Vol 73, No 6 (2022)
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Association of sleep duration and sleep quality with the risk of metabolic syndrome in adults: a systematic review and meta-analysis

Jingyao Hu1, Xiaoyue Zhu1, Defu Yuan1, Dakang Ji1, Haijian Guo2, You Li1, Zhiliang He1, Hexiang Bai1, Qiuqi Zhu1, Chenye Shen1, Haonan Ma1, Fangteng Fu1, Bei Wang1
Pubmed: 35971927
Endokrynol Pol 2022;73(6):968-987.

Abstract

Introduction: The association between sleep duration and metabolic syndrome (MetS) remains controversial, and few have considered the effects of sleep quality. We performed a meta-analysis to clarify the relationship of sleep duration and sleep quality with the risk of MetS.

Material and methods: We conducted a systematic and comprehensive literature search of electronic databases from inception to 17 February 2022. The effect sizes of covariates from each study were pooled using a random or fixed model, and a restricted cubic spline random-effects meta-analysis was performed to examine the dose-response relationship between sleep duration and MetS.

Results: A total of 62 studies were included in this meta-analysis. Compared to normal sleep duration, short sleep duration [odds ratio (OR) = 1.14, 95% confidence interval (CI): 1.10–1.19] and long sleep duration (OR = 1.15, 95% CI: 1.09–1.23) were associated with an increased risk of MetS. The restricted cubic spline analysis indicated that sleep durations of 8.5 h (OR = 0.95, 95% CI: 0.92–0.97) and 11 h (OR = 1.58, 95% CI: 1.31–1.91) were significantly associated with the risk of MetS. The pooled results showed that poor sleep quality (OR = 1.46, 95% CI: 1.03–2.06) and sleep complaints had significant positive associations with MetS.

Conclusion: Our results demonstrated that short sleep duration increased the risk of developing MetS. Long sleep duration was also associated with MetS, especially for 11 h. 8.5 h can be considered the recommended sleep duration for MetS. Poor sleep quality and sleep complaints were also associated with MetS.

Review

Endokrynologia Polska

DOI: 10.5603/EP.a2022.0058

ISSN 0423–104X, e-ISSN 2299–8306

Volume/Tom 73; Number/Numer 6/2022

Association of sleep duration and sleep quality with the risk of metabolic syndrome in adults: a systematic review and meta-analysis

Jingyao Hu1Xiaoyue Zhu1Defu Yuan1Dakang Ji1Haijian Guo2You Li1Zhiliang He1Hexiang Bai1Qiuqi Zhu1Chenye Shen1Haonan Ma1Fangteng Fu1Bei Wang1
1Key Laboratory of Environment Medicine and Engineering of the Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China
2Integrated Business Management Office, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, Jiangsu Province, China

Bei Wang, Key Laboratory of Environment Medicine and Engineering of the Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu province, China; e-mail: wangbeilxb@163.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 association between sleep duration and metabolic syndrome (MetS) remains controversial, and few have considered the effects of sleep quality. We performed a meta-analysis to clarify the relationship of sleep duration and sleep quality with the risk of MetS.
Material and methods: We conducted a systematic and comprehensive literature search of electronic databases from inception to 17 February 2022. The effect sizes of covariates from each study were pooled using a random or fixed model, and a restricted cubic spline random-effects meta-analysis was performed to examine the dose-response relationship between sleep duration and MetS.
Results: A total of 62 studies were included in this meta-analysis. Compared to normal sleep duration, short sleep duration [odds ratio (OR) = 1.14, 95% confidence interval (CI): 1.101.19] and long sleep duration (OR = 1.15, 95% CI: 1.091.23) were associated with an increased risk of MetS. The restricted cubic spline analysis indicated that sleep durations of 8.5 h (OR = 0.95, 95% CI: 0.920.97) and 11 h (OR = 1.58, 95% CI: 1.311.91) were significantly associated with the risk of MetS. The pooled results showed that poor sleep quality (OR = 1.46, 95% CI: 1.032.06) and sleep complaints had significant positive associations with MetS.
Conclusion: Our results demonstrated that short sleep duration increased the risk of developing MetS. Long sleep duration was also associated with MetS, especially for 11 h. 8.5 h can be considered the recommended sleep duration for MetS. Poor sleep quality and sleep complaints were also associated with MetS. (Endokrynol Pol 2022; 73 (6): 968–987)
Key words: sleep duration; sleep quality; metabolic syndrome; meta-analysis

Introduction

Metabolic syndrome (MetS), characterised by a cluster of interdependent risk factors including abdominal obesity, hypertension, hyperglycaemia, and dyslipidaemia [1], is significantly correlated with an increased risk of cardiovascular disease, cancer, and mortality [2–6]. The prevalence of MetS is rapidly growing worldwide; 34.7% of the adult population in the US and 33.9% in China suffer from MetS [7, 8]. Thus, the early identification and control of modifiable risk factors associated with the development of MetS are vital to public health.

Sleep is a state of energy restoration and replenishment that accounts for approximately one-third of a human’s lifetime [9]. With growing mental stress in society, sleep problems are becoming increasingly severe and have attracted widespread attention. Sleep duration and quality are the most important aspects of sleep profiles [10, 11]. Several studies have shown that sleep duration and sleep quality not only have additive effects on health outcomes, but can also independently have different effects on health [12, 13], indicating that the potential differences in health effects between the 2 independent domains of sleep assessment should be considered. Current research shows that poor sleep is related to adverse outcomes such as obesity, diabetes, cardiovascular disease, and all-cause mortality [9, 10, 14–17], which suggests that it might be associated with an increased risk of MetS.

Several epidemiological studies have investigated the association between sleep duration and MetS, with inconsistent results. The findings of a meta-analysis published recently summarised 38 articles reporting that short and long sleep duration were associated with a high prevalence of MetS, presenting a U-shaped” relationship by merging the effect sizes of different sleep groups [18], while others only found an association between short sleep duration and MetS [19–21]. The relationship between long sleep duration and MetS remains controversial [18–23]. Although previous reviews have evaluated the association between sleep duration and MetS, the dose-response association remains unclear. Additionally, many studies have focused only on sleep duration, and few have considered the influence of sleep quality [24]. Since more eligible original studies have been conducted, a reanalysis of the relationship between sleep duration and MetS is required, and specific dose-response relationships should also be verified. Therefore, we performed a systematic meta-analysis combining 53 articles to update the relationship between sleep duration and MetS, and then evaluated the association between sleep quality and MetS for the included studies involving overall sleep quality or sleep disorders, which has rarely been mentioned in previous studies.

Material and methods

The present study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines [25]. This meta-analysis was registered in the PROSPERO database (registration number: CRD42021251459).

Search strategy

We systematically searched electronic databases, including PubMed, Embase, Ovid, and Web of Science, from inception to 17 February 2022. The search strategy to identify all possible studies involved the use of the following terms: “sleep” and “syndrome X” or “metabolic syndromes” or “metabolic syndromes X” or “insulin resistance syndrome” or “MS” or “MetS”; and the literature search was limited to English language. In addition, we screened the reference lists of all obtained articles and relevant reviews, and manually searched for other studies that met the inclusion criteria to avoid missing relevant articles.

Study selection

Studies were included in this meta-analysis if they satisfied the following criteria: (1) the population comprised only adults (age ≥ 18 years); (2) used a cross-sectional or cohort design; (3) defined MetS as the outcome variable and sleep duration as the exposure variable; and (4) reported usable risk estimates and 95% confidence interval (CI) or sufficient data for calculation. The exclusion criteria were as follows: (1) review or meta-analysis; (2) editorials; (3) only defined components of MetS as outcome variables; (4) no information on sleep duration can be extracted; (5) combined with other diseases; and (6) the criteria for MetS were unclear or absent. In addition, if multiple articles were conducted in the same population, only the article with the largest sample was included in further analysis.

Data extraction and quality assessment

Two independent researchers (JY Hu and XY Zhu) separately assessed the articles for compliance with the inclusion/exclusion criteria and resolved disagreements by reaching a consensus, with adjudication by a third researcher (B Wang) if a discrepancy persisted. The following contents were collected: (1) name of the first author; (2) publication year; (3) study design; (4) country of origin; (5) participant characteristics (age and sex); (6) number of subjects in MetS cases and healthy controls; (7) measurements (sleep measurements and MetS criteria); (8) categories of sleep duration; (9) risk estimates and 95% CI; and (10) the covariates used in adjustment.

The quality of the included studies was evaluated according to the Newcastle-Ottawa Quality Assessment Scale (NOS), which carefully scrutinised 3 aspects: selection, comparability, and exposure [26]. Total scores of 0–3, 4–6, and 7-9 were considered low, moderate, and high quality, respectively.

Definitions

Because the sleep duration categories varied among different cultures and ethnicities [27], the definitions of short, normal, and long sleep durations were derived from the original articles. If sleep duration was divided into more than 3 groups, short or long sleep duration was defined as the shortest or longest range, as reported previously. In addition, we classified the source of sleep duration into 2 categories: night-time sleep duration and 24-h sleep duration (including night-time sleep and daytime naps). If the study reported both night-time and 24-h sleep duration, only the night-time sleep duration was used. Sleep duration was assessed using 3 methods: self-administered questionnaires (interview or self-reported), standard questionnaires [Pittsburgh Sleep Quality Index (PSQI)], and objective sleep measurements (polysomnography, accelerometry, or wrist actigraph).

Outcome measures

The primary outcome of this meta-analysis was the association between sleep duration and the risk of MetS. For the original articles that reported at least 4 levels of sleep categories, we performed a restricted cubic spline random-effects meta-analysis to assess the dose-response relationship between sleep duration and MetS. Daily sleep duration of 7–8 h was used as the reference group based on most of the original articles [28]. The secondary outcome was the association between sleep quality and risk of developing MetS. Among the included studies, articles referring to overall sleep quality or sleep complaints, including use of sleep medication, difficulty falling asleep, difficulty maintaining sleep, early morning awakening, insomnia symptoms, and sleep-related breathing disorders, were used to examine the relationship between sleep quality and MetS.

Statistical analysis

The heterogeneity between studies was tested using Q statistics (p < 0.1 indicates statistical significance) and I2 index (I2 > 50% indicates statistical significance). A random [29] or fixed [30] effects model was used to pool the effect sizes of covariates from each study according to heterogeneity. In this meta-analysis, we used the odds ratios (ORs), hazard ratios (HRs), and risk ratios (RRs) with corresponding 95% CIs, adjusted for most covariates according to the original articles. The HRs reported in cohort studies were regarded as RRs. Furthermore, several sensitivity analyses were conducted to evaluate the stability of the results, and publication bias was assessed using funnel plots and Begg’s test [31].

