Association between Short Sleep Duration and Overweight/Obesity among middle-school students: a cross-sectional study in Fuzhou, China

DOI: https://doi.org/10.21203/rs.3.rs-30129/v2

Abstract

Background: This study was designed to investigate the prevalence of short sleep duration and explore the association between short sleep duration and overweight or obesity in adolescents from middle school in Fuzhou, China.

Methods: Questionaires focusing on short sleep duration and overweight or obesity related factors were collected. The sleep duration was self-reported by the subjects. The body weight and height of each subject were measured by the qualified personnel. Logistic regression analysis was used to evaluate the effects of short sleep duration on overweight or obesity in school students.

Results: The overall rates of overweight and obesity were 12.1% and 7.1%, respectively. The prevalence of short sleep duration among students was 82.8%. The majority of high school students (92.5%) suffered from short sleep duration. Compared with male children with a sleep duration of over 8 hrs a day, the odds ratios (95% CI) of overweight/obesity for those with a sleep duration of less than 6 h or 6 - 8 h, were 1.63 (1.25-2.13) and 1.06 (0.88-1.27). After adjusting social and demographic status, mental health and lifestyle factors, the odds ratios of female children were 1.38(0.99-1.93) and 1.04 (0.79-1.35), respectively.

Conclusions: A large number of adolescents suffered from short sleep duration. Short sleep duration was negatively correlated with overweight/obesity among male children.

Background

Overweight and obesity, with an increased trend in the past few decades, is becoming a threat to the public health worldwide. In a recent survey, about 711.4 million individuals were diagnosed with obesity, among which 107.7 million were children [1]. Meanwhile, a large proportion of adolescents (19.4%) aged 7-18 yrs presented overweight and obesity in a national survey [2]. In China mainland, the number of obese children is very huge because of alternations in the diet compared with the previous decades [1]. Fuzhou is the provincial capital of Fujian Province localized in the southeast of China. In 2010, Lin et al reported the body development of the students in middle school in Fuzhou city [3], however, few studies focused on the incidence of overweight/obesity and the relationship between overweight/obesity and the sleep duration in these students.  

Obesity is considered to be associated with the genetic and environmental factors [4, 5]. Nowadays, more and more attention has been paid to sleep duration among the individuals with obesity [6]. Short sleep duration among students is regarded to be related to increased risk of obesity, injuries, behavior problems, attention-deficit disorder, poor academic performance and psychological problems [7-9]. In adulthood, short sleep duration is a risk factor of cardiovascular diseases [10]. Short sleep duration mainly affects metabolism, endocrine function and immune systems [11]. However, there are still some controversies on the relationship between short sleep duration and overweight or obesity. Some studies proposed a U-shaped [12, 13] or linear inverse correlation [14] between sleep duration and obesity, while others proposed no associations [15, 16]. Meanwhile, the association between short sleep duration and the overweight/obesity was somehow modulated by gender [17] and it was proposed that gender should be considered a specific trait for short sleep duration in these with overweigh/obesity [18, 19]. In this study, we aimed to investigate the prevalence of short sleep duration among students in Fuzhou. In addition, we explored the relationship between short sleep duration and overweight or obesity.

Methods

Survey design

In this survey, we utilized the multistage stratified random cluster sampling method. Firstly, sampling was randomly selected from 5 urban areas and 4 rural areas from the total 13 areas of Fuzhou City according to socioeconomic status. Secondly, the adolescents from 2 selected middle schools were sampled randomly from the selected counties. Thirdly, all students in the chosen classes were invited to participate in the study by the qualified teachers in their school after informing about the potential benefits and risks by our staff. All the subjects were invited to participate in this study at their wills. A total of 15025 students from 18 different schools were invited to this study, with 14,499 students participating in the survey (response rate: 96.5%). After exclusion of subjects with missing key variables (including sex, height, weight and sleep duration), 13,063 eligible subjects (male: 6500; female: 6553) were included in the final analyses.

