Vol 75, No 1 (2024)
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Association between sleep duration and metabolic syndrome: a population-based study in China

Ziyi Sun1, Xi Yang2, Ling Bai3, Ridong Huang4, Zuojie Luo1, Li Li1, Yingfen Qin1, Jia Zhou1, Liheng Meng1, Yang Peng5, Xinghuan Liang1
Pubmed: 38497390
Endokrynol Pol 2024;75(1):51-60.

Abstract

Introduction: The relationship between sleep duration and metabolic syndrome (MetS) remains debatable. In the present study, we analysed the link between total sleep duration (including nighttime sleep and nap duration) and MetS as well as its components among the Chinese population.

Material and methods: This was a cross-sectional study from a prospective population cohort including 8616 participants over 40 years in Guangxi, China, evaluated from April 2011 to January 2012. MetS was diagnosed using modified criteria from the National Cholesterol Education Program’s Adult Treatment Panel III. Sleep information was obtained through a standard self-report-based questionnaire. The connection between sleep duration and MetS prevalence as well as its components was evaluated using a logistic regression model.

Results: After adjusting for potential confoundings, the longer daily sleep duration (≥ 10 hours) group was observed to have the higher odds of having MetS than the reference group with ≥ 7 and < 8 hours of sleep [odds ratio (OR): 1.25, 95% confidence interval (CI): 1.03–1.52, p = 0.023], as well as the highest odds of having elevated triglycerides (OR: 1.25, 95% CI: 1.03–1.52) and fasting blood glucose (OR: 1.21, 95% CI: 1.01–1.45). Further analysis demonstrated that sleeping > 9 hours per night was correlated to MetS in females (OR: 1.27, 95% CI: 1.02–1.58), while napping ≥ 90 minutes was correlated to MetS (OR: 1.44, 95% CI: 1.11–1.87) in males.

Conclusion: Both longer nighttime sleep duration and longer naps may be associated with the development of MetS.

Original paper

Endokrynologia Polska

DOI: 10.5603/ep.96448

ISSN 0423–104X, e-ISSN 2299–8306

Volume/Tom 75; Number/Numer 1/2024

Submitted: 11.07.2023

Accepted: 14.09.2023

Early publication date: 09.11.2023

Association between sleep duration and metabolic syndrome: a population-based study in China

Ziyi Sun*1Xi Yang*2Ling Bai3Ridong Huang4Zuojie Luo1Li Li1Yingfen Qin1Jia Zhou1Liheng Meng1Yang Peng5Xinghuan Liang1
1Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Endocrine Clinical Key Specialty, Nanning, China
2Department of Geriatric Endocrinology and Metabolism & Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention & Guangxi Clinical Research Centre for Cardio-Cerebrovascular Diseases, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
3Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
4Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
5Department of Occupational and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, China
*These authors have contributed equally to this work and share first authorship.

Xinghuan Liang, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuang Yong Road, Nanning, Guangxi 530021, China; e-mail: liangxinghuan@gxmu.edu.cn
Yang Peng, School of Public Health, Guangxi Medical University, No.22 Shuang Yong Road, Nanning, Guangxi 530021, China; e-mail: pyu098@126.com

This article is available in open access under Creative Common Attribution-Non-Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license, allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially

Abstract
Introduction: The relationship between sleep duration and metabolic syndrome (MetS) remains debatable. In the present study, we analysed the link between total sleep duration (including nighttime sleep and nap duration) and MetS as well as its components among the Chinese population.
Material and methods: This was a cross-sectional study from a prospective population cohort including 8616 participants over 40 years in Guangxi, China, evaluated from April 2011 to January 2012. MetS was diagnosed using modified criteria from the National Cholesterol Education Program’s Adult Treatment Panel III. Sleep information was obtained through a standard self-report-based questionnaire. The connection between sleep duration and MetS prevalence as well as its components was evaluated using a logistic regression model.
Results: After adjusting for potential confoundings, the longer daily sleep duration (≥ 10 hours) group was observed to have the higher odds of having MetS than the reference group with7 and < 8 hours of sleep [odds ratio (OR): 1.25, 95% confidence interval (CI): 1.03–1.52, p = 0.023], as well as the highest odds of having elevated triglycerides (OR: 1.25, 95% CI: 1.03–1.52) and fasting blood glucose (OR: 1.21, 95% CI: 1.01–1.45). Further analysis demonstrated that sleeping > 9 hours per night was correlated to MetS in females (OR: 1.27, 95% CI: 1.02–1.58), while napping90 minutes was correlated to MetS (OR: 1.44, 95% CI: 1.11–1.87) in males.
Conclusion: Both longer nighttime sleep duration and longer naps may be associated with the development of MetS. (Endokrynol Pol 2024; 75 (1): 51–60)
Key words: sleep duration; daily nap; MetS

Introduction

Metabolic syndrome (MetS) is a clinical disorder that includes central obesity, hypertension, hyperglycaemia, and dyslipidaemia [1]. MetS can lead to cardiovascular problems and all-cause mortality risk, and it has developed as a global public health concern [2]. In recent years, MetS incidence has risen considerably in numerous nations [3], with a global incidence ranging from 20 to 45% [4]. According to epidemiological research, the total prevalence of MetS among the Chinese population was 24.5% in 2016 [5]. Therefore, identifying modifiable factors is crucial to preventing the onset of MetS.

