Skip to main content
Download PDF

Sleep characteristics of middle-aged adults with non-alcoholic fatty liver disease: findings from the Shahrekord PERSIAN cohort study

BMC Public Health volume 23, Article number: 312 (2023) Cite this article

Abstract

Background

Several studies have reported short sleep duration in people with non-alcoholic fatty liver disease (NAFLD) but other sleep characteristics have been less studied. We aimed to assess the cross-sectional association of NAFLD with sleep duration and quality in an Iranian population sample.

Methods

We used data from 9,151 participants in the Shahrekord Prospective Epidemiological Research Studies in Iran (PERSIAN) Cohort Study, including 1,320 that were diagnosed with NAFLD. Log-binomial regression models sequentially adjusted for sociodemographic, lifestyle, clinical and biological variables were used to estimate relative risks (RR) and 95% confidence intervals (95% CI) for the association between NAFLD and sleep characteristics.

Results

Participants with NAFLD had shorter sleep duration, later wake-up time and bedtime, worse sleep efficiency, and more frequent daytime napping and use of sleeping pills, in age- and sex-adjusted models. After controlling for sociodemographic, lifestyle, clinical, and biological variables the associations remained strong for sleep efficiency (per 10%, RR = 0.92, 95%CI: 0.88–0.96) and use of sleeping pills (RR = 1.48, 95%CI: 1.17–1.88). The association between NAFLD and sleep efficiency was stronger in participants aged > 60 years (RR = 0.81, 0.70–0.93) and 40–60 years (RR = 0.87, 0.82–0.94), compared with those aged < 40 years (P-heterogeneity < 0.001). More frequent daytime napping in participants with NAFLD, compared with non-NAFLD, was observed in males but not females (P-heterogeneity = 0.007), and in those with body mass index (BMI) < 30 but not in obese participants (P-heterogeneity < 0.001).

Conclusions

Diagnosis of NAFLD is associated with several poor sleep characteristics in middle-aged Iranians. Although longitudinal studies would help to clarify the direction of causality, our study shows that poor sleep is an important aspect of NAFLD.

Peer Review reports

Background

Non-alcoholic fatty liver disease (NAFLD) is one of the most prevalent liver diseases worldwide and may lead to serious complications such as non-alcoholic steatohepatitis, cirrhosis, end-stage liver disease and liver cancer [1, 2]. NAFLD is characterized by the excessive accumulation of hepatic fat, with the presence of steatosis in > 5% of hepatocytes, assessed by magnetic resonance imaging or histology image analysis of liver proton density fat fraction [2]. This chronic liver disease is recognized as the hepatic manifestation of obesity and metabolic syndrome (MetS). MetS is defined as having three or more of excess central obesity, raised blood pressure, high levels of triglycerides, low levels of high-density lipoproteins and impaired fasting glucose or diabetes, and is thought to be both a cause and a consequence of NAFLD [3]. Several other non-metabolic factors play a role in NAFLD including lifestyle factors such as low levels physical activity and poor dietary habits [4, 5]

Sleep disturbances are known to be associated with insulin resistance and dysregulation of glucose metabolism, which may lead to metabolic disorders such as type 2 diabetes (T2D), obesity, and MetS [6,7,8,9,10,11,12,13,14,15]. Two recent meta-analyses investigated sleep issues in NAFLD patients but these included only a handful of studies and focused exclusively on sleep duration [9]

To the best of our knowledge, other sleep characteristics have rarely been studied. A case–control study in South Korea suggested that NAFLD patients were more likely to suffer from obstructive sleep apnea, particularly in those with excessive daytime sleepiness [16]. In Chinese middle-aged and elderly adults, participants with longer daytime napping and shorter sleep duration were more frequently diagnosed with NAFLD [17]. The large cross-sectional study by Kim et al. found that both poor sleep quality and short duration were associated with NAFLD [18].

Given the strong interplay between NAFLD and other metabolic conditions, thorough adjustment for potential confounding variables when analyzing sleep patterns in relation to NAFLD patients is paramount, and this aspect may have been neglected in some of the previous studies. We hypothesized that people with NAFLD have worse sleep quality, independently of their other lifestyle and clinical characteristics. To our knowledge, few studies have assessed the sleep characteristics of NAFLD patients, particularly in the Middle Eastern population, and these only focused on specific sleep variables (mainly duration). The aim of this study was to clarify the cross-sectional relationship between NAFLD diagnosis and sleep characteristics in an Iranian population sample.

