Yu QT, Lin YY, Gui ZH, Duan HL, Yang JJ, Meng Y, Peng XT, Liu L, Wan H, Shen J. Mediating roles of insulin resistance and inflammatory markers between healthy sleep scores and metabolic dysfunction-associated steatotic liver disease. World J Gastroenterol 2026; 32(21): 118057 [DOI: 10.3748/wjg.v32.i21.118057]
Corresponding Author of This Article
Jie Shen, Department of Endocrinology and Metabolism, The Eighth Affiliated Hospital, Southern Medical University (The First People’s Hospital of Shunde), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan 528308, Guangdong Province, China. sjiesy@smu.edu.cn
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Yu QT, Lin YY, Gui ZH, Duan HL, Yang JJ, Meng Y, Peng XT, Liu L, Wan H, Shen J. Mediating roles of insulin resistance and inflammatory markers between healthy sleep scores and metabolic dysfunction-associated steatotic liver disease. World J Gastroenterol 2026; 32(21): 118057 [DOI: 10.3748/wjg.v32.i21.118057]
World J Gastroenterol. Jun 7, 2026; 32(21): 118057 Published online Jun 7, 2026. doi: 10.3748/wjg.v32.i21.118057
Mediating roles of insulin resistance and inflammatory markers between healthy sleep scores and metabolic dysfunction-associated steatotic liver disease
Qing-Tao Yu, Zi-Hao Gui, Hua-Lin Duan, Lan Liu, Heng Wan, Jie Shen, Department of Endocrinology and Metabolism, The Eighth Affiliated Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan 528308, Guangdong Province, China
Yun-Ying Lin, Yuan Meng, Xue-Tao Peng, Lan Liu, Heng Wan, Jie Shen, Guangdong Engineering Technology Research Center of Metabolic Disorders Interdisciplinary Precision Prevention and Digital Healthcare, The Eighth Affiliated Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan 528308, Guangdong Province, China
Yun-Ying Lin, Jie Shen, School of Nursing, Southern Medical University, Guangzhou 510000, Guangdong Province, China
Jun-Jie Yang, The Chronic Disease Prevention and Treatment Center of Shunde District, The Eighth Affiliated Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan 528308, Guangdong Province, China
Author contributions: Yu QT, and Lin YY contributed equally as co-first authors; Wan H and Shen J contributed equally as co-corresponding authors; concept and design were performed by Shen J, Wan H, and Liu L; acquisition, analysis, or interpretation of data and statistical analysis were performed by Yu QT, Gui ZH, Lin YY, Duan HL, Yang JJ, Meng Y, and Peng XT; drafting of the manuscript were performed by Yu QT, Wan H, and Gui ZH; critical review of the manuscript and supervision were performed by Shen J, Wan H, and Liu L; all authors read and approve the final manuscript.
Supported by the Guangdong Basic and Applied Basic Research Foundation, No. 2023A1515140062; and Guangdong-Hong Kong Technology Cooperation Funding Scheme, No. 2024A0505040004.
Institutional review board statement: The Medical Ethics Committee of Shunde Hospital of Southern Medical University approved the study protocol (Approval No. Research Ethics Review 20211103) according to the ethical guidelines of the 1975 Declaration of Helsinki.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All authors declare no disclosure of interest for this contribution.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: The data and material supporting the findings of the study are available from the corresponding authors upon reasonable request.
Corresponding author: Jie Shen, Department of Endocrinology and Metabolism, The Eighth Affiliated Hospital, Southern Medical University (The First People’s Hospital of Shunde), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan 528308, Guangdong Province, China. sjiesy@smu.edu.cn
Received: December 29, 2025 Revised: February 5, 2026 Accepted: March 11, 2026 Published online: June 7, 2026 Processing time: 151 Days and 1.8 Hours
Abstract
BACKGROUND
Suboptimal sleep quality is increasingly recognized as a potential modifiable risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD). However, epidemiological evidence remains limited and inconsistent. Importantly, the underlying biological mechanisms, particularly the potential mediating pathways linking sleep to MASLD pathogenesis, are still poorly understood.
