Background: Both insufficient and excessive catch-up sleep durations have been implicated in influencing the risk of cardiovascular diseases (CVDs) and metabolic disorders. However, the specific impact of weekend catch-up sleep (WCS) on the relationship between hepatic steatosis (HS) and atherosclerotic cardiovascular disease (ASCVD) remains unclear.
Aims: This cross-sectional study aims to examine the potential regulatory effect of WCS on the association between controlled attenuation parameter (CAP) and ASCVD.
Methods: Weighted logistic regression analyses were employed to evaluate the associations of CAP and WCS with ASCVD, expressed in terms of odds ratios (ORs) and 95% confidence intervals (CIs). The study also explored the effect of WCS on the CAP-ASCVD relationship and assessed the potential regulatory role of WCS in subgroups based on age, gender, body mass index, and obesity status.
Results: Eligible participants were categorized into two groups: those with an ASCVD risk < 7.5% (n = 1536) and those with an ASCVD risk ≥ 7.5% (n = 1612). After adjusting for covariates, CAP ≥ 274 dB/m was associated with a higher likelihood of ASCVD compared to the CAP < 274 dB/m (OR, 1.84, 95% CI, 1.24-2.73). When compared to a WCS duration of 0-1 hour, WCS ≥ 1 hour was found to increase the potential ASCVD risk associated with CAP (OR, 3.29, 95% CI, 1.41-7.68). Furthermore, among individuals with WCS ≥ 1 hour, CAP ≥ 274 dB/m was linked to a higher ASCVD risk than among those with CAP < 274 dB/m (OR, 3.72, 95% CI, 1.99-6.93). Additionally, in subgroups of participants aged ≥ 60 years, females, non-obese and obese individuals, WCS ≥ 1 hour was associated with an increased ASCVD risk related to CAP (all, p < 0.05).
Conclusion: A WCS duration of ≥ 1 hour may be associated with an increased ASCVD risk in adults with HS aged ≥ 40 years. However, further research is necessary to clarify the causal relationships between WCS, HS, and ASCVD.
Hepatic steatosis (HS) continues to be highly prevalent globally, imposing a significant disease burden.1,2 In addition to hepatic complications like cirrhosis and hepatocellular carcinoma, cardiovascular diseases (CVDs), particularly atherosclerotic cardiovascular disease (ASCVD), represent critical adverse outcomes in HS patients, potentially linked to metabolic aberrations associated with HS.3-6 Recent evidence indicates that both shortened and prolonged sleep durations may influence CVD risk and associated risk factors, including metabolic syndrome and arterial stiffness.7-10 Specifically, in patients with non-alcoholic fatty liver disease (NAFLD), reduced sleep duration and compromised sleep quality have been correlated with increased arterial stiffness.11 Additionally, the duration of catch-up sleep has garnered increasing research attention. Data suggest that extending sleep, termed catch-up sleep, among sleep-deprived individuals may mitigate CVD and metabolic disease risk.12 For instance, moderate durations of catch-up sleep (e.g., ≤ 2 hours) have been associated with decreased risks of metabolic syndrome, hypertension, and dyslipidemia.13-16 Conversely, a recent study highlighted that individuals with catch-up sleep durations of less than 1 hour exhibited higher all-cause mortality risks related to hypertension compared to those with durations exceeding 1 hour.17 However, excessive catch-up sleep, also referred to as sleep insufficiency, may detrimentally impact cardiovascular health by increasing the risk of obesity and hypertension, and it correlates with reduced overall cardiovascular health metrics.18 Furthermore, in patients with diabetes mellitus (DM), extended catch-up sleep has been linked to heightened risks of obesity and insulin resistance.19 To date, the impact of catch-up sleep duration on ASCVD risk in the context of HS remains unexplored. This study aims to examine the influence of weekend catch-up sleep (WCS) on the association between HS and ASCVD, offering insights for the prevention of ASCVD related to HS.
Study population
This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) database spanning 2017-2020. NHANES is a nationally representative program designed to assess the health and nutritional status of the U.S. population through a complex, multistage stratified probability sampling method. The survey collects data from approximately 5,000 individuals biennially, covering 15 geographic regions. Detailed methodology is available at: NHANES Documentation. Ethical approval and informed consent requirements for this study were waived by the Institutional Review Board (IRB) due to the public availability of de-identified NHANES data.