We conducted subgroup analyses based on the study design, ethnicity, sex, study population, source of sleep duration, measurements of sleep duration, MetS criteria, and reference of sleep duration on the association between sleep duration and the risk of MetS. A restricted cubic spline random-effects meta-analysis with 4 knots at fixed percentiles (5, 35, 65, and 95%) of the distribution was performed to examine the dose-response relationship between sleep duration and MetS. STATA 15.0 (version 15.1, StataCorp, College Station, TX) was used for all analyses, and a two-sided p ≤ 0.05 was considered as statistically significant.

Results

Characteristics of studies

The study selection process is illustrated in Figure 1. A total of 7132 potentially relevant articles were identified through literature and manual search. A total of 2141 duplicate articles were excluded by preliminary screening, and 4771 articles were excluded due to apparent irrelevance after reading the titles and abstracts. After reviewing 220 full texts, 53 articles were involved, including 46 cross-sectional studies and 7 cohort studies. Since the data of 8 studies were provided by sex and one by menopausal status, they were considered as separate studies in the subsequent data analysis. Therefore, 62 studies (874,367 participants) were included in the final meta-analysis, of which 54 were cross-sectional studies and 8 were cohort studies [32–84]. In cross-sectional studies, 40 studies assessed short sleep duration using self-administered questionnaires, 5 used PSQI, and 3 used objective sleep measurements. For the measurements of long sleep duration, 38 studies used self-administered questionnaires, 3 used PSQI, and 3 used objective sleep measurements. Self-administered questionnaires were used measure short and long sleep durations in the cohort study. Among the 62 included studies, 13 (15 separate studies involving 93,773 participants) reported an association between sleep quality and the risk of MetS (48, 49, 52, 55, 56, 63, 68, 70-72, 74, 75, 80), which included 14 cross-sectional studies and one cohort study. The primary characteristics of the included studies are presented in Table 1.

223494.png
Figure 1. Flow chart of meta-analysis for exclusion/inclusion studies. OR odds ratio; RRrelative risk; CIconfidence interval

Table 1. Characteristics of included studies

Study

Country

Study design

Participants (N)

Cases (N)

Male (%)

Mean age (range)

Study population

MetS criteriaa

Measurement (sleep duration/sleep quality)

Source of sleep duration

Sleep category [h]

Adjustment covariatesb

Santosa, 2007 [39]

Portugal

Cross-sectional

832

140

100.00

18–92

Community

NCEP ATP III

Self-administered questionnaires

24 h

Short ≤ 6

Ref ≥ 9

1, 5, 6, 7

Santosb, 2007 [39]

Portugal

Cross-sectional

1332

296

0.00

18–92

Community

NCEP ATP III

Self-administered questionnaires

24 h

Short ≤ 6

Ref ≥ 9

1, 5, 6, 7

Choi, 2008 [40]

Korea

Cross-sectional

4222

1100

43.15

44.10 (≥ 20)

Community

NCEP ATP III (modified)

Self-administered questionnaires

Night

Short ≤ 5

Ref 7

Long ≥ 9

1, 2, 3, 5, 6, 7, 8, 13, 14, 15

Hall, 2008 [41]

USA

Cross-sectional

1214

272

46.79

44.40 (30–54)

Community

AHA/NHLBI

Self-administered questionnaires

Night

Short < 6

Ref 7–8

Long > 8

1, 2, 4, 5, 6, 8, 12, 25

Kobayashi, 2011 [42]

Japan

Cross-sectional

44,452

3876

49.46

44.80 (≥ 20)

Hospital

Japanese criteria

Self-administered questionnaires

Night

Short < 6

Ref ≥ 8

1, 2, 6, 7, 8, 30

Najafian, 2011 [43]

Iran

Cross-sectional

12,492

2936

48.91

38.89 (> 19)

Community

AHA/NHLBI

Self-administered questionnaires

Night

Short ≤ 5

Ref 7–8

Long ≥ 9

1, 2

Arora, 2011 [44]

China

Cross-sectional

29,333

8222

27.59

61.51 (50–96)

Community

AHA/NHLBI

Self-administered questionnaires

24 h

Short < 6

Ref 7–8

Long ≥ 9

1, 2, 5, 6, 7, 8, 11, 19, 23, 26, 30, 31, 32, 37

McCanlies, 2012 [45]

USA

Cross-sectional

98

14

60.20

39.61

Company or office

AHA/NHLBI

Self-administered questionnaires

Night

Short < 6

Ref ≥ 6

1, 2, 5, 6,

Sabanayagam, 2012 [46]

USA

Cross-sectional

6122

2284

51.81

44.63 (≥ 20)

Community

AHA/NHLBI

Self-administered questionnaires

Night

Short ≤ 5

Ref 7

Long ≥ 9

1, 2, 4, 5, 6, 7, 8, 12

Wua, 2012 [47]

China

Cross-sectional

4298

760

100.00

20–90

Hospital

NCEP ATP III (modified)

Self-administered questionnaires

Night

Short < 6

Ref 6–8

Long > 8

1, 3, 5, 6, 7, 8

Wub, 2012 [47]

China

Cross-sectional

2802

275

0.00

20–90

Hospital

NCEP ATP III (modified)

Self-administered questionnaires

Night

Short < 6

Ref 6–8

Long > 8

1, 3, 5, 6, 7, 8

Lee, 2013 [48]

Korea

Cross-sectional

301

106

62.13

50.80 (≥ 20)

Hospital

NCEP ATP III (modified)

PSQI/PSQI

24h

Short ≤ 5

Ref 7

Long ≥ 9

1, 2, 5, 6, 7, 8, 12, 15, 16, 17

Yoo, 2013 [49]

USA

Cross-sectional

106

35

82.08

42.30 (22–60)

Company or office

AHA/NHLBI (modified)

PSQI/PSQI

Night

Short ≤ 6

Ref 6–8

Long ≥ 8

1, 2, 6, 8, 12, 17, 38

Chaput, 2013 [50]

Canada

Cross-sectional

810

199

42.96

40.05 (18–65)

Community

AHA/NHLBI

Self-administered questionnaires

Night

Short ≤ 6

Ref 7–8

Long ≥ 9

1, 2, 5, 6, 7, 8, 9, 15, 29

Stefani, 2013 [51]

Korea

Cross-sectional

24,511

6103

40.79

20–79

Community

AHA/NHLBI

Self-administered questionnaires

24 h

Short ≤ 5

Ref 7

Long ≥ 9

1, 2, 3, 5, 6, 7, 8, 16

Ikeda, 2014 [52]

Japan

Cross-sectional

3936

757

40.45

56.50 (≥ 20)

Community

Japanese criteria

Self-administered questionnaires/self-administered questionnaires

24 h

Short ≤ 5

Ref 6–7

Long ≥ 8

1, 2, 6, 7, 8, 17, 27

Yu, 2014 [53]

China

Cross-sectional

11,496

4488

46.18

53.82 (≥ 35)

Community

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Ref ≤ 7

Long ≥ 9

1, 4, 5, 6, 7, 8, 10, 13, 15, 27, 28, 44

Saleh, 2014 [54]

USA

Cross-sectional

1371

512

56.16

49.00 (≥ 20)

Community

AHA/NHLBI

Objective (accelerometry)

Night

Short 3.0–7.2

Ref 7.2–8.6

Long 9.7–11.8

1, 2, 4, 5, 6, 7, 8,1 8, 29, 39, 40

Okuboa, 2014 [55]

Japan

Cross-sectional

549

105

100.00

57.10 (20–80)

Community

Japanese criteria

PSQI/PSQI

Night

Short < 5

Ref > 7

1, 6, 7, 8, 12, 41

Okubob, 2014 [55]

Japan

Cross-sectional

932

63

0.00

57.90 (20–80)

Community

Japanese criteria

PSQI/PSQI

Night

Short < 5

Ref > 7

1, 6, 7, 8, 12, 41

Chang, 2015 [56]

China

Cross-sectional

796

195

100.00

37.36 (20–60)

Company or office

AHA/NHLBI (modified)

Self-administered questionnaires/ PSQI

24 h

Short < 5

Ref 7–8

Long ≥ 8

1, 6, 7, 8, 25, 32, 35

Canuto, 2015 [57]

Brazil

Cross-sectional

902

84

34.04

31.00 (18–50)

Company or office

AHA/NHLBI

Self-administered questionnaires

Night

Short < 5

Ref ≥ 5

1, 4, 5, 6, 7, 8, 10, 15, 27, 35

Wua, 2015 [58]

China

Cross-sectional

11,370

2720

100.00

63.60

Company or office

IDF

Self-administered questionnaires

24 h

Short < 7

Ref 7–8

Long ≥ 10

1, 3, 5, 6, 7, 8, 10, 13

Wub, 2015 [58]

China

Cross-sectional

13,814

5,326

0.00

63.60

Company or office

IDF

Self-administered questionnaires

24 h

Short < 7

Ref 7–8

Long ≥ 10

1, 3, 5, 6, 7, 8, 10, 13

Haba-Rubio, 2015 [59]

Switzerland

Cross-sectional

2162

659

48.84

58.40 (35–75)

Community

NCEP ATP III

Objective (polysomnography)

Night

Short < 6

Ref 7–8

Long ≥ 8

1, 2, 6, 7, 8, 12, 30

Lim, 2015 [60]

Korea

Cross-sectional

1728

N/A

34.14

51.20 (> 20)

Hospital

NCEP ATP III

Self-administered questionnaires

N/A

Ref < 5

Long >8

1, 2, 5, 6, 7, 17, 18

Xiao, 2016 [61]

China

Cross-sectional

20,502

4422

34.13

54.20 (18–74)

Community

AHA/NHLBI

Self-administered questionnaires

24 h

Ref ≤ 7

Long > 8

1, 2, 3, 5, 6, 7, 8, 10, 15, 16, 27, 28, 39, 40

Min, 2016 [62]

Korea

Cross-sectional

8505

1338

0.00

43.30 (20–75)

Community

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Short ≤ 5

Ref 7

Long ≥ 9

1, 3, 5, 6, 7, 8, 15, 27

Lin, 2016 [63]

China

Cross-sectional

4197

880

46.41

47.94

Community

AHA/NHLBI

Self-administered questionnaires/ ISAI

Night

Short < 7

Ref 7–8

Long ≥ 9

1, 2, 6, 27

Rao, 2016 [64]

Canada

Cross-sectional

2901

551

N/A

≥ 18

Community

AHA/NHLBI

Self-administered questionnaires

24 h

Short < 7

Ref 7–8

Long ≥ 8

1, 2, 5, 6, 7, 15

Yoon, 2016 [65]

Korea

Cross-sectional

72,673

19,125

33.48

40–69

Hospital

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Short < 6

Ref 6–7

Long ≥ 10

Cole, 2017 [66]

Ghana

Cross-sectional

263

52

41.06

47.60 (≥ 25)

Community

AHA/NHLBI

Objective (wrist actigraphs)

Night

Short < 7

Ref 7–8

Long >8

1, 2, 8, 12, 14, 18, 33

Suliga, 2017 [67]