Sleep is an important factor related to human health, and it is closely related to hormone secretion and metabolic balance in the body [6]. Meanwhile, insufficient or excessive sleep has been demonstrated in epidemiological research to have adverse implications for the body, such as obesity [7], diabetes [8], or cardiovascular events [9]. MetS has long been assumed to be caused by lack of exercise, irregular diets, and a sedentary lifestyle [10]. Moreover, the onset of MetS has been related to sleep duration, which has been identified as a contributing factor as modern civilization has progressed. To date, many studies have analysed the connection between sleep duration and MetS. However, the outcomes of prior investigations were inconsistent. Toshiaki et al. reported a U-shaped relationship in Japanese patients, with both short- and long-duration sleepers presenting a higher risk of MetS [11]. Recent data from a meta-analysis of 9 prospective cohorts suggests that short sleep duration, rather than extended sleep duration, increases the risk of MetS [12]. On the other hand, another cross-sectional investigation showed no link between sleep duration and MetS in Chinese individuals [13]. These inconsistent results might be related to differences in experimental study designs, race, and sleep duration classification.

Daytime napping is a common behaviour worldwide. In China, around 68.6% of middle-aged and older people nap daily [14]. Adequate napping can aid in energy recovery, memory consolidation [15], and cognitive function [16]. However, obesity [17], type 2 diabetes (T2DM) [18], and cardiovascular issues have also been linked to taking longer naps in several studies [19]. Also, previous studies have reported a correlation between napping and MetS. In the Netherlands, a cross-sectional study indicated that napping for both < 30 and 30 min was related to an elevated incidence of MetS in 1679 older subjects compared to those who did not nap [20]. Another cross-sectional study conducted in China found that only napping for90 min was correlated with significantly higher MetS occurrence among 5129 subjects with an average age of 39 years [21]. Because more middle-aged and elderly people nap during the day in China, understanding the health impacts of napping on MetS is of significant interest.

Most studies on sleep duration and MetS have concentrated on nighttime sleep duration, with relatively few studies looking at nap and total daily sleep duration. An even greater lack of studies is related to the link between the 3 sleep durations and MetS. At the same time, there is controversy regarding the length of sleep duration that affects MetS. Hence, we used data from the REACTION study in Guangxi to evaluate the correlation between total sleep duration (including nighttime sleep duration and naps) and MetS and its components, and to further explore the impacts of gender, age, and BMI status on the sleep-MetS relationship.

Material and methods

Study population

The data for this study were retrieved from the Guangxi division of the Risk Evaluation of cAncers in Chinese diabeTic Individuals: A LONgitudinal (REACTION) study, which is a population-based prospective cohort study with respondents over the age of 40 years. This study was designed to evaluate the association between type 2 diabetes, prediabetes, and cancer risk in the Chinese population [22–24]. A total of 9028 respondents were recruited from April 2011 to January 2012 in 8 communities in Nanning, Guangxi, China. Respondents who did not match the eligibility requirements of age (≥ 40 years, n = 134), lacked nighttime sleep duration (n = 239), or could not determine their MetS status (n = 39) were not included in the current research. Thus, 8616 respondents were included to evaluate nighttime sleep duration and MetS and its components. Respondents lacking nap information (n = 1954) were then excluded, and the remaining population (n = 6662) had their daytime nap time and daily sleep time with MetS and its components examined (Fig. 1). The present study followed the Declaration of Helsinki and was authorized by the Human Research Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. Written informed consent form was signed by all the respondents.

178829.png
Figure 1. Flow chart of crowd data analysis from the REACTION study (2011–2012)

Clinical information and biochemical data

Professional researchers performed personal interviews using a systematic questionnaire. The questionnaire included general information, lifestyle factors, and medical and family histories. Never, ever (stopped smoking or drinking for more than 6 months), or currently (smoked or drunk regularly in the past 6 months) were used to categorize smoking and drinking behaviours. The intensity, duration, and frequency of physical activity during leisure time were evaluated using a short version of the International Physical Activity Questionnaire (IPAQ) [25]. The anthropometric measures of all respondents were evaluated using a standard methodology by qualified personnel. The blood pressure (BP) of sitting respondents was tested using an automated electronic device (OMRON, Omron, China). The same observer took BP readings 3 times in a row, each 5 minutes apart. The BP value for analysis was calculated as the average of the 3 readings. The waist circumference (WC) was estimated at the umbilical cord in the natural standing position. The body mass index (BMI) (kg/m2) was computed by multiplying the weight (kg) by the square of the height (m2). Obesity was defined as a BMI28, and overweight was defined as a BMI24 and < 28, according to the standards of the Chinese population [26].

Venous blood samples were collected in the early morning when respondents were fasting. Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG), and triglyceride (TG) levels were tested using an automated analyser (ARCHITECTc16000System, Abbott Laboratories, IL, United States).

Definition of MetS

MetS was defined following the 2005 National Cholesterol Education Program Modified Adult Standard Treatment Group III (NCEP ATP III) [27] and was diagnosed as a condition in which 3 or more of the following features were present:

  • women’s and men’s WCs of 90 and 80 cm, respectively;
  • TG1.70 mmol/L or medication therapy for high triglycerides;
  • HDL-C levels of < 1.03 and < 1.30 mmol/L for men and women, respectively;
  • systolic blood pressure (SBP)130 mmHg, diastolic blood pressure (DBP)85 mm Hg or medication therapy for high BP;
  • FPG5.6 mmol/L or medication therapy for high fasting glucose.
Definition criteria of total sleep duration, nighttime sleep duration, and nap duration

Sleep duration was evaluated based on the following questions: When do you generally sleep at night? When do you normally get up in the morning? Do you take naps during the day? How long do you generally slumber minutes or hours? Waking and falling asleep times were used to compute nighttime sleep duration. Nighttime sleep was separated into 5 categories: < 6 h,6 and < 7 h,7 and < 8 h,8 and < 9 h, and 9 h. Daytime naps were classified into 3 categories:1 and < 45 min,45 and < 90 min, and 90 min. Based on the sum of nighttime sleep and nap duration, the total sleep duration was divided into 5 categories: < 7 h,7 and < 8 h,8 and < 9 h,9 and < 10 h, and 10 h.

