404 |
| TC (mmol/L) | −0.029 | .107 | −0.007 | .679 | −0.007 | .723 | −0.005 | .778 | −0.010 | .608 |
| TG (mmol/L) | −0.004 | .721 | −0.010 | .451 | −0.003 | .833 | −0.002 | .855 | −0.010 | .492 |
| HDL-C (mmol/L) | −0.003 | .637 | 0.006 | .388 | 2.38E−04 | .970 | 0.003 | .610 | 0.010 | .197 |
| LDL-C (mmol/L) | −0.016 | .322 | −0.004 | .817 | −9.24E−05 | .996 | −0.002 | .916 | −0.010 | .606 |
| SBP (mm Hg) | 0.198 | .459 | −0.187 | .513 | 0.117 | .647 | −0.065 | .810 | −0.116 | .697 |
| DBP (mm Hg) | 0.357 | .068 | 0.125 | .551 | 0.281 | .159 | 0.196 | .344 | 0.116 | .599 |
BMI = body mass index; DBP = diastolic blood pressure; FAT% = fat mass percentage; FBG = fasting blood-glucose; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; SBP = systolic blood pressure; TC = total cholesterol; TG = triglyceride; WC = waist circumference.
β means unstandard coefficients.
βa was adjusted for age, gender, and Tanner stage.
βb was adjusted for model 1 and family history, diet, physical activity, parental education and vocation, and residence.
βc was adjusted for model 2 and BMI.
βd was adjusted for model 2 and leptin.
βe was adjusted for model 2 and HMW-adiponectin for 6–12 years old or RBP4 for 13–18 years old.
Values in bold are significant at p < .05.
Table 5.
Mediation Effect of Leptin on the Association of Sleep Duration With Cardiometabolic Risk Factors in 6–12 Years Old.
| Coefficient | Bootstrapped 95% confidence intervals | Proportion mediated (% of total effect) | |
|---|---|---|---|
| BMI | −0.429 | (−0.611 to −0.263) | 81.6 |
| WC (cm) | −1.088 | (−1.537 to −0.602) | 85.8 |
| FAT% | −0.856 | (−1.220 to −0.503) | —a |
| FBG (mmol/L) | −0.006 | (−0.010 to −0.002) | 10.0 |
| ln insulin | −0.056 | (−0.078 to −0.033) | 71.5 |
| ln HOMA-IR | −0.057 | (−0.082 to −0.032) | 63.0 |
| HDL-C (mmol/L) | 0.014 | (0.007 to 0.021) | 65.0 |
Models were adjusted for age, gender, Tanner stage, family history, diet, physical activity, parental education, vocation, and residence.
BMI = body mass index; FAT% = fat mass percentage; FBG = fasting blood-glucose; HDL-C = high-density lipoprotein cholesterol; HOMA-IR = homeostasis model assessment of insulin resistance; WC = waist circumference.
aProportion mediated is undefined because the point estimate of the mediated effect is not in the same direction as the direct effect.
Values in bold are significant at p < .05.
For the 13–18 years old group, after adjusting for other covariates, an inverse association was also found between sleep duration and BMI (p = .014), WC (p = .007), and FAT% (p = .011), although the association was less prominent compared with that in the 6–12 years old group. After further adjusting for leptin levels, the associations of sleep duration with BMI and FAT% were disappeared, but were not changed by adjustment for RBP4 levels. Mediation analysis also showed that leptin accounted for 49.2% and 40.9% of the total effects of sleep on BMI and WC, respectively, although compared to the 6–12 years old group the proportions decreased nearly by a half (data not shown).
DISCUSSION
To the best of our knowledge, this is the first study to examine the association of self-reported sleep duration in Chinese children with respect to the adipokines associated with inflammation and IR, and how they might mediate adverse health effects of short sleep. The results reveal that reduced sleep is associated with increased cardiometabolic risk, including adiposity, hyperglycemia, and IR, plus unfavorable adipokine levels; indeed, these associations differ by age. Moreover, leptin appears to play a role in mediating the detrimental effects of short sleep, particularly in younger children.