Poland

Cross-sectional

10,367

5333

29.48

37–66

Community

AHA/NHLBI

Self-administered questionnaires

Night

Short ≤ 6

Ref 7–8

Long ≥ 9

1, 2, 5, 6, 7, 8, 10, 14, 29, 36, 40

Zohal, 2017 [68]

Iran

Cross-sectional

1079

330

46.43

40.08 (20–72)

Community

AHA/NHLBI

Self-administered questionnaires/PSQI

Night

Short < 6

Ref 6–8

Long >8

1, 2, 3

Kim a, 2018 [69]

Korea

Cross-sectional

44,930

13,072

100.00

53.60 (40–69)

Community

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Short < 6

Ref 6–8

Long ≥ 10

1, 5, 6, 7, 8, 9, 16, 27

Kim b, 2018 [69]

Korea

Cross-sectional

88,678

21,754

0.00

52.30 (40–69)

Community

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Short < 6

Ref 6–8

Long ≥ 10

1, 5, 6, 7, 8, 9, 16, 27

van der Pal, 2018 [70]

Netherlands

Cross-sectional

1679

447

47.41

60.80 (40–75)

Community

NCEP ATP III

Self-administered questionnaires/ESS

Night

Short < 7

Ref 7–8

Long ≥ 9

1, 2, 3, 5, 6, 8, 12, 16

Titova, 2018 [71]

Sweden

Cross-sectional

19,691

4941

43.43

60.80

Community

AHA/NHLBI

Self-administered questionnaires/self-administered questionnaires

24 h

Short ≤ 6

Ref 7–8

Long ≥ 9

1, 2, 5, 6, 7, 8

Ostadrahimi, 2018 [72]

Iran

Cross-sectional

14,916

5104

44.75

35–70

Community

AHA/NHLBI

PSQI/PSQI

24 h

Short < 6

Ref 6–9

Long >9

1, 2, 3, 10, 14, 39, 42

Kima, 2019 [73]

Korea

Cross-sectional

2049

579

100.00

19–64

Company or office

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Ref < 6

Long ≥ 8

1, 2, 3, 5, 6, 7, 8, 16, 17, 18, 27, 41

Kimb, 2019 [73]

Korea

Cross-sectional

2617

442

0.00

19–64

Company or office

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Ref < 6

Long ≥ 8

1, 2, 3, 5, 6, 7, 8, 16, 17, 18, 27, 41

Qian, 2019 [74]

China

Cross-sectional

4579

919

48.05

67.64 (≥ 60)

Community

NCEP ATP III (modified)

Self-administered questionnaires/ Self-administered questionnaires

Night

Short < 7

Ref 7–8

Long ≥ 11

1, 2, 5, 6, 7, 15, 27, 28, 42

Gastona, 2019 [75]

USA

Cross-sectional

13,988

787

0.00

46.80 (35–74)

Community

AHA/NHLBI

Self-administered questionnaires/ Self-administered questionnaires

N/A

Short < 7

Ref 7–9

1, 5, 6, 7, 8, 12, 15, 27, 30

Gastonb, 2019 [75]

USA

Cross-sectional

24,019

3725

0.00

59.80 (35–74)

Community

AHA/NHLBI

Self-administered questionnaires/ Self-administered questionnaires

N/A

Short < 7

Ref 7–9

1, 5, 6, 7, 8, 12, 15, 27, 30

Fan, 2020 [76]

China

Cross-sectional

8272

2503

40.57

51.50 (≥ 18)

Community

Chinese criteria

Self-administered questionnaires

24 h

Short < 6

Ref 6–9

Long > 9

Xu, 2020 [77]

China

Cross-sectional

20,862

2926

47.28

43.40 (18–80)

Community

AHA/NHLBI

Self-administered questionnaires

24 h

Short < 6

Ref ≥ 6

1, 2, 3, 4, 6, 7, 13, 16, 27

Lu, 2020 [78]

China

Cross-sectional

4144

1509

100.00

47.04 (> 18)

Company or office

Chinese criteria

Self-administered questionnaires

N/A

Short < 6

Ref 7

Long > 8

1, 35, 6, 7, 8, 12

Ghazizadeh, 2020 [79]

Iran

Cross-sectional

9652

3859

39.96

48.01

Community

IDF

Self-administered questionnaires

24 h

Short < 6

Ref 6–8

Long > 8

1, 3, 5, 6, 8, 15, 16, 42, 43

Wang, 2021 [80]

China

Cross-sectional

7052

1533

46.87

45.70 (18–64)

Community

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Short < 7

Ref 7–9

Long > 9

1, 2, 5, 6, 7, 8, 14, 27

Aryannejad, 2021 [81]

Iran

Cross-sectional

30504

9742

35.75

41.70 (20–65)

Community

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Ref ≤ 5

Long ≥ 10

1, 2, 4, 5, 6 ,8, 13, 14, 45

Li, 2021 [82]

China

Cross-sectional

4785

209

50.20

≥ 65

Community

Chinese criteria

Self-administered questionnaires

Night

Short < 7

Ref 7–8

Long > 8

1, 2, 5, 6, 7, 8, 10, 11, 14, 45, 46, 47, 48, 49

Feng, 2021 [83]

USA

Cross-sectional

11,181

2917

48.39

≥ 16

Community

AHA/NHLBI

Self-administered questionnaires

Night

Short < 7

Ref 7–8

Long ≥ 8

1, 2, 4, 5, 6, 7, 10, 40, 45

Katsuura-Kamanoa, 2021 [84]

Japan

Cross-sectional

14,907

3371

100.00

54.60 (35–69)

Community

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Short < 6

Ref 6–8

Long ≥ 8

1, 5, 6, 7, 8, 27, 50, 51

Katsuura-Kamanob, 2021 [84]

Japan

Cross-sectional

14,873

1562

0.00

53.80 (35–69)

Community

NCEP ATP III (modified)

Self-administered questionnaires

24 h

Short < 6

Ref 6–8

Long ≥ 8

1, 5, 6, 7, 8, 9, 27, 50, 51

Choia, 2011 [32]

Korea

Cohort

386

82

100.00

48.97 (40–70)

Community

AHA/NHLBI (modified)

Self-administered questionnaires

Night

Short < 6

Ref 6–8

Long ≥ 10

1, 3, 6, 7, 8

Choib, 2011 [32]

Korea

Cohort

721

122

0.00

48.23 (40–70)

Community

AHA/NHLBI (modified)

Self-administered questionnaires

Night

Short < 6

Ref 6–8

Long ≥ 10

1, 3, 6, 7, 8, 9

Kim, 2015 [33]

Korea

Cohort

2579

558

35.40

55.75 (40–70)

Community

AHA/NHLBI (modified)

Self-administered questionnaires

24 h

Short < 6

Ref 6–8

Long ≥ 10

1, 2, 5, 6, 7, 8, 27

Li, 2015 [34]

China

Cohort

4774

1506

52.28

50.22 (30–65)

Community

AHA/NHLBI

Self-administered questionnaires

Night

Short < 6

Ref 7–8

Long ≥ 9

1, 2, 5, 6, 7, 8, 11, 13, 17, 19, 21, 22, 26, 30, 31, 32, 33

Song, 2016 [35]

China

Cohort

15,753

6302

82.77

47.33 (19–98)

Hospital

AHA/NHLBI (modified)

Self-administered questionnaires

Night

Short ≤ 5.5

Ref 7

Long ≥ 8.5

1, 2, 3, 5, 6, 7, 8, 10, 15, 32, 34

Itani, 2017 [36]

Japan

Cohort

39,182

6622

100.00

42.40 (18–65)

Company or office

Japanese criteria

Self-administered questionnaires

24 h

Short < 5

Ref ≥ 5

1, 6, 7, 11, 27, 35, 36

Deng, 2017 [37]

China

Cohort

162,121

24,637

47.41

20–80

Community

AHA/NHLBI

Self-administered questionnaires

24 h

Short < 6

Ref 6–8

Long >8

1, 2, 3, 5, 6, 7, 8, 10, 19, 20, 21, 22, 23, 24, 26

Wang, 2021 [38]

China

Cohort

3005

406

51.48

71.31 (≥ 60)

Community

NCEP ATP III (modified)

Self-administered questionnaires/self-administered questionnaires

Night

Short ≤ 6

Ref 7–8

Long ≥ 9

1, 2, 5, 6, 7

Sleep duration and MetS

Compared with normal sleep duration, short sleep duration (OR = 1.14, 95% CI: 1.10–1.19, p < 0.001) and long sleep duration (OR = 1.15, 95% CI: 1.09–1.23, p < 0.001) were significantly associated with an increased risk of MetS. Short sleep duration was related to the risk of MetS both in cross-sectional studies (OR = 1.13, 95% CI: 1.08–1.18, p < 0.001) and cohort studies (RR = 1.21, 95% CI: 1.10–1.34, p < 0.001). However, a significant relationship was observed only between long sleep duration and the risk of MetS in cross-sectional studies (OR = 1.15, 95% CI: 1.08–1.22, p < 0.001), and no association was found in cohort studies (RR = 1.21, 95% CI: 0.93–1.58, p = 0.150) (Fig. 2).

223655.png
Figure 2. Forest plots of associated with sleep duration and the risk of metabolic syndrome. A. Short sleep duration; B. Long sleep duration
Subgroup analysis of sleep duration and MetS

The results of subgroup analyses are presented in Table 2. In cross-sectional studies, there were significant associations between short or long sleep duration and the risk of MetS in Caucasians and Asians; however, no association was detected in Africans (Tab. 2). After stratification by sex, short sleep duration increased the risk of MetS both in men (RR = 1.08, 95% CI: 1.03–1.13, p = 0.004) and women (RR = 1.80, 95% CI: 1.06–3.05, p = 0.030) among cohort studies, and a significant association was observed only in men (OR = 1.26, 95% CI: 1.11–1.42, p < 0.001) among cross-sectional studies. Moreover, the correlation between long sleep duration and the risk of MetS in both men and women was only found in cross-sectional studies (Tab. 2). The summary ORs/RRs and their 95% CI indicated that short sleep duration was associated with the risk of MetS among the community, hospital, and company or office populations for both cross-sectional and cohort studies. Similar findings were also found in cross-sectional studies on the relationship between long sleep duration and an increased risk of MetS (Tab. 2). Additionally, we observed that short sleep duration defined by night-time or 24-h sleep duration was related to the risk of MetS in both cross-sectional and cohort studies. Long sleep duration defined by 24-h sleep duration (OR = 1.16, 95% CI: 1.08–1.25, p < 0.001) was associated with increased prevalence of MetS in cross-sectional studies, while the association between long sleep duration defined by night-time sleep duration (RR = 1.42, 95% CI: 1.18–1.71, p < 0.001) and increased prevalence of MetS was found in cohort studies (Tab. 2). Furthermore, subgroup analysis stratified by measurements of sleep duration revealed a significant relationship between either short (OR = 1.14, 95% CI: 1.08–1.19, p < 0.001) or long sleep duration (OR = 1.15, 95% CI: 1.08–1.23, p < 0.001) recorded by self-administered questionnaires and MetS in cross-sectional studies, while the association was not found in PSQI and objective sleep measurements (Tab. 2).