Statistical analyses

The Kolmogorov-Smirnov (K-S) test was applied to determine normality. Continuous variables are presented as means ± standard deviations or medians (interquartile ranges). Frequencies and percentages are used to express categorical variables. To contrast non-MetS and MetS groups on continuous variables, the Wilcoxon rank-sum test was employed, while the c2 test was utilized to compare categorical data. The odds ratios (ORs) and 95% confidence intervals (CIs) for different sleep durations and MetS were computed using logistic regression analyses. The reference groups were the 1 and < 45 min nap group and the 7 and < 8 h sleep group. We performed 2 types of logistic model analyses: model 1 was unadjusted for confounding factors, model 2 was adjusted for age and gender, ethnicity, education, smoking status, drinking status, work intensity, physical activity and BMI.

We also focused on whether age and BMI affected the connection between sleep duration and MetS. After stratifying the research sample by gender, we analysed the connection between MetS and sleep duration in subgroups of age (40–60 and < 60 years) and obesity degree (normal, overweight, or obese). To make our research more comprehensive, a multivariate logistic regression model was used to evaluate the relationship between sleep duration and MetS components. The SAS software (version 9.4) was used for all statistical analyses, and the significance was determined by a p value < 0.05.

Results

Characteristics of respondents

The characteristics of respondents with and without MetS are compared in Table 1. Among the 8616 respondents (3268 males and 5348 females), 3252 (37.7%) had MetS. The respondents’ average age was 56.1 ± 10.7 years, and men (34.0%) showed a lower incidence of MetS than women (40.0%). The mean WC and FPG were 83.0 ± 9.1 cm and 5.9 ± 1.7 mmol/L, respectively. Respondents with MetS had higher WC, FPG, BP, and TG levels and BMI compared with non-MetS respondents (all p < 0.05).

Table 1. Characteristics of respondents with and without metabolic syndrome (MetS)

Characteristic

Total (n = 8616)

Non-MetS (n = 5364)

MetS (n = 3252)

p-value

Male (n%)

3268 (37.93)

2156 (40.19)

1112 (34.19)

Age [years]

56.11 ± 10.65

54.12 ± 10.24

59.40 ± 10.51

< 0.001

BMI [kg/m²]

24.13 ± 3.31

23.13 ± 3.00

25.76 ± 3.14

< 0.001

Ethnicity

< 0.001

Han (n%)

5492 (64.90)

3338 (62.23)

2254 (69.31)

Zhuang (n%)

2666 (30.94)

1811 (33.76)

855 (26.29)

Other (n%)

114 (1.32)

77 (1.44)

37 (1.14)

Missing (n%)

244 (2.83)

133 (2.57)

106 (3.26)

Education

< 0.001

Primary school and below (n%)

1437 (16.68)

658 (12.27)

779 (23.95)

Junior high school (n%)

2811 (32.63)

1729 (32.23)

1082 (33.27)

High school and technical Secondary school (n%)

3062 (35.54)

2092 (39.00)

970 (29.83)

College degree or above (n%)

1268 (14.72)

859 (16.01)

409 (12.58)

Missing (n%)

38 (0.44)

25 (0.48)

12 (0.37)

Smoking status

0.001

Never (n%)

6597 (76.57)

4024 (75.02)

2573 (79.12)

Former (n%)

621 (7.21)

387 (7.21)

234 (7.20)

Current (n%)

1252 (14.53)

859 (16.01)

393 (12.08)

Missing (n%)

146 (1.69)

94 (1.75)

52 (1.60)

Alcohol consumption status

< 0.001

Never (n%)

5143 (59.69)

3091 (57.62)

2052 (63.10)

Former (n%)

603 (7.00)

378 (7.05)

225 (6.92)

Current (n%)

2729 (31.67)

1805 (33.65)

924 (28.41)

Missing (n%)

141 (1.64)

90 (1.68)

51 (1.57)

Work intensity

< 0.001

No work (n%)

5145 (59.71)

2860 (53.32)

2285 (70.26)

Low intensity (n%)

2686 (31.17)

1952 (36.39)

734 (22.57)

Moderate intensity (n%)

566 (6.57)

400 (7.46)

166 (5.10)

High intensity (n%)

100 (1.16)

64 (1.19)

36 (1.11)

Missing (n%)

119 (1.38)

84 (1.63)

31 (0.95)

Physical activity

< 0.001

No (n%)

1453 (16.86)

943 (17.58)

510 (15.68)

Low level (n%)

4579 (53.15)

2709 (50.50)

1870 (57.50)

Moderate (n%)

1250 (14.51)

804 (14.99)

446 (13.71)

High level (n%)

1153 (13.38)

789 (14.71)

364 (11.19)

Missing (n%)

181 (2.10)

119 (2.22)

62 (1.91)

Changes in Mets components

WC [cm]

83.04 ± 9.07

79.73 ± 8.13

88.41 ± 7.89

< 0.001

TG [mmol/L]

1.55 ± 1.27

1.16 ± 0.72

2.19 ± 1.65

< 0.001

HDL-c [mmol/L]

1.31 ± 0.41

1.39 ± 0.43

1.18 ± 0.34

< 0.001

SBP [mmHg]

132.03 ± 20.21

125.78 ± 18.52

142.20 ± 18.66

< 0.001

DBP [mmHg]

78.45 ± 11.42

76.02 ± 10.73

82.40 ± 11.41

< 0.001

FPG [mmol/L]