Existing investigation was modeled from the questionnaire including the Youth Risk Behavior Survey, performed by the Centers for Disease Control and Prevention [20], and the international Global School-based Student Health Survey conducted with the support of WHO [21]. Questionnaires distributed to the respondents included the demographic characteristics (e.g. age and gender), athletic activity, screen-time, nutrient supplements, as well as sleep and mental health (e.g. sadness, annoyance and loneliness). This survey was conducted between May and June in 2019. The age of the youngest participants included in the study was 12 years. Written parental consent was obtained on behalf of participants below the age of 16. The study was approved by the Ethical Committee of Fujian Medical University (approval No. 2019-117).

The sample size was calculated using the following formula: N=deff×µ2×P×(1−P)/d2. In addition, the 95% CI (2-sided for µ=1.96) was determined, and the measure of probability (P) was the obese rate (8.6%) of China [2]. The design effect (deff) value was set to 2 and the relative error (d value) was d=r×0.01 (r=15%). On this basis, the sample size was 2,680 for each stratum. After taking 4 strata (i.e. boy and girl, urban and rural area) and an assumed potential non-response rate of 10% into consideration, the final sample size was 11,929.

Definition of the variables

The key schools were defined as these supported by the local government and the education bureau, with more high quality teachers and students. The ordinary schools were those supported less by the local government and the education bureau, and lower in the quality of the teachers and students. The environment for the learning and capacity of the key schools was much better than that of the ordinary schools.

Overweight and obesity screening

The body weight and height of each subject were measured by the qualified staff annually in the routine physical examination. Body mass index (BMI) was the result of measured weight (kg) divided by the square of height in meters. The diagnostic criteria for overweight and obesity among adolescents were conducted based on the guidelines proposed by the Chinese Working Group on Obesity for Children (WGOC) [22]. Overweight was defined as increase of one standard deviation (SD) of BMI compared with the normal children of the same gender at the same age, and obesity was defined as increase of two SDs of BMI. The disease history was inquired by the staff, followed by physical examination to exclude the secondary obesity induced by severe diseases, endocrine or metabolic disorders. The subjects not willing to participate in this study were excluded from this study. 

Sleep duration

Sleep duration was calculated by self-administered questionnaires by recalling the average time of falling asleep and getting up in the preceding 7 days. The average time of using electronic products each day was also self-administered. A short sleep duration was defined as a sleep duration less than 8 hrs a day for teenagers aged ≥13 according to the recommendation of American Academy of Sleep Medicine (AASM) [23]. Sleep duration was subdivided into 3 groups: “<6h”,“6-8 h”, and “>8h”, as a categorical variable in the multivariable logistic regression.

Co-variates

The questionnaire also investigated other factors related to obesity, including the gender and age, days for consuming breakfast and night snack, physical activity, homework time, high-energy snacks ,fast food intake and nutrient supplements.

Statistical analysis

SPSS version 21was used for the statistical analysis. Continuous variables were described by mean±standard deviation. Chi square test was utilized to analyze the differences in overweight and obese rates of different groups. Logistic regression analysis was conducted to investigate the relationship between sleep duration and obesity. Adjusted odds ratios (ORs) and 95% CI were evaluated through three logistic regression models. P< 0.05 was considered to statistical significance. In order to further validate whether the gender would modulate the effects of overweight on the short sleep duration, we analyzed the correlation between short sleep duration and overweight/obesity among the male and female adolescence. In the Logistics regression analysis, no additional variable was given in Model 0. The Model 1 of total subject involved adjustment for gender, age, region, types of school. For the Model 1 of male and female, we adjusted gender, region, and types of school. For the Model 2 of the three aspects, we additionally adjusted for whether using electronic product daily during school, electronics in bedroom, someone smoking at home, strenuous physical activity, moderate physical activity, days of consuming breakfast and night snack per week, having fried food or pastry in the past 12 months, having taken nutrient supplements in the past 12 months. Besides, there was further adjusted for feeling annoy, sad or lonely in the Model 3 of the three aspects.

Results

Respondent characteristics

Finally, 13,063 subjects (male: 6500; female: 6553) were included in this study. The respondents with short sleep duration had longer screen-time than that of the counterparts with short sleep duration, (P<0.001, Table 1). Meanwhile, compared with those with short sleep duration, they less frequently participated in physical activity (P<0.001) or consuming breakfast (P<0.001). Instead, short sleep duration was significantly linked to consuming night snack (P<0.001) and feeling irritated (P<0.001), sad (P<0.001) and lonely (P<0.001).

