The metabolic score for insulin resistance as a Predictor of Obstructive Sleep Apnea: The Mediating Effects of Liver Fat and Steatosis

The metabolic score for insulin resistance as a Predictor of Obstructive Sleep Apnea: The Mediating Effects of Liver Fat and Steatosis | 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 The metabolic score for insulin resistance as a Predictor of Obstructive Sleep Apnea: The Mediating Effects of Liver Fat and Steatosis Shangyi Song, Xuhao Li, Yecun Liu, Xingxin Wang, Wenhui Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5330011/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background Obstructive sleep apnea (OSA) is associated with metabolic disorders such as insulin resistance and liver fat accumulation. However, the specific mediating role of liver-related metabolic indicators in this association has not been fully studied. The purpose of this study was to investigate the relationship between Metabolic Score for Insulin Resistance (METS-IR) and OSA, focusing on the mediating effects of liver fat percentage (PLF) and hepatic steatosis index (HSI). Understanding these mechanisms may provide insights into targeted interventions for OSA. Methods A total of 12,655 participants from the National Health and Nutrition Examination Survey (NHANES) were included in this analysis. Obstructive sleep apnea (OSA) was assessed using the NHANES questionnaire. Weighted multivariate logistic regression was employed to assess the relationship between METS-IR and OSA, with a mediation model constructed to explore the mediating roles of key liver and metabolic markers, including PLF, HSI, SII, and OBS. Results Among 12,655 subjects, 31.04% had OSA. METS-IR was closely related to the increased risk of OSA, and the highest quartile group of METS-IR had a significantly increased risk of OSA ( OR = 2.35, 95% CI : 1.72–3.21 ). Mediating effect analysis showed that PLF and HSI mediated 11.22% and 22.78% of the effects, respectively, while systemic immunity-inflammation index (SII) and oxidative balance score (OBS) had no significant mediating effect. Conclusions METS-IR is an important predictor of OSA risk, primarily mediated by hepatic lipid accumulation. Addressing insulin resistance and hepatic metabolic health is crucial for the effective management of OSA and provides valuable guidance for clinical risk assessment in susceptible populations. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Pathogenesis Health sciences/Risk factors metabolic score for Insulin resistance obstructive sleep apnea percentage of liver fat hepatic steatosis index NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.Introduction Obstructive sleep apnea syndrome (OSA) is a common sleep disorder characterized by the repeated partial or complete blockage of the upper airway during sleep, resulting in intermittent breathing interruptions and hypoxemia [ 1 ]. These recurring episodes of apnea not only severely disrupt the patient's nighttime sleep quality, but also lead to excessive daytime sleepiness, cognitive impairment, and a significant reduction in overall quality of life [ 2 ]. More critically, OSA is closely linked to a range of metabolic disorders and cardiovascular diseases, including type 2 diabetes, hypertension, coronary artery disease, and stroke [ 3 ][ 4 ]. In recent years, the global rise in obesity rates has contributed to a rapid increase in OSA prevalence, posing significant challenges for public health [ 5 ]. Although a large number of studies have revealed the correlation between OSA and cardiovascular and metabolic diseases, there is still a lack of in-depth understanding of its potential metabolic mechanisms [6]. Insulin resistance is considered to be one of the core mechanisms linking OSA and metabolic syndrome. Insulin resistance is not only closely related to metabolic syndrome, but also interacts with pathological processes such as visceral fat accumulation, chronic inflammation and oxidative stress, which may play a key role in the pathogenesis of OSA [ 7 ].The excessive activation of sympathetic nerve and oxidative stress induced by intermittent hypoxemia in OSA patients are important causes of insulin resistance, and insulin resistance further aggravates metabolic disorders.. In recent years, the metabolic insulin resistance score (METS-IR), as a new metabolic index, can effectively evaluate the individual 's insulin resistance [ 8 ]. METS-IR combines a variety of metabolic-related parameters, such as waist circumference, triglyceride ( TG ) and fasting blood glucose, and is a