Methods

The Shahrekord cohort study

This cross-sectional study was conducted using data collected from participants in the prospective Shahrekord Cohort Study (SCS), a population-based study of 10,075 men and women aged 35 to 70 years. The SCS is one of the Prospective Epidemiological Research Studies in Iran (PERSIAN) [19] and recruited people from Chaharmahal and Bakhtiari province, located in the southwest of Iran. The SCS was designed as a regional cohort study in Shahrekord and is planned to have a 20-year follow-up. Enrolment in the SCS started in November 2015 and ended in June 2018. More details about the PERSIAN cohorts and the SCS protocol can be found in other publications [20, 21]. The SCS protocol was approved by the Ethics Committee of Shahrekord University of Medical Sciences (IR.SKUMS.REC 1394.286).

Study sample

We excluded from the study: pregnant women (n = 18), participants with alcoholic liver disease, or who consumed a large amount of alcohol (more than 20 g daily for women and 30 g daily for men) and those with grade III NAFLD (N = 52), and those with ischemic heart disease (N = 554), myocardial infarction (N = 123), hepatitis B and hepatitis C (N = 49), and cancer (N = 71). No participants with viral or autoimmune hepatitis or cognitive disorders were recruited in this study. After exclusions, 9,151 participants were available for the analysis.

Sociodemographic and lifestyle variables

Sociodemographic variables including ethnicity group, educational attainment, marital status and socioeconomic status were collected using the SCS general questionnaire. Lifestyle variables such as smoking habits and alcohol and drug consumptions were collected using medical questionnaires. Physical activity was recorded using the general questionnaire and self-reported daily activities were converted to metabolic equivalent of tasks (METs).

Anthropometric and clinical variables

The anthropometric characteristics of the participants were measured using a wall height meter (Seca 206) for height, an analog scale (Seca) for weight, and standard tape for waist, hip, and wrist circumferences. Blood pressure at the left and right arm was measured twice at a 15-min interval using a standard barometer (Richter), and the average of these two measures was calculated. A Pars Azmoon kit was used to measure the following biochemical variables in blood serum of all participants: fasting blood glucose, serum cholesterol (total, high-density lipoprotein [HDL], low-density lipoprotein [LDL]), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and triglycerides (TG). MetS was diagnosed using the National Cholesterol Education Program Adult Treatment Panel III criteria (ATPIII) definition (13), i.e. meeting any three of the following criteria: fasting serum glucose ≥ 100 mg/dl, waist circumference (WC) (> 102 cm in men; > 88 cm in women), TG ≥ 150 mg/dl, HDL (< 40 mg/dl in men; < 50 mg/dl in women), systolic blood pressure (SBP) ≥ 140 or diastolic blood pressure (DBP) ≥ 90 mmHg (25).

NAFLD diagnosis

NAFLD status was determined using liver ultrasound imaging, which was retrieved from the medical records of participants and checked by a clinician [22].

Sleep characteristics

Sleep characteristics were assessed with the validated 9-item sleep and circadian rhythm questionnaire used in all PERSIAN cohorts [19]. The variables retained for this study were sleep duration (hours), sleep efficiency (%), bedtime, daytime napping (hours), falling asleep unintentionally, presence of leg restless syndrome, and use of sleeping pills. Total time in bed was calculated as the difference between the times at which participants reported they went to bed at night and left their bed in the morning. Sleep efficiency was calculated as the ratio of sleep time duration to total time in bed multiplied by 100 [23].

Statistical analysis

Continuous variables were presented as their means and standard deviations (SD) and categorical variables as numbers and percentages [24]. Although logistic regression is typically used when the outcome of interest is binary, the odds ratio is not a good estimate of the relative risk when this outcome is not rare; the log-binomial model is a reliable alternative to estimate the relative risk [25, 26]. In this study, as the prevalence of NAFLD was not rare (14.4%), log-binomial regression was used to estimate relative risks (RR) and 95% confidence intervals (95% CI) for the association between sleep characteristics and NAFLD status. Associations were first evaluated using Model 0 adjusting for age and sex, and then using four multivariable models sequentially adjusted for potential confounders: Model 1 adjusted for sociodemographic variables including age, sex, ethnicity, marital status, education, and socioeconomic status; Model 2 adjusted for variables in Model 1 and lifestyle variables including physical activity, smoking status, alcohol consumption, and use of drugs; Model 3 adjusted for the variables in Model 2 along with clinical variables (T2D, hypertension and BMI); Model 4 adjusted for the variables in Model 3 and biological markers of metabolic disturbances (TG, HDL, and LDL).