Overweight and obesity

The prevalence of overweight and obesity were 12.1% and 7.1%, respectively (Table 2). They were statistical differences in the rate of obesity and overweight among various regions (P<0.001). No statistical differences were noticed in the rate of obesity and overweight between the students in the key schools and ordinary schools (P=0.566). The prevalence of obesity rate of among the male children was significantly higher than that of female counterparts (P<0.001).

Short sleep duration

The total prevalence of short sleep duration was 82.8%. The prevalence of short sleep duration among the female children was significantly higher than that in male counterparts (86.0% vs. 79.5% P<0.001). There were statistical differences among the prevalence of short sleep duration in the students aged ≤13 yrs (67.7%), those aged 14-15 yrs (77.8%) and those aged ≥16 yrs (92.5%, P<0.001). The prevalence of short sleep duration in the students lived in the urban was significantly lower than those lived in the rural areas (80.5% vs. 84.6%, P<0.001). No statistical difference was noticed in the prevalence of short sleep duration in the students of the key schools and ordinary schools (82.9% vs. 82.6%, P=0.726).

Relationship between sleep duration and overweight/obesity

Model 1 showed that, compared with all the subjects and male children who slept more than 8 hrs per day, those who slept for less than 6 hrs a day showed higher possibility for overweight/obesity after adjusting socio-demographic status [OR=1.32(95%CI: 1.12-1.55) and 1.25(95%CI: 1.02-1.54) (Table 3 and Table 4)]. After additionally adjusting life-style factors in Model 2, all the subjects and male children who slept for less than 6 hrs a day showed a significantly higher association for overweight/obesity by comparing to the reference group [OR=1.55 (95%CI:1.27-1.89) and 1.65, (95%CI:1.27-2.13)]. The odds ratios were 1.53 (1.24-1.87) and 1.63 (1.25-2.13), after further adjustment for mental health such as feeling irritation, sad or lonely. Nevertheless, there were no significant differences among the female children. The homologous odds ratios (95%CI) for female children were 1.41 (1.02-1.96) in Model 2 and 1.38 (0.99-1.93) in Model 3, respectively. Nevertheless, the lower CI was very close to significance.

Discussion

This study revealed the link between short sleep duration and overweight/obesity in adolescents of Fuzhou, China. After adjusting socio-demographic status, lifestyle factors, and mental health, there was a negative correlation between short sleep duration and overweight/obesity among male children.

The prevalence of overweight or obesity in our study was 19.2%, which was higher than the national average [2]. Moreover, our findings demonstrated the differences among gender, age and region, which were similar with the Report on Childhood Obesity in China [24]. The prevalence of obesity showed decline with the elevation of the ages, which may be possibly related to physical development and attention to their appearances[25, 26].

The prevalence of obesity in male children was significantly higher than that of female children. In addition, the prevalence of short sleep duration in adolescents showed strike increase with age. About 92.4% of adolescents in high school suffered from short sleep duration. Study pressure, especially pressure from the college entrance examination, may contribute to such a high prevalence.

Our study revealed that short sleep duration was negatively related to overweight/obesity which was consistent with previous study showing that sleep deficiency seemed to parallel the increased prevalence of obesity[27]. Meanwhile, it could be an independent risk factor for obesity[10, 28, 29]. One study provided causal evidence on the relationship between short sleep duration and weight gain in the population-level [30]. Moreover, Krietsch et al reported that there was a  U-shaped correlation between those with short sleep duration and obesity only in the female children [31]. These differences may be related to the physiology of adolescence between female and male.

To date, little is known about the relationship between sleep and obesity. Sleep duration involved in the regulation of cerebral function such as controlling the appetite, which could lead to over-eating in an obesogenic environment [32]. The homeostatic control of appetite was achieved by complex interactions among numerous neuroendocrine hormones [33]. Many pivotal hormones (e.g. insulin, leptin, cortisol and ghrelin) [34] may involve in the correlation between sleep and obesity. Even after adjusting the BMI, sleep duration was negatively correlated with circulating leptin [35]. Eptin pathway could explain the key mechanism via a modification effect [36]. Under some circumstances, the short sleep duration could lead to disruption of insulin, leptin, cortisol and ghrelin expression [37, 38]. After a period of sleep loss, people could experience a 24% increase in hunger with largely whetting the appetite for high carbohydrate foods [37]. In our study, students with short sleep duration spent more time on night snack than those with adequate sleep, which may lead to weight gain. Fatigue caused by short sleep duration may result in reduced physical activity, which then promoted the weight gain [39, 40]. In this study, adolescents with adequate sleep did more exercise of moderate-intensity than those with short sleep duration.