In this large cohort, we demonstrate that short sleep duration is associated with increased risk of obesity both in children and adolescents, with the effects being stronger among younger children. Our finding is consistent with most previous studies from different regions in Asia and Europe.22–25 Although the evidence for what can be defined as an optimal sleep duration in children has been questioned,2 according to the new US National Sleep Foundation’s recommendation in 2015,26 the proportions of children with short sleep duration (<9 hours/day for 6–13 years old and <8 hours/day for 14–18 years old) were over 30% in our study, which was comparable to another previous finding that 28.3% of children had short sleep duration (<9 hours/day), based on a large survey conducted in eight cities with a random sample of 20778 Chinese children aged 5–12 years.27
Besides the relation to obesity, we also found that short sleep duration was associated with other cardiometabolic risk factors, including IR, higher FBG, higher TG, and lower HDL-C but only in the 6–12 years old group. A similar age-dependent trend was also evident in previous studies.28 In addition, we found that the associations of sleep duration with FBG and IR among 6–12 years olds were independent of adiposity, in line with previous reports.29–31 However, the lack of association between sleep duration and cardiometabolic risk in adolescents could be accounted for by the start of sexual development, which may modify the association between sleep duration and metabolic status. In addition, changes in lifestyle factors, such as the different school schedules and homework load from primary to secondary school could have an impact.28
Among the six adipokines in relation to sleep, leptin is the most studied in the literature given it is a satiety hormone that raises energy expenditure and slows body weight gain, but with inconsistent reports.8,10–12,14,32–37 Typical obesity in humans is commonly associated with elevation in leptin levels, which is usually interpreted as leptin resistance. In such cases, elevated leptin level or hyperleptinemia has a proinflammatory effect, and is associated with IR and weight gain. Consequently, higher leptin levels in obesity predict the risk of type 2 diabetes (T2D), metabolic syndrome, and coronary heart disease.38 There are many factors that could explain differences in the association of sleep duration with leptin during childhood and adolescence10 included changes in body composition, pubertal changes in leptin levels,39 and sensitivity to leptin, as well as changes in sleep patterns from childhood to adolescence. Given our cohort consisted of high proportions of subjects with metabolic syndrome (12.5%) and overweight and obesity (53.2%), among whom hyperleptinemia or leptin resistance were relatively common,3 our finding may be interpreted as chronic sleep deprivation reduces leptin sensitivity; thus, an elevation in circulating leptin may reflect a secondary increase in an attempt to overcome this leptin resistance. Accordingly, based on our further correlation analyses, stratified by normal weight and overweight (Supplementary Table S2), the reverse association between sleep duration and leptin levels mainly exists among the group with overweight or obese children. Therefore, an effect of reduced sleep on leptin resistance could explain the associations between short sleep and increased risk for obesity and cardiometabolic factors.
In contrast to leptin, adiponectin is produced exclusively by adipose tissue but inversely correlated with body fat content. Adiponectin can improve metabolic status via anti-inflammatory processes, improving insulin sensibility and antiarteriosclerosis, and thus is considered a “good adipokine”. Importantly, the HMW-adiponectin is the active form to have such beneficial metabolic function.40 Decrease circulating levels of total and HMW have been observed in patients with obesity and contributes to the development of IR, diabetes, and MS. In our large cohort study, we measured both total and HMW adiponectin and we found a significantly positive association of sleep duration with HMW-adiponectin, but not with total adiponectin in 6- to 12-year-old children, and no association was observed in adolescents over 12 years old. However, previous studies only observed the relationship between sleep duration and total-adiponectin.11,12,14,31 However, our results suggest that sleep duration may significantly alter the levels of the bioactive HMW-adiponectin in young children. Notably, the association was no longer significant after adjustment for adiposity suggesting it was at least partly explained by adiposity.
FGF21, produced mainly by the liver and adipose tissue, has been shown to be protective against weight gain and IR, increasing energy expenditure and modulating phosphorylation cascades and gene expression in the hypothalamus,41,42 thereby leading us to hypothesize that secretion of this factor or its related pathway might be affected by sleep. In line with expectations, our study found that shorter sleep duration was associated with decreased level of FGF21 among 6- to 12-year-old children.
RBP4 is derived primarily by liver under normal conditions, while its secretion from adipose tissue was dramatically elevated in the state of IR; elevation of RBP4 induced adipose tissue inflammation and promoted systemic IR, thus its association with increased cardiovascular risk.43 Up to now, only one study in adults has assessed the association between sleep duration and RBP4, but found no significant correlation.8 Our analysis revealed a negative correlation between sleep duration and RBP4 levels in the 13–18 years old group. However, after further adjusted for BMI, this association was attenuated, indicating that the impact of sleep duration on RBP4 levels may be via indirect influence on obesity.