AIM
To investigate the relationship between sleep quality and MASLD prevalence, along with its potential mechanisms, in a community-based Chinese population.
METHODS
A total of 7871 adults were enrolled from 10 communities in Foshan, Guangdong Province, China. Sleep health status over the preceding month was assessed using the healthy sleep scores (HSS). The associations between HSS and MASLD were analyzed using multivariable logistic regression models. Mediation analyses were conducted to quantify the mediating effects of homeostasis model assessment of insulin resistance (HOMA-IR), remnant cholesterol (RC), non-high-density lipoprotein cholesterol (non-HDL-C), and high-sensitivity C-reactive protein (hs-CRP) on the relationship between HSS and MASLD.
RESULTS
Participants with an optimal HSS of 5 exhibited a significantly reduced prevalence of MASLD compared to those with an HSS of 0-2, corresponding to a 35.5% reduction in risk (OR = 0.65; 95%CI: 0.52-0.80, P < 0.001). Among the five components of the HSSs, the absence of sleep apnea was most strongly associated with a decreased risk of MASLD, indicating a 31.8% reduction in risk (OR = 0.68; 95%CI: 0.47-0.98, P = 0.040). Mediation analyses revealed that the relationship between HSS and MASLD was partially mediated by HOMA-IR (mediation proportion 23.2%), hs-CRP (9.7%), RC (5.3%), and non-HDL-C (6.1%).
CONCLUSION
Higher HSS scores were associated with lower prevalence of MASLD, with HOMA-IR, hs-CRP, RC, and non-HDL-C serving as partial mediators of this association.
Core Tip: In a Chinese population, better sleep health, as measured by a composite healthy sleep score, is associated with a reduced prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD). This association is partially explained by mediating factors including insulin resistance, inflammation, remnant cholesterol, and non-high-density lipoprotein cholesterol. The findings highlight the improvement of overall sleep patterns as a potential public health strategy for MASLD prevention.
Citation: Yu QT, Lin YY, Gui ZH, Duan HL, Yang JJ, Meng Y, Peng XT, Liu L, Wan H, Shen J. Mediating roles of insulin resistance and inflammatory markers between healthy sleep scores and metabolic dysfunction-associated steatotic liver disease. World J Gastroenterol 2026; 32(21): 118057
Metabolic dysfunction-associated steatotic liver disease (MASLD), a new term proposed by the Delphi consensus in 2023 to replace nonalcoholic fatty liver disease (NAFLD), which removes potentially stigmatizing terminology, emphasizes the crucial role of metabolism in MASLD[1]. Studies indicate that the global prevalence of MASLD among adults is approximately 30%, and this figure is projected to exceed 55% by 2040[2]. As one of the countries bearing the highest absolute burden of metabolic diseases, China reports an alarmingly annual incidence rate of MASLD as high as 5.1%[3]. Furthermore, multinational retrospective cohort studies have shown that MASLD is a significant risk factor for hepatocellular carcinoma[4]. Additionally, it is associated with an increased risk of developing heart disease, chronic kidney disease, diabetes, and obesity[3-5]. Therefore, identifying risk factors for MASLD and developing health management strategies to prevent its occurrence are urgent priorities.
Approximately one-third of human life is spent in a sleep state. Insufficient sleep and circadian rhythm disruption can induce metabolic dysregulation through mechanisms including hormonal alterations, dyslipidemia, and aberrant energy expenditure[6]. A United Kingdom Biobank-based study involving approximately 400000 participants demonstrated that healthy sleep patterns are associated with a 20% reduction in liver infection risk among hospitalized patients[7]. Sleep fragmentation has been shown to trigger pathological injury in the liver and heart via steatosis and oxidative damage, with these injuries persisting even after more than two weeks of sleep recovery[8]. Moreover, obstructive sleep apnea syndrome (OSA) leads to intermittent hypoxemia, sleep fragmentation, and sympathetic hyperactivity, all of which are established risk factors for the pathogenesis and progression of MASLD[9,10].