Adults aged ≥ 40 years, with available data on liver ultrasound transient elastography, weekday/weekend sleep duration, and ASCVD risk calculation were included. Exclusion criteria included: (1) history of CVD, (2) hepatitis B or C virus infection, and (3) ≥ 8 drinks per week for women and ≥ 15 for men, per NHANES guidelines. The NHANES database has been ethically approved by relevant IRBs, and ethical approval for this secondary analysis was waived by our institution’s IRB.
Definition of hepatic steatosis
HS was defined using the controlled attenuation parameter (CAP), with a cutoff value of ≥ 274 dB/m, as established in prior studies.20
Calculation of weekend catch-up sleep
WCS duration was calculated using NHANES questionnaire responses to two questions: (1) “Number of hours usually slept on weekdays/workdays” and (2) “Number of hours usually slept on weekends/non-workdays.” WCS was defined as the difference between weekend and weekday sleep durations. Normal WCS was categorized as ≤ 1 hour, and participants were stratified into three groups: < 0 hours, 0-1 hour, and ≥ 1 hour.
Assessment of 10-year ASCVD risk
ASCVD risk was calculated using the 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations,24 which predict the 10-year risk of stroke, coronary artery disease, or non-fatal myocardial infarction in adults aged 40-79 years with low-density lipoprotein < 190 mg/dl and no prior ASCVD events. The equation incorporates age, sex, race, high-density lipoprotein cholesterol, total cholesterol, systolic blood pressure, antihypertensive medication use, smoking status, and DM. ASCVD risk was dichotomized into < 7.5% and ≥ 7.5% based on prior studies.25
Variables selection
Potential confounding variables extracted from the NHANES database include variables such as age, sex, race, education level, marital status, poverty-to-income ratio (PIR), smoking, alcohol consumption, physical activity, antidiabetic medication, dyslipidemia treatment, chronic kidney disease (CKD), family history of myocardial infarction, body mass index (BMI), total energy intake, liver stiffness measurement, alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), C-reactive protein, and uric acid (UA).
Physical activity data were converted to weekly energy expenditure (MET·min/week) using standardized formulas and categorized by a 450 MET·min/week threshold. BMI was classified as non-obesity (< 30 kg/m2) or obesity (≥ 30 kg/m2) per World Health Organization criteria. Smoking status was categorized as non-smoker, current smoker, or former smoker based on self-reported data. Alcohol consumption was defined as < 1 time/week or ≥ 1 time/week. CKD was diagnosed as estimated glomerular filtration rate < 60 ml/min/1.73 m2 or UACR ≥ 30 mg/g.27 Dietary intake was assessed via two 24-hour recalls, and total energy intake was calculated from food and supplement consumption. Median liver stiffness was dichotomized at 8.0 kPa.
Statistical analysis
Continuous variables were expressed as mean ± standard error, while categorical variables were described using frequencies and percentages (n, %). Comparisons between the low and high ASCVD risk groups were conducted using weighted t-tests for continuous variables and chi-square tests (c2) for categorical variables, adhering to NHANES guidelines for incorporating the full-sample MEC examination weights.
Weighted univariate logistic regression analyses were employed to identify covariates associated with ASCVD. Subsequent weighted multivariate logistic regression models were developed to evaluate the associations between CAP, WCS, and ASCVD. Model 1 included adjustments for demographic variables (age, sex, education level, PIR, and marital status), whereas model 2 incorporated all selected covariates, including smoking, antidiabetic medication, dyslipidemia treatment, CKD, BMI, median liver stiffness, ALP, GGT, and UA. The results were expressed as odds ratios (ORs) with 95% confidence intervals (CIs), considering a two-sided p value < 0.05 as statistically significant. Additionally, the study examined the effect of WCS on the CAP-ASCVD relationship and assessed the potential moderating role of WCS in age, gender, and BMI obesity subgroups. All analyses were performed using R version 4.2.3 (Institute for Statistics and Mathematics, Vienna, Austria). Missing data were addressed through random forest interpolation, with sensitivity analyses conducted to compare variables before and after imputation (Supplementary Table 1).
Participant characteristics
The study flowchart (Figure 1) illustrates the participant selection process. Initially, 5,135 adults aged ≥ 40 years with liver ultrasound transient elastography data were included. After excluding those lacking sleep duration information (n = 69), ASCVD risk data (n = 686), or with a history of CVD (n = 646), hepatitis B/C infection (n = 123), or significant alcohol use (n = 463), a final sample of 3,148 participants was obtained.
