Abstract
Sleep can affect nonalcoholic fatty liver disease (NAFLD). We investigated the association between sleep duration, sleep quality, and NAFLD. From January to December 2018, 1,073 patients (age: 37.94 ± 10.88, Body Mass Index (BMI): 22.85 ± 3.27) were enrolled. Pittsburgh Sleep Quality Index Questionnaire and Munich Chronotype Questionnaire were used to assess sleep duration, quality, and habits. Ultrasonography was used to diagnose NAFLD. Multivariate logistic regression models were used to calculate the odds ratio (OR) and 95% confidence interval (CI) of the risk of NAFLD by different types of sleep duration and sleep quality. No significant differences in sleep time, sleep quality, and sleep habits between the NAFLD and the non-NAFLD groups were observed (P > 0.05). There was no correlation between sleep duration and NAFLD in the whole cohort. After adjusting for age, exercise, fasting plasma glucose, and BMI, the group with long sleep duration showed a decreased risk of NAFLD in men (OR = 0.01, 95% CI: 0.001–0.27, P = 0.032). However, in all four adjusted models, no correlation between sleep duration, quality, and NAFLD was found in women. In conclusion, sleep duration was significantly and negatively associated with NAFLD in men but not women. Prospective studies are required to confirm this association.
1 Introduction
Nonalcoholic fatty liver disease (NAFLD) is defined as the accumulation of triglycerides in the liver of individuals without significant alcohol consumption or viral hepatitis infection [1]. NAFLD has become one of the most common liver diseases worldwide, with a global prevalence of approximately 25% [2]. Unhealthy lifestyle habits, such as excess nutritional intake, less physical activity, or insufficient sleep are thought to be associated with obesity and diabetes. NAFLD is strongly associated with metabolic diseases such as obesity, diabetes, or dyslipidemia which are risk factors for nonalcoholic steatohepatitis, the severe form of NAFLD [3]. There are currently no approved medical therapies for NAFLD [4]. The first-line treatment is lifestyle interventions, such as diet modification and exercise [5,6]. However, compared with nutrition or exercise, sleep improvement has drawn less attention.
Sleep disturbance and deprivation are common medical complaints in modern society [7]. Insufficient sleep may cause metabolic disorders such as insulin sensitivity, obesity, and type 2 diabetes mellitus, and may therefore contribute to the development of NAFLD [8,9]. The associations between sleep duration or quality and the prevalence of NAFLD have been reported in several studies [8–11]. However, the relationships between sleep duration, quality, and NAFLD remain controversial [7–12]. In a community-based cohort study, long sleep duration was associated with the elevation of NAFLD scores in Korean middle-aged adults [12]. In another larger middle-aged Korean population study, short sleep duration and poor sleep quality were significantly associated with an increased risk of NAFLD [10]. A meta-analysis pooled data from six studies and found a small but significantly increased risk of NAFLD among short sleep duration subjects [13].
In the study, we investigated the associations between sleep duration, quality, and NAFLD, as determined by ultrasonography which is a common clinical diagnostic tool for detecting fatty liver [10]. Besides, we examined the gender differences in sleep duration, quality, and their effects on the risk of NAFLD.
2 Methods
2.1 Study population
The subjects in this study were from Lihuili Hospital of Ningbo Medical Center and the Physical Examination Center of Lihuili East Hospital of Ningbo. The study population was restricted to individuals who underwent a health screening examination with information on sleep duration and sleep quality from January 2018 to December 2018 (N = 1,714). All subjects received a questionnaire survey and physical examination. The exclusion criteria are incomplete medical examination data, uncompleted sleep questionnaire, lack of hepatic ultrasonography, lack of BMI information, lack of triglycerides data, lack of fasting blood glucose data, history of liver disease, and pregnancy and breastfeeding. A total of 641 subjects met one or more of the exclusion criteria at baseline (Figure 1). The total number of eligible subjects for the study was 1,073. The protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the ethics committee of the Lihuili Hospital of Ningbo (ethics review number: 2018036). All the subjects knew the purpose of the questionnaire survey and signed the informed consent before entering the study.
Flowchart of the included subjects.