Heterogeneity in the associations by age, sex, and BMI was tested by comparing models (Model 2) with and without interaction terms of these factors with the sleep variables using likelihood ratio tests. The participants’ characteristics were described in the presence of missing values. All other analyses were performed using participants without missing values in any of the models (96%, N = 8,790).

All statistical analyses were conducted using R software version 3.6.3.

Results

Baseline characteristics of participants

Of the 9,151 participants included in the study, 4,211 (46.4%) were male, their mean ± SD age was 49.1 ± 9.2, and 1,320 participants (479 men and 841 women) were diagnosed with NAFLD (Table 1). Compared with non-NAFLD participants, those with NAFLD had strongly elevated fatty liver index (64.9 vs 46.9) and hepatic steatosis index (43.7 vs 36.9), as well as on average higher BMI (mean ± SD: 29.9 ± 4.4), SBP (117.6 ± 16.2), DBP (77.3 ± 10.5), and metabolic variables including total cholesterol (191 ± 40), triglycerides (174 ± 97), fasting glucose (102 ± 31), AST (21 ± 10), and ALT (26 ± 17). The NAFLD group also had higher prevalence of T2D (29%), hypertension (25%) and metabolic syndrome (33%) than the non-NAFLD group (13%, 13%, and 19%, respectively) (Table 1).

Table 1 Baseline characteristics of participants in the Shahrekord cohort study (n = 9,151)
Full size table

Association between sleep characteristics and NAFLD

In age- and sex-adjusted models (Model 0), several sleep characteristics were negatively associated with NAFLD: sleep duration (per one hour, RR = 0.95, 95%CI: 0.92–0.99), waking up earlier than 6a.m. (RR = 0.76, 95%CI: 0.62–0.94, compared with ≥ 9a.m.), bedtime hour ≤ 10p.m. compared with ≥ 12a.m. (RR = 0.65, 95%CI: 0.56–0.74), bedtime hour 10p.m.-12a.m. compared with ≥ 12a.m. (RR = 0.88, 95%CI: 0.79–0.99), sleep efficiency (per 10%, RR = 0.93, 95%CI: 0.89–0.97), and reporting restless legs syndrome (RR = 0.81, 95%CI: 0.71–0.93). Participants were more likely to have NAFLD when they reported daytime napping (RR = 1.29, 95%CI: 1.17–1.44) and using sleeping pills (RR = 1.57, 95%CI: 1.33–1.86) (Table 2).

Table 2 Association between NAFLD status and sleep characteristics using log-binomial models (N = 1320 cases of NAFLD; 7831 non-cases)
Full size table

As shown in Table 2, adjustment for sociodemographic variables (Model 1) moderately attenuated the association observed for waking up earlier than 6a.m. (RR = 0.82, 95%CI: 0.67–1.00), going to bed earlier than 10p.m. (RR = 0.79, 95%CI: 0.68–0.91), use of sleeping pills (RR = 1.52, 95%CI: 1.28–1.79), daytime napping (RR = 1.18, 95%CI: 1.06–1.31), sleep efficiency (RR = 0.92, 95%CI: 0.88–0.96), and restless legs syndrome (RR = 0.78, 95%CI: 0.66–0.92). Additional adjustment for physical activity, alcohol consumption, smoking and use of drugs (Model 2) also left the associations virtually unchanged, compared with Model 1, except for waking up earlier than 6a.m. (point RR from 0.82 to 0.93) and daytime napping (point RR from 1.18 to 1.12) (Table 2).

