Association between Metabolic Dysfunction-Associated Steatotic Liver Disease and Obstructive Sleep Apnea: A Nationwide Retrospective Cohort Study

Association between Metabolic Dysfunction-Associated Steatotic Liver Disease and Obstructive Sleep Apnea: A Nationwide Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between Metabolic Dysfunction-Associated Steatotic Liver Disease and Obstructive Sleep Apnea: A Nationwide Retrospective Cohort Study Chan Ho Park, Sang Yi Moon, Bongjo Kim, Minkook Son This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7423767/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Obstructive sleep apnea (OSA) and metabolic liver disease share overlapping mechanisms, yet the association between metabolic dysfunction-associated steatotic liver disease (MASLD) and OSA remains unclear. We conducted a nationwide retrospective cohort study using data from the Korean National Health Insurance Service (NHIS). Adults aged ≥40 years who underwent health screening in 2009–2010 were categorized into four groups: (1) no steatotic liver disease (SLD) and no cardiometabolic risk factors (CMRFs); (2) no SLD with CMRFs; (3) MASLD without alcohol; and (4) MASLD with alcohol and MetALD. Incident OSA was identified using ICD-10 codes. Cox proportional hazards models and restricted cubic spline analyses were applied. Among 265,452 participants (mean age 58.9 years; 52.3% men), MASLD was independently associated with increased OSA risk (adjusted HR 1.48; 95% CI: 1.16–1.89; p = 0.002), with a slightly higher risk in the MASLD with alcohol and MetALD groups (adjusted HR 1.50; 95% CI: 1.16–1.93; p = 0.002). CMRFs alone were not significantly associated with OSA. Spline analysis showed a nonlinear dose–response relationship between fatty liver index and OSA risk. These findings suggest that MASLD—especially with alcohol involvement—is a significant risk factor for OSA, supporting routine sleep screening in this population. Health sciences/Diseases Health sciences/Gastroenterology Health sciences/Medical research Health sciences/Risk factors Figures Figure 1 Figure 2 Figure 3 Introduction Obstructive sleep apnea (OSA) is a common and increasingly recognized sleep disorder characterized by recurrent episodes of partial or complete upper airway obstruction during sleep. These episodes result in intermittent hypoxia, sleep fragmentation, and sympathetic nervous system activation, contributing to a wide range of adverse health consequences. OSA is estimated to affect 9–38% of the global adult population, with higher prevalence among older adults, males, and individuals with obesity [ 1 , 2 ] . It is independently associated with increased risks of cardiovascular disease, stroke, insulin resistance, and all-cause mortality [ 3 , 4 ] . Despite its clinical significance, OSA remains underdiagnosed and undertreated, particularly in populations with coexisting metabolic disorders. OSA has been closely linked to several metabolic conditions, with particularly strong associations observed with type 2 diabetes mellitus (T2DM), hypertension, and obesity. For instance, OSA has been shown to increase insulin resistance and worsen glycemic control in patients with T2DM through sympathetic overactivity and nocturnal hypoxemia [ 5 ] . Similarly, obesity—a major risk factor for both OSA and metabolic syndrome—exacerbates upper airway collapsibility and systemic inflammation [ 6 ] . Nonalcoholic fatty liver disease (NAFLD), the hepatic manifestation of metabolic syndrome, has also been increasingly associated with OSA. Previous studies have reported a bidirectional relationship between OSA and NAFLD, with intermittent hypoxia accelerating liver injury and hepatic steatosis, while fatty liver disease exacerbates cardiometabolic profiles that predispose to OSA [ 7 – 10 ] . Recently, the term metabolic dysfunction-associated steatotic liver disease (MASLD) was introduced to replace NAFLD, in order to better reflect the underlying pathophysiology and clinical heterogeneity of fatty liver disease. MASLD is diagnosed based on evidence of hepatic steatosis in the presence of at least one of five cardinal cardiometabolic risk factors (CMRFs): overweight/obesity, hypertension, hypertriglyceridemia, low HDL-C, or T2DM [ 11 – 13 ] . This redefinition has garnered substantial interest and has prompted renewed investigations into the role of MASLD in systemic metabolic and cardiovascular diseases [ 14 ] . Despite this, few large-scale epidemiological studies have examined the association between MASLD and OSA. Given their shared metabolic origins and overlapping pathophysiological mechanisms, understanding this association has important clinical and public health implications. To address this gap, we conducted a nationwide retrospective cohort study using data from the Korean National Health Insurance Service (NHIS) to assess the risk of OSA in individuals with MASLD, with and without alcohol-related hepatic involvement. Results Baseline Characteristics of the Study Population A total of 265,452 individuals were included in the analysis, with 52.3% being male. The mean age of participants was 58.9 ± 8.8 years. The mean body mass index (BMI) and waist circumference were 23.9 ± 3.0 kg/m² and 81.5 ± 8.3 cm, respectively. Participants were classified into four mutually exclusive groups according to the presence of steatotic liver disease(SLD) and CMRFs: (1) no SLD without CMRF (n = 26,067), (2) no SLD with CMRFs (n = 138,580), (3) MASLD without alcohol consumption (n = 50,526), and (4) MASLD with alcohol consumption and MetALD (n = 50,279). Notable differences in demographic and metabolic characteristics were observed across the groups. The MASLD with alcohol and MetALD groups exhibited the highest proportion of males (92.6%) and the youngest mean age (56.7 ± 7.5 years), whereas the MASLD without alcohol group had the highest mean BMI (26.5 ± 2.6 kg/m²) and waist circumference (88.4 ± 6.4 cm). The prevalence of hypertension, diabetes, and dyslipidemia progressively increased across the spectrum, with the highest rates observed in the MASLD groups, particularly among non-drinkers. Additionally, liver enzymes (AST, ALT, γ-GTP), triglycerides, and the Charlson Comorbidity Index(CCI) were elevated in the MASLD groups, while HDL-C levels were reduced, indicating a worsening metabolic burden. Association Between SLD and OSA As shown in Table 2 , during a mean follow-up of 9.6 years, the incidence of OSA varied according to the presence of SLD and CMRFs. Using the no SLD without CMRF group as the reference, the incidence rate and adjusted hazard ratio (HR) for OSA in the group with no SLD but with CMRF were 0.33 per 1,000 person-years and 1.18 (95% CI: 0.93–1.50; p = 0.179), respectively. The MASLD without alcohol group showed an incidence rate of 0.40 per 1,000 person-years and an adjusted HR of 1.46 (95% CI: 1.12–1.91; p = 0.006). In the MASLD with alcohol and MetALD groups, the corresponding incidence rate and HR were 0.68 per 1,000 person-years and 1.50 (95% CI: 1.16–1.93; p = 0.002). Table 1 Baseline characteristics of the study population. Variables No SLD without CMRF (n = 26067) No SLD with CMRF (n = 138580) MASLD without alcohol (n = 50526) MASLD with alcohol & MetALD (n = 50279) P-value Sex (%) Male 12701 (48.7) 58832 (42.5) 23788 (47.1) 46534 (92.6) < 0.001 Female 13366 (51.3) 79748 (57.5) 26738 (52.9) 3745 (7.4) Age (years) Mean (SD) 55.9 (7.8) 59.7 (9.0) 61.5 (8.8) 56.7 (7.5) < 0.001 Income level (%) 1st quartile 3564 (13.7) 20425 (14.7) 7737 (15.3) 5459 (10.9) < 0.001 2nd quartile 5586 (21.4) 29294 (21.1) 10624 (21.0) 8751 (17.4) 3rd quartile 7264 (27.9) 40019 (28.9) 15644 (31.0) 14626 (29.1) 4th quartile 9653 (37.0) 48842 (35.2) 16521 (32.7) 21443 (42.6) Residence (%) Rural 8280 (31.8) 48551 (35.0) 19575 (38.7) 16109 (32.0) < 0.001 Urban 17787 (68.2) 90029 (65.0) 30951 (61.3) 34170 (68.0) Hypertension (%) 0 (0.0) 62924 (45.4) 30834 (61.0) 27180 (54.1) < 0.001 Diabetes (%) 0 (0.0) 15721 (11.3) 10903 (21.6) 9280 (18.5) < 0.001 Dyslipidemia (%) 0 (0.0) 50460 (36.4) 29085 (57.6) 22634 (45.0) < 0.001 Charlson comorbidity index (%) 0 16397 (62.9) 65574 (47.3) 18895 (37.4) 26250 (52.2) < 0.001 1 6494 (24.9) 37300 (26.9) 13623 (27.0) 13053 (26.0) 2 2253 (8.6) 18728 (13.5) 8176 (16.2) 5980 (11.9) ≥ 3 923 (3.5) 16978 (12.3) 9832 (19.5) 4996 (9.9) Body mass index (kg/m 2 ) Mean (SD) 20.9 (1.5) 23.0 (2.2) 26.5 (2.6) 25.6 (2.4) < 0.001 Waist circumference (cm) Mean (SD) 73.6 (5.9) 78.4 (6.3) 88.4 (6.4) 88.1 (6.1) < 0.001 Systolic blood pressure (mmHg) Mean (SD) 113.8 (10.7) 124.5 (15.2) 128.8 (15.1) 129.3 (14.4) < 0.001 Diastolic blood pressure (mmHg) Mean (SD) 71.0 (7.5) 76.8 (9.8) 79.2 (9.7) 80.9 (9.7) < 0.001 Fasting blood glucose (mg/dL) Mean (SD) 87.9 (7.4) 98.8 (21.9) 105.5 (28.5) 106.8 (28.2) < 0.001 Total cholesterol (mg/dL) Mean (SD) 189.6 (25.7) 198.4 (37.1) 207.7 (39.9) 203.8 (36.8) < 0.001 Triglyceride (mg/dL) Mean (SD) 81.8 (28.3) 107.1 (48.9) 186.0 (94.3) 191.3 (102.6) < 0.001 HDL cholesterol (mg/dL) Mean (SD) 62.2 (19.6) 56.0 (23.1) 50.4 (29.0) 51.5 (21.8) < 0.001 LDL cholesterol (mg/dL) Mean (SD) 111.3 (24.4) 121.7 (36.0) 121.9 (40.4) 115.3 (39.0) < 0.001 Aspartate aminotransferase (U/L) Mean (SD) 23.5 (9.7) 23.7 (8.2) 27.1 (14.6) 29.0 (17.5) < 0.001 Alanine aminotransferase (U/L) Mean (SD) 18.9 (11.0) 20.3 (10.1) 28.7 (18.2) 30.2 (18.9) < 0.001 r-glutamyl transpeptidase (U/L) Mean (SD) 22.1 (17.0) 22.1 (14.4) 39.1 (38.4) 66.6 (69.9) < 0.001 Hemoglobin (g/dL) Mean (SD) 13.5 (1.4) 13.5 (1.4) 13.9 (1.5) 14.8 (1.3) < 0.001 Glomerular filtration rate (mL/min/1.73 m 2 ) Mean (SD) 81.7 (30.5) 78.7 (29.3) 75.7 (28.9) 78.8 (35.3) < 0.001 Smoking (%) Non-smoker 17993 (69.0) 102157 (73.7) 37953 (75.1) 16114 (32.0) < 0.001 Ex-smoker 3585 (13.8) 19734 (14.2) 7202 (14.3) 17150 (34.1) Smoker 4489 (17.2) 16689 (12.0) 5371 (10.6) 17015 (33.8) Alcohol consumption (%) 9676 (37.1) 42849 (30.9) 226 (0.4) 50279 (100.0) < 0.001 Amount of alcohol consumption (g/week) Mean (SD) 41.3 (97.4) 35.4 (94.7) 0.0 (0.0) 135.1 (104.1) < 0.001 Regular exercise (%) No 17435 (66.9) 94884 (68.5) 37608 (74.4) 27880 (55.5) < 0.001 1–2 times/week 5297 (20.3) 24704 (17.8) 7381 (14.6) 14286 (28.4) 3–4 times/week 2087 (8.0) 11475 (8.3) 3307 (6.5) 5381 (10.7) 5 times/week 1248 (4.8) 7517 (5.4) 2230 (4.4) 2732 (5.4) Fatty liver index Mean (SD) 7.9 (5.5) 15.1 (7.7) 49.1 (15.1) 54.1 (16.8) < 0.001 Table 2 Association between SLD and OSA. Group Number Events Follow-up duration (person-years) Incidence rate (per 1000 person-years) Crude HR (95% CIs, P-value) Adjusted HR (95% CIs, P-value) No SLD without CMRF 26067 79 248970.6 0.32 1 (Reference) 1 (Reference) No SLD with CMRF 138580 428 1315323.0 0.33 1.03 (0.81–1.30, p = 0.839) 1.18 (0.93–1.50, p = 0.179) MASLD without alcohol 50526 194 479543.9 0.40 1.27 (0.98–1.65, p = 0.070) 1.46 (1.12–1.91, p = 0.006) MASLD with alcohol & MetALD 50279 324 474256.9 0.68 2.16 (1.69–2.77, p < 0.001) 1.50 (1.16–1.93, p = 0.002) MASLD with alcohol 38207 251 360506.2 0.70 1 (Reference) 1 (Reference) MetALD 12072 73 113750.7 0.64 0.92 (0.71–1.20, p = 0.543) 0.91 (0.70–1.19, p = 0.499) *The model was adjusted for age, sex, income level, residence area, Charlson comorbidity index, hemoglobin level, glomerular filtration rate, and smoking and regular exercise status. In subgroup analysis, the risk of OSA did not differ significantly between patients with the MASLD and alcohol use and those with MetALD (adjusted HR for MetALD: 0.91; 95% CI: 0.70–1.19; p = 0.499). Dose–Response Relationship Between the Number of CMRFs and OSA Risk Among individuals without SLD, a dose–response trend in OSA risk was not observed according to the number of CMRFs. Compared to the reference group (no SLD and no CMRF), adjusted HRs for OSA increased with a greater number of CMRFs, although the associations were not statistically significant. The adjusted HRs were 1.24 (95% CI: 0.95–1.63; p = 0.114) for one CMRF, 1.06 (95% CI: 0.81–1.40; p = 0.664) for two CMRFs, and 1.26 (95% CI: 0.95–1.67; p = 0.112) for three or more CMRFs. In contrast, individuals with MASLD (regardless of alcohol status) showed a significantly increased risk of OSA, with an adjusted HR of 1.48 (95% CI: 1.16–1.89; p = 0.002), suggesting that hepatic steatosis with metabolic dysfunction may serve as a stronger determinant of OSA risk than the presence of CMRFs alone. (Table 3 ) Table 3 Association between SLD with CMRFs and OSA. Group Number Events Follow-up duration (person-years) Incidence rate (per 1000 person-years) Crude HR (95% CIs, P-value) Adjusted HR (95% CIs, P-value) No SLD without CMRF 26067 79 248970.6 0.32 1 (Reference) 1 (Reference) No SLD with 1 CMRF 43976 156 418482.0 0.37 1.17 (0.90–1.54, p = 0.244) 1.24 (0.95–1.63, p = 0.114) No SLD with 2 CMRF 50999 142 484116.0 0.29 0.92 (0.70–1.22, p = 0.574) 1.06 (0.81–1.40, p = 0.664) No SLD with 3–4 CMRF 43605 130 412724.9 0.31 0.99 (0.75–1.31, p = 0.959) 1.26 (0.95–1.67, p = 0.112) MASLD 100805 518 953800.8 0.54 1.71 (1.35–2.17, p < 0.001) 1.48 (1.16–1.89, p = 0.002) *The model was adjusted for age, sex, income level, residence area, Charlson comorbidity index, hemoglobin level, glomerular filtration rate, and smoking Graphical Representation of Risk Gradients Figure 1 presents the results of restricted cubic spline analyses examining the association between selected continuous metabolic parameters and the risk of incident OSA, after adjusting for relevant confounders. Across the entire cohort, a positive, linear association was observed between BMI and OSA risk. The 95% confidence intervals remained narrow across most of the BMI distribution, reinforcing the reliability of the observed pattern. A similar trend was noted for waist circumference. OSA risk began to rise at waist circumferences exceeding 80 cm, with a steeper increase observed beyond 85–90 cm, suggesting a central adiposity threshold beyond which OSA risk becomes substantially elevated. The spline curve showed no clear linear dose-response relationship between alcohol consumption and the risk of OSA. A J-shaped association was noted, with an increased HR in the range of 120–150 g/week of alcohol consumption. At higher levels of alcohol intake, the HR appeared to decline below 1.0, although confidence intervals widened in these extreme ranges. The Kaplan–Meier analysis (Fig. 2 ) further illustrated distinct differences in the cumulative incidence of OSA across the four defined groups. The MASLD with alcohol and MetALD groups showed the highest cumulative incidence throughout the follow-up period, followed by the MASLD without alcohol group. Both SLD groups demonstrated higher incidence curves than the non-SLD groups, with the lowest incidence observed in the reference group. Log-rank testing confirmed that these differences in OSA incidence were statistically significant (p < 0.001). Discussion In this large, nationally representative Korean cohort of over 260,000 adults, we observed that individuals with MASLD had a significantly increased risk of incident OSA, independent of traditional metabolic risk factors. The association was even more pronounced among those with MASLD and concomitant alcohol use (MASLD with alcohol and MetALD). These findings underscore the role of metabolic liver dysfunction in the pathogenesis of OSA and support the hypothesis that hepatic steatosis—particularly in the context of metabolic dysregulation—may serve as an independent contributor to sleep-disordered breathing. CMRFs are central to the definitions of both MASLD and metabolic syndrome, and prior studies have suggested their contribution to OSA risk [15, 16] . However, in our study, increasing numbers of CMRFs were not significantly associated with incident OSA after adjusting for confounders. In contrast, the presence of MASLD—defined by hepatic steatosis and at least one CMRF—was significantly associated with increased OSA risk. This suggests that fatty liver itself, rather than the accumulation of traditional metabolic risk factors alone, may play a pivotal role in OSA pathogenesis. Hepatic steatosis may contribute to systemic inflammation, oxidative stress, and endocrine dysregulation, which in turn could impair upper airway neuromuscular control and ventilatory stability, thereby increasing OSA susceptibility [7, 9, 17] . The presence of CMRFs is a core component in both MASLD and metabolic syndrome definitions, and their contribution to OSA risk is well documented [16,18] . In our study, although an increasing number of CMRFs showed a trend toward elevated OSA risk, MASLD emerged as a stronger predictor. This finding suggests that liver fat accumulation and metabolic dysfunction may exert synergistic effects on OSA development, beyond the impact of individual risk factors such as obesity or insulin resistance [18, 19] . The hepatic production of pro-inflammatory cytokines, increased oxidative stress, and hormonal dysregulation in MASLD may contribute to neuromuscular instability of the upper airway and impaired ventilatory control [20-22] . The mechanistic interplay between OSA and MASLD is complex and likely bidirectional. Intermittent hypoxia resulting from OSA promotes sympathetic activation, oxidative stress, and systemic inflammation, which may exacerbate insulin resistance and hepatic lipid accumulation [23, 24] . Conversely, hepatic steatosis can impair glucose and lipid metabolism, elevate systemic inflammation, and worsen sleep-disordered breathing through altered leptin and adiponectin signaling [25, 26] . Imaging- and biopsy-based studies have also demonstrated increased liver fibrosis and steatohepatitis severity in patients with OSA, suggesting that sleep apnea may contribute to disease progression along the MASLD spectrum [27] . Alcohol consumption contributes to the development and exacerbation of OSA by promoting upper airway collapsibility, suppressing arousal responses, and reducing ventilatory drive during sleep [28, 29] . In particular, acute alcohol intake before bedtime has been shown to increase the apnea–hypopnea index and prolong episodes of oxygen desaturation [30] . However, in our study, while the MetALD group exhibited the highest absolute incidence rate of OSA, the adjusted HR was not significantly different between MetALD and MASLD with alcohol (adjusted HR 0.91, 95% CI: 0.70–1.19, p = 0.499). Furthermore, in the restricted cubic spline analysis, no clear linear dose–response relationship was