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WHAT IS ALREADY KNOWN ON THIS TOPIC Sleep duration is established as being linked to insulin resistance and metabolic health, with estimated glucose disposal rate being a validated marker for this condition.WHAT THIS STUDY ADDS This study identifies a nonlinear, inverted U-shaped relationship with an optimal sleep duration of about 7.3 hours.It reveals that weekend catch-up sleep has a dual, conditional effect—beneficial only in moderation for those with sleep debt, but potentially harmful for others.HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY The findings advocate for more personalized sleep guidelines in clinical practice and public health, encouraging consistent, adequate sleep over weekend compensation, and highlight the need for research into the mechanisms behind sleep patterns and metabolic risk.Introduction Metabolic syndrome (MetS) is a complex clinical condition defined by the coexistence of multiple metabolic abnormalities, including elevated blood pressure, abdominal obesity, impaired glucose metabolism, and dyslipidemia; it is common in approximately 34% of Americans and 25% of the global population. 1 2 The prevalence of MetS is increasingly rising worldwide, and it is widely recognized for its predictive value in all-cause mortality and cardiovascular disease (CVD) mortality, making it a significant public health issue.3 MetS is physiologically defined as a state in which insulin-targeted tissues have a reduced reactivity to high physiological insulin levels.4 Researchers commonly employ surrogate measures such as the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and the Triglyceride-Glucose (TyG) index to screen patients for insulin sensitivity.5 6 Nevertheless, HOMA-IR values are influenced by fasting glucose and insulin levels, and the TyG index may not fully capture the multifaceted nature of insulin resistance (IR), potentially reducing its accuracy, particularly in patients with diabetes.7 8 The estimated glucose disposal rate (eGDR), an emerging standard clinical measure calculated using waist circumference (WC), hypertension status, and glycated hemoglobin A1c (HbA1c), has been validated as relevant to MetS and can predict the mortality rate of patients with MetS.9 Although this formula was originally derived from diabetes research, current evidence indicates that the eGDR index reflects IR and can be generalized to the general population.10 11 Furthermore, eGDR has also been established as a predictor of long-term mortality in non-diabetic individuals and the elderly.12 13 Therefore, screening and early identification of high-risk populations based on eGDR may represent a crucial step in addressing CVD and MetS.Sleep duration is a fundamental regulator of metabolic homeostasis, with profound implications for glucose metabolism. Experimental studies demonstrate that sleep restriction induces acute IR within days, primarily through altered hypothalamic–pituitary–adrenal axis activity, increased sympathetic tone, and elevated proinflammatory cytokines.14 15 Chronic short sleep further impairs β-cell function and reduces glucose utilization in peripheral tissues, contributing to hyperglycemia and type 2 diabetes risk.16 In modern societies, weekday sleep curtailment is ubiquitous. Adults averaging <6 hours/night on workdays exhibit impaired glucose tolerance.17 Studies have shown that sleep deprivation and excessive sleep increase the risk of developing type 2 diabetes, and sleep patterns may influence the onset of diabetes by modulating functional brain networks.18 Moreover, studies have shown that compared with those who sleep 8–9 hours a night during adolescence, people who sleep 6 hours or less have a higher risk of CVD in adulthood.19 Despite extensive research on sleep duration and metabolic dysregulation, the specific association between weekday sleep patterns and eGDR remains underexplored.Chronic sleep deprivation can lead to a range of adverse health consequences, including CVD, MetS, and mood disorders.20 To compensate for weekday sleep loss, many individuals attempt weekend catch-up sleep (WCS). Some studies suggest that WCS may help mitigate inflammation associated with chronic sleep deprivation21 and improve glucose regulation.22 Moderate WCS has also been linked to a reduced risk of MetS in middle-aged