2.2 Data collection
Sleep duration and quality were assessed using the validated Pittsburgh Sleep Quality Index (PSQI), a self-administered questionnaire. PSQI, comprised of 19 items, generates seven component scores that reflect subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime function [14,15]. Poor sleep quality is defined as having a sum of scores greater than 5 for these seven components. Sleep duration was estimated using component 3, which asked for the number of hours of actual night-time sleep during the past month. Sleep duration means the sum of all sleep time in a day. According to the 2015 National Sleep Foundation recommendations for sleep disorders, we classified sleep duration by age group [16]. During health examinations, information such as medical history, medication use, health-related behavior, physical measurements, and serum biochemical measurements is collected. There were questions regarding the amount and frequency of alcohol consumed weekly and daily. We also collected information on smoking history (duration and daily consumption of cigarettes) using questionnaires.
Sleep habits were assessed using the Munich Chronotype Questionnaire (MCTQ) [17]. The assessment of daily life includes the distribution and assessment of daily behaviors, as well as a reminder of working days and rest days [18]. We define 0 as a very early bedtime, 1 as an appropriate early bedtime, 2 as an earlier bedtime, 3 as a normal bedtime, 4 as a slightly late bedtime, 5 as an appropriate late bedtime, and 6 as an extremely late bedtime.
Any type of exercise which lasts longer than 20 min, every time more than 3 times a week, is classified as physical exercise. Depending on the eating habits in the past month, three types of eating habits have been identified: meat-based, vegetable-based, and meat and vegetable equivalent. For Body Mass Index (BMI), we defined BMI < 18.5 kg/m2 as lean, 18.5 kg/m2 ≤ BMI < 24 kg/m2 as normal, 24 kg/m2 ≤ BMI < 28 kg/m2 as overweight; 28 kg/m2 ≤ BMI as obesity. According to the classification by the American Diabetes Association, fasting plasma glucose (FPG) levels were divided into three grades: normal (FPG < 5.6 mmol/L), impaired (5.6 mmol/L ≤ FPG < 7.0 mmol/L), and diabetes mellitus (FPG ≥ 7.0 mmol/L) [19].
To make the questionnaire reliable, the survey was designed according to the basic situation of the Chinese population preliminary design and was discussed, evaluated, and revised by epidemiologists and clinical experts before large-scale application. Before the beginning of the survey, all personnel participating in the survey were given unified training, the questions in the questionnaire were explained one by one, and the relevant controversial issues were standardized. The questionnaire was completed after face-to-face, one-to-one guidance between the investigators and the respondents.
2.3 Statistical analysis
The subject characteristics were expressed as mean ± standard deviation for continuous variables or as percentages for categorical variables. The significance of differences in gender, age, smoking, and drinking between NAFLD and non-NAFLD groups was determined by the chi-square test. Continuous variables such as age and BMI were analyzed by a two-sided t-test or by analysis of variance. A univariate and multivariate logistic regression model was used to analyze the contributions of different variables. The odds ratio (OR) and 95% confidence interval (95% CI) were used to evaluate the effect of sleep-related variables on NAFLD. The multivariate model was controlled for potential covariates such as age, smoking, drinking, physical exercise, eating habits, and BMI. All statistical analysis was performed by SPSS 18.0 software. All reported P values are two-tailed, and the statistically significant threshold was set at 0.05.
3 Results
3.1 General characteristics of subjects
We enrolled 1,073 valid participants comprising 282 NAFLD and 791 non-NAFLD controls. The demographic and clinical characteristics of the study population are shown in Table 1. The average age of the NAFLD group (41.30 ± 10.04 years) and the non-NAFLD group (36.74 ± 10.92 years) was significantly different (t = 6.153, P < 0.001). Participants were further divided into six age groups and the differences in age distribution were also significant between NAFLD and non-NAFLD groups (P < 0.001). The average BMI of the NAFLD group (25.86 ± 2.76 kg/m2) and non-NAFLD group (21.7 ± 2.72 kg/m2) was significantly different (t = −2.102, P = 0.036). The BMI segment distribution was also significantly different (χ 2 = 319.772, P < 0.001). The composition of smokers between NAFLD and non-NAFLD groups was different (χ 2 = 4.906, P = 0.027). The eating habits between the NAFLD group and the non-NAFLD group were also different (χ 2 = 9.444, P = 0.009). The two groups also have significant differences in marital status (χ 2 = 4.544, P = 0.033). However, there was no significant difference in the composition of drinkers (χ 2 = 0.064, P = 0.800) or exercisers (χ 2 = 2.082, P = 0.149) or occupations (χ 2 = 0.284, P = 0.594) between the two groups.