Table 2. Subgroup meta-analyses of cross-sectional and cohort studies

Subgroups

Short sleep duration

Long sleep duration

N

OR/RR (95% CI)

pa

I2 (%)

pb

N

OR/RR (95% CI)

pa

I2 (%)

pb

Cross–sectional studies

Ethnicity

Caucasian

17

1.12 (1.04–1.20)

0.004

66.60

< 0.001

11

1.15 (1.08–1.22)

< 0.001

43.70

0.059

Asian

30

1.14 (1.07–1.21)

< 0.001

83.60

< 0.001

32

1.16 (1.07–1.24)

< 0.001

77.00

< 0.001

African

1

0.75 (0.32–1.76)

0.510

0.00

0.000

1

0.86 (0.37–2.00)

0.727

0.00

0.000

Sex

Men

8

1.26 (1.11–1.42)

< 0.001

55.60

0.027

7

1.10 (1.03–1.18)

0.040

51.40

0.055

women

9

1.02 (0.93–1.13)

0.666

73.60

< 0.001

6

1.28 (1.19–1.38)

< 0.001

53.50

0.057

Study population

Community

36

1.09 (1.04–1.13)

< 0.001

75.00

< 0.001

32

1.12 (1.05–1.19)

0.001

72.90

< 0.001

Hospital

5

1.31 (1.12–1.54)

0.001

64.50

0.024

5

1.41 (1.27–1.58)

< 0.001

28.90

0.229

Company or office

7

1.36 (1.20–1.55)

< 0.001

38.50

0.135

7

1.26 (1.05–1.51)

0.014

56.10

0.034

Source of sleep duration

24 h

22

1.07 (1.01–1.13)

0.021

82.80

< 0.001

24

1.16 (1.08–1.25)

< 0.001

76.40

< 0.001

Night-time

23

1.24 (1.10–1.38)

< 0.001

73.90

< 0.001

18

1.11 (0.98–1.26)

0.100

61.50

< 0.001

Measurements of sleep duration

Self–administered questionnaires

40

1.14 (1.08–1.19)

< 0.001

81.00

< 0.001

38

1.15 (1.08–1.23)

< 0.001

73.80

< 0.001

PSQI

5

2.37 (0.88–6.35)

0.087

77.00

0.002

3

2.78 (0.82–9.49)

0.103

78.30

0.010

Objective sleep measurements

3

0.92 (0.73–1.16)

0.474

0.00

0.876

3

0.96 (0.74–1.25)

0.768

0.00

0.000

Cohort studies

Sex

Men

2

1.08 (1.03–1.13)

0.004

21.30

0.260

1

1.57 (0.61–4.03)

0.348

0.00

0.000

Women

1

1.80 (1.06–3.05)

0.030

0.00

0.000

1

1.66 (0.71–3.89)

0.241

0.00

0.000

Study population

Community

6

1.37 (1.09–1.73)

0.007

80.70

< 0.001

6

1.22 (0.88–1.69)

0.229

74.80

0.001

Hospital

1

1.22 (1.00–1.49)

0.049

0.00

0.000

1

1.24 (0.93–1.66)

0.146

0.00

0.000

Company or office

1

1.08 (1.03–1.14)

0.003

0.00

0.000

Source of sleep duration

24 h

3

1.09 (1.06–1.12)

< 0.001

38.00

0.199

2

0.93 (0.87–0.98)

0.011

19.00

0.267

Night-time

5

1.43 (1.12–1.82)

0.004

58.80

0.046

5

1.42 (1.18–1.71)

< 0.001

0.00

0.640

Dose-response relationship between sleep duration and MetS

The results of the restricted cubic spline random-effects meta-analysis, which included 22 studies (16 cross-sectional studies and 6 cohort studies), demonstrated a nonlinear relationship between sleep duration and MetS (p < 0.001). Compared with normal sleep duration (7–8 h per day), 8.5 h (OR = 0.95, 95% CI: 0.92–0.97) and 11 h (OR = 1.58, 95% CI: 1.31–1.91) were significantly associated with the risk of MetS. Similar findings were also observed in cross-sectional studies (p < 0.001) (Fig. 3). However, for cohort studies, the restricted cubic spline analysis presented a nonlinear relationship (p < 0.001), in which 5.25 h and 11 h were positively related to MetS and 8.5 h was negatively related to MetS, compared with the normal sleep duration. The combined ORs of MetS were 1.08 (95% CI: 1.01–1.16) for 5.25 h, 0.84 (95% CI: 0.77–0.93) for 8.5 h and 2.58 (95% CI: 1.25–5.33) for 11 h, respectively (Fig. 3).

223558.png
Figure 3. The dose-response relationship between sleep duration and the risk of metabolic syndrome. OR odds ratio; RRrelative risk

Sleep quality and MetS

Among the 15 studies included to assess the association between sleep quality and the risk of MetS, 8 studies reported overall sleep quality, and 7 reported sleep complaints. To measure overall sleep quality, PSQI was used in 6 studies, and 2 used self-administered questionnaires. The total PSQI scores ranged from 0 to 21 points, with higher scores indicating poorer sleep quality. PSQI scores greater than 5 points were defined as poor sleep quality [85].

As shown in Table 3, the pooled results indicated that poor sleep quality increased the risk of MetS (OR = 1.46, 95% CI: 1.03–2.06, p = 0.033), and this relationship was observed in cross-sectional studies (OR = 1.54, 95% CI: 1.02–2.32, p = 0.041) and was not found in cohort studies (RR = 1.20, 95% CI: 0.82–1.76, p = 0.349). Furthermore, sleep complaints, including use of medication, difficulty falling asleep, difficulty maintaining sleep, and sleep-related breathing disorder, had significant positive associations with MetS (Tab. 3). Excluding one cohort study, the findings of subgroup analysis stratified by sex revealed that overall sleep quality was related to the risk of MetS in women among cross-sectional studies (OR = 2.71, 95% CI: 1.45–5.07, p = 0.002); in contrast, no association was found in men (OR = 1.31, 95% CI: 0.44–3.89, p = 0.625). Additionally, we only observed a significant relationship between overall sleep quality evaluated by PSQI (OR = 1.76, 95% CI: 1.03–3.01, p = 0.039) and the risk of MetS. Our study failed to detect an association between overall sleep quality and MetS risk in different ethnicities (Tab. 3).

Table 3. Meta-analyses of sleep quality and the risk of metabolic syndrome (MetS)

Variables

N

OR/RR (95% CI)

pa

I2 (%)

pb

Overall sleep quality

8

1.46 (1.03–2.06)

0.033

78.30

< 0.001

Study design

Cross-sectional

7

1.54 (1.02–2.32)

0.041

81.30

< 0.001

Cohort

1

1.20 (0.82–1.76)

0.349

0.00

0.000

Sleep complaints

Use of sleep medication

2

1.32 (1.14–1.52)

< 0.001

0.00

0.625

Difficulty falling asleep

3

1.12 (1.06–1.19)

< 0.001

0.00

0.588

Difficulty maintaining sleep

3

1.20 (1.02–1.40)

0.028

67.20

0.047

Early morning awakening

1

1.07 (0.86–1.33)

0.536

0.00

0.000

Insomnia symptoms

2

1.05 (0.74–1.51)

0.771

70.80

0.064

Sleep-related breathing disorder

2

1.62 (1.25–2.11)

< 0.001

85.50

0.009

Subgroup analyses (cross-sectional)*

Sex

Men

2

1.31 (0.44–3.89)

0.625

88.30

0.003

Women

1

2.71 (1.45–5.07)

0.002

0.00

0.000

Ethnicity

Caucasian

1

2.25 (0.70–7.21)

0.172

0.00

0.000

Asian

6

1.49 (0.97–2.30)

0.069

83.80

< 0.001

Measurements of sleep quality

Self–administered questionnaires

1

0.95 (0.74–1.22)

0.688

0.00

0.000

PSQI

6

1.76 (1.03–3.01)

0.039

82.70

< 0.001

Publication bias and sensitivity analysis

The NOS scores of the 62 eligible studies were all5 (Supplementary File Tab. S1). No publication bias was found for the association between short and long sleep durations and the risk of MetS, which was identified by funnel plots and Begg’s text (short sleep duration: p = 0.445; long sleep duration: p = 0.673) (Supplementary File Fig. S1). Sensitivity analyses were conducted to confirm the robustness of the results.

Because the 7 original articles (8 studies) included subjects of more than one race [41, 45, 46, 54, 57, 75, 83], we conducted a sensitivity analysis by removing these studies. After excluding the 8 studies, the effect did not change substantially in short (OR = 1.13, 95% CI: 1.08–1.18, p < 0.001) and long sleep duration (OR = 1.15, 95% CI: 1.09–1.23, p < 0.001). In the study reported by Gaston et al. [75], the results were presented according to menopausal status and only provided data for short sleep duration. After excluding the study, the pooled OR still showed a stable association (short sleep duration: OR = 1.14, 95% CI: 1.09–1.19, p < 0.001). For the definition of MetS, the original data of 11 articles (13 studies) were adopted as non-internationally recognised criteria for MetS [36, 38, 42, 49, 52, 55, 74, 76, 78, 82, 84]. Subgroup analyses stratified by the criteria for MetS indicated significant associations between short and long sleep duration and the risk of MetS, which was defined by the modified NECP ATP-III criteria and the AHA/NHLBI criteria in cross-sectional studies (Supplementary File Tab. S2 and S3). After deleting studies using non-internationally recognised criteria for MetS, the results did not present any major changes (short sleep duration: OR = 1.11, 95% CI: 1.07–1.16, p < 0.001; long sleep duration: OR = 1.15, 95% CI: 1.07–1.23, p < 0.001). Although the reference of sleep duration selected in the included studies was different, most of them were about 7–8 h, which presented a significant relationship between short (7–8 h: OR = 1.14, 95% CI: 1.05–1.24, p = 0.003) (Supplementary File Tab. S2) and long sleep duration with MetS (7–8 h: OR = 1.14, 95% CI: 1.04–1.26, p = 0.005) (Supplementary File Tab. S3).

Discussion

The correlation between sleep duration and the risk of MetS has been controversial [18–22]. Our meta-analysis included 62 studies with more than 870,000 participants. To the best of our knowledge, this is the most comprehensive study that provides quantitative pooled estimates of the associations of sleep quantity and quality with the risk of MetS in adults, in which we updated the relationship between sleep duration and MetS, and evaluated the association between sleep quality and MetS for the included studies involving overall sleep quality or sleep disorders. Our findings demonstrate that short sleep duration is significantly associated with an increased risk of MetS, and long sleep duration is more likely to lead to the development of MetS, especially for 11 h. Additionally, 8.5 h presented a decreased risk for MetS. Furthermore, we found that poor sleep quality and sleep complaints were significantly positively correlated with MetS.

