Submitted: 10 Jun 2018
Revised: 17 Sep 2018
Accepted: 17 Sep 2018
First published online: 27 Oct 2018
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - FireFox Plugin)

Abstract Page Views: 81
PDF Downloads: 62
Full Text Views: 14
Does long sleep duration increase risk of metabolic syndrome in Azar cohort study population?

Health Promotion Perspectives, 8(4), 290-295; DOI:10.15171/hpp.2018.41

Original Article

Does long sleep duration increase risk of metabolic syndrome in Azar cohort study population?

Alireza Ostadrahimi1, Zeinab Nikniaz2 ,*, Elnaz Faramarzi2, Asghar Mohammadpoorasl3, Khalil Ansarin4, Mohammad Hossein Somi2 ,*

1 Nutrition Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
2 Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
3 Department of Statistics and Epidemiology, Tabriz University of Medical Sciences, Tabriz, Iran
4 Tuberculosis & Lung Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran


© 2018 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background: We decided to assess the correlation between metabolic syndrome (MetS) risks,sleep and napping duration in Azar cohort population according to the increasing incidence of MetS in the world and inconsistence results about sleep duration and MetS. Methods: In this cross-sectional study, MetS and sleep habits of 14916 subjects (35-70 years old) who inhabited in Shabestar city were determined by ATPIII and Pittsburg questionnaire respectively. Inclusion criteria were subjects with 35-70 years old and living in Shabestar for at least 9 months of the year.Results: According to the results, age, living place, body mass index, hypnotic drug use, sleep and napping duration and TV time were the risk factors of MetS. In this regard, long sleep duration (>9 h/24 h), napping (0.25-2 h/day), hypnotic drug use and watching TV (2 h/day)increased the risk of MetS by 1.18 (1.05-1.33), 1.16(1.07-1.26), 1.35(1.13-1.60), and 1.13(1.04-1.23) respectively.Conclusion: According to these results, it appears that proper education for improvement of sleep habit is necessary to reduce incidence of MetS and its consequences. However, there is need for more longitudinal researches and using objective method of sleep habits evaluation for more precise results.

Keywords: Sleep duration, Napping, Metabolic syndrome, Hypnotic drugs

Citation: Ostadrahimi A, Nikniaz Z, Faramarzi E, Mohammadpoorasl A, Ansarin K, Somi MH. 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.


Metabolic syndrome (MetS) includes a set of abnormalities such as hypertension, dyslipidemia, impaired glucose tolerance and abdominal obesity.1 According to the results of recent studies, prevalence of MetS along with obesity is increasing in the world especially in developing countries that may be due to the changes in life styles and food habit. It is estimated that about a quarter of the world population has MetS.2 Traditionally, obesity and sedation life style are considered as the main contributors of the MetS development.3 Nowadays, other new factors such as socio-economic status4 and sleep duration,5 have been proposed as risk factors in metabolic syndrome development.

Recently , Koren and Taveras noted that sleep deprivation and long sleep duration are associated with increasing the risk of central obesity, metabolic syndrome, cardiovascular diseases and mortality.5 While, recent systematic review and meta-analysis studies revealed that the relationship between sleep duration and MetS are inconsistence.6-8 In this regard, Xi et al reported no significant relationship between long sleep duration and MetS.7 Moreover, Iftikhar et al could not confirm a significant correlation between long sleep duration and metabolic syndrome.6 Well designed studies with large sample size are needed to clarify the relationship between sleep duration and MetS on the basis of aforementioned studies.

Considering the increasing incidence of MetS in the world and inconsistent results about the associations between sleep duration and MetS, we decided to assess the correlations between MetS risks, sleep and napping duration in Azar cohort population.

Materials and Methods

Participants and procedures

This cross-sectional study was approved by Ethics Committee of Tabriz University of Medical Sciences. We used a part of Azar’s cohort study data in this study.