observed between alcohol consumption and OSA risk. Although a modest increase in risk was noted in the 120–150 g/week range, the association was not consistent at higher intake levels, likely due to statistical uncertainty from smaller sample sizes. These findings suggest that chronic low-to-moderate alcohol exposure, as assessed in our cohort, may not independently increase OSA risk beyond the effect of metabolic liver dysfunction alone. This underscores the predominant role of MASLD, rather than alcohol consumption, in determining OSA susceptibility in this population. Several limitations should be acknowledged. First, the diagnosis of OSA relied on ICD-10 codes within NHIS claims data, without confirmatory polysomnography, which may have led to underestimation or misclassification. Second, the Fatty Liver Index (FLI) used to define hepatic steatosis is an indirect, algorithm-based surrogate that, while validated, may be influenced by confounding metabolic variables. Third, the observational design of this study limits causal inference, and unmeasured confounding factors—such as dietary habits, unreported alcohol use, or genetic predispositions—cannot be excluded. Fourth, the cohort consisted exclusively of individuals aged 40 years and older, limiting generalizability to younger populations at risk for OSA. Despite these limitations, the study's strengths include its large sample size, extended follow-up duration, and comprehensive adjustment for a broad range of demographic and clinical variables. Our findings highlight a novel at-risk population for OSA—individuals with MASLD—and provide compelling evidence to support the inclusion of sleep assessments in the routine evaluation of patients with metabolic liver disease. In conclusion, MASLD is significantly associated with an increased risk of incident OSA, independent of other metabolic risk factors. These findings underscore the need for heightened clinical awareness and potentially early screening for OSA in patients with MASLD. Future research should explore whether timely diagnosis and treatment of OSA in this population can mitigate hepatic and cardiometabolic complications. Methods Study Population and Design This retrospective cohort study was based on data from the NHIS, a nationwide claims database that provides universal healthcare coverage. The database includes eligibility information, diagnoses (based on ICD-10 code), prescriptions, mortality data, and biennial national health screening results for adults aged ≥40 years. Health screening data comprise anthropometric measurements, laboratory test results, and standardized self-reported questionnaires on lifestyle behaviors (e.g., alcohol consumption, smoking status, and physical activity), with all components subject to national quality control standards. Using this dataset, we initially identified 377,641 individuals who underwent health examinations between January 1, 2009, and December 31, 2010. Among them, 86,577 were excluded due to diagnoses of viral hepatitis, autoimmune hepatitis, alcoholic liver disease, toxic liver disease, Wilson's disease, biliary cholangitis, or any form of vasculitis that could potentially lead to chronic liver disease. An additional 11,973 individuals were excluded due to a prior diagnosis of OSA. Further exclusions were applied for non-cirrhosis (n = 6,880), incomplete health screening data (n = 11,973), and extreme values of the aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio (n = 5,209). After applying these criteria, a total of 265,452 individuals remained and were followed from the date of their baseline examination until the earliest of the following: diagnosis of OSA, death, or the end of the study period (December 31, 2019) (Figure 3). Group Classification Using CMRFs and FLI The CMRFs used in this study were defined based on the five components of the diagnostic criteria for MASLD. These included (1) a BMI ≥23 kg/m² or a waist circumference ≥90 cm for men and ≥85 cm for women, as specified in the Korean Obesity Study Guidelines; (2) a fasting blood glucose level ≥100 mg/dL or the diagnosis and treatment of type 2 diabetes; (3) a blood pressure reading of ≥130/85 mmHg or the use of antihypertensive medication; (4) a serum triglyceride level ≥150 mg/dL or the use of lipid-lowering therapy; and (5) an HDL-C level ≤40 mg/dL for men or ≤50 mg/dL for women, or the use of lipid-lowering therapy. The presence of at least one of the above criteria qualified an individual as having CMRF. SLD was defined using the fatty liver index (FLI), a validated non-invasive marker calculated from BMI, waist circumference, triglyceride levels, and γ-glutamyl transferase (γ-GTP). A threshold of FLI ≥30 was used to define hepatic steatosis, in accordance with established epidemiological practice. Based on the presence or absence of SLD, CMRFs, and alcohol consumption, participants were categorized into four mutually exclusive groups: (1) individuals with no SLD and no CMRFs (FLI <30 and no CMRFs); (2) individuals with no SLD but with one or more CMRFs (FLI <30 and ≥1 CMRF); (3) individuals with MASLD without alcohol consumption (FLI ≥30 and ≥1 CMRF); and (4) individuals classified as MetALD (FLI ≥30 and ≥1 CMRF, with alcohol consumption ≥ 210 g/week for men or ≥ 140 g/week for women). This classification scheme is consistent with the most recent international consensus on the nomenclature and diagnostic criteria for MASLD and MetALD, reflecting both hepatic steatosis and the associated metabolic burden. To further investigate the relationship between the number of accompanying CMRFs and the risk of OSA, participants without SLD were additionally subclassified into five mutually exclusive groups: (1) those without SLD and without any CMRFs; (2) those without SLD but with one CMRF; (3) those without SLD and two CMRFs; (4) those without SLD and three to four CMRFs; and (5) those with MASLD, regardless of the number of CMRFs. Outcome Definition The primary outcome was incident OSA, defined using ICD-10 code G47.3, based on ≥1 inpatient or outpatient claim, along with at least one additional visit for OSA within 1 year to ensure diagnostic validity. Covariates Baseline demographic and clinical variables included age, sex, household income (quartile), region (urban vs. rural), and CCI. Comorbidities such as hypertension, diabetes, and dyslipidemia were identified using ICD-10 codes and prescription records. Laboratory and clinical measures included BMI, waist circumference, systolic and diastolic blood pressure, fasting glucose, lipid profile, liver enzymes (AST, ALT, γ-GTP), hemoglobin, and estimated glomerular filtration rate (eGFR). Lifestyle behaviors—including smoking status, alcohol consumption, and physical activity—were assessed through standardized self-reported questionnaires. Statistical Analysis Baseline characteristics were presented as means (standard deviations) or frequencies (percentages). Between-group differences were evaluated using ANOVA or the chi-square test, as appropriate. Cox proportional hazards regression models were used to estimate HRs and 95% CIs for incident OSA. The reference group was “no SLD without CMRF.” Multivariable models adjusted for age, sex, income level, region, CCI, hemoglobin level, eGFR, smoking status, and frequency of physical activity. Kaplan–Meier curves were constructed to depict cumulative OSA incidence, and log-rank tests were used to evaluate differences across groups. Restricted cubic spline regression was used to assess nonlinear associations between FLI and OSA risk. Two sensitivity analyses were performed to assess the robustness of the findings. First, a higher FLI cutoff of ≥60 was used to define SLD. Second, the main analysis was repeated using a hepatic steatosis index (HSI) ≥36 as an alternative marker of hepatic steatosis. All analyses were performed using SAS version 9.4 and R version 4.3.0. Statistical significance was defined as a two-sided p < 0.05. Declarations Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author, C.H.P., upon reasonable request. Author Contributions C.H.P. and S.Y.M. conceived and designed the study. B.K. and M.S. were responsible for data acquisition, analysis, interpretation, and drafting of the manuscript. All authors critically revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work to ensure its integrity and accuracy. Funding The authors declare no competing financial interests. References Kazeminia, M. et al. Global prevalence of obstructive sleep apnea in the elderly and related factors: a systematic review and metaanalysis. Sleep. Health . 9 , 232–241 (2023). Iannella, G. et al. The global burden of obstructive sleep apnea. Diagnostics (Basel) . 15 , 1088 (2025). Javaheri, S. et al. JACC stateoftheart review: interactions of obstructive sleep apnea with the pathophysiology of cardiovascular disease. J. Am. Coll. Cardiol. 84 , 1208–1223 (2024). Koh, H. E. et al. Effect of obstructive sleep apnea on glucose metabolism. Eur. J. Endocrinol. 186 , 457–467 (2022). Wang, C., Tan, J., Miao, Y. & Zhang, Q. 