adults with chronic short sleep.23 However, other research indicates that WCS does not significantly prevent MetS induced by recurrent sleep deprivation.24 Studies have shown that alternating cycles of sleep restriction and extension may lead to more severe metabolic dysregulation compared with chronic sleep deprivation, and weekend sleep recovery is not an effective strategy for counteracting metabolic disturbances caused by repeated sleep loss,24 25 possibly due to circadian rhythm disruption. The physiological basis of compensatory sleep involves the regulation of sleep homeostasis. Studies indicate that only about 18.2% of individuals with significant sleep debt can restore balance through WCS,26 suggesting a limited and potentially incomplete compensatory effect. Furthermore, discrepant sleep–wake schedules between weekdays and weekends lead to ‘social jetlag’, causing circadian rhythm disruption.27 Prolonged or excessive WCS duration may be detrimental to the health of individuals who already obtain sufficient sleep on weekdays,28 as irregular sleep patterns between weekdays and weekends can disrupt metabolic and physiological functions.29 This metabolic disruption induced by irregular sleep is closely related to oxidative stress and inflammation, which are key mediators of IR. A study demonstrated that sinomenine protects against myocardial ischemia-reperfusion injury by preventing oxidative stress, cellular apoptosis, and inflammation—mechanisms that are also implicated in sleep-related metabolic disturbances.30 This parallel underscores the importance of considering oxidative stress and inflammation when exploring the relationship between sleep patterns and eGDR. Since the eGDR is a more accessible and reliable surrogate marker for IR, large-scale population studies are essential to further investigate the relationship between WCS, weekday sleep patterns, and eGDR.The objective of this study is (1) to investigate the association between weekday sleep duration and eGDR and (2) to examine the moderating role of WCS on this relationship. We hypothesize that shorter weekday sleep duration predicts lower eGDR, but WCS may attenuate this association. By quantifying the relationship between weekday sleep duration, WCS, and eGDR, this study aims to elucidate their interaction, thereby providing a basis for targeted sleep hygiene strategies.Methods Study population The original dataset comprised 67 932 participants, from which we excluded 44 457 individuals based on the following criteria: (1) age <20 years (n=27 625) or pregnancy (n=629); (2) missing data on weekday sleep duration or the primary outcome eGDR (n=10 120); and (3) missing information on age, sex, ethnicity, marital status, education level, family income-to-poverty ratio (PIR), body mass index (BMI), drink, and smoke (n=6083). Our final analytical sample included 23 475 participants, among whom 10 817 had available data on weekend sleep duration ( online supplemental figure S1).SP110.1136/bmjdrc-2025-005692.supp1Supplementary data Assessments of weekday sleep duration and WCS In the National Health and Nutrition Examination Survey (NHANES) 2009–2023 survey, weekday sleep duration was determined based on participants’ responses to the following separate question regarding weekdays: ‘the number of hours usually he/she sleep on weekdays or workdays’.In the NHANES 2017–2023 survey, weekend sleep duration was assessed based on participants’ responses to the following separate question regarding weekends: ‘the number of hours he/she usually sleep on weekends or non-workdays’. The WCS duration was defined as weekend sleep duration minus weekday sleep duration, and in this study, WCS was categorized into four groups31 32: 0, >0 to ≤1, >1 to 2, >2hours. The ‘0 hours’ category includes participants whose weekend sleep duration was less than or equal to their weekday sleep duration; the ‘>0 to ≤1 hour’ category includes those with a WCS greater than 0 but less than or equal to 1 hour; the ‘>1 to ≤2 hours’ category includes those with a WCS greater than 1 hour but less than or equal to 2 hours; and the ‘>2 hours’ category includes those with a WCS greater than 2 hours.Assessments of eGDR According to the anthropometric procedure manual, WC was measured for the participants. To determine the level of HbA1c in the whole blood sample, an automatic HbA1c analyzer