Since nightly sleep results from the synergistic interplay of various sleep behaviors, a composite assessment of these components offers a more holistic and clinically relevant measure of overall sleep health[11,12]. However, prior studies on sleep and MASLD have predominantly focused on individual sleep patterns[13]. Therefore, this study employs a composite healthy sleep score (HSS), which integrates five key sleep parameters: Optimal sleep duration (7-8 hours per day), morning chronotype, absence of insomnia, absence of sleep apnea, and absence of excessive daytime sleepiness. The HSS provides a rapid and convenient method for the quantitative assessment of an individual’s sleep status. Furthermore, it has been shown to be a significant predictor of cardiovascular disease and is associated with an increased risk of liver cancer incidence[11,14]. However, epidemiological evidence regarding the association between MASLD prevalence and the HSS remains limited in Chinese populations.
Furthermore, studies investigating the relationship between multiple sleep patterns and MASLD have generally been limited to a simple summation of sleep patterns and have rarely explored the mediating factors underlying this association. The development of MASLD is influenced by sleep disturbances through several distinct mechanistic pathways. Firstly, abnormal sleep patterns activate the hypothalamic-pituitary-adrenal axis and sympathetic nervous system, resulting in increased insulin resistance—as quantified by the homeostasis model assessment of insulin resistance (HOMA-IR)—and promoting hepatic lipid accumulation[15,16]. Secondly, sleep impairment disrupts lipid metabolism, raising atherogenic lipoproteins [remnant cholesterol (RC) and non-high-density lipoprotein cholesterol (non-HDL-C)] that promote hepatic steatosis[17]. Additionally, sleep dysregulation induces systemic low-grade inflammation, reflected by elevated high-sensitivity C-reactive protein (hs-CRP), worsening hepatic inflammation and injury to drive MASLD progression[16,18]. Therefore, this study aims to systematically investigate the mediating effects of metabolic and inflammatory markers in the association between the HSS and MASLD.
This study innovatively employs a composite HSS to assess multidimensional sleep health and its association with MASLD in a large Chinese population. Using causal mediation analysis, we aim to examine this association and to explore the underlying mediating roles of key metabolic and inflammatory factors. These findings may help inform the development of evidence-based, sleep-focused preventive strategies for individuals at high risk of MASLD.
MATERIALS AND METHODS
Study design and participants
This study utilized a stratified cluster sampling method to select participants. Individuals were excluded for the following reasons: Absence of transient elastography measurements (n = 3646); missing blood samples (n = 432); diagnosis of other liver diseases, including hepatitis A, hepatitis B, or alcoholic liver disease (n = 1107); and missing sleep questionnaire data (n = 479). After applying these criteria, 7871 participants were included in the final analysis (Figure 1). For variables with missing rates each below 1%, including body mass index (BMI), systolic blood pressure, diastolic blood pressure, waist circumference (WC), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), missing data were handled using mean imputation for the primary analysis[19]. Additionally, multiple imputation was performed as a sensitivity analysis, with results presented in Supplementary Table 1[19].
Figure 1 The enrollment of population.
CAP: Controlled attenuation parameter; LSM: Liver stiffness measurement.
Between November 2021 and September 2022, we conducted a community-based cross-sectional study in Shunde, Guangdong Province, China. A multistage stratified random sampling approach was employed to recruit a representative sample of adults. The study was registered at www.chictr.org.cn (ChiCTR210005413)[20,21]. All participants provided written informed consent. The study protocol was approved by the Medical Ethics Committee of Shunde Hospital of Southern Medical University (Approval No. Research Ethics Review 20211103) and was conducted in accordance with the ethical principles of the Declaration of Helsinki.