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Med. 42 , 38–46 (2018). Burgos-Sanchez, C. et al. Impact of alcohol consumption on snoring and sleep apnea: A systematic review and meta-analysis. Otolaryngol. Head Neck Surg. 163 , 1078–1086 (2020). Kim, H. J. et al. Enhanced alcohol metabolism and sleep quality with continuous positive airway pressure following alcohol consumption. Sci. Rep. 13 , 16382 (2023). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 30 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 26 Sep, 2025 Reviewers agreed at journal 20 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers invited by journal 18 Sep, 2025 Editor assigned by journal 18 Sep, 2025 Editor invited by journal 29 Aug, 2025 Submission checks completed at journal 26 Aug, 2025 First submitted to journal 26 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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07:14:17","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118363,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7423767/v1/85a138b9fb4140145d771a1f.html"},{"id":92474965,"identity":"14be1cdb-1cc8-400e-8673-b33ac6174088","added_by":"auto","created_at":"2025-09-30 07:14:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline of hazard ratio with 95% confidence intervals for obstructive sleep apnea\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*The model was adjusted for age, sex, income level, residence area, Charlson comorbidity index, hemoglobin level, glomerular filtration rate, and smoking and regular exercise status\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7423767/v1/06d0087b3eb74828143b9ec9.png"},{"id":92474966,"identity":"d6a9b804-08ed-4853-945d-e9fa0e3356b3","added_by":"auto","created_at":"2025-09-30 07:14:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for the association between SLD and OSA.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7423767/v1/a606f03a4aef8a2865c161e0.png"},{"id":92478328,"identity":"03d653c0-5e7a-4d13-b09d-00df7e84adb4","added_by":"auto","created_at":"2025-09-30 07:30:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of the study population\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7423767/v1/161a894df00130cdcbdf96d5.png"},{"id":106344435,"identity":"64cf060b-a594-4268-b21a-d5c61b26e02a","added_by":"auto","created_at":"2026-04-07 16:14:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1780701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7423767/v1/5fa1ceeb-6e0a-4594-a98b-26a9645d65b3.pdf"},{"id":92474967,"identity":"068efa18-ab20-42f3-9d2c-224dc1aacb5b","added_by":"auto","created_at":"2025-09-30 07:14:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19469,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7423767/v1/c592ce0aa111193f7a93556b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Metabolic Dysfunction-Associated Steatotic Liver Disease and Obstructive Sleep Apnea: A Nationwide Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObstructive sleep apnea (OSA) is a common and increasingly recognized sleep disorder characterized by recurrent episodes of partial or complete upper airway obstruction during sleep. These episodes result in intermittent hypoxia, sleep fragmentation, and sympathetic nervous system activation, contributing to a wide range of adverse health consequences. OSA is estimated to affect 9\u0026ndash;38% of the global adult population, with higher prevalence among older adults, males, and individuals with obesity \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It is independently associated with increased risks of cardiovascular disease, stroke, insulin resistance, and all-cause mortality \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Despite its clinical significance, OSA remains underdiagnosed and undertreated, particularly in populations with coexisting metabolic disorders.\u003c/p\u003e\u003cp\u003eOSA has been closely linked to several metabolic conditions, with particularly strong associations observed with type 2 diabetes mellitus (T2DM), hypertension, and obesity. For instance, OSA has been shown to increase insulin resistance and worsen glycemic control in patients with T2DM through sympathetic overactivity and nocturnal hypoxemia \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Similarly, obesity\u0026mdash;a major risk factor for both OSA and metabolic syndrome\u0026mdash;exacerbates upper airway collapsibility and systemic inflammation \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Nonalcoholic fatty liver disease (NAFLD), the hepatic manifestation of metabolic syndrome, has also been increasingly associated with OSA. Previous studies have reported a bidirectional relationship between OSA and NAFLD, with intermittent hypoxia accelerating liver injury and hepatic steatosis, while fatty liver disease exacerbates cardiometabolic profiles that predispose to OSA \u003csup\u003e[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecently, the term metabolic dysfunction-associated steatotic liver disease (MASLD) was introduced to replace NAFLD, in order to better reflect the underlying pathophysiology and clinical heterogeneity of fatty liver disease. MASLD is diagnosed based on evidence of hepatic steatosis in the presence of at least one of five cardinal cardiometabolic risk factors (CMRFs): overweight/obesity, hypertension, hypertriglyceridemia, low HDL-C, or T2DM\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. This redefinition has garnered substantial interest and has prompted renewed investigations into the role of MASLD in systemic metabolic and cardiovascular diseases \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite this, few large-scale epidemiological studies have examined the association between MASLD and OSA. Given their shared metabolic origins and overlapping pathophysiological mechanisms, understanding this association has important clinical and public health implications. To address this gap, we conducted a nationwide retrospective cohort study using data from the Korean National Health Insurance Service (NHIS) to assess the risk of OSA in individuals with MASLD, with and without alcohol-related hepatic involvement.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of the Study Population\u003c/h2\u003e\u003cp\u003eA total of 265,452 individuals were included in the analysis, with 52.3% being male. The mean age of participants was 58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8 years. The mean body mass index (BMI) and waist circumference were 23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 kg/m\u0026sup2; and 81.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 cm, respectively. Participants were classified into four mutually exclusive groups according to the presence of steatotic liver disease(SLD) and CMRFs: (1) no SLD without CMRF (n\u0026thinsp;=\u0026thinsp;26,067), (2) no SLD with CMRFs (n\u0026thinsp;=\u0026thinsp;138,580), (3) MASLD without alcohol consumption (n\u0026thinsp;=\u0026thinsp;50,526), and (4) MASLD with alcohol consumption and MetALD (n\u0026thinsp;=\u0026thinsp;50,279).\u003c/p\u003e\u003cp\u003eNotable differences in demographic and metabolic characteristics were observed across the groups. The MASLD with alcohol and MetALD groups exhibited the highest proportion of males (92.6%) and the youngest mean age (56.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5 years), whereas the MASLD without alcohol group had the highest mean BMI (26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 kg/m\u0026sup2;) and waist circumference (88.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 cm). The prevalence of hypertension, diabetes, and dyslipidemia progressively increased across the spectrum, with the highest rates observed in the MASLD groups, particularly among non-drinkers. Additionally, liver enzymes (AST, ALT, γ-GTP), triglycerides, and the Charlson Comorbidity Index(CCI) were elevated in the MASLD groups, while HDL-C levels were reduced, indicating a worsening metabolic burden.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation Between SLD and OSA\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, during a mean follow-up of 9.6 years, the incidence of OSA varied according to the presence of SLD and CMRFs. Using the no SLD without CMRF group as the reference, the incidence rate and adjusted hazard ratio (HR) for OSA in the group with no SLD but with CMRF were 0.33 per 1,000 person-years and 1.18 (95% CI: 0.93\u0026ndash;1.50; p\u0026thinsp;=\u0026thinsp;0.179), respectively. The MASLD without alcohol group showed an incidence rate of 0.40 per 1,000 person-years and an adjusted HR of 1.46 (95% CI: 1.12\u0026ndash;1.91; p\u0026thinsp;=\u0026thinsp;0.006). In the MASLD with alcohol and MetALD groups, the corresponding incidence rate and HR were 0.68 per 1,000 person-years and 1.50 (95% CI: 1.16\u0026ndash;1.93; p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo SLD without CMRF\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26067)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo SLD with CMRF\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;138580)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMASLD without alcohol\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;50526)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMASLD with alcohol \u0026amp; MetALD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;50279)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12701 (48.