5.90 ± 1.67

5.49 ± 1.24

6.56 ± 2.02

< 0.001

Association between sleep duration and MetS

The ORs (95% CI) for MetS according to different sleep durations are displayed in Figure 2. In the total population, sleeping9 hours at night and 10 hours per day exhibited a higher incidence of MetS compared to the control group, with ORs (95% CI) of 1.32 (1.14–1.52) and 1.41 (1.19–1.67), respectively (Model 1). After controlling for age, gender, ethnicity, education, smoking status, drinking status, work intensity, physical activity, and BMI, this correlation (OR: 1.18,95% CI: 1.00–1.40, p = 0.047; OR: 1.25, 95% CI: 1.03–1.52, p = 0.023) is attenuated (Model 2). In male subjects, respondents in the napping duration90 min group and with longer daily sleep duration (≥ 10 hours) were significantly linked with MetS (OR: 1.44, 95% CI: 1.11–1.87, p = 0.007; OR: 1.42, 95% CI: 1.04–1.95, p = 0.028). In female subjects a similar connection was only detected between longer night sleep duration (≥ 9 hours) and MetS (OR: 1.27, 95% CI: 1.02–1.58, p = 0.031). We did not detect a significant correlation between short sleep duration and MetS.

178844.png
Figure 2. The odds ratios (ORs) of metabolic syndrome (MetS) based on nighttime sleep duration (A), nap duration (B), and daily sleep duration (C) in the REACTION study (2011–2012). Model 1: crude ratio. Model 2: adjusted for age, gender (as appropriate), ethnicity, education, smoking status, alcohol consumption status, work intensity, physical activity, and body mass index (BMI)

Table 2 shows how the correlation between sleep duration and MetS varied among subgroups stratified by age and BMI. After correcting for multiple confounders, napping time90 minutes in male subjects was positively related to MetS in the following subgroups: 40–60 years (OR: 1.53, 95% CI: 1.08–2.15) and overweight (OR: 1.79, 95% CI: 1.26–2.53). Female subjects with9 hours of nighttime sleep were significantly more susceptible to MetS in the subgroup aged60 years (OR: 1.66, 95% CI: 1.22–2.27). The interaction p-values for gender, age, and BMI were significant for MetS (all p < 0.001).

Table 2. Subgroup analysis of sleep duration and metabolic syndrome (MetS)

Variables

Age

BMI

40–60 y

≥ 60 y

Normal

Overweight

Obesity

Male

Nighttime sleep duration (n = 8616)

Number

2137

1129

1455

1352

388

< 6 h

0.51 (0.31–0.85)

1.08 (0.60–1.92)

0.79 (0.38–1.66)

0.74 (0.44–1.24)

0.70 (0.33–1.48)

≥ 6 and < 7 h

0.85 (0.64–1.14)

1.24 (0.83–1.85)

1.08 (0.71–1.64)

0.82 (0.60–1.12)

1.52 (0.80–2.89)

≥ 7 and < 8 h

Ref

Ref

Ref

Ref

Ref

≥ 8 and < 9 h

1.07 (0.82–1.41)

1.42 (1.02–1.97)

1.40 (0.98–2.02)

1.01 (0.77–1.34)

1.41 (0.75–2.67)

9 h

1.07 (0.74–1.53)

1.29 (0.86–1.94)

1.14 (0.72–1.82)

1.22 (0.85–1.76)

0.77 (0.37–1.60)

Nap sleep duration (n = 6662)

Number

1653

936

1123

1094

310

≥ 1 and < 45 min

Ref

Ref

Ref

Ref

Ref

≥ 45 and < 90 min

1.26 (0.95–1.67)

0.88 (0.59–1.31)

0.83 (0.55–1.25)

1.32 (0.97–1.79)

0.90 (0.49–1.67)

≥ 90 min

1.53 (1.08–2.15)

1.32 (0.86–2.02)

1.22 (0.77–1.93)

1.79 (1.26–2.53)

0.97 (0.48–1.97)

Daily sleep duration (n = 6662)

Number

1653

936

1123

1094

310

< 7 h

0.89 (0.55–1.43)

0.79 (0.41–1.55)

0.73 (0.30–1.81)

0.89 (0.53–1.50)

0.73 (0.30–1.81)

≥ 7 and < 8 h

Ref

Ref

Ref

Ref

Ref

≥ 8 and < 9 h

1.10 (0.80–1.53)

0.76 (0.49–1.19)

0.75 (0.38–1.47)

1.21 (0.85–1.71)

0.75 (0.38–1.47)

≥ 9 and < 10 h

1.47 (1.02–2.13)

0.96 (0.61–1.50)

0.78 (0.36–1.66)

1.46 (1.00–2.12)

0.78 (0.36–1.66)

≥ 10 h

1.44 (0.94–2.21)

1.35 (0.83–2.19)

0.78 (0.32–1.90)

1.67 (1.09–2.58)

0.78 (0.32–1.90)

Female

Nighttime sleep duration (n = 8616)

Number

3323

2024

2854

1851

557

< 6 h

0.95 (0.61–1.47)

0.88 (0.58–1.34)

0.69 (0.43–1.12)

0.95 (0.60–1.52)

0.99 (0.43–2.28)

≥ 6 and < 7 h

1.01 (0.80–1.27)

0.99 (0.75–1.32)

1.10 (0.84–1.43)

0.93 (0.71–1.22)

0.79 (0.48–1.32)

≥ 7 and < 8 h

Ref

Ref

Ref

Ref

Ref

≥ 8 and < 9 h

1.11 (0.91–1.37)

1.14 (0.90–1.45)