All eligible individuals from 35 up to 70 years old in Shabestar region were invited to participate in the study from October 2014 to January 2017, and those who were signed the consent form are included in the Azar cohort study. Those included the inhabitants in Shabestar for at least 9 months. The participants with severe psychiatric or physical illness, body mass index (BMI) <18.5 kg/m2, and pregnant women were excluded from the study. The pilot and enrolment phase recruited further 15 006 participants, giving 16 138 in total. The overall response rate of those invited was equal to 93%. We included 14 916 subjects on the basis of inclusion criteria of the present study. Demographic information of participants was collected by questionnaire including age, gender, marital status, education level, etc. Blood samples were collected after an overnight fast of 12 hours. Fasting blood sugar (FBS), serum triglyceride (TG), high density lipoprotein (HDL) were determined by enzymatic method.

Moreover, height, weight, waist circumference and blood pressure of participants were measured. The method of measurements of mentioned factors has been explained in previously published article9 with more details. Women with waist circumference ≥88 cm and men with waist circumference of ≥102 cm were considered as abdominally obese.10 Subjects with SBP ≥130 and DBP ≥85 or antihypertensive drug treatment in a patient with a history of hypertension is an alternate indicator considered as patients with hypertension.11

Study area

Azar cohort study is a part of a large Persian cohort study (The Prospective Epidemiological Research Studies of the Iranian Adults)9,12 which was launched at October 2014 and finished at January 2017. Azar cohort was established in Shabestar in East Azarbaijan province (North-west of Iran). It is conducted in 3 phases of pilot, enrollment and follow-up.

Shabestar is the capital of Shabestar city, East Azerbaijan province, Iran. At the 2011 census, its population was equal to 25 663, with 4824 families. It is located in proximity to Tabriz, the provincial capital, on the main Iranian-International railway line which connects Tehran and Tabriz to Turkey and Europe. Considering the proximity to Lake Urmia, the city experiences mild weather with wet winters, and summers are somewhat hot and dry during day time and cooler at night.


Socioeconomic status

Socioeconomic status (SES) was defined based on job categories, education levels, and the family assets by using principal component analysis (PCA). SES classified into very high, high, middle, low and very low based on quintuple of obtained scores.

Metabolic syndrome definition

We defined metabolic syndrome according to the National Cholesterol Education Program’s Adult Treatment Panel III report (ATPIII) criteria.13 Subjects with 3 or more, the following conditions were defined as having the MetS: Waist circumference ≥102 cm in men and ≥88 cm in women, TG ≥150 mg/dL (drug treatment for elevated TGs is an alternate indicator), HDL-C <40 mg/dL in men and <50 mg/dL in women. Elevated blood pressure systolic ≥130 and/or diastolic ≥85 mm Hg (antihypertensive drug treatment in a patient with a history of hypertension is an alternate indicator). Elevated fasting glucose ≥100 (drug treatment of elevated glucose is an alternate indicator).

Sleep habits

Sleep habits of population were assessed using Pittsburgh questionnaire. Sleep habits information were presented in 3 forms: 24 hours sleep duration which was a sum of nocturnal and day time sleep hours, night sleep duration showed sleep duration at night and day time napping indicated sleep duration in day. Total sleep duration and night sleep duration was classified into 3 groups: <6 hours, 6-9 hours, and >9 hours. In addition, day time napping was categorized to: subjects, who were not napped, 0.25-2 hours, and >2 hours.

Statistical analysis

Data were reported as mean, ± SD and also number (percent) where applicable. Comparisons were done using χ2 test for the categorical variables between groups (education level, marital status, etc). We used backward logistic regression analysis for estimating adjusted odds ratios (ORs) and their corresponding 95% CI. 6-9 hours’ sleep was considered as reference group in all analyses and those who not napped was considered as reference group in day time napping. Moreover, those who watched TV less than 2 hours and those who not have night shift work were determined as reference group. Data analysis was performed with SPSS Statistics for Windows version 21.0 (IBM Inc., Armonk, NY, USA) and the level of significance was set at 0.05.


As indicated in Table 1, among 14 916 participants, 5104 (34.22%; 95% CI: 33.44% to 34.96%) had MetS which was higher in women rather than men (41.0% vs. 25.8%). The prevalence of MetS among illiterates, those who divorced and those lived in rural area was significantly more than other groups (P < 0.001). Moreover, the prevalence of MetS in low and very low SES classification was significantly higher than other classifications (P < 0.001). Our findings indicated that MetS was more prevalent in those who used hypnotic drugs rather than those who did not use (P < 0.001). The prevalence of MetS in people who were in long sleep duration (>9 hours per night) and napping (>2 h/day) categories was higher than the other groups. In addition, we observed that a greater percentage of subjects who were watched TV over 2 hours had MetS compared to those who watched less than 2 h/day (36.9% vs 32.9; P < 0.001 ) (Table 1).