Comparison of the general demographic of participants
| Variables | NAFLD (n = 282) | Non-NAFLD (n = 791) | P | ||
|---|---|---|---|---|---|
| N | Percentage | N | Percentage | ||
| Age | 41.30 ± 10.04 | 36.74 ± 10.92 | <0.001a | ||
| <0.001b | |||||
| <20 | 0 | 0 | 4 | 0.51 | |
| 20–30 | 34 | 12.06 | 241 | 30.47 | |
| 30–40 | 102 | 36.17 | 268 | 33.88 | |
| 40–50 | 75 | 26.60 | 152 | 19.22 | |
| 50–60 | 64 | 22.70 | 108 | 13.65 | |
| ≥60 | 7 | 2.47 | 18 | 2.27 | |
| Gender | 0.001b | ||||
| Men | 253 | 89.72 | 459 | 58.03 | |
| Women | 29 | 10.28 | 332 | 41.97 | |
| BMI (kg/m2) | 25.86 ± 2.76 | 21.7 ± 2.72 | 0.036a | ||
| <0.001b | |||||
| <18.5 | 0 | 0 | 84 | 10.62 | |
| 18.5–24 | 63 | 22.34 | 545 | 68.90 | |
| 24–28 | 163 | 57.80 | 146 | 18.46 | |
| ≥28 | 56 | 19.85 | 16 | 2.03 | |
| Smoking history | 0.027b | ||||
| Smoking now | 65 | 23.05 | 135 | 17.07 | |
| No smoking | 217 | 76.95 | 656 | 82.93 | |
| Drinking history | 0.800b | ||||
| Drinking now | 50 | 17.73 | 135 | 17.07 | |
| No drinking | 232 | 82.27 | 656 | 82.93 | |
| Eating habits | 0.009b | ||||
| Meat | 73 | 25.89 | 148 | 18.71 | |
| Vegetable | 35 | 12.41 | 144 | 18.20 | |
| Equal | 174 | 61.70 | 499 | 63.08 | |
| Physical exercise | 0.149b | ||||
| Yes | 108 | 38.30 | 342 | 43.24 | |
| No | 174 | 61.70 | 449 | 56.76 | |
| Marriage | 0.033b | ||||
| Married | 231 | 81.91 | 599 | 75.73 | |
| Singlec | 51 | 18.09 | 192 | 24.27 | |
| Occupation | 0.594b | ||||
| Yes | 265 | 93.97 | 736 | 93.05 | |
| No | 17 | 6.03 | 55 | 6.95 | |
a t-Test was used for continuous variables. bCategorical variables were tested by chi-square test. cBeing single includes divorce, bereft of one’s spouse, and unmarried. The values in age and BMI rows indicate their average and standard deviation.
3.2 Association between sleep duration, quality, habits, and NAFLD
We further examined the associations between sleep duration, sleep quality, sleep habits, and NAFLD in the whole cohort. No significant associations were observed between sleep and NAFLD (Table 2). Multivariate logistic regression analysis for independent variables in NAFLD identified physical exercise as a protective factor (OR: 1.59, 95% CI: 1.11–2.28), while age (OR: 0.97, 95% CI: 0.96–0.99), gender (OR: 0.36, 95% CI: 0.22–0.60), BMI (OR: 0.61, 95% CI: 0.57–0.66), and FPG (OR: 0.63, 95% CI: 0.48–0.81) as risk factors for NAFLD with P < 0.05. However, sleep duration, sleep quality, and sleep habits showed no association with NAFLD in the whole cohort (Table 3). To test if the associations exist in different genders, we performed logistic regression analysis for NAFLD and sleep in men and women separately. We found no correlation between sleep habits and NAFLD in both men and women subjects, whether the covariate was adjusted or not. However, we found that the risk of NAFLD was significantly lower in men with recommended or longer sleep duration than in those with too short sleep duration after adjustment for age, exercise, FPG, and BMI (OR = 0.01, 95% CI: 0.001–0.27, P = 0.032). No associations between sleep duration, sleep quality, and NAFLD was observed in women (Table 4).