reliable alternative indicator of metabolic syndrome and metabolic dysfunction. In view of the important role of insulin resistance in the pathogenesis of OSA, METS-IR provides a new perspective to understand how metabolic disorders affect OSA. Hepatic steatosis involves abnormal liver fat accumulation, often linked to obesity, insulin resistance, and metabolic syndrome. Percentage of liver fat (PLF) and hepatic steatosis index (HSI) are key markers to assess liver fat buildup and functional impairment [ 9 ][ 10 ]. In OSA patients, liver fat accumulation is common and correlates with heightened insulin resistance and metabolic disturbances [ 11 ]. Over time, excessive liver fat can lead to non-alcoholic fatty liver disease (NAFLD), further disrupting metabolic balance and elevating cardiovascular risk [ 12 ]. Liver dysfunction may also exacerbate OSA by worsening insulin resistance and systemic inflammation. Systemic inflammation plays a significant role in OSA's metabolic consequences. OSA often triggers sympathetic overactivation, driving a chronic inflammatory state. The systemic immunity-inflammation index (SII), based on neutrophil, platelet, and lymphocyte ratios, measures systemic inflammation. Elevated SII in OSA patients suggests ongoing low-grade inflammation, which not only aggravates insulin resistance but may also directly contribute to OSA progression by affecting the respiratory system's inflammatory state [13]. Oxidative stress arises when the body’s antioxidant defenses are overwhelmed by free radicals, resulting in cellular damage [ 14 ]. OSA-induced intermittent hypoxia increases oxidative stress, particularly in the cardiovascular and respiratory systems [15]. This stress impairs endothelial function, promoting vascular sclerosis and increasing cardiovascular event risk [ 16 ]. Elevated oxidative balance score (OBS) in OSA patients indicate persistent oxidative stress, highlighting its role as another key mechanism linking OSA with metabolic dysfunction [ 17 ][ 18 ]. Based on this, the purpose of this study is to systematically evaluate the association between METS-IR and OSA by analyzing large-scale data from the National Health and Nutrition Survey ( NHANES ), and to focus on the mediating role of PLF, HSI, SII and OBS in this association. By revealing the mediating effects of these metabolic markers, this study provides a new clinical perspective for the early identification and intervention of OSA. 2. Methods 2.1 Study design and participants The National Health and Nutrition Examination Survey (NHANES) is an ongoing national program designed to gather comprehensive data regarding the dietary patterns and overall health of the U.S. population. Before beginning data collection, participants provided written informed consent, and all study procedures received approval from the ethical review board of the National Center for Health Statistics. Additional details about the NHANES program are available on its official website. This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2005–2008 and 2015–2020, including participants aged 20 years and older (n = 29,763). Participants with missing data required to calculate the Metabolic Score for Insulin Resistance (METS-IR) and the four mediators — percentage of liver fat (PLF), hepatic steatosis index (HSI), systemic immunity-inflammation index (SII) and oxidative balance score (OBS) were excluded (n = 17,105). Additionally, data from participants with missing or incomplete information required for an obstructive sleep apnea syndrome (OSA) diagnosis were excluded from the analysis (n = 3). The final sample size included in the analysis was 12,655 participants. 2.2 Definitions of the exposure variable, mediating variable and outcome variable The exposure variable in this study is the Metabolic Score for Insulin Resistance (METS-IR), which serves as a predictor for insulin resistance and metabolic health. Data for calculating METS-IR were obtained from the NHANES database. The formula used is: \(\:\text{M}\text{E}\text{T}\text{S}-\text{I}\text{R}=\frac{Ln(2\times\:FPG+TG)}{Ln(BMI\times\:HDL-\text{C})}\) , where FPG stands for fasting plasma glucose, TG stands for triglycerides, BMI is the body mass index, and HDL-C refers to high-density lipoprotein cholesterol [ 8 ]. The mediating variables in this study were percentage of liver fat (PLF), hepatic steatosis index (HSI), systemic immunity-inflammation index (SII) and oxidative balance score (OBS). These markers are linked to liver fat accumulation, inflammation, and oxidative stress, which are key metabolic