Table 1. Baseline characteristics of subjects stratified by metabolic syndrome
Metabolic syndrome P value*
No Yes Total
No. % No. % No. %
Men 4951 74.2 1724 25.8 6675 44.8 <0.001
Women 4861 59 3380 41 8241 55.2
Total 9812 65.8 5104 34.2 14916 100
Education level
Illiterate 1315 51.8 1226 48.2 2541 16.85 <0.001
Elementary 4778 65.1 2560 34.9 7338 49.2
Middle school 1472 72 573 28 2045 13.71
Diploma 1259 74.3 436 25.7 1695 11.36
College 988 76.3 307 23.7 1295 8.68
Marital status
Single 199 85.8 33 14.2 232 1.55 <0.001
Married 9194 66.5 4632 33.5 13826 92.69
Divorced 416 48.9 434 51.1 850 5.69
Age groups (y)
35-39 2009 80.5 487 9.5 2496 16.7 <0.001
40-44 1984 75.1 658 12.9 2642 17.7
45-49 1813 67.8 860 16.8 2673 17.9
50-54 1491 61.2 944 38.8 2435 16.3
55-59 1128 54.7 936 45.3 2064 13.8
60-64 832 54.8 685 45.2 1517 10.2
65-72 555 51.0 534 49 1089 7.3
BMI (kg/m2) classification
18.5-24.9 2861 92.4 234 7.6 3095 20.9 <0.001
25-29.9 4347 71.0 1772 29.0 6119 41.3
≥30 2513 44.8 3098 55.2 5611 37.8
Socioeconomic status
Very high 1991 70.2 847 29.8 2838 19.1 <0.001
high 1937 67.7 925 32.3 2862 19.3
Middle 1925 66.3 980 33.7 2905 19.6
low 1903 63.8 1081 36.2 2984 20.1
Very low 2011 62.0 1231 38 3242 21.9
Living place
Urban 6934 66.8 3444 33.2 10378 70 <0.001
rural 2878 63.4 1660 36.6 4538 30
Hypnotic drug use
Yes 399 53.5 347 46.5 746 5 <0.001
no 9413 66.4 4757 33.6 14170 95
Total Sleep duration, h/day
6-9 7733 67.0 3809 33.2 11542 77.37 <0.001
<6 994 63.0 583 37.3 1577 10.5
>9 1085 60.0 712 39.8 1797 12.04
Nocturnal sleep duration, h/day
6-9 7051 66.8 3511 33.2 10562 70.8 <0.001
<6 2358 64.4 1306 35.6 3664 24.6
>9 403 58.4 287 41.6 690 4.62
TV time, h/day
≤2 6793 67.1 3337 32.9 10130 44.40 <0.001
>2 3019 63.1 1767 36.9 4786 55.58
Napping duration, h/day
0 4821 68.3 2236 31.7 7057 47.05 <0.001
0.25-2 4884 63.6 2793 36.4 7677 51.18
>2 107 58.8 75 41.2 182 1.7
Shift work at night
Yes 916 74.6 312 25.4 1228 8.23 <0.001
No 8896 65 4792 35 13688 91.8
Shift work at night (day/year)
0 8895 65.0 4792 35 13687 91.2 <0.001
1-24 688 75.5 223 24.5 911 6.0
≥24 229 72 89 28 318 2.1
* P value: χ2 test.