Summary data for sleep status and subjects composition
| NAFLD | Non-NAFLD | P | |||
|---|---|---|---|---|---|
| N | Percentage | N | Percentage | ||
| Sleep duration | 0.671a | ||||
| Too short | 6 | 2.13 | 25 | 3.16 | |
| Recommend | 223 | 79.08 | 639 | 80.78 | |
| Appropriate | 51 | 18.09 | 124 | 15.68 | |
| Too long | 2 | 0.71 | 3 | 0.38 | |
| Sleep quality | 0.891a | ||||
| Good (<5) | 164 | 58.16 | 479 | 60.46 | |
| Poor (>5) | 77 | 27.30 | 220 | 27.81 | |
| PSQI = 5 | 41 | 14.54 | 92 | 11.63 | |
| Sleep habits | 0.951a | ||||
| 0 (too early) | 6 | 2.13 | 22 | 2.78 | |
| 1 | 13 | 4.61 | 38 | 4.80 | |
| 2 | 43 | 15.25 | 133 | 16.81 | |
| 3 (normal) | 92 | 32.62 | 230 | 29.08 | |
| 4 | 87 | 30.85 | 249 | 31.48 | |
| 5 | 32 | 11.35 | 94 | 11.88 | |
| 6 (too late) | 9 | 3.19 | 25 | 3.17 | |
aThe categorical variables were tested by chi-square test.
Multivariate logistic regression analysis for NAFLD
| Variables | B estimate | Standard error | Wald | P | OR (95% CI) |
|---|---|---|---|---|---|
| Gender | −1.01 | 0.25 | 16.23 | <0.001 | 0.36 (0.22–0.60) |
| Marriage | 0.01 | 0.23 | 0.01 | 0.950 | 1.02 (0.65–1.59) |
| Age | −0.03 | 0.01 | 8.61 | 0.003 | 0.97 (0.96–0.99) |
| BMI | −0.49 | 0.04 | 165.65 | <0.001 | 0.61 (0.57–0.66) |
| Smoking history | 0.08 | 0.23 | 0.12 | 0.732 | 1.08 (0.69–1.68) |
| Drinking history | 0.33 | 0.24 | 1.87 | 0.171 | 1.39 (0.87–2.21) |
| Eating habits | −0.04 | 0.11 | 0.33 | 0.732 | 0.96 (0.78–1.20) |
| Physical exercise | 0.46 | 0.18 | 6.37 | 0.012 | 1.59 (1.11–2.28) |
| FPGa | −0.47 | 0.13 | 12.40 | <0.001 | 0.63 (0.48–0.81) |
| Sleep duration | −0.11 | 0.21 | 0.26 | 0.613 | 0.90 (0.60–1.35) |
| Sleep quality | −0.01 | 0.03 | 0.03 | 0.871 | 1.00 (0.93–1.06) |
| Sleep habits | −0.30 | 0.07 | 153.13 | 0.680 | 0.97 (0.84–1.12) |
aFasting plasma glucose.
Relationships between sleep duration, sleep quality, and NAFLD
| Univariate analysis OR (95% CI) | Multivariate analysis derived OR (95% CI) | ||||
|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | ||
| Sleep duration | |||||
| Men | |||||
| Too short | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Recommend | 0.34 (0.10–1.18) | 0.38 (0.11–1.33) | 0.37 (0.11–1.30) | 0.28 (0.06–1.22) | 0.13 (0.02–0.72) |
| Appropriate | 0.30 (0.08–1.10) | 0.34 (0.09–1.23) | 0.33 (0.09–1.21) | 0.25 (0.05–1.12) | 0.14 (0.02–0.79) |
| Too long | 0.09 (0.01–1.39) | 0.08 (0.01–1.14) | 0.08 (0.01–1.16) | 0.06 (0.004–1.04) | 0.01 (0.001–0.27) |
| P trend | 0.223 | 0.221 | 0.218 | 0.186 | 0.032 |
| Women | |||||
| Too short | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Recommend | 4.5 (1.13–17.95) | 4.32 (0.95–19.71) | 3.50 (0.74–16.49) | 3.29 (0.70–15.36) | 3.60 (0.71–18.25) |
| Appropriate | 2.83 (0.60–13.44) | 3.47 (0.63–19.00) | 2.40 (0.42–13.73) | 2.27 (0.40–12.86) | 4.17 (0.61–28.35) |
| P trend | 0.085 | 0.165 | 0.250 | 0.281 | 0.276 |
| Sleep quality | |||||
| Men | |||||
| Good | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Poor | 1.08 (0.76–1.55) | 1.05 (0.73–1.51) | 1.05 (0.73–151) | 1.13 (0.78–1.65) | 1.15 (0.75–1.79) |
| P trend | 0.662 | 0.790 | 0.795 | 0.508 | 0.521 |
| Women | |||||
| Good | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| Poor | 0.59 (0.26–1.34) | 0.61 (0.26–1.43) | 0.58 (0.25–1.39) | 0.59 (0.25–1.40) | 0.45 (0.16–1.30) |
| P trend | 0.205 | 0.257 | 0.224 | 0.231 | 0.140 |
Note: Model 1: adjusted for age. Model 2: Model 1 adjusted for physical exercise. Model 3: Model 2 adjusted for FPG. Model 4: Model 3 adjusted for BMI. The too-long sleep duration group for women was deleted as there was only one participant in it.