Circadian Locomotor Output Cycles Kaput (CLOCK) genes involved in regulation of diurnal rhythm, and their effects on neuroendocrine systems might have an impact on obesity [41]. The variants of CLOCK gene was related to sleep duration [42], as with calorie intake [43], metabolic syndrome [44], and obesity [45]. Meanwhile, methylation of CLOCK gene was associated with carbohydrate intake, total energy intake, insulin resistance, and BMI [46]. REV-ERBα rs2071570 and rs2071427 were related to BMI and sleep duration in male children, confirming the association of the REV-ERBα gene with human obesity, mainly in males [47]. In line with our findings, this theory supported a negative correction between sleep duration and overweight/obesity among male children [48].

Obesity is not simply related to biology and behavior, but also to the our social context [49]. Social support from friends and awareness/internalization of thinness ideals were significantly related to odds of overweight/obesity in youth. Such association varied by age and sex, and persisted after control for intra-familial factors such as overall family support/function, diet and activity specific support [50]. A cross-sectional study on societal risk factors for overweight and obesity in women in Zimbabwe showed that the key social factors associated with overweight and obesity were older age, wealthy and the use of hormonal contraception. Besides, a higher education and being Christian also increased the risk of obese and overweight, respectively [51].

In this study, there were no statistical differences between the short sleep duration and overweight/obesity in the female children, however, the lower CI (95% CI 0.99-1.93) was very close to significance. It appeared that this association was just smaller than what was observed for males. It may be that the stratified analysis lacked statistical power. In our subsequent study, studies of a large sample size involving more social variables are required to investigate the effects of short sleep duration on the overweight/obesity in female children. 

A large and representative sample was included in the survey. Meanwhile, we analyzed a wide range of covariance to verify the relationships between sleep duration and overweight or obesity by adjusting potential confounding factors correlated with overweight or obesity. Nevertheless, there are some limitations in our study. First, causal inference will be limited in the cross-sectional design, although there are several theories supporting our findings. Second, the determination of sleep duration was reported by the students themselves, which was a limiting factor. Indeed, the facilities may contribute to the reduction of the errors to some extent, however, it was a challenge for the promotion of the facility as the sample size was too large. In the subsequent study, representative samples will be selected for the monitor of the sleep duration of the subjects including sleep quality. Third, the impact of prolonged sleep duration on overweight or obesity among adolescents was not explored in our study, as we laid emphasis on the risk of overweight or obesity induced by short sleep duration in middle-school students. Then we will focus on the effects of long sleep duration on the overweight and obesity in the students. Fourth, the psychologic status of the subjects when self-reporting may lead to some bias of the results. In a follow-up study, qualified mental scales will be required to evaluate the psychologic status including depression and sorrow, which can correct the effects of psychologic variables on overweight and obesity. 

Conclusion

Adolescents suffered from short sleep duration. There was a negative correlation between short sleep duration and overweight/obesity in male children. In female children, further studies are needed to investigate the potential association between short sleep duration and overweight/obesity. In future, further researches are required to investigate the relationship between sleep duration and specific hormones in adolescents, in order to explore the potential mechanisms of overweight or obesity.

Abbreviations

Body mass index (BMI)  

Chinese Working Group on Obesity for Children (WGOC) 

American Academy of Sleep Medicine (AASM) 

Odds ratios

Circadian Locomotor Output Cycles Kaput (CLOCK)

Declarations

Ethics approval and consent to participate

The study was approved by the Ethical Committee of Fujian Medical University.

 Consent for Publication 

Written parental consent was obtained on behalf of participants below the age of 16.

Availability of data and material 

All the data were available upon appropriate request.

Competing interests 

The author(s) declare(s) that there is no conflict of interest regarding the publication of this paper.