Adjustment for key clinical variables (Model 3, Table 2) resulted in null associations for bedtime hour ≤ 10p.m., compared with ≥ 12a.m., sleep duration and daytime napping. The associations nevertheless remained quite strong for sleep efficiency and use of sleeping pills. In the comprehensively adjusted Model 4, participants with better sleep efficiency were less likely to have NAFLD (per 10%, RR = 0.92, 95%CI: 0.88–0.98). Participants using sleeping pills were 48% more likely to have a NAFLD diagnosis (RR = 1.48, 95%CI: 1.17–1.88) (Table 2).

Effect modification by age, sex, and BMI

Results stratified by age, sex, and BMI are shown in Table 3. Most associations appeared consistent across subgroups, in particular for restless legs syndrome, use of sleeping pills, and waking-up time. For sleep efficiency, the associations varied by age, being substantially stronger in older participants (> 60 years: RR = 0.81, 95%CI: 0.70–0.93; ≤ 40 years: RR = 1.01, 95%CI: 0.97–1.04; P-interaction < 0.001), and by BMI, being stronger in obese participants (BMI ≥ 30: RR = 0.89, 95%CI: 0.84–0.94; BMI ≤ 25: RR = 1.01, 95%CI: 0.98–1.04; P-heterogeneity = 0.01). Male participants who reported daytime napping were 27% more likely to have NAFLD (RR = 1.27, 95%CI: 1.05–1.53) but no association was observed in females (RR = 0.94, 95%CI: 0.83–1.06), P-heterogeneity = 0.007. Daytime napping participants with a BMI ≥ 30 were less likely to have NAFLD (RR = 0.81, 95%CI: 0.70–0.94), but the opposite was found in those with BMI ≤ 25 and between 25 and 30 (RR = 1.24 and 1.29, respectively, P-heterogeneity < 0.001), Table 3. Weak evidence of effect modification by sex and age was also observed for the bedtime hour variable (P-interaction = 0.03 and 0.02, respectively); male participants with early bedtime were less likely to have NAFLD than those with late bed time (RR = 0.73, 95%CI: 0.58–0.93), but the same was not observed in females (RR = 0.99, 95%CI: 0.85–1.15). Older participants (age ≥ 60) with early bedtime were substantially less likely to have NAFLD (RR = 0.66, 95%CI: 0.49–0.90), but the same was not observed in participants aged < 60.

Table 3 Interaction between sleeping characteristics and the factors in association with NAFLD using likelihood ratio test
Full size table

Discussion

Our study examined the association between NAFLD and several sleep-related variables, including sleep duration and efficiency, wake-up and bed time, and variables indicative of poor sleep such as falling sleep unintentionally, having restless legs syndrome, and using sleeping pills, in a large cohort of ethnically-diverse middle-aged Iranian men and women. We found that a diagnosis of NAFLD with associated with worse sleep outcomes. Although part of these associations was explained by sociodemographic, lifestyle, or clinical variables, some remained strong after comprehensive confounder adjustment, in particular for sleep efficiency and use of sleeping pills, which suggests that NAFLD independently affects sleep. Across population subgroups, we found that: i) the association of NAFLD with sleep efficiency was stronger in older participants and those who were overweight / obese; and ii) the association of NAFLD with daytime napping was stronger in male participants and those with a BMI less than 30.

The strengths of our study were i) its large sample size, including 1,320 NAFLD cases drawn from a population-based cohort, ii) the clinical diagnosis of NAFLD made by ultrasonography; and iii) the adjustment for an extensive collection of sociodemographic, lifestyle, and clinical/biochemical variables. Our study also has several limitations. First, while ultrasonography and medical records were used to diagnose NAFLD, no liver biopsies could be obtained from the participants to improve diagnosis accuracy; liver biopsies are an invasive procedure associated with complications such as death and hemorrhage, so it was not a safe, feasible and cost-effective approach in this study. Patients with higher grades of NAFLD stated in their medical records and blood samples were referred to clinicians to take biopsy for cancer detection and excluded from the study. We also excluded participants with a history of excessive alcohol use to minimize the risk of NAFLD misclassification. The questionnaire we used to assess sleep characteristics was validated and used in several PERSIAN cohorts [20, 21], but other common sleep quality assessments such as the Pittsburgh Sleep Quality Index (PSQI) were not made. Like for all questionnaire-collected variables, the fact that sleep characteristics were self-reported may have resulted in measurement error, which usually yields estimates of association biased towards the null [27]. Due to its cross-sectional nature, our study did not allow conclusions about whether sleep disturbances were present before NAFLD development, or were caused by the presence of NAFLD; it nevertheless provides evidence that poor sleep is an important issue in NAFLD patients.