4 Discussion
There are few studies about associations of sleep and NAFLD in Chinese population. In the study, the associations of NAFLD with sleep duration, sleep quality, and sleep habits were evaluated. Sleep duration, sleep quality, and sleep habits showed no statistically significant association with NAFLD in the whole cohort when the model was adjusted for other parameters. It was found that the risk of NAFLD was significantly lower in men with long sleep duration than in those with short sleep duration. However, sleep quality and sleep habits in both genders were not associated with NAFLD.
Previous studies have suggested that sleep quality was associated with NAFLD, and there were sex differences [6]. However, results showed no associations of NAFLD with the global PSQI score, subjective sleep quality score, sleep duration score, and sleep disturbance score [6]. A recent study also showed that a mean sleep time of 7 h or more had a significant negative relationship with NAFLD [20]. Short sleep duration was associated with an increased risk of prevalent NAFLD in Chinese, two South Korean, and American populations [8–11]. Different results were also reported in some South Korean populations. Poor sleep quality but not sleep duration was associated with a lower risk of NAFLD in men. In women, the association of sleep quality and duration with the risk of NAFLD was insignificant [7]. A relationship between long sleep duration and the elevation of NAFLD scores was found after adjusting for several confounding factors in Korean middle-aged adults [12]. Thus, these findings are controversial, although a recent study pooled six datasets and meta-analysis demonstrated a small but significantly increased risk of NAFLD among participants who had short sleep duration [13].
Many reasons may account for the above phenomenon. NAFLD is the most common chronic liver disease worldwide. The disease is a heterogeneous group of liver diseases characterized by the accumulation of fat in the liver [21]. The pathogenesis of NAFLD is not yet fully understood. Many factors may contribute to NAFLD, including diet, medications, genetic predisposition, and gut microbiota [22]. Although this study had a large enough sample size to conclude, it had some limitations. First, the PSQI assessment is a self-reported questionnaire and is subjective. Thus, the associations between sleep quality and NAFLD need to be confirmed by objective methods, such as polysomnography. Second, the study was cross-sectional. The collected data reflect the recent sleep characteristics as well as NAFLD status. Therefore, longitudinal evaluation is essential to reveal associations between sleep quality, duration, and NAFLD in the future. Third, the influences of nutritional factors, genetic background, and gut microbiota are hard to control in the study [22]. Hypercaloric nutrition, including the effects of saturated fat and fructose, as well as adipose tissue dysfunction and intestinal dysbiosis, may contribute to the incidence of NAFLD [23]. In the future study, these factors can be recorded and incorporated into the disease model. Finally, more large-scale cohorts are needed to draw an explicit conclusion. The multi-center study can be carried out to increase statistical power for associations between sleep and NAFLD.
Our result suggests that men with short sleep duration should be cautious about their sleep habits as they have a higher probability to have NAFLD. It has been reported that individuals with NAFLD were more likely to be men and had a higher prevalence of sleep disorders [24]. Thus, the differences in gender should be considered when designing large-scale epidemiologic studies. The molecular mechanisms of chronic sleep deprivation have been extensively studied. Transcriptome analysis identified genes affected by insufficient sleep were associated with circadian rhythms, sleep homeostasis, oxidative stress, and metabolism [25]. Short sleep may increase diabetes risk through three pathways that are alterations in glucose metabolism, upregulation of appetite, and decreased energy expenditure. Type 2 diabetes mellitus is a risk factor often linked with NAFLD [26].