The definition of MASLD
MASLD was defined according to established multisociety Delphi consensus criteria[20,21]: The presence of hepatic steatosis detected via vibration-controlled transient elastography (VCTE), in addition to at least one of the following five metabolic abnormalities: (1) BMI ≥ 23 kg/m2 or WC > 80 cm (female)/> 94 cm (male); (2) Fasting plasma glucose ≥ 5.6 mmol/L, or 2-hour post-load glucose levels ≥ 7.8 mmol/L, or glycated hemoglobin (HbA1c) ≥ 5.7%, or a confirmed diagnosis of type 2 diabetes, or treatment for type 2 diabetes; (3) Blood pressure ≥ 130/85 mmHg or specific antihypertensive drug treatment; (4) Plasma TG ≥ 1.70 mmol/L or lipid lowering treatment; and (5) Plasma HDL-C ≤ 1.3 mmol/L (female) or ≤ 1.0 mmol/L (male), or lipid lowering treatment. Among individuals diagnosed with MASLD, those with weekly alcohol consumption of 140-350 g for females or 210-420 g for males were further classified as having MASLD with increased alcohol intake (MetALD). Liver steatosis and fibrosis were assessed by trained technicians using VCTE (FibroScan® 402 Touch; Echosens, France) with M/XL probes. Hepatic steatosis was defined as a controlled attenuation parameter ≥ 248 dB/m, and significant fibrosis as a liver stiffness measurement ≥ 8 kPa[22-24].
The definition of HSS
The HSS integrates five key parameters: (1) Sleep (7-8 hours/day); (2) Early chronotype; (3) Never/rarely insomnia; (4) No apnea symptoms; and (5) No excessive daytime sleepiness. In the SPEED-Shunde cohort, sleep-related data were collected using self-administered questionnaires. Sleep duration was calculated from self-reported bedtimes and wake times on weekdays and weekends, using the formula: [(weekday sleep duration × 5) + (weekend sleep duration × 2)]/7. An average duration of 7-8 hours/day was defined as the optimal criterion for scoring. Chronotype was classified as “early” if the calculated sleep midpoint (derived from self-reported bedtime and wake time) occurred before 3:00 AM[25]. Never/rarely insomnia was defined as answering “never” or “rarely” to experiencing difficulty sleeping; no apnea symptoms was defined as responding “never” to loud snoring or coughing during sleep; and no excessive daytime sleepiness was defined as answering “never” or “rarely” to feeling drowsy or lacking energy. Each optimal sleep characteristic was assigned 1 point, otherwise 0. Thus, the total HSS ranged from 0 to 5, with higher scores indicating a healthier overall sleep profile.
Definition of clinical and laboratory parameters
The following demographic information was collected: Sex, education level (categorized as less than high school, completed high school, or beyond high school), and smoking history (current smokers: Having smoked ≥ 100 cigarettes and currently smoking, previous smokers: Quit for > 6 months, or never smoke). Hypertension was defined as systolic blood pressure ≥ 140 mmHg, a diastolic blood pressure ≥ 90 mmHg, current use of antihypertensive medication, or a documented history of hypertension[26]. Diabetes was defined as a fasting plasma glucose level ≥ 7.0 mmol/L, a HbA1c level ≥ 6.5%, or a documented history of diabetes[27]. Physical measurements included height, weight, WC, and systolic and diastolic blood pressure. BMI was calculated as weight (kg)/height2 (m2). All participants underwent laboratory assessments after an overnight fast, including measurements of TC, TG, LDL-C, HDL-C, hs-CRP, fasting plasma glucose, and serum creatinine levels. The following indices were calculated: RC = TC - LDL-C - HDL-C[28]; HOMA-IR = [fasting plasma glucose (mmol/L) × fasting serum insulin (μU/mL)]/22.5[29]; non-HDL-C = total cholesterol - HDL-C[30].
Statistical methodology and analytical approach
Continuous variables were summarized using mean ± SD, and categorical variables were presented using frequencies. Binary logistic regression models were used to analyze the association between the HSS and MASLD, with the group scoring 0-2 on the HSS serving as the reference.
Mediation analysis was used to assess the role of mediators in the relationship between HSS and MASLD[31]. The analysis required the following conditions to be met: (1) The HSS/mediator must be associated with MASLD; (2) The HSS must be associated with the mediator; and (3) After adjusting for the mediator, the association between the HSS and MASLD should be attenuated. When these conditions were met, bootstrapping with 1000 iterations was used to estimate the significance of the indirect (mediation) effect. The mediation proportion was calculated as the ratio of the indirect effect to the total effect.