Table 1 presents the baseline characteristics of participants stratified by ASCVD risk (< 7.5% vs. ≥ 7.5%). The mean age of the cohort was 57.57 years, with a majority being female (56.85%). Most participants exhibited median liver stiffness < 8.0 kPa (91.13%). Significant differences were observed between the two groups, with the low ASCVD risk group having lower CAP values (262.31 dB/m vs. 281.64 dB/m) and higher WCS durations (0.86 hours vs. 0.47 hours) compared to the high ASCVD risk group.
Associations of CAP and WCS with ASCVD
Prior to analyzing the associations between CAP, WCS, and ASCVD, covariates related to ASCVD risk were identified through weighted univariate logistic regression (Supplementary Table 2). Variables such as age, gender, education level, PIR, marital status, smoking, antidiabetic medication, dyslipidemia treatment, CKD, median liver stiffness, ALP, GGT, and UA were significantly associated with ASCVD (all, p < 0.05). After adjusting for these covariates, individuals with CAP ≥ 274 dB/m demonstrated a higher likelihood of ASCVD (OR, 1.84, 95% CI, 1.24-2.73) compared to those with CAP < 274 dB/m. However, no significant association was found between WCS duration and ASCVD risk (all, p > 0.05) (Table 2).
Moderating effect of WCS on CAP-ASCVD association
Table 3 highlights the moderating effect of WCS on the CAP-ASCVD relationship. After adjusting for covariates, individuals with WCS ≥ 1 hour exhibited a stronger association between CAP and ASCVD risk compared to those with WCS durations of 0-1 hour (OR, 3.29, 95% CI, 1.41-7.68). This interaction was visually represented in Figure 2. Further analysis under varying WCS conditions (Table 4) confirmed that CAP ≥ 274 dB/m was associated with higher ASCVD odds in individuals with WCS ≥ 1 hour (OR, 3.72, 95% CI, 1.99-6.93).
Subgroup analyses
Subgroup analyses (Table 5) revealed that individuals aged ≥ 60 years or females who had WCS ≥ 1 hour had lower ASCVD odds compared to those with WCS durations of 0-1 hour. Conversely, in subgroups of individuals aged ≥ 60 years, females, and both obese and non-obese individuals, WCS ≥ 1 hour intensified the CAP-ASCVD association. Notably, in participants with WCS ≥ 1 hour, CAP was linked to elevated ASCVD odds, particularly in females (OR, 7.86, 95% CI, 2.24-27.56), non-obese (OR, 3.58, 95% CI, 1.14-11.27), and obese individuals (OR, 3.04, 95% CI, 1.10-8.37) (Table 6).
The present study examined the associations between WCS and CAP with ASCVD in adults aged ≥ 40 years and evaluated the potential moderating effect of WCS on the relationship between CAP and ASCVD. The findings revealed that individuals with CAP ≥ 274 dB/m exhibited higher odds of ASCVD compared to those with CAP < 274 dB/m. Notably, WCS durations of ≥ 1 hour were associated with an increased likelihood of ASCVD compared to durations of 0-1 hour. Furthermore, this moderating effect of WCS on the CAP-ASCVD relationship was observed across various subgroups, including individuals aged ≥ 60 years, females, and both non-obese and obese populations.
This study is the first to investigate the moderating role of WCS in the association between CAP and ASCVD. Prior research has explored the relationship between WCS and cardiovascular/metabolic diseases, but none has specifically addressed its interaction with CAP. For instance, Jang et al.16 reported a negative association between WCS and dyslipidemia in male workers using data from the Korea National Health and Nutrition Examination Survey (KNHANES). Other cross-sectional studies based on KNHANES have linked WCS to NAFLD28 and demonstrated that optimal WCS durations of 1-2 hours improved blood glucose regulation, whereas durations exceeding 3 hours were associated with impaired glucose metabolism.29
A recent analysis of the 2017-2018 NHANES data further highlighted that WCS durations > 2 hours were strongly associated with reduced CVD prevalence when weekday sleep durations were < 6 hours.21 Consistent with Zhu et al.’s21 approach, our study utilized NHANES data to investigate the moderating effect of WCS on CAP-related ASCVD risk, leveraging its large, representative U.S. population sample. However, our analysis of 2017-2020 data (two 2-year cycles) revealed that WCS durations ≥ 1 hour may elevate ASCVD risk in individuals with CAP ≥ 274 dB/m. These discrepancies may stem from racial and lifestyle differences between Asian and European populations. Nonetheless, the causal relationships between CAP, WCS, and ASCVD warrant further investigation.