According to the results of backward logistic regression analysis, age, living place, BMI, hypnotic drug use, sleep and napping duration and TV time were the risk factors of MetS (Table 2). The odds of MetS in women was equal to 1.51 (95% CI: 1.39-1.67) times more than men (P < 0.001). Marriage and divorce in comparison to single, increase in risk of MetS respectively by 1.92 (1.27-2.19) and 2.32 (1.49-3.60) times. We observed that, the risk of MetS increased by increasing the age and BMI. Moreover, long sleep duration (>9 h/24 h), napping (0.25-2 h/day), hypnotic drug use and watching TV (2 h/day) increased the risk of MetS. In this regard, long sleep duration (>9 h/24 h), napping (0.25-2 h/day), hypnotic drug use and watching TV (2 h/day) increased the risk of MetS by 1.18 (1.05-1.33), 1.16 (1.07-1.26), 1.35 (1.13-1.60), and 1.13 (1.04-1.23) respectively.

Table 2. Odds ratios (with 95% CIs) of metabolic syndrome on the basis of sleep habits
Variables Metabolic syndrome P value*
OR 95% CI
Men Reference
Women 1.51 1.39-1.64 <0.001
Marital status
Single Reference
Married 1.92 1.27-2.19 0.002
Divorced 2.32 1.49-3.60 <0.001
Age groups (y)
35-39 Reference
40-44 1.22 1.06-1.41 0.004
45-49 1.68 1.46-1.93 <0.001
50-54 2.41 2.1-2.77 <0.001
55-59 3.30 2.86-3.81 <0.001
60-64 3.51 3.00-4.11 <0.001
65-72 4.58 3.84-5.47 <0.001
Living place
Urban Reference
Rural 1.10 1.01-1.20 0.01
BMI (kg/m2) classification
18.5-24.9 Reference
25-29.9 4.99 4.30-5.78 <0.001
≥30 14.48 12.48-16.80 <0.001
Hypnotic drug use
No Reference
Yes 1.35 1.13-1.60 0.001
Total Sleep duration, h/day
6-9 Reference
<6 0.97 0.85-1.10 0.64
>9 1.18 1.05-1.33 0.006
Napping duration, h/day
0 Reference
0.25-2 1.16 1.07-1.26 <0.001
>2 1.16 0.83-1.64 0.37
TV time, h/day
≤2 Reference
>2 1.13 1.04-1.23 0.002
*P value: backward logistic regression.


Nowadays, prevalence of MetS parallel is increasing worldwide by increase in obesity. So, it can be considered as a silent killer. Moreover, sleep disturbance becomes a common problem in recent years.14 On the other hand, it has been known that sleep duration plays an important role in maintaining a good health.5 In this regard, U shaped association has been reported between sleep duration and increased risk of mortality.15,16

The results of this cross-sectional study with large sample size showed that long sleep duration were significantly increased the risk of MetS which is consistence with Jike et al’s study. They reported that sleep more than 8-9 h/day are significantly associated with cardiovascular disease and mortality.17 The findings of another study indicated that short and long sleep duration increased the risk of metabolic syndrome.8 In contrast to our results, previous studies reported that there is a positive correlation between short sleep duration (<6 h) and MetS components.7,18,19 the discrepancy between our results and other studies may be due to the self-report sleep and different definitions of short or long sleep duration.

Also, we found that, day time napping increased the risk of MetS. In line with these results, Guo et al observed that day time napping over 1 hour increased the risk of type II diabetes.20 In a cohort study on retired worker indicated that long afternoon napping can be considered as a novel risk factor of Type II diabetes. In another population based study, Lin et al reported that napping increased the risk of MetS in non-obese Chinese women.21 Yang et al found that long duration of napping was positively associated with MetS incidence.22

By these mechanisms, long duration of napping which increased the risk of MetS has not been fully understood. On the basis of previous studies, some mechanisms have been proposed for this association. Long duration napping reduced energy expenditure which is linked with obesity and its consequences. Moreover, it has been reported that disrupting of circadian rhythm involved in insulin resistance and diabetes mellitus. On the other hand, evening cortisol concentration elevated in the nap takers, that lead to subjects become predisposed to insulin resistance.