Binary logistic regression models assessing the association between MASLD prevalence and HSS were adjusted for the following covariates: Age, sex, physical activity, smoking status, educational attainment, BMI, hypertension, and diabetes. Subgroup analyses were performed by stratifying the entire cohort based on sex (male, female), age (< 50 years, ≥ 50 years), and BMI (≤ 24.9 kg/m2: Underweight and normal weight; > 24.9 kg/m2: Overweight and obesity).
Sensitivity analyses were also performed by using the HSS 0-1 group and the HSS 0-3 group as alternative reference groups. Additionally, multiple imputation was employed to handle missing data to assess the robustness of our findings[32]. The robustness of the chronotype classification was further tested using alternative cutoff points (2:00 AM) for the sleep midpoint[33]. Furthermore, we examined the consistency of the primary association by applying different categorizations of the HSS.
Statistical significance was defined as a two-sided P < 0.05. All analyses were conducted using R software (version 4.4.2) and IBM SPSS Statistics (version 27).
RESULTS
Study population and characteristics
A total of 7871 participants were included in this study, of whom 1716 (21.8%) were diagnosed with MASLD (Table 1). The overall cohort comprised 62.9% females. A higher prevalence of MASLD was observed in males, individuals with lower educational attainment, current smokers, and those with elevated BMI or larger WC. Compared to those without MASLD, participants with MASLD had significantly higher serum levels of TG, TC, LDL-C, LDL, and RC. Additionally, they had a higher prevalence of hypertension and diabetes, and were more likely to have significant liver fibrosis.
Table 1 General characteristics of participants in the study.
Regarding the components of the HSS among MASLD patients, 59.9% reported sleep 7-8 hours/day, 67.6% were of early chronotype, 75.9% reported rarely/never insomnia, 96.5% had no apnea symptoms, and 32.2% reported no excessive daytime sleepiness. Participants with higher HSS scores (4-5) had a significantly lower prevalence of MASLD (Table 1). And higher BMI was associated with lower HSS scores (Supplementary Table 2).
The associations between sleep quality and MASLD
As shown in Table 2, after full adjustment for covariates, among the five components of the HSS, no apnea symptoms showed the strongest inverse association with MASLD (OR = 0.68; 95%CI: 0.47-0.98; P = 0.04), followed by early chronotype (OR = 0.72; 95%CI: 0.62-0.83; P < 0.001). Compared with the reference group (HSS 0-2), individuals with scores of 3, 4, and 5 had progressively lower odds of MASLD, 12.1% (P < 0.001), 29.6% (P < 0.001), and 35.5% (P < 0.001), respectively. This inverse dose-response relationship between the HSS and MASLD remained consistent across all sensitivity analyses. Using the HSS 0-1 group as the reference, participants with an HSS of 5 had significantly lower odds of MASLD (OR = 0.51; 95%CI: 0.36-0.72; P < 0.01). Similarly, individuals with high sleep scores (4-5) showed significantly reduced odds of MASLD compared to those with low sleep scores (0-3) after full adjustment (OR = 0.74; 95%CI: 0.66-0.84; P < 0.01). Additionally, we conducted a sensitivity analysis by redefining early chronotype using an earlier sleep midpoint cutoff (< 2:00 AM) to examine the robustness of its association with MASLD. The consistency of the findings was further confirmed through a sensitivity analysis using multiple imputation (Supplementary Tables 1, 3 and 4).
Table 2 Association between the individual components and total score of healthy sleep score with metabolic dysfunction-associated steatotic liver disease.
Mediating effects of metabolic and inflammatory indicators
Our analysis revealed a significant mediating role of metabolic and inflammatory indicators in the association between HSS and MASLD. We categorized four key indicators—RC, non-HDL-C, HOMA-IR, and hs-CRP—into quartiles and examined their role in mediating the HSS-MASLD association. Given the close association between HOMA-IR and diabetes, diabetes was not adjusted for in the mediation analysis involving HOMA-IR.