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58832 (42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23788 (47.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46534 (92.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13366 (51.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79748 (57.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26738 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3745 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.9 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.7 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61.5 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56.7 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome level (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1st quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3564 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20425 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7737 (15.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5459 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2nd quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5586 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29294 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10624 (21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8751 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3rd quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7264 (27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40019 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15644 (31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14626 (29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4th quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9653 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48842 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16521 (32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21443 (42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8280 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48551 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19575 (38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16109 (32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17787 (68.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90029 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30951 (61.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34170 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62924 (45.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30834 (61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27180 (54.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15721 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10903 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9280 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50460 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29085 (57.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22634 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCharlson comorbidity index (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16397 (62.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65574 (47.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18895 (37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26250 (52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6494 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37300 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13623 (27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13053 (26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2253 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18728 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8176 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5980 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e923 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16978 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9832 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4996 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody mass index (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.9 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.0 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.5 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.6 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist circumference (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.6 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.4 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88.4 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.1 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSystolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e113.8 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e124.5 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e128.8 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e129.3 (14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiastolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.0 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.8 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.2 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80.9 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFasting blood glucose (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.9 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.8 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e105.5 (28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e106.8 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal cholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e189.6 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e198.4 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e207.7 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e203.8 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglyceride (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.8 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107.1 (48.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e186.0 (94.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e191.3 (102.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL cholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.2 (19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.0 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.4 (29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51.5 (21.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL cholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111.3 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e121.7 (36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e121.9 (40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e115.3 (39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAspartate aminotransferase (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.5 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.7 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.1 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.0 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlanine aminotransferase (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.9 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.3 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.7 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.2 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003er-glutamyl transpeptidase (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.1 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.1 (14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.1 (38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66.6 (69.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHemoglobin (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.5 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.5 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.9 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.8 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlomerular filtration rate (mL/min/1.73 m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.7 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.7 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.7 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e78.8 (35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17993 (69.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102157 (73.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37953 (75.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16114 (32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEx-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3585 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19734 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7202 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17150 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4489 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16689 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5371 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17015 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol consumption (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9676 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42849 (30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e226 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50279 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAmount of alcohol consumption (g/week)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.3 (97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.4 (94.