Our findings are partially in line with two recent cross-sectional studies [18, 28]. Kim et al. [18] found that NAFLD patients had shorter sleep duration in 45,293 middle-aged South Korean workers; the association was however only observed in the group with ≤ 5 h of sleep, compared with > 7 h and appeared stronger in women; men: odds ratio (OR) = 1.24 (95%CI: 1.11–1.39), women: OR = 1.55 (95%CI: 1.28–1.89), with effect modification by sex, P-interaction = 0.003. We found no effect modification by sex for sleep duration (P-interaction = 0.78) [18] and that short sleep duration was associated with only moderately increased NAFLD prevalence (+ 18% in age- and sex-adjusted models, and + 11% after comprehensive adjustments). Miyake et al. [28], in a community-based study of 2,429 Japanese participants aged 18–80 years, found a significant negative association between NAFLD and short sleep in men: adjusted OR = 0.55, 95%CI: 0.36–0.83, the same was not observed in women: OR = 0.97, 95%CI: 0.67–1.42. In contrast to our study this association was slightly increased in multivariable models after adjusting for age, SBP, TG, HDL, fasting plasma glucose, uric acid, ALT, creatinine, snacking habit, periodic exercise [28]. The meta-analysis by Wijarnpreecha et. al [9] included five cross-sectional and one cohort studies and participants with NAFLD were found to have shorter sleep duration: pooled RR: 1.19; 95%CI: 1.04–1.36. However, the meta-analysis of Shen et al. [12] did not find long sleep or short sleep to be associated with NAFLD. These findings taken together suggest that the association between sleep duration and NAFLD is relatively small. The choice of reference categories has also differed across studies which may complicate the interpretation of the findings. Additional studies are required to confirm whether this association varies by sex.

A common tool used to evaluate sleep quality in recent studies [18, 29, 30] was the global PSQI score of seven components including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, sleep medication use, and daytime dysfunction [31]. Our study used a different self-reported questionnaire, which was validated and detailed in a previous publication [20], and we evaluated each item individually. We found that participants with higher sleep efficiency were less frequently diagnosed with NAFLD, particularly in older participants and those with a higher BMI. A cross-sectional study of 4,828 Japanese participants found no association of NAFLD with global PSQI score and subjective sleep quality in both sexes, but similar to our results, NAFLD was associated with habitual sleep efficiency in women although this association did not remain after adjusting for BMI [29]. Chou et al. [30] examined the association of NAFLD with sleep quality using the global PSQI score in a study of 6,663 Taiwanese participants aged over 18 years. After adjusting for many confounders similar to our study, they found poor sleep quality to be associated with lower prevalence of both mild and moderate-to-severe NAFLD in men, but not in women [30]. Kim et al.’s study also examined the association between NAFLD and sleep quality [18]. A global PSQI score ≥ 5 (indicative of poor sleep quality) was positively associated with NAFLD in men after adjustment for sociodemographic, lifestyle and clinical variables [18]. The studies by Chou et. al [30] and Kim et. al [18] did not evaluate individual sleeping components separately as was done in Takahashi et al. [29] and our study.

Only few other studies have evaluated other sleep-related variables. Wang et al. [32] showed that bed time was associated with a diagnosis of NAFLD after adjusting for age, sex, and several metabolic variables in 22,807 Chinese participants; participants with later bed time were more likely to have NAFLD (compared with < 10 pm, 10 pm-12am: OR = 1.80 [1.05–1.32) and > 12am: OR = 1.42 [1.21–1.68)) [32]. This association was nevertheless substantially attenuated in comprehensively adjusted models in our study. In agreement with our findings, the authors found no interaction between BMI and bedtime [32]. A population-based study of 6,881 Chinese night-shift steelworkers suggested that the duration, length and frequency of night shift work were associated with NAFLD [33]. Late sleepers may be prone to metabolic disorders due to imbalances between external and internal clock, which can lead to postponing sleep and eating meals late at night, resulting in reducing melatonin secretion and disrupting circadian rhythm. Genetic studies have also found that genetic predispositions to late bed time and metabolic disorders were correlated [34,35,36].