CAP serves as a non-invasive tool for early NAFLD detection, with CAP-defined NAFLD (≥ 222 dB/m) independently associated with coronary plaques, particularly non-calcified plaques.30 NAFLD is a leading global cause of liver disease and is linked to heightened cardiovascular risk.31 Our results align with prior evidence, demonstrating that CAP ≥ 274 dB/m is associated with higher ASCVD odds in adults aged ≥ 40 years. While the mechanisms underlying the relationship between WCS and ASCVD risk remain unclear, several plausible pathways exist. The hypothalamic-pituitary-adrenal axis and autonomic nervous system play critical roles in regulating physical and mental health. Dysregulation of these systems, influenced by sleep parameters such as duration, quality, and circadian alignment, may increase NAFLD risk.32,33 NAFLD-induced low-grade systemic inflammation promotes the release of pro-inflammatory cytokines, which may drive ASCVD progression via endothelial dysfunction and plaque formation.34 Given the interplay between sleep and immune function,35 moderate WCS may mitigate NAFLD-related inflammation, reducing cytokine release and ASCVD risk. However, excessive WCS durations may negate these benefits and increase mortality risk.36,37 Our findings suggest that limiting WCS to 0-1 hours may be optimal for minimizing ASCVD risk.
Age, DM, and hypertension are well-established risk factors for coronary heart disease in NAFLD patients.38 Additional factors include dyslipidemia, insulin resistance, metabolic syndrome, dietary habits, smoking, and physical inactivity.39 After adjusting for covariates significantly associated with ASCVD, subgroup analyses revealed that WCS durations ≥ 1 hour were associated with elevated ASCVD risk in older adults (≥ 60 years), females, and both non-obese and obese individuals. These findings contrast with Son et al.’s40 observation that WCS ≥ 1 hour reduced metabolic syndrome risk in younger populations, underscoring the need for age-specific considerations in WCS recommendations. Additionally, gender-specific differences in sleep patterns, such as women’s higher insomnia prevalence,42 may influence WCS effects. Dysregulated glucose metabolism and insulin resistance, common to both NAFLD and CVD, further highlight the importance of optimizing sleep patterns. While WCS may offer protective effects against obesity and metabolic syndrome in chronic short sleepers,14,45 our results emphasize that appropriate WCS durations are critical for ASCVD risk reduction, regardless of obesity status.
This study is the first to explore the moderating effect of WCS on the CAP-ASCVD relationship, offering novel insights for ASCVD prevention. HS and hepatic fibrosis were assessed using transient elastography, a highly accurate method, and potential confounders comprehensively addressed using the representative NHANES database. However, limitations exist. The cross-sectional design precludes causal inferences, and NHANES WCS data are only available for 2017-2020. Future prospective studies should clarify the dose-response relationship between WCS and ASCVD risk.
In conclusion, WCS durations ≥ 1 hour may heighten ASCVD risk in adults with HS. Further research should focus on refining WCS duration recommendations to guide ASCVD prevention strategies.
Ethics Committee Approval: Not applicable.
Informed Consent: Not applicable.
Data Sharing Statement: The datasets analyzed during the current study are available from the corresponding author upon reasonable request.
Authorship Contributions: Concept- Y.L.; Design- Y.L.; Supervision- Y.L.; Fundings- Y.L.; Materials- Y.L., Z.W., N.Z.; Data Collection or Processing- Z.W., N.Z.; Analysis or Interpretation- Y.L., Z.W., N.Z.; Writing- Y.L.; Critical Review- Z.W., N.Z.
Conflict of Interest: The authors declare that they have no conflict of interest.
Funding: The authors declared that this study received no financial support.
Supplementary Table 1: https://balkanmedicaljournal.org/img/files/87-SUPPLEMENTARY%20TABLE%201.%281%29.pdf
Supplementary Table 2: https://balkanmedicaljournal.org/img/files/87-SUPPLEMENTARY%20TABLE%202.%281%29.pdf
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