5 Conclusions
According to the study, we found that sleep duration is an independent influencing factor of NAFLD in men, and the risk of NAFLD decreases with the increase in sleep duration, but there are no significant associations in women. Thus, men with short sleep duration should be cautious about their sleep habits as they have a higher probability to have NAFLD.
Acknowledgements
We acknowledge all patients involved in this study.
-
Funding information: The research reported in this publication was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LGF19H030006 and LQ20H030001), Ningbo Science and Technology Project (No. 2019C50100), and Ningbo Clinical Medicine Research Center Project (No. 2019A21003).
-
Author contributions: H.L.: conceptualization, methodology, data curation, writing – original draft. S.H.: investigation. M.X.: investigation, writing – review and editing. D.Z.: investigation, writing – review and editing. X.W.: data curation. L.Z.: investigation. D.C.: data curation. J.D.: investigation. R.Y.: investigation. H.L.: investigation. H.Y.: investigation, writing – review and editing, supervision.
-
Conflict of interest: The authors declare no conflict of interest.
-
Data availability statement: The datasets generated during the current study are available from the corresponding author upon reasonable request.
References
[1] Jullian-Desayes I, Trzepizur W, Boursier J, Joyeux-Faure M, Bailly S, Benmerad M, et al. Obstructive sleep apnea, chronic obstructive pulmonary disease and NAFLD: an individual participant data meta-analysis. Sleep Med. 2021;77:357–64. 10.1016/j.sleep.2020.04.004.Search in Google Scholar PubMed
[2] Loomba R, Sanyal AJ. The global NAFLD epidemic. Nat Rev Gastroenterol Hepat. 2013;10:686–90. 10.1038/nrgastro.2013.171.Search in Google Scholar PubMed
[3] Kasper P, Martin A, Lang S, Kütting F, Goeser T, Demir M, et al. NAFLD and cardiovascular diseases: a clinical review. Clin Res Cardiol. 2021;110:921–37. 10.1007/s00392-020-01709-7.Search in Google Scholar PubMed PubMed Central
[4] Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, et al. The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American association for the study of liver diseases. Hepatology. 2018;67:328–57. 10.1002/hep.29367.Search in Google Scholar PubMed
[5] Caylak E. The genetics of sleep disorders in humans: narcolepsy, restless legs syndrome, and obstructive sleep apnea syndrome. Am J Med Genet A. 2009;149A(11):2612–26. 10.1002/ajmg.a.33087.Search in Google Scholar PubMed
[6] Takahashi A, Anzai Y, Kuroda M, Kokubun M, Kondo Y, Ogata T, et al. Effects of sleep quality on non-alcoholic fatty liver disease: a cross-sectional survey. BMJ Open. 2020;10:e039947. 10.1136/bmjopen-2020-039947.Search in Google Scholar PubMed PubMed Central
[7] Chou YT, Cheng HJ, Wu JS, Yang YC, Chou CY, Chang CJ, et al. The association of sleep duration and sleep quality with non-alcoholic fatty liver disease in a Taiwanese population. Obes Res Clin Pract. 2018;12:500–5. 10.1016/j.orcp.2018.05.002.Search in Google Scholar PubMed
[8] Peng K, Lin L, Wang Z, Ding L, Huang Y, Wang P, et al. Short sleep duration and longer daytime napping are associated with non-alcoholic fatty liver disease in Chinese adults. J Diabetes. 2017;9:827–36. 10.1111/1753-0407.12489.Search in Google Scholar PubMed
[9] Um YJ, Chang Y, Jung HS, Cho IY, Shin JH, Shin H, et al. Sleep duration, sleep quality, and the development of nonalcoholic fatty liver disease: a cohort study. Clin Transl Gastroenterol. 2021;12:e00417. 10.14309/ctg.0000000000000417.Search in Google Scholar PubMed PubMed Central
[10] Kim CW, Yun KE, Jung HS, Chang Y, Choi ES, Kwon MJ, et al. Sleep duration and quality in relation to non-alcoholic fatty liver disease in middle-aged workers and their spouses. J Hepatol. 2013;59:351–7. 10.1016/j.jhep.2013.03.035.Search in Google Scholar PubMed