calibrated by an equal percentage equivalence method was used in this study. The standard for defining hypertension was as follows: an average systolic blood pressure of ≥140 mm Hg, an average diastolic blood pressure of ≥90 mm Hg, use of antihypertensive medication, or self-reported diagnosis of hypertension. The formula for calculating eGDR is as follows 8: eGDR (mg/kg/min)=21.158−(0.09×WC)−(3.407×hypertension)−(0.551×HbA1c),[WC (cm), hypertension (yes = 1 / no = 0), and HbA1c (%)] Covariates The covariates included age (20–39, 40–59, ≥60 year), sex (male, female), ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black and other races), marital status (married or living with a partner, other), education level (less than high school, high school, or more than high school), PIR (<1, 1–2.99, or ≥3), BMI (<25, 25–30, or >30 kg/m 2), drink (yes or no), smoke (yes or no). Drinking and smoking statuses were categorized as ‘Yes’ or ‘No’.33 Smoking status was obtained during the interview, with a smoker being defined as having smoked ≥100 cigarettes in their life. Alcohol consumption was determined from a single 24 hours dietary recall and classified as ‘Yes’ or ‘No’. All confounding variables were determined based on a directed acyclic graph (online supplemental figure S2).Statistical analysis Considering the complex survey design of NHANES, appropriate weights were applied to each analysis in accordance with the recommendations of the NCHS. For categorical variables, data were presented as numbers and weighted proportions, while for continuous variables, data were expressed as weighted means±SD.Given the potential threshold and saturation effects between sleep duration and eGDR, wherein both extremely short and long sleep durations may increase the risk of eGDR through distinct physiological pathways, and to avoid the limitations associated with categorical modeling, we employed restricted cubic splines (RCS) for model fitting and analysis.34 RCS analysis was employed to explore the potential nonlinear relationship between weekday sleep duration and eGDR. We tested knots ranging from 3 to 5 and selected the model with the lowest Akaike Information Criterion (AIC) value for the RCS.35 If the RCS analysis revealed a U-shaped, inverted U-shaped, or L-shaped curve with identifiable inflection points, the data were divided into two distinct segments based on these inflection points. Threshold effects were then assessed using piecewise regression models, log-likelihood ratio tests, and Bootstrap resampling. Further analysis of the nonlinear association between weekday sleep duration and eGDR was conducted using generalized linear models, with adjustments made for the identified inflection points. To further investigate the potential moderating role of WCS in the association between weekday sleep duration and eGDR, we employed moderation models to examine whether WCS moderated this relationship. Additionally, multivariable regression models were applied to explore the associations between weekday sleep duration categories, WCS, and eGDR.Subgroup analyses were conducted to examine whether these associations varied by demographic variables, including age, sex, ethnicity, marital status, education level, PIR, BMI, drink, and smoke. Interaction models were tested using likelihood ratio tests. Additionally, we separately assessed the association between weekday sleep duration and eGDR among participants with and without diabetes. Diabetes was defined as self-reported physician-diagnosed diabetes, current use of glucose-lowering medications, fasting blood glucose ≥7.0 mmol/L, and/or HbA1c ≥6.5% at baseline.All statistical analyses were performed using IBM-SPSS V.27.0 and R V.4.4, while moderation models were fitted using Stata V.17.0. A p value <0.05 was considered statistically significant.Results Baseline characteristics The total number of participants in this study was 23 475, with males accounting for 51.16%. The age range of participants was 20–80 years old, with a median eGDR of 8.23 (IQR: 5.52–9.94). The median sleep duration on weekdays was 7.50 hours (IQR: 6.00–8.00). Additionally, among 10 817 participants, the median weekend sleep duration was 8.00 hours (IQR: 7.00–9.00). On weekends, 48.29% of participants reported taking catch-up sleep. Table 1 summarizes the weighted estimates of individual baseline