1.17 (0.94–1.47)

1.11 (0.87–1.42)

1.01 (0.63–1.63)

9 h

1.04 (0.77–1.41)

1.66 (1.22–2.27)

1.36 (1.01–1.82)

1.09 (0.77–1.55)

1.37 (0.68–2.75)

Nap sleep duration (n = 6662)

Number

2492

1578

2172

1403

425

≥ 1 and < 45 min

Ref

Ref

Ref

Ref

Ref

≥ 45 and < 90 min

1.05 (0.83–1.32)

0.93 (0.72–1.21)

0.95 (0.74–1.21)

1.00 (0.76–1.31)

1.08 (0.62–1.87)

≥ 90 min

0.89 (0.68–1.17)

1.04 (0.77–1.40)

1.01 (0.76–1.33)

0.87 (0.63–1.18)

1.02 (0.57–1.83)

Daily sleep duration (n = 6662)

Number

2492

1578

2172

1403

425

< 7 h

0.90 (0.60–1.38)

1.14 (0.71–1.84)

1.06 (0.67–1.68)

0.90 (0.55–1.46)

1.03 (0.42–2.49)

≥ 7 and < 8 h

Ref

Ref

Ref

Ref

Ref

≥ 8 and < 9 h

1.13 (0.86–1.47)

1.04 (0.76–1.44)

1.32 (0.97–1.80)

0.95 (0.69–1.29)

1.32 (0.70–2.51)

≥ 9 and < 10 h

1.05 (0.79–1.41)

0.99 (0.71–1.37)

1.02 (0.74–1.42)

1.04 (0.74–1.45)

1.21 (0.63–2.33)

≥ 10 h

0.98 (0.70–1.39)

1.56 (1.07–2.26)

1.27 (0.88–1.83)

1.10 (0.75–1.63)

1.37 (0.66–2.85)

Relationship between sleep duration and MetS components

Figure 3 presents the results of the multivariate logistic regression analysis between sleep duration and MetS components. For all participants, with elevated TG (OR: 1.25, 95% CI: 1.03–1.52) and FPG (OR: 1.21, 95% CI: 1.01–1.45) being linked to10 hours of sleep per day compared to the reference group. Of elevated TG,9 hours of sleep per night also presented similar results (OR: 1.23, 95% CI: 1.05–1.45). For males, respondents who napped for90 minutes had a higher risk of elevated TG (OR: 1.48 95% CI: 1.15–1.90). For females, although the statistically relevant connection between longer nighttime sleep duration and elevated TG was weaker, we still noticed a trend (OR: 1.24 95% CI: 1.00–1.54).

Sun-3.png
Figure 3. Adjusted odds ratios (ORs) for metabolic syndrome (MetS) components based on nighttime sleep duration, nap duration, and daily sleep duration in the REACTION study (2011–2012) adjusted for age, gender (as appropriate), body mass index (BMI), ethnicity, education, smoking status, alcohol consumption status, work status, and physical activity
Sensitivity analysis

The association between nighttime sleep duration and MetS was investigated in Supplementary File Table S1 using the data excluding missing nap information. The sensitivity analysis was similar to the primary analysis. Compared with women who slept 7–8 hours at night, those who slept more than 9 hours at night exhibited a higher chance of MetS in the non-adjusted model. The association in the sensitivity analysis was slightly attenuated compared to that in the primary analysis in the adjusted model.

Discussion

In the current cross-sectional study, we presented epidemiological evidence that excessive night sleep duration and total sleep duration might contribute to the onset of MetS among the Chinese population. Further subgroup analyses revealed that sleep duration9 hours per night was linked to MetS development in older Chinese females. Daytime naps of90 minutes were linked to a considerable increase in MetS prevalence in middle-aged overweight Chinese males. According to gender variations in the night sleep and nap connections with MetS, men might be more vulnerable to napping, whereas women may be more influenced by nighttime sleep. Our current findings indicate that longer nighttime sleep and total daily sleep duration might be detrimental to MetS components, including TG and FPG.

Previous studies have only shown that a short or long nighttime sleep duration was linked to MetS, or that sleep duration presented a U-shaped pattern with MetS risk [28–30]. Hence, the findings regarding the link between nighttime sleep duration and MetS are controversial. Longer nightly sleep (≥ 9 h) was related to MetS in women over 60 years old, but not in men, according to our survey. The exact cause for these gender differences remains unclear. Hormonal changes and their effects on sleep could be one explanation for the gender-specific differences in this connection [31]. Previously, fluctuations in reproductive hormone levels in women during menopause have been associated with decreased oestrogen levels and sleep disturbances [32, 33]. Studies have also indicated that perimenopausal women tend to have hot flashes during nighttime sleep, which are characterized by intense heat, sweating, and dilation of skin blood vessels [34]. Moreover, post-hot flashes in women were negatively correlated with sleep efficiency and increased nocturnal awakenings, which might lead to decreased sleep quality and increased sleep demand, finally resulting in increased nighttime sleep [35]. Also, a meta-analysis mentioned that women who slept longer had an elevated degree of C-reactive protein and interleukin 6 (IL-6) inflammatory markers than men [36]. The levels of inflammatory markers also affect BP, WC, and insulin sensitivity, which might contribute to MetS [37].