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e135.1 (104.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegular exercise (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17435 (66.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94884 (68.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37608 (74.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27880 (55.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;2 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5297 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24704 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7381 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14286 (28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;4 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2087 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11475 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3307 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5381 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1248 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7517 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2230 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2732 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFatty liver index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.9 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.1 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49.1 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54.1 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between SLD and OSA.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFollow-up duration (person-years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncidence rate \u003c/p\u003e\u003cp\u003e(per 1000 person-years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCrude HR \u003c/p\u003e\u003cp\u003e(95% CIs, P-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdjusted HR \u003c/p\u003e\u003cp\u003e(95% CIs, P-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo SLD without CMRF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e248970.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 \u003c/p\u003e\u003cp\u003e(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 \u003c/p\u003e\u003cp\u003e(Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo SLD with CMRF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e138580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1315323.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03 \u003c/p\u003e\u003cp\u003e(0.81\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.18 \u003c/p\u003e\u003cp\u003e(0.93\u0026ndash;1.50, p\u0026thinsp;=\u0026thinsp;0.179)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMASLD without alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e479543.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27 \u003c/p\u003e\u003cp\u003e(0.98\u0026ndash;1.65, p\u0026thinsp;=\u0026thinsp;0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.46 \u003c/p\u003e\u003cp\u003e(1.12\u0026ndash;1.91, p\u0026thinsp;=\u0026thinsp;0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMASLD with alcohol \u0026amp; MetALD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e474256.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.16 \u003c/p\u003e\u003cp\u003e(1.69\u0026ndash;2.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.50 \u003c/p\u003e\u003cp\u003e(1.16\u0026ndash;1.93, p\u0026thinsp;=\u0026thinsp;0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMASLD with alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e360506.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 \u003c/p\u003e\u003cp\u003e(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 \u003c/p\u003e\u003cp\u003e(Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetALD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e113750.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003cp\u003e(0.71\u0026ndash;1.20, p\u0026thinsp;=\u0026thinsp;0.543)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.91 \u003c/p\u003e\u003cp\u003e(0.70\u0026ndash;1.19, p\u0026thinsp;=\u0026thinsp;0.499)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*The model was adjusted for age, sex, income level, residence area, Charlson comorbidity index, hemoglobin level, glomerular filtration rate, and smoking and regular exercise status.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn subgroup analysis, the risk of OSA did not differ significantly between patients with the MASLD and alcohol use and those with MetALD (adjusted HR for MetALD: 0.91; 95% CI: 0.70\u0026ndash;1.19; p\u0026thinsp;=\u0026thinsp;0.499).\u003c/p\u003e\n\u003ch3\u003eDose–Response Relationship Between the Number of CMRFs and OSA Risk\u003c/h3\u003e\n\u003cp\u003eAmong individuals without SLD, a dose\u0026ndash;response trend in OSA risk was not observed according to the number of CMRFs. Compared to the reference group (no SLD and no CMRF), adjusted HRs for OSA increased with a greater number of CMRFs, although the associations were not statistically significant. The adjusted HRs were 1.24 (95% CI: 0.95\u0026ndash;1.63; p\u0026thinsp;=\u0026thinsp;0.114) for one CMRF, 1.06 (95% CI: 0.81\u0026ndash;1.40; p\u0026thinsp;=\u0026thinsp;0.664) for two CMRFs, and 1.26 (95% CI: 0.95\u0026ndash;1.67; p\u0026thinsp;=\u0026thinsp;0.112) for three or more CMRFs. In contrast, individuals with MASLD (regardless of alcohol status) showed a significantly increased risk of OSA, with an adjusted HR of 1.48 (95% CI: 1.16\u0026ndash;1.89; p\u0026thinsp;=\u0026thinsp;0.002), suggesting that hepatic steatosis with metabolic dysfunction may serve as a stronger determinant of OSA risk than the presence of CMRFs alone. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between SLD with CMRFs and OSA.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFollow-up duration (person-years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncidence rate \u003c/p\u003e\u003cp\u003e(per 1000 person-years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCrude HR \u003c/p\u003e\u003cp\u003e(95% CIs, P-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdjusted HR \u003c/p\u003e\u003cp\u003e(95% CIs, P-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo SLD without CMRF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e248970.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 \u003c/p\u003e\u003cp\u003e(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 \u003c/p\u003e\u003cp\u003e(Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo SLD with 1 CMRF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e418482.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.17 \u003c/p\u003e\u003cp\u003e(0.90\u0026ndash;1.54, p\u0026thinsp;=\u0026thinsp;0.244)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.24 \u003c/p\u003e\u003cp\u003e(0.95\u0026ndash;1.63, p\u0026thinsp;=\u0026thinsp;0.114)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo SLD with 2 CMRF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e484116.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92 \u003c/p\u003e\u003cp\u003e(0.70\u0026ndash;1.22, p\u0026thinsp;=\u0026thinsp;0.574)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.06 \u003c/p\u003e\u003cp\u003e(0.81\u0026ndash;1.40, p\u0026thinsp;=\u0026thinsp;0.664)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo SLD with 3\u0026ndash;4 CMRF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e412724.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99 \u003c/p\u003e\u003cp\u003e(0.75\u0026ndash;1.31, p\u0026thinsp;=\u0026thinsp;0.959)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.26 \u003c/p\u003e\u003cp\u003e(0.95\u0026ndash;1.67, p\u0026thinsp;=\u0026thinsp;0.112)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMASLD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e953800.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.71 \u003c/p\u003e\u003cp\u003e(1.35\u0026ndash;2.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.48 \u003c/p\u003e\u003cp\u003e(1.16\u0026ndash;1.89, p\u0026thinsp;=\u0026thinsp;0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*The model was adjusted for age, sex, income level, residence area, Charlson comorbidity index, hemoglobin level, glomerular filtration rate, and smoking\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eGraphical Representation of Risk Gradients\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of restricted cubic spline analyses examining the association between selected continuous metabolic parameters and the risk of incident OSA, after adjusting for relevant confounders. Across the entire cohort, a positive, linear association was observed between BMI and OSA risk. The 95% confidence intervals remained narrow across most of the BMI distribution, reinforcing the reliability of the observed pattern.