Restless legs syndrome, one of the most common sleep disturbances in people with chronic conditions [37, 38], was negatively associated with NAFLD in our study, although the association was attenuated in comprehensively-adjusted models (RR = 0.87, 95% CI: 0.73–1.04). Mir et al.’s population-based study of 10,541 in the US found a weakly positive association [39]. Similarly, for daytime napping, our results were partially in agreement with two cross-sectional studies [17, 40]. A community-based population study of 8,559 middle-aged and elderly Chinese participants found that those with long nap duration (> 0.5 h) (OR = 1.22, 95% CI: 1.06–1.41) were more likely to have NALFD compared with those who did not nap; the authors did not find effect modification by obesity status as in our study, but findings are difficult to compare because of different BMI cut-off points used, and a lower proportion of obese participants in the Chinese study [17]. Another cross-sectional study of middle-aged Chinese participants suggested that NALFD was more frequent in participants reporting > 0.5 h napping per day compared with no napping [40]. Studies taken together are consistent with a weak association between daytime napping and NAFLD.

Finally, the use of sleeping pills was substantially more frequent (+ 50%) in our participants with NAFLD, which likely reflected their history of poor quality / short sleep or medical conditions. This association was i) robust to comprehensive adjustments and ii) consistent across age, sex, and BMI subgroups. Since sleeping pills improve sleep quality and extend sleeping time, this better regulation of the circadian rhythm could theoretically be beneficial for patients with metabolic conditions via an effect on glucose metabolism and insulin sensitivity [41]. Such beneficial effect in reducing the risk of NAFLD is nevertheless unclear and should be examined in prospective studies.

Conclusions

Poor sleep quality is a common issue in middle-aged Iranians with NAFLD, and the associations we observed were not fully explained by sociodemographic, lifestyle, or other clinical conditions. Although our study could not unravel the causal relationship between quality and duration of sleep and NAFLD, it highlights that sleep disturbances may affect the quality of life of these patients, and potentially worsen their prognosis.

Availability of data and materials

The SCS data are not open-access, but external researchers can use the data for collaborative projects. The process relative to data access and collaboration can be obtained from the corresponding author Dr. Ali Ahmadi, or at info@ persiancohort.com.

Abbreviations

NAFLD:

Non-alcoholic fatty liver disease

PERSIAN:

Prospective Epidemiological Research Studies in Iran

BMI:

Body mass index

MetS:

Metabolic syndrome

T2D:

Type 2 diabetes

SCS:

Shahrekord Cohort Study

HDL:

High-density lipoprotein

LDL:

Low-density lipoprotein

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

TG:

Triglycerides

WC:

Waist circumference

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

SD:

Standard deviation

RR:

Relative risks

CI:

Confidence intervals

ATPIII:

National Cholesterol Education Program Adult Treatment Panel III criteria

PSQI:

The Pittsburgh Sleep Quality Index

OR:

odds ratio

References

  1. Wong T, Wong RJ, Gish RG. Diagnostic and treatment implications of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Gastroenterology & hepatology. 2019;15(2):83.

    Google Scholar 

  2. Leoni S, et al. Current guidelines for the management of non-alcoholic fatty liver disease: a systematic review with comparative analysis. World J Gastroenterol. 2018;24(30):3361.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ahmadi A, et al. Metabolic Control And Care Assessment in Patients with Type 2 Diabetes In Chaharmahal & Bakhtiyari Province. Iran J Endocrinol Metab. 2009;11(1):33–9.

  4. Hu X-Y, et al. Risk factors and biomarkers of non-alcoholic fatty liver disease: an observational cross-sectional population survey. BMJ Open. 2018;8(4): e019974.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Centre, N.G., National Institute for Health and Care Excellence: Guidance. Non-Alcoholic Fatty Liver Disease: Assessment and Management. 2016, London: National Institute for Health and Care Excellence (UK) Copyright © National Institute for Health and Care Excellence 2016.

  6. Spiegel K, et al. Effects of poor and short sleep on glucose metabolism and obesity risk. Nat Rev Endocrinol. 2009;5(5):253–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jennings JR, et al. Self-reported sleep quality is associated with the metabolic syndrome. Sleep. 2007;30(2):219–23.