characteristics. Online supplemental table S1 summarizes the weighted comparison of baseline characteristics between with-WCS and without-WCS.Table 1Baseline characteristics of included participantsN=23 475Weighted %Sex Male11 40651.16 Female12 06948.84Age, year 20–39740535.61 40–59769736.52 ≥60837327.87Ethnicity Mexican American29227.65 Non-Hispanic White10 51867.32 Non-Hispanic Black467810.11 Other535714.92Education level Less than high school448112.19 High school diploma520323.45 More than high school13 79164.36Marital status Married or living with partner13 79063.43 Other968536.57PIR <1457913.29 1–2.99963734.97 ≥3925951.74BMI, kg/m2 <25635227.68 25–30765332.84 ≥30947039.48Drinking Yes19 35786.41 No411813.59Smoking Yes599425.78 No17 48174.22Hypertension Yes858136.55 No14 89463.45Diabetes Yes317813.54 No20 29786.46eGDR, median (Q1–Q3)8.23 (5.52–9.94)Weekday sleep duration, median (Q1–Q3)7.50 (6.00–8.00)Weekend sleep duration (n=10 817), median (Q1–Q3)8.00 (7.00–9.00)Weekend catch-up sleep (n=10 817)Yes486148.29No595651.71BMI, body mass index; eGDR, estimated glucose disposal rate; PIR, family income-to-poverty ratio.Nonlinear association between weekday sleep duration and eGDR We tested knots ranging from 3 to 5 and ultimately chose the 5-node model with the smallest AIC ( online supplemental figure S3). Figure 1 presents the results of the RCS analysis after adjusting for covariates (age, sex, ethnicity, marital status, education level, PIR, BMI, drink, and smoke), revealing an inverted U-shaped relationship between weekday sleep duration and eGDR (p for nonlinearity <0.001). Using maximum likelihood estimation, the inflection point of the curve was identified at a weekday sleep duration of 7.32 hours. Before reaching this inflection point, eGDR increased with longer weekday sleep duration, whereas beyond this threshold, further increases in sleep duration were associated with a decline in eGDR (table 2). After adjusting for covariates, the results showed that for participants with weekday sleep duration <7.32 hours, each 1 hour increase in sleep duration was associated with a 0.273-unit increase in eGDR (β-Coefficient=0.273, 95% CI 0.224 to 0.322, p<0.001). Conversely, among those with weekday sleep duration ≥7.32 hours, each additional hour of sleep was linked to a 0.222-unit decrease in eGDR (β-Coefficient=−0.222, 95% CI −0.272 to –0.171, p<0.001), as detailed in table 3. Given these opposing trends before and after the inflection point, subsequent segmented analyses were conducted using the threshold of 7.32 hours.Figure 1Restricted cubic spline curve for the association between weekday sleep duration and the eGDR. (The model was adjusted for age, sex, ethnicity, marital status, education level, PIR, BMI, drink, and smoke. Blue lines represent references for β-Coefficients, and blue areas represent 95% CIs). BMI, body mass index; eGDR, estimated glucose disposal rate; PIR, family income-to-poverty ratio.Table 2Analysis of the threshold effectOutcomeEffectPModel 1 Fitting model by standard linear regression0.01 (−0.00, 0.03)0.15Model 2 Fitting model by two-piecewise linear regressionInflection point7.32 <7.320.09 (0.06, 0.12)<0.001 ≥7.32−0.07 (−0.10, –0.03)<0.001P for likelihood test<0.001Table 3Relationship between weekday sleep duration and eGDRWeekday sleep durationModel 1Model 2Hoursβ-Coefficients (95% CI)Pβ-Coefficients (95% CI)P<7.320.273 (0.224 to 0.322)<0.0010.0185 (0.080 to 0.152)<0.001≥7.32−0.222 (−0.272 to –0.171)<0.001−0.098 (−0.134 to –0.062)<0.001Model 1: unadjusted covariates. Model 2: adjusted for age, sex, ethnicity, marital status, education level, PIR, BMI, drink, and smoke.BMI, body mass index; eGDR, estimated glucose disposal rate; PIR, family income-to-poverty ratio. Online supplemental figure S4A–J displays interaction models for the association between weekday sleep duration and eGDR, stratified by age, sex, ethnicity, marital status, education level, PIR, BMI, drink, and smoke. The results indicated statistically significant interaction for age and BMI (P for interaction <0.05), suggesting heterogeneity in the relationship between weekday sleep duration and eGDR across these subgroups.Association between threshold-based weekday sleep duration and eGDR Stratified analyses based on the inflection point were conducted to examine the association between weekday sleep duration and eGDR