Previous meta-analyses have indicated that short sleep duration is related to an increased risk of MetS in cross-sectional and cohort studies [18, 21, 22], consistent with our findings. Several meta-analyses have shown that short sleep duration is associated with components of MetS, such as obesity, hypertension, and diabetes [86–89], which demonstrate a positive association between short sleep duration and the risk of MetS. Several possible underlying mechanisms may explain the correlation between short sleep duration and MetS. Short sleep duration is associated with reduced leptin and elevated ghrelin levels [90, 91], which leads to increased appetite, facilitates the development of obesity, and impairs glycaemic control [71, 92, 93]. Cortisol levels also increased with reduced sleep [94, 95], and elevated tumour necrosis factor alpha (TNF-a), interleukin (IL-6), and C-reactive protein (CRP) levels could partially explain insulin resistance and the rise in blood pressure [96–99]. Although the mechanisms of the inflammatory state that occurs after short sleep duration are still unclear, increased sympathetic activity is probably involved [100, 101].

The effects of long sleep duration remain uncertain [19, 20, 22]. Although the latest meta-analysis found an association between long sleep duration and MetS in cohort studies [23], the evidence of this study was limited, and the results may not be reliable because they mistakenly included cross-sectional studies as cohort studies in the meta-analysis. Additionally, the results of the latest original studies have been inconsistent [38, 78–80]. Our meta-analysis only observed a relationship between long sleep duration and MetS in cross-sectional studies. The exact mechanisms for the association between long sleep duration and MetS are not fully understood. Epidemiological studies have indicated that it is related to sleep fragmentation, fatigue, and depression [102–105], which could lead to MetS and increase the need for sleep. Increased levels of IL-6 and CRP have also been observed in long sleepers [99, 106, 107]. Additionally, individuals with long sleep duration may compress the waking time of physical activity and have a higher propensity toward unhealthy behaviour [108, 109], thus influencing the overall well-being of adults, attributed to obesity, hypertension, and diabetes, all of which can trigger MetS [110, 111]. We theorise that habitual long sleep may elicit a proinflammatory metabolic state, combined with an unhealthy lifestyle, which may create optimal conditions for the development and progression of MetS. The definitions of short and long sleep duration vary in different studies, complicating the interpretation of the results. Therefore, whether short or long sleep duration is a real cause of MetS should be investigated and verified in other populations.

We conducted a restricted cubic spline random-effects meta-analysis to further explore the correlation between precise sleep duration and MetS. The previous meta-analysis reported by Ju et al. [22] indicated a U-shaped” association between sleep duration and the risk of MetS assessed by the restricted cubic spline in 8 cross-sectional studies, in which5 h, 5.5 h, 6 h, 6.5 h, 8 h, 8.5 h, 9 h, and 9.5 h positively related to the risk of MetS compared with 7 h per day. However, our findings demonstrated that sleep of 11 h was associated with an increased risk of developing MetS, and 8.5 h presented a decreased risk for MetS, consistent with the optimal sleep duration for adults recommended by the National Sleep Foundation (18–64 years: 7–9 h;65 years: 7–8 h) [28]. Compared with the study by Ju et al. [22], our study involved more articles, including 16 cross-sectional studies and 6 cohort studies, which increased the credibility of the results. Although the restricted cubic spline analysis may strengthen the plausibility of a causal association, the estimates of risk in this approach are slightly less accurate than in the individual patient data meta-analyses because their calculations depend on the means, median, or midpoints of the sleep duration categories [22, 112, 113]. Different sleep duration categories may also partially contribute to the discrepancy in the results. Therefore, more original studies on the relationship between precise sleep duration and MetS should be conducted and included in subsequent meta-analysis updates to provide a scientific basis for the development of clear guidelines in the future.

We conducted comprehensive subgroup analyses of cross-sectional and cohort studies. Previous meta-analyses reported that no difference between sex was observed between sleep duration and MetS [19, 22]. However, we only observed a significant relationship between short sleep duration and increased risk of MetS in men, consistent with a recent meta-analysis by Xie et al. [18]. The potential mechanisms of sex subgroup differences in the relationship are unclear, but sex discrepancies in insulin sensitivity may partially explain this finding. Many aspects of energy balance and glucose metabolism have been regulated differently in men and women [114], and men are less sensitive to insulin than equally fit women [115]. In addition, related studies have also demonstrated that a lack of sleep is related to decreased insulin sensitivity and glucose tolerance in men [116]. The underlying mechanisms of sex subgroup differences require further investigation. For the measurements of sleep duration, previous meta-analyses found no distinction between sleep duration recorded by subjective or objective measurements and MetS [21]. However, in this study, we observed a significant association between either short or long sleep duration recorded by self-administered questionnaires and MetS in cross-sectional studies, while the association was not found in PSQI and objective measurements, which may be due to limited literature inclusion. Subjective and objective measurements are valuable methods for estimating sleep, assessing different and complementary dimensions of sleep [117, 118]. Although subjective measurements, such as questionnaires, may overestimate actual sleep duration [119, 120], it is inexpensive and allows the collection of information related to personal perception of sleep besides timing variables [118, 121], making them practical and widely used. Objective measurements such as polysomnography and accelerometry may provide more valid or accurate measurements, but the machinery may impede natural sleep and thus fail to reflect habitual sleep patterns [115, 122]. Considering the advantages and disadvantages of subjective and objective methods, their combination may be the most effective and practical method for population surveys. Moreover, it is worth mentioning that short sleep duration, defined by night-time or 24-h sleep duration, was associated with the risk of MetS in both cross-sectional and cohort studies. While a significant relationship was observed between MetS and long sleep duration defined by 24-h sleep in cross-sectional studies or long sleep duration defined by night-time sleep in cohort studies, only one of the published meta-analyses reported similar findings [18]. Human hormone levels and mechanisms were different during the day and night [123, 124], but most of the published studies did not distinguish sleep defined by night-time sleep duration and 24-h sleep duration. A meta-analysis reported by Yamada et al. indicated a J-curve” relationship between daytime nap duration and MetS [125], which may confuse the potential association between sleep duration defined by 24-h sleep duration and MetS. Therefore, we suggest using sleep defined by night-time sleep duration to explore the relationship between sleep quality and quantity and MetS, as well as a separate analysis of nap time. In addition, sleep duration on weekdays and rest days should be reported separately.

We found that poor sleep quality emerged as an essential risk factor for MetS, observed only in cross-sectional studies and not found in cohort studies with one study included. Similar findings were detected in a meta-analysis reported by Lian et al. [24]. We also observed that sleep complaints, including use of medication, difficulty falling asleep, difficulty maintaining sleep, and sleep-related breathing disorders, were significantly correlated with MetS. Although the underlying biological mechanisms of the relationship between sleep quality and MetS are not known in detail, a growing body of research indicates that poor sleep quality and sleep complaints could affect energy regulation by upregulating appetite, thereby reducing insulin resistance [126], and alterations of neuroendocrine functioning and inflammation have also been found to be involved [99, 127]. Nevertheless, it remains possible that residual confounders result in an underestimation or overestimation of the association and should be interpreted with caution until further studies have been conducted.

We conducted a much more comprehensive meta-analysis of the relationship between sleep and the risk of MetS, which updated the relationship between sleep duration and MetS, and evaluated the association between sleep quality and MetS for the included studies involving overall sleep quality or sleep disorders, which has rarely been mentioned in previous studies. Our results may be more objective than the latest meta-analysis because they mistakenly treated the cross-sectional studies as cohort studies [23]. In addition, a restricted cubic spline random-effects meta-analysis was performed as a complementary investigation to evaluate the relationship between the exact sleep duration and MetS. Moreover, a series of sensitivity analyses were conducted to make the results more sensible and stable. However, our results should be interpreted with caution due to several limitations. First, most of the original studies included in this meta-analysis measured sleep duration and sleep quality using subjective measurements, and only 3 original studies used objective measurements such as polysomnography and accelerometry. Objective measurements may provide more valid or accurate results but often are not feasible in large prospective population studies. Therefore, a combination of subjective and objective measurements should be considered in future studies. Second, the categories of sleep duration and criteria of MetS varied across countries and studies, and were limited in translating the results of the restricted cubic spline analysis into practical recommendations for the public. Third, all included studies were assessed at a specific time point, and only 15 of them involved sleep quality, which may not sufficiently identify the sustained effect of sleep problems in individuals with MetS. Therefore, investigations of the longitudinal effects of sleep problems, including quality and quantity, are the focus of future research. Despite these limitations, the findings of this meta-analysis provide the latest evidence to evaluate the effects of sleep duration and quality on MetS.

In conclusion, short sleep duration, long sleep duration, and poor sleep quality are associated with the risk of MetS. Additional original studies on the relationship between precise sleep duration and MetS should be conducted to provide a scientific basis for developing clear guidelines in the future.

Conflicts of interest

The authors declare that they have no competing interests.

Funding

This work was supported by the National Key Research and Development Program of China (2016YFC1305700).

Author contributions

All authors certified that they participated in the conceptual design, data analysis, and manuscript writing to take public responsibility for it, and reviewed the final version of the manuscript and approved it for publication.