Resistin is mainly secreted by adipocytes in rodents and by mononuclear cells in humans, promoting both inflammation and IR.44 We observed no association between sleep time and resistin, in contrast to a previous report.14 Like resistin, we also did not observe an association between sleep duration and SPARC, despite being suggested to be a key player in the pathology of obesity, T2D, and IR.20
Notably, to address whether these adipokine-sleep associations play a role in mediating the correlation between sleep duration and cardiometabolic risk, we further compared the association with or without adjusting for the sleep-associated adipokines. Our study showed that the significance of associations between sleep duration and obesity, dyslipidemia, and IR disappeared when accounting for leptin, but not for HWM-adiponectin or RBP4. In addition, mediation analyses suggested that leptin played a major role as a mediator of the association of sleep duration with obesity, IR, and lipid with mediated proportion approximate 50% or more in 6- to 12-year-old children, thus suggesting that leptin may at least partly explain the influence of short sleep duration on those metabolic disorders in younger children through leptin sensitivity. However, it should be noted that the strength of the relationship between sleep duration and glucose levels was modified by neither adiposity nor adipokines.
The strength of our study is a large, well-characterized cohort of individuals with a wealth of metabolic traits and covariates measured. Thus, unlike prior studies, we can adjust many potential confounders known to be associated with both cardiometabolic risk and the sleep duration, which makes our results more refined and robust. However, there are several limitations that should be noted. First, sleep time and some potential confounding variables were collected by self-report, and thus may influence the accuracy of the data. Second, no validation of our sleep questionnaire was a major limitation, and we did not collect other sleep dimensions, eg, sleep problems, patterns, and quality, so further research is needed to assess the impact of this. Finally, because of the cross-sectional nature of the study, it is not possible to imply causality. Future studies should address the longitudinal associations between objectively measured sleep and adipokine regulation, as well as cardiometabolic risk factors.
In summary, our results provide evidence that reduced sleep during childhood has detrimental effects on adipose tissue function, as well as cardiometabolic outcomes. Leptin may play a role in explaining the association of inadequate sleep with certain cardiometabolic risk factors. Indeed, these associations differ by age. Therefore, further study of the effect of sleep on adipose tissue function in diverse pediatric populations should be pursued. Nonetheless, we recommend getting adequate sleep as an important measure to prevent cardiometabolic disorders, especially among young children.
SUPPLEMENTARY MATERIAL
Supplementary data are available at SLEEP online.
FUNDING
This work was supported by grants from the Key Program of Beijing Municipal Science &Technology Commission (D111100000611001, D111100000611002, H030930030130, H030930030230), National Key Research program of China (2016YFC1304800), Beijing Science & Technology Star Program (2004A027), Novo Nordisk Union Diabetes Research Talent Fund (2011A002), National Key Program of Clinical Science of China (WBYZ2011-873),Young Research Grant of PUMCH (2013–091), and Beijing Training Project for the Leading Talents in S, T (2011LJ07). Dr Grant is funded by the Daniel B. Burke Endowed Chair for Diabetes Research and NIH grant R01 HD056465.
DISCLOSURE STATEMENT
This was not an industry supported study. The other authors have indicated no financial conflicts of interest. This study was presented in abstract form at the American Diabetes Association’s 76th Scientific Sessions, June 10–14, 2016 in New Orleans, Louisiana.
Supplementary Material
ACKNOWLEDGMENTS
The authors thank all the BCAMS study members and participants for their continuing participation in this research effort. JM and ML are equal contribution to this study. LJL analyzed data and wrote the manuscript. JLF and XTY contributed to data collection and data analysis; GL, LX, JHY, HC, DQH, and XYZ contributed to data collection in the BCAMS; SG contributed to the design and the data analysis; WHL, CHL, MYL, and YX contributed to the data analysis and reviewed the manuscript. SFG contributed to the design and reviewed/edited the manuscript. JM directed the implementation of BCAMS. ML was responsible for the conception and design of this work, acquisition, and interpretation of the data, and revised the manuscript.
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