Funding

This work was funded by Joint Funds for the Innovation of Science and Technology, Fujian Province (2018Y9089), the Natural Science Foundation of Fujian Province (Grant No. 2019 J01315), and Professor Development Fund Project of Fujian Medical University (JS15002). These fundings showed no roles in the design of the study

Authors' contributions 

Designed and modified the manuscript: WSC and WSY; Designed the research and participated in the experimental design, coordinated and drafted the manuscript: LGB, ZFH, XXY, CYY; Data collection, achievement interpretation and manuscript writing: LGB, ZFH, XXY, CYY; Analysed the data: LGB, XXY, CYY. All of the authors have given final approval of the version to be published.

Acknowledgements 

Not applicable.

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Tables

Table 1 Sleep duration of middle school students in Fuzhou, China

Characteristics

Total

(n=13063)

6h

(n=1762)

6-8h

(n=9051)

>8h

(n=2250)

 

F/c2

 

P value

Age (years)

14.44±1.64

14.94±1.65

14.53±1.64

13.66±1.36

369.68

<0.001

Gender

 

 

 

 

103.26

<0.001

Boys

6520(49.9)

802(45.5)

4381(48.4)

1337(59.4)

 

 

Girls

6543(50.1)

960(50.1)

4670(51.6)

913(40.6)

 

 

Types of school

 

 

 

 

3.86

0.145

Key school

7817(59.8)

1092(62.0)

5386(59.5)

1339(59.5)

 

 

Ordinary school

5246(40.2)

670(38.0)

3665(40.5)

911(40.5)

 

 

Areas

 

 

 

 

57.41

<0.001

Urban

5924(45.3)

860(48.8)

3910(43.2)

1154(51.3)

 

 

Rural

7139(54.7)

902(51.2)

5141(56.8)

1096(48.7)

 

 

Screen time per day

during school

 

 

 

 

210.20.

<0.001

30minute

5386(41.2)

596(33.8)

3659(40.4)

1131(50.3)

 

 

30-60minute

3949(30.2)

469(26.6)

2814(31.1)

666(29.6)

 

 

≥60minute

3728(28.5)

697(18.7)

2578(28.5)

453(20.1)

 

 

Using electronic products

in bedroom

8422(64.5)

1254(71.2)

5898(65.2)

1270(56.4)

99.70

<0.001

Someone smoking at home

7163(55.4)

1015(58.2)

4954(55.2)

1194(53.6)

8.69

0.013

Doing strenuous exercise

in spare time

8362(65.4)

1032(59.8)

5749(65.0)

1581(71.8)

64.95

<0.001

Exercise at moderate intensity in spare time

7057(55.8)

837(49.0)

4896(55.8)

1324(60.9)

55.43

<0.001

Days for consuming breakfast

6.13±1.56

5.52±1.99

6.17±1.48

6.40±1.36

163.74

<0.001

Days for consuming

night snack

2.10±2.34

2.39±2.51

2.09±2.29

1.91±2.35

19.44

<0.001

Having fried food often in the past 12 months

7468(70.9)

950(70.2)

5279(71.6)

1239(68.7)

6.17

0.046

Having pastry

often in the past 12 months

8342(80.6)

1035(78.5)

5880(81.1)

1427(80.3)

5.16

0.076

Nutrition supplements in thepast 12 months

4982(51.3)

612(49.6)

3518(51.6)

852(51.7)

1.64

0.441

Frequency of

feeling lonely

 

 

 

 

316.78

<0.001

Never

4760(36.7)

443(25.3)

3255(36.2)

1062(47.7)

 

 

Sometimes

6397(49.4)

888(50.8)

4584(51.0)

925(41.5)

 

 

Always

1803(13.9)

417(23.9)

1145(12.7)

241(10.8)

 

 

Frequency of

feeling sad

 

 

 

 

245.29

<0.001

Never

4795(37.0)

489(28.0)

3291(36.7)

1015(45.5)

 

 

Sometimes

6877(53.1)

945(54.1)

4919(54.8)

1013(45.5)

 

 

Always

1283(9.9)

314(18.0)

768(8.6)

201(9.0)

 

 

Frequency of

feeling irritated

 

 

 

 

425.93

<0.001

Never

2084(16.3)

207(12.0)