References

  1. Grundy SM. Metabolic syndrome pandemic. Arterioscler Thromb Vasc Biol. 2008; 28(4): 629–636, doi: 10.1161/ATVBAHA.107.151092, indexed in Pubmed: 18174459.
  2. Kim CJ, Park J, Kang SW. Prevalence of metabolic syndrome and cardiovascular risk level in a vulnerable population. Int J Nurs Pract. 2015; 21(2): 175–183, doi: 10.1111/ijn.12258, indexed in Pubmed: 24666551.
  3. Guembe MJ, Fernandez-Lazaro CI, Sayon-Orea C, et al. RIVANA Study Investigators. Risk for cardiovascular disease associated with metabolic syndrome and its components: a 13-year prospective study in the RIVANA cohort. Cardiovasc Diabetol. 2020; 19(1): 195, doi: 10.1186/s12933-020-01166-6, indexed in Pubmed: 33222691.
  4. Dibaba DT, Ogunsina K, Braithwaite D, et al. Metabolic syndrome and risk of breast cancer mortality by menopause, obesity, and subtype. Breast Cancer Res Treat. 2019; 174(1): 209–218, doi: 10.1007/s10549-018-5056-8, indexed in Pubmed: 30465158.
  5. Gacci M, Russo GI, De Nunzio C, et al. Meta-analysis of metabolic syndrome and prostate cancer. Prostate Cancer Prostatic Dis. 2017; 20(2): 146–155, doi: 10.1038/pcan.2017.1, indexed in Pubmed: 28220805.
  6. O’Neill S, O’Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev. 2015; 16(1): 1–12, doi: 10.1111/obr.12229, indexed in Pubmed: 25407540.
  7. Hirode G, Wong RJ. Trends in the Prevalence of Metabolic Syndrome in the United States, 2011-2016. JAMA. 2020; 323(24): 2526–2528, doi: 10.1001/jama.2020.4501, indexed in Pubmed: 32573660.
  8. Lu J, Wang L, Li M, et al. 2010 China Noncommunicable Disease Surveillance Group. Metabolic Syndrome Among Adults in China: The 2010 China Noncommunicable Disease Surveillance. J Clin Endocrinol Metab. 2017; 102(2): 507–515, doi: 10.1210/jc.2016-2477, indexed in Pubmed: 27898293.
  9. 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.
  10. Lao XQ, Liu X, Deng HB, et al. Sleep Quality, Sleep Duration, and the Risk of Coronary Heart Disease: A Prospective Cohort Study With 60,586 Adults. J Clin Sleep Med. 2018; 14(1): 109–117, doi: 10.5664/jcsm.6894, indexed in Pubmed: 29198294.
  11. Buysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014; 37(1): 9–17, doi: 10.5665/sleep.3298, indexed in Pubmed: 24470692.
  12. Chien KL, Chen PC, Hsu HC, et al. Habitual sleep duration and insomnia and the risk of cardiovascular events and all-cause death: report from a community-based cohort. Sleep. 2010; 33(2): 177–184, doi: 10.1093/sleep/33.2.177, indexed in Pubmed: 20175401.
  13. Rod NH, Kumari M, Lange T, et al. The joint effect of sleep duration and disturbed sleep on cause-specific mortality: results from the Whitehall II cohort study. PLoS One. 2014; 9(4): e91965, doi: 10.1371/journal.pone.0091965, indexed in Pubmed: 24699341.
  14. Beccuti G, Pannain S. Sleep and obesity. Curr Opin Clin Nutr Metab Care. 2011; 14(4): 402–412, doi: 10.1097/MCO.0b013e3283479109, indexed in Pubmed: 21659802.
  15. Cappuccio FP, D’Elia L, Strazzullo P, et al. Quantity and quality of sleep and incidence of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2010; 33(2): 414–420, doi: 10.2337/dc09-1124, indexed in Pubmed: 19910503.
  16. St-Onge MP, Grandner MA, Brown D, et al. American Heart Association Obesity, Behavior Change, Diabetes, and Nutrition Committees of the Council on Lifestyle and Cardiometabolic Health; Council on Cardiovascular Disease in the Young; Council on Clinical Cardiology; and Stroke Council. Sleep Duration and Quality: Impact on Lifestyle Behaviors and Cardiometabolic Health: A Scientific Statement From the American Heart Association. Circulation. 2016; 134(18): e367–e386, doi: 10.1161/CIR.0000000000000444, indexed in Pubmed: 27647451.
  17. Kwok CS, Kontopantelis E, Kuligowski G, et al. Self-Reported Sleep Duration and Quality and Cardiovascular Disease and Mortality: A Dose-Response Meta-Analysis. J Am Heart Assoc. 2018; 7(15): e008552, doi: 10.1161/JAHA.118.008552, indexed in Pubmed: 30371228.
  18. 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.
  19. Xi Bo, He D, Zhang M, et al. Short sleep duration predicts risk of metabolic syndrome: a systematic review and meta-analysis. Sleep Med Rev. 2014; 18(4): 293–297, doi: 10.1016/j.smrv.2013.06.001, indexed in Pubmed: 23890470.
  20. 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.
  21. Hua J, Jiang H, Wang H, et al. Sleep Duration and the Risk of Metabolic Syndrome in Adults: A Systematic Review and Meta-Analysis. Front Neurol. 2021; 12: 635564, doi: 10.3389/fneur.2021.635564, indexed in Pubmed: 33679592.
  22. Ju SY, Choi WS. Sleep duration and metabolic syndrome in adult populations: a meta-analysis of observational studies. Nutr Diabetes. 2013; 3: e65, doi: 10.1038/nutd.2013.8, indexed in Pubmed: 23670223.
  23. Che T, Yan C, Tian D, et al. The Association Between Sleep and Metabolic Syndrome: A Systematic Review and Meta-Analysis. Front Endocrinol (Lausanne). 2021; 12: 773646, doi: 10.3389/fendo.2021.773646, indexed in Pubmed: 34867820.
  24. Lian Y, Yuan Q, Wang G, et al. Association between sleep quality and metabolic syndrome: A systematic review and meta-analysis. Psychiatry Res. 2019; 274: 66–74, doi: 10.1016/j.psychres.2019.01.096, indexed in Pubmed: 30780064.
  25. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372: n71, doi: 10.1136/bmj.n71, indexed in Pubmed: 33782057.
  26. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010; 25(9): 603–605, doi: 10.1007/s10654-010-9491-z, indexed in Pubmed: 20652370.
  27. Adenekan B, Pandey A, McKenzie S, et al. Sleep in America: role of racial/ethnic differences. Sleep Med Rev. 2013; 17(4): 255–262, doi: 10.1016/j.smrv.2012.07.002, indexed in Pubmed: 23348004.
  28. Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s updated sleep duration recommendations: final report. Sleep Health. 2015; 1(4): 233–243, doi: 10.1016/j.sleh.2015.10.004, indexed in Pubmed: 29073398.
  29. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986; 7(3): 177–188, doi: 10.1016/0197-2456(86)90046-2, indexed in Pubmed: 3802833.
  30. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959; 22(4): 719–748, indexed in Pubmed: 13655060.
  31. Begg C, Mazumdar M. Operating Characteristics of a Rank Correlation Test for Publication Bias. Biometrics. 1994; 50(4): 1088, doi: 10.2307/2533446, indexed in Pubmed: 7786990.
  32. Choi JK, Kim MY, Kim JK, et al. Association between short sleep duration and high incidence of metabolic syndrome in midlife women. Tohoku J Exp Med. 2011; 225(3): 187–193, doi: 10.1620/tjem.225.187, indexed in Pubmed: 22001675.
  33. Kim JY, Yadav D, Ahn SV, et al. A prospective study of total sleep duration and incident metabolic syndrome: the ARIRANG study. Sleep Med. 2015; 16(12): 1511–1515, doi: 10.1016/j.sleep.2015.06.024, indexed in Pubmed: 26611949.
  34. Li X, Lin L, Lv L, et al. U-shaped relationships between sleep duration and metabolic syndrome and metabolic syndrome components in males: a prospective cohort study. Sleep Med. 2015; 16(8): 949–954, doi: 10.1016/j.sleep.2015.03.024, indexed in Pubmed: 26116460.
  35. Song Q, Liu X, Zhou W, et al. Changes in sleep duration and risk of metabolic syndrome: the Kailuan prospective study. Sci Rep. 2016; 6: 36861, doi: 10.1038/srep36861, indexed in Pubmed: 27857185.
  36. Itani O, Kaneita Y, Tokiya M, et al. Short sleep duration, shift work, and actual days taken off work are predictive life-style risk factors for new-onset metabolic syndrome: a seven-year cohort study of 40,000 male workers. Sleep Med. 2017; 39: 87–94, doi: 10.1016/j.sleep.2017.07.027, indexed in Pubmed: 29157594.
  37. Deng HB, Tam T, Zee BCY, et al. Short Sleep Duration Increases Metabolic Impact in Healthy Adults: A Population-Based Cohort Study. Sleep. 2017; 40(10), doi: 10.1093/sleep/zsx130, indexed in Pubmed: 28977563.
  38. Wang Y, Qian YX, Liu JH, et al. Longitudinal association between sleep and 5-year incident metabolic syndrome in older Chinese adults: a community-based cohort study. Sleep Med. 2021; 81: 1–7, doi: 10.1016/j.sleep.2021.02.004, indexed in Pubmed: 33621789.
  39. Santos AC, Ebrahim S, Barros H. Alcohol intake, smoking, sleeping hours, physical activity and the metabolic syndrome. Prev Med. 2007; 44(4): 328–334, doi: 10.1016/j.ypmed.2006.11.016, indexed in Pubmed: 17239432.
  40. Choi KM, Lee JS, Park HS, et al. Relationship between sleep duration and the metabolic syndrome: Korean National Health and Nutrition Survey 2001. Int J Obes (Lond). 2008; 32(7): 1091–1097, doi: 10.1038/ijo.2008.62, indexed in Pubmed: 18475274.
  41. Hall MH, Muldoon MF, Jennings JR, et al. Self-reported sleep duration is associated with the metabolic syndrome in midlife adults. Sleep. 2008; 31(5): 635–643, doi: 10.1093/sleep/31.5.635, indexed in Pubmed: 18517034.
  42. Kobayashi D, Takahashi O, Deshpande GA, et al. Relation between metabolic syndrome and sleep duration in Japan: a large scale cross-sectional study. Intern Med. 2011; 50(2): 103–107, doi: 10.2169/internalmedicine.50.4317, indexed in Pubmed: 21245632.
  43. Najafian J, Toghianifar N, Mohammadifard N, et al. Association between sleep duration and metabolic syndrome in a population-based study: Isfahan Healthy Heart Program. J Res Med Sci. 2011; 16(16): 801–806, indexed in Pubmed: 22091310.
  44. Arora T, Jiang CQ, Thomas GN, et al. Self-reported long total sleep duration is associated with metabolic syndrome: the Guangzhou Biobank Cohort Study. Diabetes Care. 2011; 34(10): 2317–2319, doi: 10.2337/dc11-0647, indexed in Pubmed: 21873559.
  45. McCanlies EC, Slaven JE, Smith LM, et al. Metabolic syndrome and sleep duration in police officers. Work. 2012; 43(2): 133–139, doi: 10.3233/WOR-2012-1399, indexed in Pubmed: 22927620.