Napping has long been considered a beneficial lifestyle habit. However, recent studies have shown that prolonged napping can be detrimental to health. According to a previous meta-analysis, prolonged napping was related to the occurrence of cardiovascular events and all-cause mortality [38]. Additionally, a cohort study found that longer napping was a possible risk factor for the onset of MetS and negatively affected MetS recovery [30]. Although we did not identify a link between napping and MetS in the general population, in the subgroup analyses by gender and BMI, we found that napping for90 min was linked to an elevated risk of MetS in overweight men between the ages of 40 and 60 years. The mechanism behind this connection remains unknown. In previous studies, increased sympathetic activity was detected after waking from a daytime nap. In particular, prolonged napping might lead to sympathetic and vagal equilibrium breakdowns, thereby activating the renin-angiotensin system that regulates insulin production and glycaemic regulation, finally resulting in abnormal glucose metabolism [39]. Meanwhile, prolonged daytime napping might cause circadian rhythm disturbances, leading to increased nighttime cortisol concentrations and predisposition to insulin resistance and other metabolic abnormalities [40]. Furthermore, prolonged napping might be caused by lack of sleep or nocturnal sleep disorders. Thus, the increased risk of diabetes or metabolic illness on these individuals might be related to chronic nocturnal sleep deprivation or sleep disturbances rather than prolonged daytime napping [41].

Few studies have explored the link between MetS and the total sleep duration (nighttime sleep and napping throughout the day). In Korea, a prospective study reported an OR (95% CI) of 1.41 (1.06–1.08) for the occurrence of MetS when comparing a total sleep time of6 and < 8 h with < 6 h [42], but it did not analyse gender interactions. Moreover, Wu et al. [13] reported a link between daily sleep duration and MetS in a Chinese population and showed that female respondents with sleep duration8 h were more prone to MetS. However, this study did not detect the difference between sleep duration and MetS in men. Regarding the relative connection between daily sleep duration and MetS, we showed that among overweight men and women aged60 years, excessive daily sleep duration (≥ 10 hours/day) was related to MetS. Excessive total daily sleep duration leading to MetS might be due to deprivation of physical activity and excessive fat accumulation in prolonged sleepers. Additionally, lipocalin is an important adipocytokine that protects against hypertension, inflammation, and atherosclerotic vascular disease. Lipocalin expression decreases in people with visceral fat build-up, which might be one of the reasons why prolonged sleep leads to MetS [43]. Second, in terms of MetS composition, we showed that longer daily sleep duration was linked to higher TG and FPG levels, consistent with previous observations.

Our present study has some limitations. First, we described a relationship between sleep duration and metabolic risk factors, but the underlying causes could not be inferred. Second, although we adjusted for different possible risk variables, residual confounding can be caused by unobserved or inaccurately calibrated variables, including sleep breathing disorders and other sleep disturbances. Finally, we relied on self-reported personal information such as sleep time, which might contribute to bias in the experimental findings. The lack of objective measures of sleep to go with the objective measures of MetS was a major limitation. On the other hand, our study has the advantage of adjusting for more covariates in a large sample of individuals and adding the effects of longer naptime and MetS in men.

Conclusion

This study reveals that excessive daily sleep duration (both nap and night sleep duration) seems to be a detrimental factor for MetS and its components, with overweight middle-aged men being primarily affected by nap duration and older women being primarily affected by night sleep duration. To maintain a proper sleep schedule and limit the development of MetS, it is desirable to enhance sleep-related health education in the prevention and control of MetS.

Data availability statement

The datasets generated and/or analysed during the current study are not publicly available due to their containing information that could compromise research participant privacy, but they are available from the corresponding author on reasonable request.

Ethics statement:

This study was approved by the Human Research Ethics Committee of the First Affiliated Hospital of Guangxi Medical University, and all procedures were performed in accordance with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all participants or their proxies.

Author contributions

Z.Y.S. and L.B. conceived and analysed the data and drafted the initial manuscript; Z.J.L., Y.F.Q., X.H.L., and L.H.M. designed the study and assisted with data collection, R.D.H. and L.L. were responsible for data image processing, Y.P. and J.Z. analysed the data and revised the manuscript. X.Y. reviewed the manuscript. All authors approved the final manuscript as submitted and agreed to take responsibility for all aspects of the work.

Funding

This research was funded by the National Key Research & Development Plan for Precision Medicine Key Program (Grant No. 2016YFC0901200, 2016YFC0901205), the National Health Commission of China Public Welfare Research Project (Grant No. 201502007), and the “Excellent Medical Talents” Research and Innovation Training Program of the First Affiliated Hospital of Guangxi Medical University (Grant No. 2017025).

Acknowledgments

We thank the Department of Endocrinology of the First Affiliated Hospital of Guangxi Medical University for providing the data for this study.

Conflict of interest

The authors have no conflicts of interest to disclose.

Supplementary material

There are 3 Supplementary Tables.