1281(14.4)

596(27.1)

 

 

Sometimes

8771(68.5)

1054(61.1)

6353(71.5)

1364(62.1)

 

 

Always

1946(15.2)

463(26.9)

1246(14.0)

237(10.8)

 

 

 

Table 2 Prevalence of overweight and obesity among different groups [n(%)]

Characteristics

Normal

Overweight/

Obesity

Overweight

Obesity

Total

c2

P

Gender

 

 

 

 

 

365.53

<0.001

Boys

4849(74.4)

1671(25.6)

1106(17.0)

565(8.7)

6520

 

 

Girls

5704(87.2)

839(12.8)

473(7.2)

366(5.6)

6543

 

 

Area

 

 

 

 

 

48.51

<0.001

Urban

4635(78.2)

1289(21.8)

789(13.3)

500(8.4)

5924

 

 

Rural

5918(82.9)

1221(17.1)

790(11.1)

431(6.0)

7139

 

 

Age(y)

 

 

 

 

 

94.38

<0.001

≤13

3866(78.3)

1071(21.7)

592(12.0)

479(9.7)

4937

 

 

14-15

3133(81.3)

721(18.7)

470(12.2)

251(6.5)

3854

 

 

≥16

3559(83.3)

713(16.7)

517(12.1)

196(4.6)

4272

 

 

Types of School

 

 

 

 

 

1.14

0.566

Key school

6335(81.0))

1482(19.0)

939(12.0)

543(6.9)

7817

 

 

Ordinary school

4218(80.4))

1028(19.6)

640(12.2)

388(7.4)

5246

 

 

Total

10553(80.8)

2510(19.2)

1579(12.1)

931(7.1)

13063

 

 

 

Table 3 Odds ratios of overall overweight/obese based on sleep duration

Characteristics

6h

6-8h

>8h

Total (n)

1762

9051

2250

Overweight/obese (n)

376

1669

46

Model 0

1.20(1.06-1.36) *

1.04(0.89-1.21)

1.00

Model 1

1.32(1.12-1.55)*

1.04(0.92-1.17)

1.00

Model 2

1.55(1.27-1.89)**

1.06(0.92-1.23)

1.00

Model 3

1.53(1.24-1.87)*

1.06(0.91-1.23)

1.00

Model 0, no additional variables

Model 1, adjustment for gender, age, region, types of school.

Model 2, additionally adjusted for whether using electronic product daily during school, electronics in bedroom, someone smoking at home, strenuous physical activity, moderate physical activity, days of consuming breakfast and night snack per week, having fried food or pastry in the past 12 months, having taken nutrient supplements in the past 12 months.

Model 3, further adjusted for feeling annoy, sad or lonely

*P < 0.05; **P < 0.01

 

Table 4 Odds ratios of overweight/obese based on sleep duration in different gender

Characteristics

6h

6-8h

>8h

Female

 

 

 

Total (n)

960

4670

913

Overweight/obese (n)

152

568

119

Model 0

1.25(0.97-1.63)

1.08(0.87-133)

1.00

Model 1

1.38(1.06-1.81)*

0.99(0.80-1.23)

1.00

Model 2

1.41(1.02-1.96)*

1.04(0.80-1.35)

1.00

Model 3

1.38(0.99-1.93)

1.04(0.79-1.35)

1.00

Male

 

 

 

Total (n)

802

4381

1337

Overweight/obese (n)

224

1101

346

Model 0

1.26(1.03-1.65) *

0.92(0.75-1.14)

1.00

Model 1

1.25(1.02-1.54)*

1.07(0.92-1.23)

1.00

Model 2

1.65(1.27-2.13)**

1.06(0.89-1.27)

1.00

Model 3

1.63(1.25-2.13)**

1.06(0.88-1.27)

1.00

Model 0, no addition variables

Model 1, adjustment for age, region, types of school.

Model 2, additionally adjusted for whether using electronic product daily during school, electronics in bedroom, someone smoking at home, strenuous physical activity, moderate physical activity, days of consuming breakfast and night snack per week, having fried food or pastry in the past 12 months, having taken nutrient supplements in the past 12 months.

Model 3, further adjusted for feeling annoy, sad or lonely

*P < 0.05; **P < 0.01