across different subgroups ( figure 2). After stratification, a positive correlation between eGDR and weekday sleep duration (<7.32 hours) remained significant in most subgroups. However, subgroups including sex, age, ethnicity, education level, marital status, PIR, smoking status, drinking status, and diabetes did not profoundly change the relationship between weekday sleep duration (<7.32 hours) and eGDR (all P for interactions p>0.05). The results showed that when BMI ≥30, longer weekday sleep duration was associated with higher eGDR levels (β-Coefficient=0.1300, 95% CI 0.0723 to 0.1870, P for interaction=0.002).Figure 2β-Coefficients of eGDR for weekday sleep <7.32 hours group versus participant characteristics in the group of weekday sleep <7.32 hours. BMI, body mass index; eGDR, estimated glucose disposal rate.In figure 3, subgroups including ethnicity, education level, marital status, PIR, smoking status, drinking status, and diabetes did not profoundly change the relationship between weekday sleep duration (≥7.32 hour) and eGDR (all P for interactions p>0.05). The results showed that longer weekday sleep duration was associated with lower eGDR levels among women (β-Coefficient=−0.0992, 95% CI: −0.1450 to –0.0535, P for interaction=0.001), individuals aged 40–59 years (β-Coefficient=−0.1310, 95% CI: −0.2060 to –0.0553, P for interaction=0.014), and BMI ≥30 (β-Coefficient=−0.0983, 95% CI: −0.1590 to –0.0375, P for interaction=0.032).Figure 3β-Coefficients of eGDR for weekday sleep ≥7.32 hours group versus participant characteristics in the group of weekday sleep ≥7.32 hours. BMI, body mass index; eGDR, estimated glucose disposal rate.The role of WCS Association of WCS with weekday sleep and eGDR To explore the relationship between WCS, weekday sleep, and eGDR, we performed a stratified analysis by dividing weekday sleep duration into two groups: <7.32 hours and ≥7.32 hours, and categorized WCS into 0, >0 to ≤1, >1 to 2, >2 hours. Multivariable regression analysis was then conducted. The results ( table 4) show that in the group with weekday sleep <7.32 hours, compared with the group without WCS, the 1 hour<WCS≤2 hour group was significantly associated with an increase in eGDR levels (β=0.296, 95% CI 0.107 to 0.484, p=0.002), followed by the 0<WCS≤ 1 hour group (β=0.249, 95% CI 0.074 to 0.425, p=0.005). However, among those with weekday sleep ≥7.32 hours, WCS showed no significant association with eGDR.Table 4The role of weekend catch-up sleep on weekday sleep and eGDRModel 1Model 2Weekday sleep <7.32 hours No change in sleep durationReference Weekend catch-up sleep ≤1 hour0.730 (0.497, 0.963)<0.0010.249 (0.074, 0.425)0.005 1–2 hours0.842 (0.595, 1.089)<0.0010.296 (0.107, 0.484)0.002 >2 hours0.686 (0.461, 0.911)<0.0010.118 (−0.061, 0.297)0.197Weekday sleep ≥7.32 hours No change in sleep durationReference Weekend catch-up sleep ≤1 hour0.761 (0.586, 0.935)<0.0010.075 (−0.054, 0.205)0.255 1–2 hours0.764 (0.533, 0.995)<0.0010.139 (−0.032, 0.311)0.112 >2 hours0.693 (0.378, 1.007)<0.001−0.082 (−0.032, 0.311)0.487Model 1: β-Coefficients (95% CI), unadjusted covariates.Model 2: β-Coefficients (95% CI), adjusted for age, sex, ethnicity, marital status, education level, PIR, BMI, drink, and smoke.BMI, body mass index; eGDR, estimated glucose disposal rate; PIR, family income-to-poverty ratio.The moderating role of WCS on the relationship between weekday sleep duration and eGDR To further examine whether WCS moderates the association between weekday sleep duration and eGDR, we conducted a moderation analysis using NHANES 2017–2023 data (n=10 817). WCS (yes/no) was tested as a moderator between weekday sleep duration (continuous) and eGDR, but no statistically significant moderation was observed (β=0.003, p=0.438) ( online supplemental figure S5). We conducted sensitivity analyses by WCS as a continuous variable in the moderation model. The results indicated that WCS did not exert a statistically significant moderation on the association between weekday sleep duration and eGDR (β=−0.0143, p=0.546) (online supplemental figure S6). Subsequently, we categorized WCS into four groups: 0, >0 to ≤1, > 1 to 2, >2 hours, and repeated the moderation analysis. Compared with the no-WCS group, the >2 hour WCS group moderated the negative association between weekday sleep and eGDR (β=−0.568, 95% CI −0.970 to –0.167, p=0.005) (figure 