\u003c/p\u003e\u003cp\u003eA similar trend was noted for waist circumference. OSA risk began to rise at waist circumferences exceeding 80 cm, with a steeper increase observed beyond 85\u0026ndash;90 cm, suggesting a central adiposity threshold beyond which OSA risk becomes substantially elevated.\u003c/p\u003e\u003cp\u003eThe spline curve showed no clear linear dose-response relationship between alcohol consumption and the risk of OSA. A J-shaped association was noted, with an increased HR in the range of 120\u0026ndash;150 g/week of alcohol consumption. At higher levels of alcohol intake, the HR appeared to decline below 1.0, although confidence intervals widened in these extreme ranges.\u003c/p\u003e\u003cp\u003eThe Kaplan\u0026ndash;Meier analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) further illustrated distinct differences in the cumulative incidence of OSA across the four defined groups. The MASLD with alcohol and MetALD groups showed the highest cumulative incidence throughout the follow-up period, followed by the MASLD without alcohol group. Both SLD groups demonstrated higher incidence curves than the non-SLD groups, with the lowest incidence observed in the reference group. Log-rank testing confirmed that these differences in OSA incidence were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, nationally representative Korean cohort of over 260,000 adults, we observed that individuals with MASLD had a significantly increased risk of incident OSA, independent of traditional metabolic risk factors. The association was even more pronounced among those with MASLD and concomitant alcohol use (MASLD with alcohol and MetALD). These findings underscore the role of metabolic liver dysfunction in the pathogenesis of OSA and\u0026nbsp;support the hypothesis that hepatic steatosis—particularly in the context of metabolic dysregulation—may serve as an independent contributor to sleep-disordered breathing.\u003c/p\u003e\n\u003cp\u003eCMRFs are central to the definitions of both MASLD and metabolic syndrome, and prior studies have suggested their contribution to OSA risk\u0026nbsp;\u003csup\u003e[15, 16]\u003c/sup\u003e. However, in our study, increasing numbers of CMRFs were not significantly associated with incident OSA after adjusting for confounders. In contrast, the presence of MASLD—defined by hepatic steatosis and at least one CMRF—was significantly associated with increased OSA risk. This suggests that fatty liver itself, rather than the accumulation of traditional metabolic risk factors alone, may play a pivotal role in OSA pathogenesis. Hepatic steatosis may contribute to systemic inflammation, oxidative stress, and endocrine dysregulation, which in turn could impair upper airway neuromuscular control and ventilatory stability, thereby increasing OSA susceptibility\u0026nbsp;\u003csup\u003e[7, 9, 17]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe presence of CMRFs is a core component in both MASLD and metabolic syndrome definitions, and their contribution to OSA risk is well documented \u003csup\u003e[16,18]\u003c/sup\u003e. In our study, although an increasing number of CMRFs showed a trend toward elevated OSA risk, MASLD emerged as a stronger predictor. This finding suggests that liver fat accumulation and metabolic dysfunction may exert synergistic effects on OSA development, beyond the impact of individual risk factors such as obesity or insulin resistance\u003csup\u003e[18, 19]\u003c/sup\u003e.\u0026nbsp;The hepatic production of pro-inflammatory cytokines, increased oxidative stress, and hormonal dysregulation in MASLD may contribute to neuromuscular instability of the upper airway and impaired ventilatory control \u003csup\u003e[20-22]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe mechanistic interplay between OSA and MASLD is complex and likely bidirectional. Intermittent hypoxia resulting from OSA promotes sympathetic activation, oxidative stress, and systemic inflammation, which may exacerbate insulin resistance and hepatic lipid accumulation\u0026nbsp;\u003csup\u003e[23, 24]\u003c/sup\u003e.\u0026nbsp;Conversely, hepatic steatosis can impair glucose and lipid metabolism, elevate systemic inflammation, and worsen sleep-disordered breathing through altered leptin and adiponectin signaling\u0026nbsp;\u003csup\u003e[25, 26]\u003c/sup\u003e.\u0026nbsp;Imaging- and biopsy-based studies have also demonstrated increased liver fibrosis and steatohepatitis severity in patients with OSA, suggesting that sleep apnea may contribute to disease progression along the MASLD spectrum \u003csup\u003e[27]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlcohol consumption contributes to the development and exacerbation of\u0026nbsp;OSA by promoting upper airway collapsibility, suppressing arousal responses, and reducing ventilatory drive during sleep\u0026nbsp;\u003csup\u003e[28, 29]\u003c/sup\u003e.\u0026nbsp;In particular, acute alcohol intake before bedtime has been shown to increase the apnea–hypopnea index and prolong episodes of oxygen desaturation\u0026nbsp;\u003csup\u003e[30]\u003c/sup\u003e. However, in our study, while the MetALD group exhibited the highest absolute incidence rate of OSA, the adjusted HR was not significantly different between MetALD and MASLD with alcohol (adjusted HR 0.91, 95% CI: 0.70–1.19, p = 0.499). Furthermore, in the restricted cubic spline analysis, no clear linear dose–response relationship was observed between alcohol consumption and OSA risk. Although a modest increase in risk was noted in the 120–150 g/week range, the association was not consistent at higher intake levels, likely due to statistical uncertainty from smaller sample sizes. These findings suggest that chronic low-to-moderate alcohol exposure, as assessed in our cohort, may not independently increase OSA risk beyond the effect of metabolic liver dysfunction alone. This underscores the predominant role of MASLD, rather than alcohol consumption, in determining OSA susceptibility in this population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral limitations\u0026nbsp;should be acknowledged. First, the diagnosis of OSA relied on ICD-10 codes within NHIS claims data, without confirmatory polysomnography, which may have led to underestimation or misclassification. Second, the Fatty Liver Index (FLI) used to define hepatic steatosis is an indirect, algorithm-based surrogate that, while validated, may be influenced by confounding metabolic variables. Third, the observational design of this study limits causal inference, and unmeasured confounding factors—such as dietary habits, unreported alcohol use, or genetic predispositions—cannot be excluded. Fourth, the cohort consisted exclusively of individuals aged 40 years and older, limiting generalizability to younger populations at risk for OSA.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the study's strengths include its large sample size, extended follow-up duration, and comprehensive adjustment for a broad range of demographic and clinical variables. Our findings highlight a novel at-risk population for OSA—individuals with MASLD—and provide compelling evidence to support the inclusion of sleep assessments in the routine evaluation of patients with metabolic liver disease.\u003c/p\u003e\n\u003cp\u003eIn conclusion, MASLD is significantly associated with an increased risk of incident OSA, independent of other metabolic risk factors. These findings underscore the need for heightened clinical awareness and potentially early screening for OSA in patients with MASLD. Future research should explore whether timely diagnosis and treatment of OSA in this population can mitigate hepatic and cardiometabolic complications.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch4\u003e\u003cem\u003eStudy Population and Design\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThis retrospective cohort study was based on data from the NHIS, a nationwide claims database that provides universal healthcare coverage. The database includes eligibility information, diagnoses (based on ICD-10 code), prescriptions, mortality data, and biennial national health screening results for adults aged ≥40 years. Health screening data comprise anthropometric measurements, laboratory test results, and standardized self-reported questionnaires on lifestyle behaviors (e.g., alcohol consumption, smoking status, and physical activity), with all components subject to national quality control standards. Using this dataset, we initially identified 377,641 individuals who underwent health examinations between\u0026nbsp;January 1, 2009, and December 31, 2010. Among them, 86,577 were excluded due to diagnoses of viral hepatitis, autoimmune hepatitis, alcoholic liver disease, toxic liver disease, Wilson's disease, biliary cholangitis, or any form of vasculitis that could potentially lead to chronic liver disease. An additional 11,973 individuals were excluded due to a prior diagnosis of OSA. Further exclusions were applied for non-cirrhosis (n = 6,880), incomplete health screening data (n = 11,973), and extreme values of the aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio (n = 5,209). After applying these criteria, a total of 265,452 individuals remained and were followed from the date of their baseline examination until the earliest of the following: diagnosis of OSA, death, or\u0026nbsp;the end of the study period (December 31, 2019)\u0026nbsp;(Figure 3).