As shown in Figure 2, RC account for 5.3% (P = 0.020), non-HDL-C showed 6.1% (P = 0.016), and HOMA-IR demonstrated the most substantial effect at 23.2% (P < 0.001), the inflammatory indicator hs-CRP accounted for 9.7% (P < 0.001) of the association between HSS and MASLD. The stability of these mediation effects was further confirmed through sensitivity analyses employing alternative model specifications (Supplementary Figures 1 and 2) and by performing the analyses under both minimally and fully adjusted models (Supplementary Table 5).
Figure 2 Mediating role of metabolic and inflammatory indicators in the healthy sleep score and metabolic dysfunction-associated steatotic liver disease relationship.
A: Mediating role of remnant cholesterol in the healthy sleep score (HSS)-metabolic dysfunction-associated steatotic liver disease (MASLD) association; B: Mediating role of non-high-density lipoprotein cholesterol (non-HDL-C) in the HSS-MASLD association; C: Mediating role of homeostasis model assessment of insulin resistance (HOMA-IR) in the HSS-MASLD association; D: Mediating role of high-sensitivity C-reactive protein in the HSS-MASLD association. Diabetes was not adjusted for in the covariates when HOMA-IR was as the mediator. aP < 0.05; bP < 0.001. MASLD: Metabolic dysfunction-associated steatotic liver disease; RC: Remnant cholesterol; Non-HDL-C: Non-high-density lipoprotein cholesterol; HOMA-IR: Homeostasis model assessment of insulin resistance; hs-CRP: High-sensitivity C-reactive protein; HSS: Healthy sleep score.
Additionally, we assessed the independent association of each potential mediator with MASLD using binary logistic regression. Higher quartile levels of all mediators were significantly associated with increased prevalence of MASLD. Notably, participants in the highest quartile (Q4) of HOMA-IR had 5.86 (95%CI: 4.60-7.46; P < 0.01) times the odds of having MASLD compared to those in the lowest quartile (Q1; Supplementary Table 6).
Subgroup analysis
The inverse association between HSS and MASLD was consistent across all predefined subgroups stratified by age, sex, and BMI (Figure 3). The protective association of favorable sleep patterns appeared similar in magnitude across these subgroups. In both age subgroups (< 50 years and ≥ 50 years), a high HSS was associated with an approximately 30% lower risk of MASLD compared with the reference group. The absence of significant interaction effects (all P for interaction > 0.05) further underscores the robustness of this association across diverse demographic and metabolic profiles.
Figure 3 Subgroup analysis of the association between healthy sleep score and metabolic dysfunction-associated steatotic liver disease.
The comprehensive model was adjusted for age, gender, physical exercise, smoking, education, body mass index (BMI), hypertension and diabetes. The model was adjusted for gender, physical exercise, smoking, education, BMI, hypertension and diabetes when age categories (< 50 years and ≥ 50 years) was analyzed in group. The model was adjusted for age, physical exercise, smoking, education, BMI, hypertension and diabetes when sex categories (male and female) was analyzed in group. The model was adjusted for age, sex, physical exercise, smoking, education, hypertension and diabetes when BMI categories (under-weight and normal weight BMI ≤ 24.9 kg/m2, overweight and obesity BMI > 24.9 kg/m2) was analyzed in group. Data are presented as the logistic regression index (95%CI). BMI: Body mass index.
In the sex-stratified analysis, a high HSS was associated with a 38% lower risk of MASLD in males (P = 0.005) and a 29% lower risk in females (P = 0.02). Similarly, within BMI-based subgroups, high HSS was associated with a 30% risk reduction among underweight/normal-weight individuals (P = 0.024) and a 36% reduction among those with overweight/obesity (P = 0.002). These subgroup findings remained consistent in sensitivity analyses using alternative HSS classifications (Supplementary Figures 3 and 4).