4). This suggests that longer weekend sleep duration may exacerbate the negative correlation between weekday sleep duration and eGDR, potentially leading to worsened blood glucose control.Figure 4The moderating role of weekend catch-up sleep groups on the relationship between weekday sleep duration and eGDR. The model was adjusted for age, sex, ethnicity, marital status, education level, PIR, BMI, drink, and smoke. BMI, body mass index; eGDR, estimated glucose disposal rate; PIR, family income-to-poverty ratio.Furthermore, we performed moderation analysis by dividing weekday sleep into two groups based on the inflection point (7.32 hours). In the group with weekday sleep duration <7.32 hours, compared with the group without WCS, the >2 hour WCS group negatively moderated the association between weekday sleep and eGDR (β=−0.191, 95% CI −0.326 to –0.057, p=0.005) (online supplemental figure S7). In the group with weekday sleep duration ≥7.32 hours, using the without WCS group as the reference, the 1<WCS≤2 hours group negatively moderated the association between weekday sleep and eGDR (β=−0.356, 95% CI −0.645 to 0.067, p=0.016) (online supplemental figure S8). This suggests that excessive WCS was not recommended for optimal health outcomes.In order to identify the optimal WCS, we have reanalyzed the data by categorizing participants into two groups based on a cut-off of 7.32 hours of weekday sleep duration (<7.32; ≥7.32). We then examined the relationship between WCS and eGDR within each group. RCS were fitted separately for each group. The results showed that when weekday sleep duration was <7.32 hours, the optimal WCS duration was 1.16 hours (online supplemental figure S9). In contrast, when weekday sleep duration was ≥7.32 hours, the optimal WCS duration was 1.12 hours (online supplemental figure S10).Discussion This study used data from NHANES to investigate the association between weekday sleep duration and eGDR, as well as the moderating role of WCS. A significant association was observed between weekday sleep duration and eGDR, with RCS analysis revealing an inverted U-shaped relationship. When weekday sleep duration was <7.32 hours, a positive correlation was found (β=0.273, 95% CI 0.224 to 0.322, p<0.001), whereas a negative correlation was observed when sleep duration was ≥7.32 hours (β=−0.222, 95% CI −0.272 to –0.171, p<0.001). In moderation analysis, compared with the group without WCS, the >2 hour WCS group negatively moderated the association between weekday sleep and eGDR (β=−0.568, 95% CI −0.970 to –0.167, p=0.005).No prior research has investigated the association between weekday sleep duration and eGDR levels or the moderating role of WCS. Our study addresses this knowledge gap by identifying an inverted U-shaped relationship between weekday sleep duration and eGDR. Research has indicated that sleep is not only essential for physiological recovery but also plays a crucial role in maintaining metabolic balance and overall health through intricate and dynamic processes.36 Several hypotheses explain the mechanisms by which short sleep duration affects metabolism. Sleep restriction may suppress leptin secretion through sympathetic activation, while promoting ghrelin release mediated by parasympathetic activity, leading to an imbalance in the ghrelin/leptin ratio, which in turn stimulates appetite and caloric intake,37 38 thereby impairing metabolic function. Additionally, sleep deprivation may disrupt cortisol circadian rhythms, inhibiting insulin signaling pathways and resulting in reduced insulin sensitivity and increased hepatic glucose output.39 These signaling pathways are intertwined with key metabolic cascades such as the AKT/MAPK and AMPK pathway, which has been shown to regulate insulin sensitivity and glucose metabolism—highlighting the pathway’s role in metabolic regulation that may also be relevant to sleep-induced changes in eGDR.40 41 Furthermore, insufficient sleep may heighten daytime fatigue,42 reducing physical activity and energy expenditure or altering appetite regulation, ultimately contributing to weight gain or metabolic dysregulation.43 Thus, our study further confirms that when weekday sleep duration is below 7.32 hours, sleep duration exhibits a positive correlation with metabolic status as measured by eGDR.Furthermore, our study found a significant negative correlation