After adjusting the confounder factors, those who watched TV more than 2 hours had 13% (OR 1.13; 1.04-1.23) more risk of MetS compared to those watched ≤2 hours. Of course, it should be noted the observed significance in narrow OR may be due to large sample size. These results are in line with previous studies which conducted on leisure time and cardiometabolic risk factors. Rosique-Esteban et al observed that long watching TV increased the risk of obesity, type II diabetes and metabolic syndrome components.23 Results of another study indicated that less physical activity and more watching TV are significantly associated with MetS.24

In the present study, those who used hypnotic drugs had 35% (OR: 1.35; 1.13-1.60) more risk for metabolic syndrome compared to those who did not use. This finding is supported by previous study which showed there is a positive correlation between hypnotic drug use and MetS.25 The precise mechanism of how hypnotic drug leads to increase in the risk of MetS is not known. It has been assumed that these drugs change life style by reducing physical activity.26 Moreover, most subjects who use hypnotic drugs have other psychiatric problems such as depression27 which may increase the risk of MetS.24

The results of the present study should be interpreted considering the following limitations: (1) the cross-sectional design of the study, which implies that no causal inferences could be made. (2) Using subjective measurements of sleep pattern that may be a potential for misreporting. The strength of this study is the assessment of sleep habits and MetS components in large population.


The findings of present study showed that long sleep duration and napping in 24 hours increased the risk of MetS. Moreover, we found that watching TV more than 2 h/day is significantly associated with MetS. According to these results, it appears that proper education for improvement of sleep habit is necessary to reduce incidence of MetS and its consequences. However, for more precise results, there is need to more longitudinal researches and using objective method of sleep habits evaluation.

Ethical approval

This study was approved by Ethics Committee of Tabriz University of medical sciences (TBZMED.REC.1393.205). After a full explanation of the study was provided, and prior to commencing participation, all participants provided written informed consent.

Competing interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


Liver and gastrointestinal diseases research center, Tabriz University of Medical Sciences.

Authors’ contributions

Conception and design: MHS. Doing the field, experimental, clinical, data collection or compilation work, provided there is also a scholarly input during the process: EF. Data analysis and interpretation: EF, ZN. Preparation of draft manuscript, doing revisions or providing critique: ZN, EF, ARO. Overall and/or sectional scientific management: MHS.


The authors are grateful for the financial support of the liver and gastrointestinal disease research center, Tabriz University of Medical Sciences. The authors also are deeply indebted to all subjects who participated in this study. We appreciate the contribution by the investigators and the staff of the Azar cohort study. We thank the close collaboration of the Shabestar health center. In addition, we would like to thank the Persian cohort study staff for their technical support.