DISCUSSION
Our study demonstrates that healthy sleep patterns are strongly associated with a reduced risk of MASLD. An inverse relationship was observed between HSS and the prevalence of MASLD. Individuals with optimal sleep patterns (HSS = 5) exhibited a 35.5% lower prevalence of MASLD compared to those with poor sleep health (HSS 0-2). This study also provides the first systematic evaluation of the partial mediating role of metabolic and inflammatory indicators in the association between sleep patterns and MASLD. Four key indicators—RC and non-HDL-C (lipid metabolism), HOMA-IR (glucose metabolism), and hs-CRP (systemic inflammation)—were investigated as potential mediators. Mediation analysis quantified the proportion of the sleep-MASLD association explained by each.
A prospective cohort study of 480000 individuals from the United Kingdom Biobank revealed a U-shaped association between sleep duration and MASLD, indicating that a moderate duration of 7-8 hours is associated with the lowest risk of adverse MASLD outcomes[34]. Consistent with this, both excessively long and short sleep durations have been linked to various metabolic disturbances, including microvascular complications[18], glucose metabolic abnormalities, obesity, and metabolic disorders[35], all of which can exacerbate conditions such as MASLD and metabolic syndrome. Furthermore, experimental evidence suggests that sleep deprivation directly induces hepatic steatosis and promotes lipid accumulation[36,37]. Most hepatic circadian clocks are synchronized primarily with natural light-dark cycles. Individuals with an early chronotype and no insomnia are more likely to maintain homeostasis of circadian clock genes and hormones, as well as sustain regular feeding rhythms, thereby reducing their risk of MASLD[38-41]. OSA, characterized by intermittent hypoxia, has been shown to promote the polarization of macrophages toward the M1 phenotype in the livers of MASLD mice and increases the level of ferroptosis in human liver tissues, thereby accelerating hepatic injury and the progression of fatty liver disease[42]. Furthermore, two multinational retrospective network cohort studies involving approximately 128 million patients worldwide found that individuals with type 2 diabetes and OSA were at a higher risk for adverse liver-related outcomes—a finding supported by similar pro-inflammatory mechanisms observed in mice[9,43,44]. Similarly, excessive daytime sleepiness has been significantly associated with insulin resistance and adipose tissue dysfunction[45].
In contrast, our study adopted a comprehensive approach by integrating multiple healthy sleep characteristics into a composite score, thereby enabling a more holistic assessment of overall sleep health in relation to MASLD. This integrative method aligns with and extends previous findings. For instance, a prospective cohort study demonstrated that adherence to all five components of the HSS was associated with a 23% lower risk of developing MASLD[46]. Similarly, a cross-sectional study conducted in China involving 10089 participants revealed that individuals with poor sleep quality combined with insufficient physical activity had a 2.36-fold higher prevalence of MASLD compared to those with adequate sleep and moderate-to-vigorous physical activity levels[47]. Furthermore, a study of over 300000 participants found that low HSS scores were significantly associated with an increased risk of incident severe MASLD, with renal function biomarkers mediating 10.08% of this effect[48]. Taken together, evidence from diverse populations and study designs highlights the important predictive and clinical utility of the composite HSS in the context of metabolic liver diseases.
While the link between sleep and MASLD is established, the underlying mechanisms, particularly the mediating roles of specific metabolic and inflammatory pathways, have not been fully elucidated. Our results are consistent with RC, non-HDL-C, HOMA-IR, and hs-CRP having partially mediated the association between HSS and MASLD, explaining approximately 5.3%, 6.1%, 23.2%, and 9.7% of the association, respectively. Notably, these factors are pathophysiologically interrelated, as insulin resistance is known to promote both dyslipidemia and inflammation[49], supporting the plausibility of their combined mediating effect. Supporting the role of dyslipidemia as a mediator, a prospective United States study of 21 participants demonstrated that disrupted light exposure rhythms can induce phase shifts in plasma lipids, thereby dysregulating lipid metabolism and promoting MASLD development[50]. Similarly, regarding insulin resistance, an 8-week observational study found that individuals with sleep deficiency were more likely to exhibit increased HOMA-IR following a dietary intervention[51]. Furthermore, sleep disorders can elevate stress responses, leading to alterations in glucose metabolism and contributing to insulin resistance[45]. Multiple studies have established that various unhealthy sleep patterns promote systemic inflammation[44,52]. Reducing inflammatory factors such as tumor necrosis factor-alpha, inhibiting macrophage activation, and blocking the NF-κB pathway can alleviate the progression of hepatic steatosis[53,54]. These alterations in inflammatory cytokines may indirectly impact the systemic levels of hs-CRP. Subgroup analyses further demonstrated that healthier overall sleep patterns are significantly associated with a reduced risk of MASLD onset and progression, and this association is consistent across different genders and age groups.