References

  1. Nilsson PM, Tuomilehto J, Rydén L. The metabolic syndrome - What is it and how should it be managed? Eur J Prev Cardiol. 2019; 26(2_suppl): 33–46, doi: 10.1177/2047487319886404, indexed in Pubmed: 31766917.
  2. Engin A. The Definition and Prevalence of Obesity and Metabolic Syndrome. Adv Exp Med Biol. 2017; 960: 1–17, doi: 10.1007/978-3-319-48382-5_1, indexed in Pubmed: 28585193.
  3. Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018; 20(2): 12, doi: 10.1007/s11906-018-0812-z, indexed in Pubmed: 29480368.
  4. Alberti KG, Eckel RH, Grundy SM, et al. International Diabetes Federation Task Force on Epidemiology and Prevention, Hational Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009; 120(16): 1640–1645, doi: 10.1161/CIRCULATIONAHA.109.192644, indexed in Pubmed: 19805654.
  5. Li Ri, Li W, Lun Z, et al. Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies. BMC Public Health. 2016; 16: 296, doi: 10.1186/s12889-016-2870-y, indexed in Pubmed: 27039079.
  6. McDevitt EA, Sattari N, Duggan KA, et al. The impact of frequent napping and nap practice on sleep-dependent memory in humans. Sci Rep. 2018; 8(1): 15053, doi: 10.1038/s41598-018-33209-0, indexed in Pubmed: 30305652.
  7. Lin CL, Lin CP, Chen SW, et al. The association between sleep duration and overweight or obesity in Taiwanese adults: A cross-sectional study. Obes Res Clin Pract. 2018; 12(4): 384–388, doi: 10.1016/j.orcp.2016.07.005, indexed in Pubmed: 27520850.
  8. Reutrakul S, Van Cauter E. Sleep influences on obesity, insulin resistance, and risk of type 2 diabetes. Metabolism. 2018; 84: 56–66, doi: 10.1016/j.metabol.2018.02.010, indexed in Pubmed: 29510179.
  9. Lin Pu, Chang KT, Lin YA, et al. Association between self-reported sleep duration and serum lipid profile in a middle-aged and elderly population in Taiwan: a community-based, cross-sectional study. BMJ Open. 2017; 7(10): e015964, doi: 10.1136/bmjopen-2017-015964, indexed in Pubmed: 29084786.
  10. van Namen M, Prendergast L, Peiris C. Supervised lifestyle intervention for people with metabolic syndrome improves outcomes and reduces individual risk factors of metabolic syndrome: A systematic review and meta-analysis. Metabolism. 2019; 101: 153988, doi: 10.1016/j.metabol.2019.153988, indexed in Pubmed: 31672441.
  11. Ohkuma T, Fujii H, Iwase M, et al. U-shaped association of sleep duration with metabolic syndrome and insulin resistance in patients with type 2 diabetes: the Fukuoka Diabetes Registry. Metabolism. 2014; 63(4): 484–491, doi: 10.1016/j.metabol.2013.12.001, indexed in Pubmed: 24411997.
  12. Xie J, Li Y, Zhang Y, et al. Sleep duration and metabolic syndrome: An updated systematic review and meta-analysis. Sleep Med Rev. 2021; 59: 101451, doi: 10.1016/j.smrv.2021.101451, indexed in Pubmed: 33618187.
  13. Wu J, Xu G, Shen L, et al. Daily sleep duration and risk of metabolic syndrome among middle-aged and older Chinese adults: cross-sectional evidence from the Dongfeng-Tongji cohort study. BMC Public Health. 2015; 15: 178, doi: 10.1186/s12889-015-1521-z, indexed in Pubmed: 25885456.
  14. Fang W, Li Z, Wu Li, et al. Longer habitual afternoon napping is associated with a higher risk for impaired fasting plasma glucose and diabetes mellitus in older adults: results from the Dongfeng-Tongji cohort of retired workers. Sleep Med. 2013; 14(10): 950–954, doi: 10.1016/j.sleep.2013.04.015, indexed in Pubmed: 23831240.
  15. Mantua J, Spencer RMC. Exploring the nap paradox: are mid-day sleep bouts a friend or foe? Sleep Med. 2017; 37: 88–97, doi: 10.1016/j.sleep.2017.01.019, indexed in Pubmed: 28899546.
  16. Leng Y, Redline S, Stone KL, et al. Objective napping, cognitive decline, and risk of cognitive impairment in older men. Alzheimers Dement. 2019; 15(8): 1039–1047, doi: 10.1016/j.jalz.2019.04.009, indexed in Pubmed: 31227429.
  17. Wang N, Zou J, Fang S, et al. Association between daytime napping and obesity in Chinese middle-aged and older adults. J Glob Health. 2020; 10(2): 020804, doi: 10.7189/jogh.10.020804, indexed in Pubmed: 33312510.
  18. Yamada T, Shojima N, Yamauchi T, et al. J-curve relation between daytime nap duration and type 2 diabetes or metabolic syndrome: A dose-response meta-analysis. Sci Rep. 2016; 6: 38075, doi: 10.1038/srep38075, indexed in Pubmed: 27909305.
  19. Léger D, Torres MJ, Bayon V, et al. The association between physical and mental chronic conditions and napping. Sci Rep. 2019; 9(1): 1795, doi: 10.1038/s41598-018-37355-3, indexed in Pubmed: 30741949.
  20. van der Pal KC, Koopman ADM, Lakerveld J, et al. The association between multiple sleep-related characteristics and the metabolic syndrome in the general population: the New Hoorn study. Sleep Med. 2018; 52: 51–57, doi: 10.1016/j.sleep.2018.07.022, indexed in Pubmed: 30278295.
  21. He J, Ouyang F, Qiu D, et al. Association of Nap Duration after Lunch with Prevalence of Metabolic Syndrome in a Chinese Government Employee Population. Int J Environ Res Public Health. 2020; 17(12), doi: 10.3390/ijerph17124268, indexed in Pubmed: 32549270.