  1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988;37(12):1595-607.
  2. Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep 2018;20(2):12. doi: 10.1007/s11906-018-0812-z. [Crossref]
  3. McCracken E, Monaghan M, Sreenivasan S. Pathophysiology of the metabolic syndrome. Clin Dermatol 2018;36(1):14-20. doi: 10.1016/j.clindermatol.2017.09.004. [Crossref]
  4. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994. Arch Intern Med 2003;163(4):427-36.
  5. Koren D, Taveras EM. Association of sleep disturbances with obesity, insulin resistance and the metabolic syndrome. Metabolism 2018;84:67-75. doi: 10.1016/j.metabol.2018.04.001. [Crossref]
  6. Iftikhar IH, Donley MA, Mindel J, Pleister A, Soriano S, Magalang UJ. Sleep duration and metabolic syndrome. an updated dose-risk metaanalysis. Ann Am Thorac Soc 2015;12(9):1364-72. doi: 10.1513/AnnalsATS.201504-190OC. [Crossref]
  7. Xi B, He D, Zhang M, Xue J, Zhou D. Short sleep duration predicts risk of metabolic syndrome: a systematic review and meta-analysis. Sleep Med Rev 2014;18(4):293-7. doi: 10.1016/j.smrv.2013.06.001. [Crossref]
  8. 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. [Crossref]
  9. Poustchi H, Eghtesad S, Kamangar F, Etemadi A, Keshtkar AA, Hekmatdoost A, et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): Rationale, objectives, and design. Am J Epidemiol 2018;187(4):647-55. doi: 10.1093/aje/kwx314. [Crossref]
  10. WHO. Obesity: preventing and managing the global epidemic: report of a WHO Consultation on Obesity, Geneva, 3-5 June 1997. Geneva: World Health Organization; 1997.
  11. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003;42(6):1206-52. doi: 10.1161/01.HYP.0000107251.49515.c2. [Crossref]
  12. Eghtesad S, Mohammadi Z, Shayanrad A, Faramarzi E, Joukar F, Hamzeh B, et al. The PERSIAN Cohort: Providing the Evidence Needed for Healthcare Reform. Arch Iran Med 2017;20(11):691-5.
  13. Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C. Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 2004;109(3):433-8. doi: 10.1161/01.cir.0000111245.75752.c6. [Crossref]
  14. Sugiyama T, Healy GN, Dunstan DW, Salmon J, Owen N. Joint associations of multiple leisure-time sedentary behaviours and physical activity with obesity in Australian adults. Int J Behav Nutr Phys Act 2008;5:35. doi: 10.1186/1479-5868-5-35. [Crossref]
  15. Patel SR, Hu FB. Short sleep duration and weight gain: a systematic review. Obesity (Silver Spring) 2008;16(3):643-53. doi: 10.1038/oby.2007.118. [Crossref]
  16. Ferrie JE, Shipley MJ, Cappuccio FP, Brunner E, Miller MA, Kumari M, et al. A prospective study of change in sleep duration: associations with mortality in the Whitehall II cohort. Sleep 2007;30(12):1659-66.
  17. Jike M, Itani O, Watanabe N, Buysse DJ, Kaneita Y. Long sleep duration and health outcomes: A systematic review, meta-analysis and meta-regression. Sleep Med Rev 2018;39:25-36. doi: 10.1016/j.smrv.2017.06.011. [Crossref]
  18. Katano S, Nakamura Y, Nakamura A, Murakami Y, Tanaka T, Takebayashi T, et al. Relationship between sleep duration and clustering of metabolic syndrome diagnostic components. Diabetes Metab Syndr Obes 2011;4:119-25. doi: 10.2147/dmso.s16147. [Crossref]
  19. Chaput JP, McNeil J, Despres JP, Bouchard C, Tremblay A. Short sleep duration as a risk factor for the development of the metabolic syndrome in adults. Prev Med 2013;57(6):872-7. doi: 10.1016/j.ypmed.2013.09.022. [Crossref]
  20. Guo VY, Cao B, Wong CKH, Yu EYT. The association between daytime napping and risk of diabetes: a systematic review and meta-analysis of observational studies. Sleep Med 2017;37:105-12. doi: 10.1016/j.sleep.2017.01.018. [Crossref]
  21. Lin D, Sun K, Li F, Qi Y, Ren M, Huang C, et al. Association between habitual daytime napping and metabolic syndrome: a population-based study. Metabolism 2014;63(12):1520-7. doi: 10.1016/j.metabol.2014.08.005. [Crossref]
  22. Yang L, Xu Z, He M, Yang H, Li X, Min X, 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):1911-8. doi: 10.5665/sleep.6214. [Crossref]
  23. Rosique-Esteban N, Diaz-Lopez A, Martinez-Gonzalez MA, Corella D, Goday A, Martinez JA, et al. Leisure-time physical activity, sedentary behaviors, sleep, and cardiometabolic risk factors at baseline in the PREDIMED-PLUS intervention trial: A cross-sectional analysis. PLoS One 2017;12(3):e0172253. doi: 10.1371/journal.pone.0172253. [Crossref]
  24. Roos V, Elmstahl S, Ingelsson E, Sundstrom J, Arnlov J, Lind L. Alterations in Multiple Lifestyle Factors in Subjects with the Metabolic Syndrome Independently of Obesity. Metab Syndr Relat Disord 2017;15(3):118-23. doi: 10.1089/met.2016.0120. [Crossref]
  25. Okubo N, Matsuzaka M, Takahashi I, Sawada K, Sato S, Akimoto N, et al. 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. [Crossref]
  26. Garde AH, Hansen AM, Holtermann A, Gyntelberg F, Suadicani P. Sleep duration and ischemic heart disease and all-cause mortality: prospective cohort study on effects of tranquilizers/hypnotics and perceived stress. Scand J Work Environ Health 2013;39(6):550-8. doi: 10.5271/sjweh.3372. [Crossref]
  27. Mallon L, Broman JE, Hetta J. Is usage of hypnotics associated with mortality? Sleep Med 2009;10(3):279-86. doi: 10.1016/j.sleep.2008.12.004. [Crossref]