  46. Sabanayagam C, Zhang R, Shankar A. Markers of Sleep-Disordered Breathing and Metabolic Syndrome in a Multiethnic Sample of US Adults: Results from the National Health and Nutrition Examination Survey 2005-2008. Cardiol Res Pract. 2012; 2012: 630802, doi: 10.1155/2012/630802, indexed in Pubmed: 22577590.
  47. Wu MC, Yang YC, Wu JS, et al. Short sleep duration associated with a higher prevalence of metabolic syndrome in an apparently healthy population. Prev Med. 2012; 55(4): 305–309, doi: 10.1016/j.ypmed.2012.07.013, indexed in Pubmed: 22846501.
  48. Lee J, Choi YS, Jeong YJ, et al. Poor-quality sleep is associated with metabolic syndrome in Korean adults. Tohoku J Exp Med. 2013; 231(4): 281–291, doi: 10.1620/tjem.231.281, indexed in Pubmed: 24305464.
  49. Yoo H, Franke WD. Sleep habits, mental health, and the metabolic syndrome in law enforcement officers. J Occup Environ Med. 2013; 55(1): 99–103, doi: 10.1097/JOM.0b013e31826e294c, indexed in Pubmed: 23207742.
  50. Chaput JP, McNeil J, Després JP, et al. Seven to eight hours of sleep a night is associated with a lower prevalence of the metabolic syndrome and reduced overall cardiometabolic risk in adults. PLoS One. 2013; 8(9): e72832, doi: 10.1371/journal.pone.0072832, indexed in Pubmed: 24039808.
  51. Stefani KM, Kim HC, Kim J, et al. The influence of sex and age on the relationship between sleep duration and metabolic syndrome in Korean adults. Diabetes Res Clin Pract. 2013; 102(3): 250–259, doi: 10.1016/j.diabres.2013.10.003, indexed in Pubmed: 24168829.
  52. Ikeda M, Kaneita Y, Uchiyama M, et al. Epidemiological study of the associations between sleep complaints and metabolic syndrome in Japan. Sleep Biol Rhythms. 2014; 12(4): 269–278, doi: 10.1111/sbr.12071.
  53. Yu S, Guo X, Yang H, et al. An update on the prevalence of metabolic syndrome and its associated factors in rural northeast China. BMC Public Health. 2014; 14: 877, doi: 10.1186/1471-2458-14-877, indexed in Pubmed: 25159694.
  54. Saleh D, Janssen I. Interrelationships among sedentary time, sleep duration, and the metabolic syndrome in adults. BMC Public Health. 2014; 14: 666, doi: 10.1186/1471-2458-14-666, indexed in Pubmed: 24975509.
  55. Okubo N, Matsuzaka M, Takahashi I, et al. Hirosaki University Graduate School of Medicine. Relationship between self-reported sleep quality and metabolic syndrome in general population. BMC Public Health. 2014; 14: 562, doi: 10.1186/1471-2458-14-562, indexed in Pubmed: 24903537.
  56. Chang JH, Huang PT, Lin YK, et al. Association between sleep duration and sleep quality, and metabolic syndrome in Taiwanese police officers. Int J Occup Med Environ Health. 2015; 28(6): 1011–1023, doi: 10.13075/ijomeh.1896.00359, indexed in Pubmed: 26294202.
  57. Canuto R, Pattussi MP, Macagnan JB, et al. Metabolic syndrome in fixed-shift workers. Rev Saude Publica. 2015; 49: 30, doi: 10.1590/s0034-8910.2015049005524, indexed in Pubmed: 26061455.
  58. 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.
  59. Haba-Rubio J, Marques-Vidal P, Andries D, et al. Objective sleep structure and cardiovascular risk factors in the general population: the HypnoLaus Study. Sleep. 2015; 38(3): 391–400, doi: 10.5665/sleep.4496, indexed in Pubmed: 25325467.
  60. Lim W, So WY. Lifestyle-related factors and their association with metabolic syndrome in Korean adults: a population-based study. J Phys Ther Sci. 2015; 27(3): 555–558, doi: 10.1589/jpts.27.555, indexed in Pubmed: 25931679.
  61. Xiao J, Shen C, Chu MJ, et al. Physical Activity and Sedentary Behavior Associated with Components of Metabolic Syndrome among People in Rural China. PLoS One. 2016; 11(1): e0147062, doi: 10.1371/journal.pone.0147062, indexed in Pubmed: 26789723.
  62. Min H, Um YJ, Jang BS, et al. Association between Sleep Duration and Measurable Cardiometabolic Risk Factors in Healthy Korean Women: The Fourth and Fifth Korean National Health and Nutrition Examination Surveys (KNHANES IV and V). Int J Endocrinol. 2016; 2016: 3784210, doi: 10.1155/2016/3784210, indexed in Pubmed: 27956898.
  63. Lin SC, Sun CA, You SL, et al. The Link of Self-Reported Insomnia Symptoms and Sleep Duration with Metabolic Syndrome: A Chinese Population-Based Study. Sleep. 2016; 39(6): 1261–1266, doi: 10.5665/sleep.5848, indexed in Pubmed: 27070137.
  64. Rao DP, Orpana H, Krewski D. Physical activity and non-movement behaviours: their independent and combined associations with metabolic syndrome. Int J Behav Nutr Phys Act. 2016; 13: 26, doi: 10.1186/s12966-016-0350-5, indexed in Pubmed: 26893071.
  65. Yoon HS, Lee KM, Yang J, et al. Associations of sleep duration with metabolic syndrome and its components in adult Koreans: from the Health Examinees Study. Sleep Biol Rhythms. 2016; 14(4): 361–368, doi: 10.1007/s41105-016-0065-7.
  66. Cole HV, Owusu-Dabo E, Iwelunmor J, et al. Sleep duration is associated with increased risk for cardiovascular outcomes: a pilot study in a sample of community dwelling adults in Ghana. Sleep Med. 2017; 34: 118–125, doi: 10.1016/j.sleep.2017.03.008, indexed in Pubmed: 28522079.
  67. Suliga E, Kozieł D, Cieśla E, et al. Sleep duration and the risk of metabolic syndrome a cross-sectional study. Medical Studies. 2017; 3: 169–175, doi: 10.5114/ms.2017.70342.
  68. Zohal M, Ghorbani A, Esmailzadehha N, et al. Association of sleep quality components and wake time with metabolic syndrome: The Qazvin Metabolic Diseases Study (QMDS), Iran. Diabetes Metab Syndr. 2017; 11 Suppl 1: S377–S380, doi: 10.1016/j.dsx.2017.03.020, indexed in Pubmed: 28284911.
  69. Kim CE, Shin S, Lee HW, et al. Association between sleep duration and metabolic syndrome: a cross-sectional study. BMC Public Health. 2018; 18(1): 720, doi: 10.1186/s12889-018-5557-8, indexed in Pubmed: 29895272.
  70. 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.
  71. Titova OE, Lindberg E, Elmståhl S, et al. Associations Between the Prevalence of Metabolic Syndrome and Sleep Parameters Vary by Age. Front Endocrinol (Lausanne). 2018; 9: 234, doi: 10.3389/fendo.2018.00234, indexed in Pubmed: 29867766.
  72. Ostadrahimi A, Nikniaz Z, Faramarzi E, et al. Does long sleep duration increase risk of metabolic syndrome in Azar cohort study population? Health Promot Perspect. 2018; 8(4): 290–295, doi: 10.15171/hpp.2018.41, indexed in Pubmed: 30479983.
  73. Kim KY, Yun JM. Analysis of the association between health-related and work-related factors among workers and metabolic syndrome using data from the Korean National Health and Nutrition Examination Survey (2016). Nutr Res Pract. 2019; 13(5): 444–451, doi: 10.4162/nrp.2019.13.5.444, indexed in Pubmed: 31583064.
  74. Qian YX, Liu JH, Ma QH, et al. Associations of sleep durations and sleep-related parameters with metabolic syndrome among older Chinese adults. Endocrine. 2019; 66(2): 240–248, doi: 10.1007/s12020-019-02064-y, indexed in Pubmed: 31473919.
  75. Gaston SA, Park YM, McWhorter KL, et al. Multiple poor sleep characteristics and metabolic abnormalities consistent with metabolic syndrome among white, black, and Hispanic/Latina women: modification by menopausal status. Diabetol Metab Syndr. 2019; 11: 17, doi: 10.1186/s13098-019-0413-2, indexed in Pubmed: 30815038.
  76. Fan L, Hao Z, Gao Li, et al. Non-linear relationship between sleep duration and metabolic syndrome: A population-based study. Medicine (Baltimore). 2020; 99(2): e18753, doi: 10.1097/MD.0000000000018753, indexed in Pubmed: 31914097.
  77. Xu T, Zhu G, Han S. Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey. Medicine (Baltimore). 2020; 99(44): e22883, doi: 10.1097/MD.0000000000022883, indexed in Pubmed: 33126337.
  78. Lu K, Zhao Y, Chen J, et al. Interactive association of sleep duration and sleep quality with the prevalence of metabolic syndrome in adult Chinese males. Exp Ther Med. 2020; 19(2): 841–848, doi: 10.3892/etm.2019.8290, indexed in Pubmed: 32010244.
  79. Ghazizadeh H, Mobarra N, Esmaily H, et al. The association between daily naps and metabolic syndrome: Evidence from a population-based study in the Middle-East. Sleep Health. 2020; 6(5): 684–689, doi: 10.1016/j.sleh.2020.03.007, indexed in Pubmed: 32482574.
  80. Wang MH, Shi T, Li Q, et al. Associations of sleep duration and fruit and vegetable intake with the risk of metabolic syndrome in Chinese adults. Medicine (Baltimore). 2021; 100(10): e24600, doi: 10.1097/MD.0000000000024600, indexed in Pubmed: 33725823.
  81. Aryannejad A, Eghtesad S, Rahimi Z, et al. Metabolic syndrome and lifestyle-associated factors in the ethnically diverse population of Khuzestan, Iran: a cross-sectional study. J Diabetes Metab Disord. 2021; 20(1): 747–756, doi: 10.1007/s40200-021-00811-5, indexed in Pubmed: 34222089.
  82. Li W, Kondracki AJ, Sun N, et al. Nighttime sleep duration, daytime napping, and metabolic syndrome: findings from the China Health and Retirement Longitudinal Study. Sleep Breath. 2021 [Epub ahead of print], doi: 10.1007/s11325-021-02487-w, indexed in Pubmed: 34729674.
  83. Feng X, Wu W, Zhao F, et al. Relationship between self-reported sleep duration during week-/work-days and metabolic syndrome from NHANES 2013 to 2016. Sleep Breath. 2021 [Epub ahead of print], doi: 10.1007/s11325-021-02522-w, indexed in Pubmed: 34780004.
  84. Katsuura-Kamano S, Arisawa K, Uemura H, et al. Japan Multi-Institutional Collaborative Cohort J-MICC Study. Association of skipping breakfast and short sleep duration with the prevalence of metabolic syndrome in the general Japanese population: Baseline data from the Japan Multi-Institutional Collaborative cohort study. Prev Med Rep. 2021; 24: 101613, doi: 10.1016/j.pmedr.2021.101613, indexed in Pubmed: 34976669.