\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003eGroup Classification Using CMRFs and\u0026nbsp;\u003c/em\u003e\u003cem\u003eFLI\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThe CMRFs used in this study were defined based on the five components of the diagnostic criteria for MASLD. These included (1) a BMI ≥23 kg/m² or a waist circumference ≥90 cm for men and ≥85 cm for women, as specified in the Korean Obesity Study Guidelines; (2) a fasting blood glucose level ≥100 mg/dL or the diagnosis and treatment of type 2 diabetes; (3) a blood pressure reading of ≥130/85 mmHg or the use of antihypertensive medication; (4) a serum triglyceride level ≥150 mg/dL or the use of lipid-lowering therapy; and (5) an HDL-C level ≤40 mg/dL for men or ≤50 mg/dL for women, or the use of lipid-lowering therapy. The presence of at least one of the above criteria qualified an individual as having CMRF.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSLD was defined using the fatty liver index (FLI), a validated non-invasive marker calculated from BMI, waist circumference, triglyceride levels, and γ-glutamyl transferase (γ-GTP). A threshold of FLI ≥30 was used to define hepatic steatosis, in accordance with established epidemiological practice. Based on the presence or absence of SLD, CMRFs, and alcohol consumption, participants were categorized into four mutually exclusive groups: (1) individuals with no SLD and no CMRFs (FLI \u0026lt;30 and no CMRFs); (2) individuals with no SLD but with one or more CMRFs (FLI \u0026lt;30 and ≥1 CMRF); (3) individuals with MASLD without alcohol consumption (FLI ≥30 and ≥1 CMRF); and (4) individuals classified as MetALD (FLI ≥30 and ≥1 CMRF, with alcohol consumption ≥ 210 g/week for men or ≥ 140 g/week for women). This classification scheme is consistent with the most recent international consensus on the nomenclature and diagnostic criteria for MASLD and MetALD, reflecting both hepatic steatosis and the associated metabolic burden.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further investigate the relationship between the number of accompanying CMRFs and the risk of OSA, participants without SLD were additionally subclassified into five mutually exclusive groups: (1) those without SLD and without any CMRFs; (2) those without SLD but with one CMRF; (3) those without SLD and two CMRFs; (4) those without SLD and three to four CMRFs; and (5) those with MASLD, regardless of the number of CMRFs.\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003eOutcome Definition\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThe primary outcome was incident OSA, defined using ICD-10 code G47.3, based on ≥1 inpatient or outpatient claim, along with at least one additional visit for OSA within 1 year to ensure diagnostic validity.\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003eCovariates\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eBaseline demographic and clinical variables included age, sex, household income (quartile), region (urban vs. rural), and CCI. Comorbidities such as hypertension, diabetes, and dyslipidemia were identified using ICD-10 codes and prescription records. Laboratory and clinical measures included BMI, waist circumference, systolic and diastolic blood pressure, fasting glucose, lipid profile, liver enzymes (AST, ALT, γ-GTP), hemoglobin, and estimated glomerular filtration rate (eGFR). Lifestyle behaviors—including smoking status, alcohol consumption, and physical activity—were assessed through standardized self-reported questionnaires.\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eBaseline characteristics were presented as means (standard deviations) or frequencies (percentages). Between-group differences were evaluated using ANOVA or the chi-square test, as appropriate. Cox proportional hazards regression models were used to estimate HRs and 95% CIs for incident OSA. The reference group was “no SLD without CMRF.” Multivariable models adjusted for age, sex, income level, region, CCI, hemoglobin level, eGFR, smoking status, and frequency of physical activity. Kaplan–Meier curves were constructed to depict cumulative OSA incidence, and log-rank tests were used to evaluate differences across groups. Restricted cubic spline regression was used to assess nonlinear associations between FLI and OSA risk. Two sensitivity analyses were performed to assess the robustness of the findings. First, a higher FLI cutoff of ≥60 was used to define SLD. Second, the main analysis was repeated using a hepatic steatosis index (HSI) ≥36 as an alternative marker of hepatic steatosis. All analyses were performed using SAS version 9.4 and R version 4.3.0. Statistical significance was defined as a two-sided p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author, C.H.P., upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.H.P. and S.Y.M.\u0026nbsp;conceived and designed the study.\u0026nbsp;B.K. and M.S.\u0026nbsp;were responsible for data acquisition, analysis, interpretation, and drafting of the manuscript.\u0026nbsp;All authors\u0026nbsp;critically revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work to ensure its integrity and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKazeminia, M. et al. 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Rep.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 16382 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7423767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7423767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObstructive sleep apnea (OSA) and metabolic liver disease share overlapping mechanisms, yet the association between metabolic dysfunction-associated steatotic liver disease (MASLD) and OSA remains unclear.\u003c/p\u003e\n\u003cp\u003eWe conducted a nationwide retrospective cohort study using data from the Korean National Health Insurance Service (NHIS). Adults aged ≥40 years who underwent health screening in 2009–2010 were categorized into four groups: (1) no steatotic liver disease (SLD) and no cardiometabolic risk factors (CMRFs); (2) no SLD with CMRFs; (3) MASLD without alcohol; and (4) MASLD with alcohol and MetALD.\u003c/p\u003e\n\u003cp\u003eIncident OSA was identified using ICD-10 codes. Cox proportional hazards models and restricted cubic spline analyses were applied. Among 265,452 participants (mean age 58.9 years; 52.3% men), MASLD was independently associated with increased OSA risk (adjusted HR 1.48; 95% CI: 1.16–1.89; p = 0.002), with a slightly higher risk in the MASLD with alcohol and MetALD groups (adjusted HR 1.50; 95% CI: 1.16–1.93; p = 0.002). CMRFs alone were not significantly associated with OSA. Spline analysis showed a nonlinear dose–response relationship between fatty liver index and OSA risk.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that MASLD—especially with alcohol involvement—is a significant risk factor for OSA, supporting routine sleep screening in this population.\u003c/p\u003e","manuscriptTitle":"Association between Metabolic Dysfunction-Associated Steatotic Liver Disease and Obstructive Sleep Apnea: A Nationwide Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:14:12","doi":"10.21203/rs.3.rs-7423767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T08:27:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T04:22:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T04:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142437426304150100915923903360758246410","date":"2025-09-20T07:16:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110027080334902152339826273158148738771","date":"2025-09-18T10:26:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T07:15:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T07:10:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-29T07:15:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-26T09:03:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-26T09:00:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a06d004-b993-43a1-8e7f-c14f43ed5205","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55449466,"name":"Health sciences/Diseases"},{"id":55449467,"name":"Health sciences/Gastroenterology"},{"id":55449468,"name":"Health sciences/Medical research"},{"id":55449469,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-07T16:11:21+00:00","versionOfRecord":{"articleIdentity":"rs-7423767","link":"https://doi.org/10.1038/s41598-026-46037-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-30 15:58:52","publishedOnDateReadable":"March 30th, 2026"},"versionCreatedAt":"2025-09-30 07:14:12","video":"","vorDoi":"10.1038/s41598-026-46037-4","vorDoiUrl":"https://doi.org/10.1038/s41598-026-46037-4","workflowStages":[]},"version":"v1","identity":"rs-7423767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7423767","identity":"rs-7423767","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}