between weekday sleep durations ≥7.32 hours and eGDR, suggesting that extended sleep may be associated with impaired metabolic regulation. The underlying mechanisms for this association are likely multifaceted and complex. First, long sleep duration is frequently observed as a symptom or consequence of underlying health conditions, such as clinical depression or undiagnosed sleep disorders (eg, sleep apnea), which themselves are potent risk factors for systemic inflammation and IR.44 45 Additionally, studies have shown that participants with longer sleep durations exhibit elevated levels of interleukin-6 and C-reactive protein (CRP).46 47 Prolonged sleep may reduce time spent in wakeful, physically active states, thereby promoting sedentary behaviors that contribute to negative energy balance and weight gain, further exacerbating metabolic dysfunction.48 Importantly, there appears to be a bidirectional relationship between sleep and metabolism. For instance, poor glycemic status itself has been linked to a higher likelihood of both short and extended sleep durations, as well as sleep disorders.49 This creates a potential vicious cycle wherein metabolic dysregulation disrupts normal sleep patterns, and the resultant abnormal sleep (including extended duration) further aggravates metabolic health.Our study found that longer sleep duration (≥7.32 hours) was associated with lower eGDR, particularly among women—a finding consistent with existing evidence indicating gender differences in metabolic responses during sleep. Studies suggest that women may be more vulnerable to the adverse effects of sleep disturbances, which can alter hormonal regulation and impair metabolic function.50 The negative association between prolonged sleep and eGDR was especially pronounced in adults aged 40–59 years. This period of life is marked by age-related declines in sleep quality and metabolic resilience, along with increased oxidative stress, systemic inflammation, and elevated risk of IR, all of which may amplify susceptibility to the potential detrimental effects of extended sleep duration.51 In individuals with obesity (BMI ≥30), shorter sleep duration (<7.32 hours) was positively associated with eGDR. Obesity is known to exacerbate sleep disorders such as obstructive sleep apnea, insomnia, and restless legs syndrome, thereby increasing cardiometabolic risk.52 Moreover, longer sleep duration (≥7.32 hours) was negatively associated with eGDR in this group. Individuals with obesity often exhibit a reduced basal metabolic rate, and excessively prolonged sleep may further disrupt energy homeostasis, increase cardiovascular strain, and impair metabolic regulation.51 53 Although the interaction analysis of the diabetes subgroups did not reach statistical significance, the results showed that when weekday sleep was <7.32 hours, each 1 hour increase in sleep was linked to a 0.144-unit rise in eGDR in diabetic participants, significantly more than in non-diabetics. However, when sleep duration was ≥7.32 hours, each additional hour of sleep was associated with a 0.108-unit decrease in eGDR, again more pronounced in diabetics. These findings suggest that increasing sleep duration when insufficient can improve insulin sensitivity, while excessive sleep may harm it.54 55 Clinical focus should be on optimizing sleep management for diabetic patients to improve metabolic health.This study also found that moderation analysis, compared with the group without WCS, the >2 hour WCS group negatively moderated the association between weekday sleep and eGDR (β=−0.568, 95% CI −0.970 to –0.167, p=0.005), this was consistent with previous research findings, and excessive weekend sleep recovery is not recommended.23 The distinct contribution of this study lies in its moderation analysis of WCS. While prior studies have shown that WCS may reduce the risk of MetS—potentially by improving immune and inflammatory functions to counteract the negative effects of sleep deprivation.56 57 The current findings suggest that for individuals with longer weekday sleep durations, WCS may instead impair metabolic function. This impairment may be related to the disruption of anti-inflammatory and antioxidant mechanisms, excessive WCS may disrupt anti-inflammatory pathways, thereby worsening eGDR.58 The underlying mechanism likely centers on circadian rhythm disruption