[11] Kim D, Kim HJ, Kushida CA, Heo NY, Ahmed A, Kim WR. Short sleep duration is associated with abnormal serum aminotransferase activities and nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2018;16:588–90. 10.1016/j.cgh.2017.08.049.Search in Google Scholar PubMed
[12] Kim JH, Jung DH, Kwon YJ, Lee JI, Shim JY. The impact of the sleep duration on NAFLD score in Korean middle-aged adults: a community-based cohort study. Sleep Med. 2019;57:144–50. 10.1016/j.sleep.2019.02.012.Search in Google Scholar PubMed
[13] Wijarnpreecha K, Thongprayoon C, Panjawatanan P, Ungprasert P. Short sleep duration and risk of nonalcoholic fatty liver disease: a systematic review and meta-analysis. J Gastroenterol Hepatol. 2016;31:1802–7. 10.1111/jgh.13391.Search in Google Scholar PubMed
[14] Buysse DJ, III Reynolds CF, Monk TH, Hoch CC, Yeager AL, Kupfer DJ. Quantification of subjective sleep quality in healthy elderly men and women using the Pittsburgh Sleep Quality Index (PSQI). Sleep. 1991;14:331–8. 10.1093/sleep/14.4.331.Search in Google Scholar
[15] Buysse DJ, 3rd Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatr Res. 1989;28:193–213. 10.1016/0165-1781(89)90047-4 Search in Google Scholar PubMed
[16] Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, et al. National sleep foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1:40–3. 10.1016/j.sleh.2014.12.010.Search in Google Scholar PubMed
[17] Roenneberg T, Kuehnle T, Juda M, Kantermann T, Allebrandt K, Gordijn M, et al. Epidemiology of the human circadian clock. Sleep Med Rev. 2007;11:429–38. 10.1016/j.smrv.2007.07.005.Search in Google Scholar PubMed
[18] Roenneberg T, Wirz-Justice A, Merrow M. Life between clocks: daily temporal patterns of human chronotypes. J Biol Rhythm. 2003;18:80–90. 10.1177/0748730402239679.Search in Google Scholar PubMed
[19] American Diabetes Association. Standards of medical care in diabetes – 2009. Diabetes Care. 2009;32(Suppl 1):S13–61. 10.2337/dc09-S013.Search in Google Scholar PubMed PubMed Central
[20] Seo Y, Sung GH, Lee S, Han KJ. Weekend catch-up sleep is associated with the alleviation of non-alcoholic fatty liver disease. Ann Hepatol. 2022;27:100690. 10.1016/j.aohep.2022.100690 Search in Google Scholar PubMed
[21] Parthasarathy G, Revelo X, Malhi H. Pathogenesis of nonalcoholic steatohepatitis: an overview. Hepatol Commun. 2020;4:478–92. 10.1002/hep4.1479.Search in Google Scholar PubMed PubMed Central
[22] Yu J, Marsh S, Hu J, Feng W, Wu C. The pathogenesis of nonalcoholic fatty liver disease: interplay between diet, gut microbiota, and genetic background. Gastroenterol Res Pract. 2016;2016:2862173. 10.1155/2016/2862173.Search in Google Scholar PubMed PubMed Central
[23] Pafili K, Roden M. Nonalcoholic fatty liver disease (NAFLD) from pathogenesis to treatment concepts in humans. Mol Metab. 2021;50:101122. 10.1016/j.molmet.2020.101122.Search in Google Scholar PubMed PubMed Central
[24] Mir HM, Stepanova M, Afendy H, Cable R, Younossi ZM. Association of sleep disorders with nonalcoholic fatty liver disease (NAFLD): a population-based study. J Clin Exp Hepatol. 2013;3:181–5. 10.1016/j.jceh.2013.06.004.Search in Google Scholar PubMed PubMed Central
[25] Ye H, Huang S, Song Y, Liu H, Zhao X, Zhao D, et al. Gene co-expression analysis identifies modules related to insufficient sleep in humans. Sleep Med. 2021;86:68–74. 10.1016/j.sleep.2021.08.010.Search in Google Scholar PubMed
[26] Knutson KL, Spiegel K, Penev P, Van Cauter E. The metabolic consequences of sleep deprivation. Sleep Med Rev. 2007;11:163–78. 10.1016/j.smrv.2007.01.002.Search in Google Scholar PubMed PubMed Central
© 2023 the author(s), published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