    Article  PubMed  Google Scholar 

  8. Grandner MA, et al. Sleep disturbance is associated with cardiovascular and metabolic disorders. J Sleep Res. 2012;21(4):427–33.

    Article  PubMed  Google Scholar 

  9. Wijarnpreecha K, et al. Short sleep duration and risk of nonalcoholic fatty liver disease: a systematic review and meta-analysis. J Gastroenterol Hepatol. 2016;31(11):1802–7.

    Article  PubMed  Google Scholar 

  10. Mi SJ, et al. Associations of sleep patterns with metabolic syndrome indices, body composition, and energy intake in children and adolescents. Pediatr Obes. 2019;14(6): e12507.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zizi F, et al. Sleep duration and the risk of diabetes mellitus: epidemiologic evidence and pathophysiologic insights. Curr DiabRep. 2010;10(1):43–7.

    Google Scholar 

  12. Shen N, Wang P, Yan W. Sleep duration and the risk of fatty liver disease: a systematic review and meta-analysis. Sci Rep. 2016;6(1):1–6.

    CAS  Google Scholar 

  13. Grandner MA, et al. Relationship between sleep duration and body mass index depends on age. Obesity. 2015;23(12):2491–8.

    Article  PubMed  Google Scholar 

  14. Krističević T, Štefan L, Sporiš G. The associations between sleep duration and sleep quality with body-mass index in a large sample of young adults. Int J Environ Res Public Health. 2018;15(4):758.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Potter GD, Cade JE, Hardie LJ. Longer sleep is associated with lower BMI and favorable metabolic profiles in UK adults: findings from the national diet and nutrition survey. PLoS ONE. 2017;12(7): e0182195.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Yu JH, et al. Obstructive sleep apnea with excessive daytime sleepiness is associated with non-alcoholic fatty liver disease regardless of visceral fat. Korean J Intern Med. 2015;30(6):846.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Peng K, et al. Short sleep duration and longer daytime napping are associated with non-alcoholic fatty liver disease in Chinese adults: 夜间睡眠时间减少和午睡时间过长与中国成年人非酒精性脂肪性肝病相关. J Diabetes. 2017;9(9):827–36.

    Article  CAS  PubMed  Google Scholar 

  18. Kim C-W, 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(2):351–7.

    Article  PubMed  Google Scholar 

  19. Ahmadi A, et al. Non-communicable diseases in the southwest of Iran: profile and baseline data from the Shahrekord PERSIAN cohort study. BMC Public Health. 2021;21(1):2275.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Poustchi H, et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): rationale, objectives, and design. Am J Epidemiol. 2018;187(4):647–55.

    Article  PubMed  Google Scholar 

  21. Khaledifar A, et al. The protocol of a population-based prospective cohort study in southwest of Iran to analyze common non-communicable diseases: Shahrekord cohort study. BMC Public Health. 2018;18(1):1–10.

    Article  Google Scholar 

  22. Zarean E, et al. Prevalence and risk factors of non-alcoholic fatty liver disease in southwest Iran: a population-based case-control study. Clin Exp Hepatol. 2019;5(3):224.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Reed DL, Sacco WP. Measuring sleep efficiency: what should the denominator be? J Clin Sleep Med. 2016;12(2):263–6.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lee SW. Methods for testing statistical differences between groups in medical research: statistical standard and guideline of life cycle committee. Life Cycle. 2022;2: e1.

    Article  Google Scholar 

  25. McNutt L-A, et al. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003;157(10):940–3.

    Article  PubMed  Google Scholar 

  26. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3(1):1–13.

    Article  Google Scholar 

  27. Armstrong BG. Effect of measurement error on epidemiological studies of environmental and occupational exposures. Occup Environ Med. 1998;55(10):651–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Miyake T, et al. Short sleep duration reduces the risk of nonalcoholic fatty liver disease onset in men: a community-based longitudinal cohort study. J Gastroenterol. 2015;50(5):583–9.

    Article  PubMed  Google Scholar 

  29. Takahashi A, et al. Effects of sleep quality on non-alcoholic fatty liver disease: a cross-sectional survey. BMJ Open. 2020;10(10): e039947.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chou Y-T, 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(6):500–5.