and subsequent inflammatory dysregulation. Large weekend sleep shifts create a state akin to ‘social jet lag’, misaligning the central circadian clock in the brain with peripheral metabolic clocks in organs such as the liver and adipose tissue.59 This internal desynchrony disrupts the coordinated expression of core circadian genes (eg, CLOCK, BMAL1), and this circadian misalignment may trigger a low-grade inflammatory state.59 For individuals with sufficient weekday sleep, the metabolic cost of excessive WCS may stem from the compounded detrimental effects of circadian disturbance and immune activation.60 Therefore, adopting tailored sleep patterns based on individual weekday sleep durations could help improve metabolic outcomes and reduce the incidence of MetS.This study has several notable strengths. First, this study uses a large, nationally representative cohort of US adults, with the use of sampling weights improving the robustness and statistical strength of the outcomes. Additionally, the data were gathered using standardized procedures, reducing the risk of selection bias. Another key strength lies in the use of eGDR, which is more clinically accessible compared with other widely used metabolic markers, thereby deepening the understanding of its clinical utility.However, this study also has certain limitations. First, the cross-sectional design of the study limits the ability to draw definitive causal inferences, and the potential for reverse causality cannot be excluded. Specifically, poor metabolic health may directly impair sleep architecture and continuity,52 highlighting the necessity for longitudinal investigations and Mendelian randomization studies to elucidate temporal sequences and establish causal directions. Second, weekday and weekend sleep durations were based on self-reported data. While self-reported sleep duration shows higher validity on weekdays, its accuracy tends to be reduced on weekends, which may introduce recall bias,61 and the proportion of the extreme population in this study is approximately 3.0% (online supplemental figure S11), which may affect the accuracy of the effect size. Third, although a wide range of potential confounders was adjusted for, residual confounding from unmeasured variables such as baseline circadian rhythm phenotypes, lifestyle factors (like sleep quality, mental health status, medication use, and shift work)29 cannot be entirely ruled out and may introduce some bias. Fourth, as the study used the US NHANES database, the population for the moderating effect analysis is different from the total population, and there may be a certain restriction in the generalizability of our findings to other populations may be limited. And cross-cultural differences in sleep behaviors, such as culturally embedded napping habits and variations in the prevalence of shift work,62 63 may preclude the direct translation of our results to other settings. Future large-scale prospective studies with objective sleep measurements are needed to validate these findings across diverse populations.Finally, another limitation of this study is the inability to distinguish between nocturnal sleep and daytime napping within the overall sleep duration, as the metabolic effects of night-time sleep and naps are likely different. Therefore, the optimal sleep duration identified in this study is a combined measure, and we cannot yet separate the contributions of high-quality night-time sleep from extended daytime napping. The latter may be linked to issues like sleep fragmentation or underlying health conditions. Future research should focus on using more comprehensive sleep assessments, such as actigraphy or sleep diaries, to differentiate between nocturnal sleep and daytime napping and to verify and refine these findings.Conclusion This study highlights the correlation between sleep patterns and eGDR, with an approximate weekday sleep duration of 7.32 hours associated with the most favorable eGDR values. Among individuals with shorter weekday sleep, modest WCS (≤2 hours) was linked to higher eGDR, whereas excessive WCS showed a negative association with eGDR. These correlational findings suggest that sleep patterns, particularly weekend recovery sleep, may be relevant for metabolic regulation in diabetes and could inform considerations for healthcare professionals in managing patient care.