  85. Buysse D, Reynolds C, Monk T, et al. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989; 28(2): 193–213, doi: 10.1016/0165-1781(89)90047-4, indexed in Pubmed: 2748771.
  86. Itani O, Jike M, Watanabe N, et al. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. Sleep Med. 2017; 32: 246–256, doi: 10.1016/j.sleep.2016.08.006, indexed in Pubmed: 27743803.
  87. Johnson KA, Gordon CJ, Chapman JL, et al. The association of insomnia disorder characterised by objective short sleep duration with hypertension, diabetes and body mass index: A systematic review and meta-analysis. Sleep Med Rev. 2021; 59: 101456, doi: 10.1016/j.smrv.2021.101456, indexed in Pubmed: 33640704.
  88. Wang Y, Mei H, Jiang YR, et al. Relationship between Duration of Sleep and Hypertension in Adults: A Meta-Analysis. J Clin Sleep Med. 2015; 11(9): 1047–1056, doi: 10.5664/jcsm.5024, indexed in Pubmed: 25902823.
  89. Zhou Q, Zhang M, Hu D. Dose-response association between sleep duration and obesity risk: a systematic review and meta-analysis of prospective cohort studies. Sleep Breath. 2019; 23(4): 1035–1045, doi: 10.1007/s11325-019-01824-4, indexed in Pubmed: 30941582.
  90. Taheri S, Lin L, Austin D, et al. Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med. 2004; 1(3): e62, doi: 10.1371/journal.pmed.0010062, indexed in Pubmed: 15602591.
  91. Spiegel K, Tasali E, Penev P, et al. Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann Intern Med. 2004; 141(11): 846–850, doi: 10.7326/0003-4819-141-11-200412070-00008, indexed in Pubmed: 15583226.
  92. Chaput JP, Després JP, Bouchard C, et al. Short sleep duration is associated with reduced leptin levels and increased adiposity: Results from the Quebec family study. Obesity (Silver Spring). 2007; 15(1): 253–261, doi: 10.1038/oby.2007.512, indexed in Pubmed: 17228054.
  93. Spiegel K, Knutson K, Leproult R, et al. Sleep loss: a novel risk factor for insulin resistance and Type 2 diabetes. J Appl Physiol (1985). 2005; 99(5): 2008–2019, doi: 10.1152/japplphysiol.00660.2005, indexed in Pubmed: 16227462.
  94. Spiegel K, Leproult R, Cauter EV. Impact of sleep debt on metabolic and endocrine function. Lancet. 1999; 354(9188): 1435–1439, doi: 10.1016/s0140-6736(99)01376-8, indexed in Pubmed: 10543671.
  95. Wright KP, Drake AL, Frey DJ, et al. Influence of sleep deprivation and circadian misalignment on cortisol, inflammatory markers, and cytokine balance. Brain Behav Immun. 2015; 47: 24–34, doi: 10.1016/j.bbi.2015.01.004, indexed in Pubmed: 25640603.
  96. Vgontzas AN, Zoumakis E, Bixler EO, et al. Adverse effects of modest sleep restriction on sleepiness, performance, and inflammatory cytokines. J Clin Endocrinol Metab. 2004; 89(5): 2119–2126, doi: 10.1210/jc.2003-031562, indexed in Pubmed: 15126529.
  97. Irwin MR, Witarama T, Caudill M, et al. Sleep loss activates cellular inflammation and signal transducer and activator of transcription (STAT) family proteins in humans. Brain Behav Immun. 2015; 47: 86–92, doi: 10.1016/j.bbi.2014.09.017, indexed in Pubmed: 25451613.
  98. Meier-Ewert HK, Ridker PM, Rifai N, et al. Effect of sleep loss on C-reactive protein, an inflammatory marker of cardiovascular risk. J Am Coll Cardiol. 2004; 43(4): 678–683, doi: 10.1016/j.jacc.2003.07.050, indexed in Pubmed: 14975482.
  99. 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.
  100. Tobaldini E, Costantino G, Solbiati M, et al. Sleep, sleep deprivation, autonomic nervous system and cardiovascular diseases. Neurosci Biobehav Rev. 2017; 74(Pt B): 321–329, doi: 10.1016/j.neubiorev.2016.07.004, indexed in Pubmed: 27397854.
  101. Pongratz G, Straub RH. The sympathetic nervous response in inflammation. Arthritis Res Ther. 2014; 16(6): 504, doi: 10.1186/s13075-014-0504-2, indexed in Pubmed: 25789375.
  102. Sun X, Zheng B, Lv J, et al. China Kadoorie Biobank (CKB) Collaborative Group. Sleep behavior and depression: Findings from the China Kadoorie Biobank of 0.5 million Chinese adults. J Affect Disord. 2018; 229: 120–124, doi: 10.1016/j.jad.2017.12.058, indexed in Pubmed: 29306691.
  103. Zhai L, Zhang H, Zhang D. Sleep duration and depression among adults:A meta-analysis of prospective studies. Depress Anxiety. 2015; 32(9): 664–670, doi: 10.1002/da.22386, indexed in Pubmed: 26047492.
  104. Pan An, Keum N, Okereke OI, et al. Bidirectional association between depression and metabolic syndrome: a systematic review and meta-analysis of epidemiological studies. Diabetes Care. 2012; 35(5): 1171–1180, doi: 10.2337/dc11-2055, indexed in Pubmed: 22517938.
  105. Grandner MA, Drummond SPA. Who are the long sleepers? Towards an understanding of the mortality relationship. Sleep Med Rev. 2007; 11(5): 341–360, doi: 10.1016/j.smrv.2007.03.010, indexed in Pubmed: 17625932.
  106. Williams CJ, Hu FB, Patel SR, et al. Sleep duration and snoring in relation to biomarkers of cardiovascular disease risk among women with type 2 diabetes. Diabetes Care. 2007; 30(5): 1233–1240, doi: 10.2337/dc06-2107, indexed in Pubmed: 17322482.
  107. Patel SR, Zhu X, Storfer-Isser A, et al. Sleep duration and biomarkers of inflammation. Sleep. 2009; 32(2): 200–204, doi: 10.1093/sleep/32.2.200, indexed in Pubmed: 19238807.
  108. Beaman A, Bhide MC, McHill AW, et al. Biological pathways underlying the association between habitual long-sleep and elevated cardiovascular risk in adults. Sleep Med. 2021; 78: 135–140, doi: 10.1016/j.sleep.2020.12.011, indexed in Pubmed: 33429289.
  109. Yan LX, Chen XR, Chen Bo, et al. Gender-specific Association of Sleep Duration with Body Mass Index, Waist Circumference, and Body Fat in Chinese Adults. Biomed Environ Sci. 2017; 30(3): 157–169, doi: 10.3967/bes2017.023, indexed in Pubmed: 28427485.
  110. Ozemek C, Lavie CJ, Rognmo Ø. Global physical activity levels - Need for intervention. Prog Cardiovasc Dis. 2019; 62(2): 102–107, doi: 10.1016/j.pcad.2019.02.004, indexed in Pubmed: 30802461.
  111. Leskinen T, Stenholm S, Heinonen OJ, et al. Change in physical activity and accumulation of cardiometabolic risk factors. Prev Med. 2018; 112: 31–37, doi: 10.1016/j.ypmed.2018.03.020, indexed in Pubmed: 29605421.
  112. Shi JQ, Copas JB. Meta-analysis for trend estimation. Stat Med. 2004; 23(1): 3–19; discussion 159, doi: 10.1002/sim.1595, indexed in Pubmed: 14695636.
  113. Hartemink N, Boshuizen HC, Nagelkerke NJD, et al. Combining risk estimates from observational studies with different exposure cutpoints: a meta-analysis on body mass index and diabetes type 2. Am J Epidemiol. 2006; 163(11): 1042–1052, doi: 10.1093/aje/kwj141, indexed in Pubmed: 16611666.
  114. Tramunt B, Smati S, Grandgeorge N, et al. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia. 2020; 63(3): 453–461, doi: 10.1007/s00125-019-05040-3, indexed in Pubmed: 31754750.
  115. Ancoli-Israel S, Cole R, Alessi C, et al. The role of actigraphy in the study of sleep and circadian rhythms. Sleep. 2003; 26(3): 342–392, doi: 10.1093/sleep/26.3.342, indexed in Pubmed: 12749557.
  116. Wong PM, Manuck SB, DiNardo MM, et al. Shorter sleep duration is associated with decreased insulin sensitivity in healthy white men. Sleep. 2015; 38(2): 223–231, doi: 10.5665/sleep.4402, indexed in Pubmed: 25325485.
  117. Tobaldini E, Fiorelli EM, Solbiati M, et al. Short sleep duration and cardiometabolic risk: from pathophysiology to clinical evidence. Nat Rev Cardiol. 2019; 16(4): 213–224, doi: 10.1038/s41569-018-0109-6, indexed in Pubmed: 30410106.
  118. Aziz M, Ali SS, Das S, et al. Association of Subjective and Objective Sleep Duration as well as Sleep Quality with Non-Invasive Markers of Sub-Clinical Cardiovascular Disease (CVD): A Systematic Review. J Atheroscler Thromb. 2017; 24(3): 208–226, doi: 10.5551/jat.36194, indexed in Pubmed: 27840384.
  119. Sun J, Wang M, Yang L, et al. Sleep duration and cardiovascular risk factors in children and adolescents: A systematic review. Sleep Med Rev. 2020; 53: 101338, doi: 10.1016/j.smrv.2020.101338, indexed in Pubmed: 32619932.
  120. Holzhausen EA, Hagen EW, LeCaire T, et al. A Comparison of Self- and Proxy-Reported Subjective Sleep Durations With Objective Actigraphy Measurements in a Survey of Wisconsin Children 6-17 Years of Age. Am J Epidemiol. 2021; 190(5): 755–765, doi: 10.1093/aje/kwaa254, indexed in Pubmed: 33226072.
  121. Lee SW, Ng KY, Chin WK. The impact of sleep amount and sleep quality on glycemic control in type 2 diabetes: A systematic review and meta-analysis. Sleep Med Rev. 2017; 31: 91–101, doi: 10.1016/j.smrv.2016.02.001, indexed in Pubmed: 26944909.
  122. Quist JS, Sjödin A, Chaput JP, et al. Sleep and cardiometabolic risk in children and adolescents. Sleep Med Rev. 2016; 29: 76–100, doi: 10.1016/j.smrv.2015.09.001, indexed in Pubmed: 26683701.
  123. Morris CJ, Aeschbach D, Scheer FA. Circadian system, sleep and endocrinology. Mol Cell Endocrinol. 2012; 349(1): 91–104, doi: 10.1016/j.mce.2011.09.003, indexed in Pubmed: 21939733.
  124. Dierickx P, Van Laake LW, Geijsen N. Circadian clocks: from stem cells to tissue homeostasis and regeneration. EMBO Rep. 2018; 19(1): 18–28, doi: 10.15252/embr.201745130, indexed in Pubmed: 29258993.
  125. 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.
  126. Pan W, Kastin AJ. Leptin: a biomarker for sleep disorders? Sleep Med Rev. 2014; 18(3): 283–290, doi: 10.1016/j.smrv.2013.07.003, indexed in Pubmed: 24080454.
  127. Castro-Diehl C, Diez Roux AV, Redline S, et al. Association of Sleep Duration and Quality With Alterations in the Hypothalamic-Pituitary Adrenocortical Axis: The Multi-Ethnic Study of Atherosclerosis (MESA). J Clin Endocrinol Metab. 2015; 100(8): 3149–3158, doi: 10.1210/jc.2015-1198, indexed in Pubmed: 26046965.

Submitted: 01.03.2022

Accepted: 19.05.2022

Early publication date: 12.08.2022