    Article  PubMed  Google Scholar 

  31. Buysse DJ, et al. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

    Article  CAS  PubMed  Google Scholar 

  32. Wang H, et al. Association between bedtime and the prevalence of newly diagnosed non-alcoholic fatty liver disease in adults. Liver Int. 2018;38(12):2277–86.

    Article  PubMed  Google Scholar 

  33. Zhang S, et al. Rotating night shift work and non-alcoholic fatty liver disease among steelworkers in China: a cross-sectional survey. Occup Environ Med. 2020;77(5):333–9.

    Article  PubMed  Google Scholar 

  34. Haas JT, Francque S, Staels B. Pathophysiology and mechanisms of nonalcoholic fatty liver disease. Annu Rev Physiol. 2016;78:181–205.

    Article  CAS  PubMed  Google Scholar 

  35. Vetter C, et al. Night shift work, genetic risk, and type 2 diabetes in the UK Biobank. Diabetes Care. 2018;41(4):762–9.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Mukherji A, et al. The circadian clock and liver function in health and disease. J Hepatol. 2019;71(1):200–11.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wijarnpreecha K, et al. Insomnia and risk of nonalcoholic fatty liver disease: a systematic review and meta-analysis. J Postgrad Med. 2017;63(4):226–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kumar A, Gupta R, Gupta R. Prevalence of RLS among subjects with chronic liver disease and its effect on sleep and mood. Sleep Med. 2020;73:144–52.

    Article  PubMed  Google Scholar 

  39. Mir HM, et al. Association of Sleep Disorders with nonalcoholic fatty liver disease (NAFLD): a population-based study. J Clin Exp Hepatol. 2013;3(3):181–5.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Qu H, et al. Associations between longer habitual day napping and non-alcoholic fatty liver disease in an elderly Chinese population. PLoS ONE. 2014;9(8): e105583.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Thomas AP, et al. Administration of melatonin and metformin prevents deleterious effects of circadian disruption and obesity in male rats. Endocrinology. 2016;157(12):4720–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to Shahrekord Cohort Centre for the facilitation of the process and data collection to perform this study.

Funding

No financial support was received for this research.

Author information

Authors and Affiliations

  1. Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran

    Elaheh Zarean

  2. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia

    Elaheh Zarean & Pierre-Antoine Dugué

  3. Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Science, Tehran, Iran

    Mehdi Azizmohammad Looha

  4. Department of Biostatistics, School of Public Health, IRAN University of Medical Sciences, Tehran, Iran

    Payam Amini

  5. Department of Epidemiology and Biostatistics, School of Health and, Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran

    Ali Ahmadi

  6. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia

    Pierre-Antoine Dugué

  7. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia

    Pierre-Antoine Dugué

Authors
  1. Elaheh Zarean
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehdi Azizmohammad Looha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Payam Amini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Ahmadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pierre-Antoine Dugué
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization and study design: all authors; Data curation: EZ and AA; Data analysis: EZ-MA; Project supervision: P-AD; Manuscript writing – original draft: EZ and P-AD; Manuscript review and editing: all authors; Project administration and funding: AA. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Ali Ahmadi.

Ethics declarations

Ethics approval and consent to participate

The study was performed in line with the guidelines of the Declaration of Helsinki and the National Ethical Guidelines in Biomedical Research in Iran. This study was conducted as a part of a research project approved by the Ethics Committee of the Shahrekord University of Medical Sciences (SKUMS) (research code: IR.SKUMS.REC.1397.160.). All participants provided signed and fingerprinted informed written consent, as per the SKUMS Ethics Committee guidelines. For illiterate participants, the informed consent form was read. After authorization, the consent form was fingerprinted and signed and confirmed by their first-degree family member or legally authorized representatives. The participants can withdraw from the study whenever they wish. Data are stored in an anonymized and protected confidential database.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zarean, E., Looha, M.A., Amini, P. et al. Sleep characteristics of middle-aged adults with non-alcoholic fatty liver disease: findings from the Shahrekord PERSIAN cohort study. BMC Public Health 23, 312 (2023). https://doi.org/10.1186/s12889-023-15251-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-15251-4

Keywords

  • Sleep duration
  • Sleep quality
  • Fatty liver disease
  • Cohort study
  • Quality of life

BMC Public Health

ISSN: 1471-2458

Contact us