  22. Ning G. Reaction Study Group. Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. J Diabetes. 2012; 4(2): 172–173, doi: 10.1111/j.1753-0407.2012.00182.x, indexed in Pubmed: 22221801.
  23. Bi Y, Lu J, Wang W, et al. Cohort profile: risk evaluation of cancers in Chinese diabetic individuals: a longitudinal (REACTION) study. J Diabetes. 2014; 6(2): 147–157, doi: 10.1111/1753-0407.12108, indexed in Pubmed: 24237858.
  24. Peng K, Chen G, Liu C, et al. REACTION Study Group, REACTION Study Group. Glycemic status and chronic kidney disease in Chinese adults: Findings from the REACTION study. J Diabetes. 2017; 9(9): 837–845, doi: 10.1111/1753-0407.12490, indexed in Pubmed: 27734593.
  25. Moriarty DG, Zack MM, Kobau R. The Centers for Disease Control and Prevention’s Healthy Days Measures - population tracking of perceived physical and mental health over time. Health Qual Life Outcomes. 2003; 1: 37, doi: 10.1186/1477-7525-1-37, indexed in Pubmed: 14498988.
  26. Wang L, Gao P, Zhang M, et al. Prevalence and Ethnic Pattern of Diabetes and Prediabetes in China in 2013. JAMA. 2017; 317(24): 2515–2523, doi: 10.1001/jama.2017.7596, indexed in Pubmed: 28655017.
  27. Grundy SM, Cleeman JI, Daniels SR, et al. American Heart Association, National Heart, Lung, and Blood Institue. Diagnosis and management of the metabolic syndrome. An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Executive summary. Cardiol Rev. 2005; 13(6): 322–327, indexed in Pubmed: 16708441.
  28. Yang L, Xu Z, He M, et al. Sleep Duration and Midday Napping with 5-Year Incidence and Reversion of Metabolic Syndrome in Middle-Aged and Older Chinese. Sleep. 2016; 39(11): 1911–1918, doi: 10.5665/sleep.6214, indexed in Pubmed: 27450688.
  29. Iftikhar IH, Donley MA, Mindel J, et al. Sleep Duration and Metabolic Syndrome. An Updated Dose-Risk Metaanalysis. Ann Am Thorac Soc. 2015; 12(9): 1364–1372, doi: 10.1513/AnnalsATS.201504-190OC, indexed in Pubmed: 26168016.
  30. Smiley A, King D, Bidulescu A. The Association between Sleep Duration and Metabolic Syndrome: The NHANES 2013/2014. Nutrients. 2019; 11(11), doi: 10.3390/nu11112582, indexed in Pubmed: 31717770.
  31. Harlow SD, Gass M, Hall JE, et al. STRAW + 10 Collaborative Group, STRAW 10 Collaborative Group, STRAW + 10 Collaborative Group, STRAW+10 Collaborative Group. Executive summary of the Stages of Reproductive Aging Workshop +10: addressing the unfinished agenda of staging reproductive aging. Climacteric. 2012; 15(2): 105–114, doi: 10.3109/13697137.2011.650656, indexed in Pubmed: 22338612.
  32. Baker FC, Willoughby AR, Sassoon SA, et al. Insomnia in women approaching menopause: Beyond perception. Psychoneuroendocrinology. 2015; 60: 96–104, doi: 10.1016/j.psyneuen.2015.06.005, indexed in Pubmed: 26142241.
  33. Polo-Kantola P. Sleep problems in midlife and beyond. Maturitas. 2011; 68(3): 224–232, doi: 10.1016/j.maturitas.2010.12.009, indexed in Pubmed: 21295422.
  34. de Zambotti M, Colrain IM, Baker FC. Interaction between reproductive hormones and physiological sleep in women. J Clin Endocrinol Metab. 2015; 100(4): 1426–1433, doi: 10.1210/jc.2014-3892, indexed in Pubmed: 25642589.
  35. Joffe H, Crawford S, Economou N, et al. A gonadotropin-releasing hormone agonist model demonstrates that nocturnal hot flashes interrupt objective sleep. Sleep. 2013; 36(12): 1977–1985, doi: 10.5665/sleep.3244, indexed in Pubmed: 24293774.
  36. Irwin MR, Olmstead R, Carroll JE. Sleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation. Biol Psychiatry. 2016; 80(1): 40–52, doi: 10.1016/j.biopsych.2015.05.014, indexed in Pubmed: 26140821.
  37. Kanmani S, Kwon M, Shin MK, et al. Association of C-Reactive Protein with Risk of Developing Type 2 Diabetes Mellitus, and Role of Obesity and Hypertension: A Large Population-Based Korean Cohort Study. Sci Rep. 2019; 9(1): 4573, doi: 10.1038/s41598-019-40987-8, indexed in Pubmed: 30872696.
  38. Pan Z, Huang M, Huang J, et al. Association of napping and all-cause mortality and incident cardiovascular diseases: a dose-response meta analysis of cohort studies. Sleep Med. 2020; 74: 165–172, doi: 10.1016/j.sleep.2020.08.009, indexed in Pubmed: 32858276.
  39. Smolensky MH, Hermida RC, Castriotta RJ, et al. Role of sleep-wake cycle on blood pressure circadian rhythms and hypertension. Sleep Med. 2007; 8(6): 668–680, doi: 10.1016/j.sleep.2006.11.011, indexed in Pubmed: 17383936.
  40. Borel AL. Sleep Apnea and Sleep Habits: Relationships with Metabolic Syndrome. Nutrients. 2019; 11(11), doi: 10.3390/nu11112628, indexed in Pubmed: 31684029.
  41. Zhang Z, Cajochen C, Khatami R. Social Jetlag and Chronotypes in the Chinese Population: Analysis of Data Recorded by Wearable Devices. J Med Internet Res. 2019; 21(6): e13482, doi: 10.2196/13482, indexed in Pubmed: 31199292.
  42. Yadav D, Lee MiY, Kim JY, et al. A prospective study of serum adiponectin and regression of metabolic syndrome: The ARIRANG study. Biochem Biophys Res Commun. 2015; 466(2): 201–205, doi: 10.1016/j.bbrc.2015.09.007, indexed in Pubmed: 26361142.
  43. Okamoto Y, Kihara S, Funahashi T, et al. Adiponectin: a key adipocytokine in metabolic syndrome. Clin Sci (Lond). 2006; 110(3): 267–278, doi: 10.1042/CS20050182, indexed in Pubmed: 16464169.