The major contributions of this study are: First, utilizing large-sample cross-sectional data from a southern Chinese population, it provides systematic evidence on the association between composite sleep patterns and MASLD and elucidates the underlying mediating mechanisms. To our knowledge, this is the first study to systematically evaluate the potential mediating roles of metabolic and inflammatory indicators in the observed association between sleep patterns and MASLD. Second, the simple sleep scoring system developed in this study holds considerable translational potential for public health. Its practicality and low cost could facilitate large-scale population screening and community health management. The results underscore the cumulative impact of multiple adverse sleep factors and highlight, from a public health perspective, the importance of promoting overall healthy sleep patterns for the primary prevention of MASLD. These findings provide an evidence-based rationale for sleep-focused interventions and could inform broader strategies to reduce the population-level burden of MASLD[55].
This study has several limitations. First, sleep parameters, particularly OSA, were assessed using self-reported questionnaires rather than objective measures like polysomnography. This method is susceptible to recall and misclassification bias, as OSA is often under-diagnosed in community settings, and individuals with mild-to-moderate cases may lack typical symptoms. Consequently, a substantial proportion of participants (i.e., 96.5%) classified as “no apnea symptoms” could have been misclassified, with true OSA cases potentially included in the asymptomatic group. Second, due to the cross-sectional design of this study, the temporal sequence among sleep patterns, mediating indicators (such as HOMA-IR), and MASLD cannot be determined. The possibility of reverse causality between sleep quality and MASLD cannot be ruled out; for instance, subclinical liver injury may also affect sleep quality[45]. While our findings are consistent with a pathway whereby sleep patterns influence MASLD risk via mechanisms like insulin resistance, definitive causal inferences require validation through future prospective or intervention studies, such as longitudinal designs with repeated measures. Third, MASLD was diagnosed using VCTE instead of liver biopsy. However, VCTE is susceptible to interference from factors such as obesity, operator skill, and hepatic inflammation, and unlike biopsy, it cannot provide direct histological visualization of subtle fat droplets, which may limit its accuracy in detecting early-stage MASLD lesions[56]. Fourth, the sleep score assigns equal weight to each component, while some may have a stronger association with MASLD. Finally, the study participants were recruited exclusively from Foshan (Guangdong Province, China). This regional specificity may limit the generalizability of our findings to other populations with different demographic or geographical backgrounds. Future studies should incorporate prospective designs with objective sleep monitoring (e.g., actigraphy, polysomnography) and liver histopathology to validate our findings, and adopt multicenter frameworks to enhance the generalizability of the conclusions.
CONCLUSION
In summary, a lower HSS was significantly associated with a higher prevalence of MASLD. This association appears to be partially mediated through metabolic and inflammatory pathways. Our findings highlight the importance of addressing sleep health in clinical practice for MASLD prevention and suggest that improving sleep quality could be a valuable public health strategy to alleviate the disease burden. These conclusions, however, should be confirmed in large-scale prospective cohort studies.
ACKNOWLEDGEMENTS
We would like to express our sincere gratitude to all individuals who contributed to this study.
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Footnotes
Peer review: Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific quality: Grade B, Grade B, Grade B
Novelty: Grade B, Grade B, Grade B
Creativity or innovation: Grade B, Grade B, Grade B
Scientific significance: Grade B, Grade B, Grade B
P-Reviewer: Fan XC, MD, PhD, Post Doctoral Researcher, Research Assistant Professor, China; Mao RF, PhD, Professor, China S-Editor: Lin C L-Editor: A P-Editor: Zhang L