processes associated with OSA. The formulas for calculating each of these mediators are as follows: PLF (%) = 10 (−0.805+0.282×metabolic syndrome+0.078×type 2 diabetes+0.525×log(insulin)+0.521×log(AST)−0.454×log(AST/ALT)) . AST stands for aspartate aminotransferase (a liver enzyme indicating liver damage when elevated), ALT stands for alanine aminotransferase (another enzyme indicating liver injury), and insulin represents fasting insulin levels. This formula incorporates metabolic syndrome and type 2 diabetes as binary variables (yes/no) [ 9 ]. HSI = 8×ALT/AST + BMI + 2×(if diabetes) + 2×(if female). BMI is body mass index, and ALT and AST are the liver enzymes. Diabetes and female sex are included as binary variables [ 19 ]. \(\:\text{S}\text{I}\text{I}=\frac{Platelet\:count\times\:Neutropℎil\:count}{Lympℎocyte\:\:\text{c}\text{o}\text{u}\text{n}\text{t}}\) [ 20 ]. The OBS combines 16 dietary components and four lifestyle factors, with higher scores indicating greater antioxidant exposure. Tobacco use was assessed via the cotinine test, and alcohol consumption was categorized into nondrinkers, nonheavy drinkers, and heavy drinkers, with scores of 2, 1, and 0, respectively. Antioxidants were scored higher in upper tertiles, while pro-oxidants were inversely scored [ 17 ]. In accordance with earlier studies, the outcome variable OSA is diagnosed when a person answers 'yes' to at least one of the following three NHANES questions [ 21 ]: (1) feeling excessively sleepy during the day despite getting at least 7 hours of sleep per night, as reported 16–30 times; (2) experiencing episodes of gasping, snorting, or stopping their breath on three or more occasions per week; (3) snoring on three or more occasions every week. 2.3 Potential covariates The self-reported sociodemographic characteristics included age, sex (male/female), race/ethnicity (non-Hispanic White, Mexican American, non-Hispanic Black, other Hispanic, or other Race/multiple Races), education level (less than high school, completed high school, or more than high school), marital status (married/living with a partner, never married, widowed, divorced, or separated) and family poverty index ratio (PIR, a ratio of family income to the poverty threshold). BMI was calculated as weight (kg) divided by height (m) squared. Physical activity was assessed by asking participants to report the types of physical activities they engaged in regularly, categorized into vigorous physical activities (e.g., running, playing basketball) and moderate physical activities (e.g., brisk walking, swimming, cycling). Alcohol consumption and smoking status were also treated as categorical variables. The participants' alcohol consumption status was categorized into five groups: never (fewer than 12 drinks in a lifetime), former (12 or more drinks in a lifetime but none in the previous year), mild (1 + drinks/day for women, 2 + drinks/day for men), moderate (2 + drinks/day for women, 3 + drinks/day for men), and heavy (5 + drinks/day for women, 4 + drinks/day for men). This categorization was based on the number of alcoholic beverages consumed per day, with the following thresholds applied: ≥2 drinks/day for women and ≥ 3 drinks/day for men, binge drinking ≥ 2 days/month, and heavy drinking (≥ 3 drinks/day for women and ≥ 4 drinks/day for men, or binge drinking ≥ 5 days/month). The participants were divided into three smoking categories: never smokers (smoked fewer than 100 cigarettes), former smokers (smoked at least 100 cigarettes but not currently smoking), and current smokers (smoked at least 100 cigarettes and currently smoking daily or some days). The definition of metabolic syndrome (MetS) was in accordance with the updated National Cholesterol Education Program/Adult Treatment Panel III criteria for Americans [ 22 ]. All missing values in this study were handled using appropriate imputation methods: continuous variables were imputed with mean values, while categorical variables were assigned dummy variables.The confounding variables and their detailed definitions are collated in Table S1 for reference. 2.4 Statistical analyses The statistical analyses for this study were performed using the R statistical computing environment (version 4.4.0). All analyses accounted for the complex, multistage survey design of NHANES by applying sample weights, stratification, and clustering to ensure the results are representative of the U.S. population. Baseline characteristics were summarized using weighted means and 95% confidence intervals (CI) for continuous variables, while categorical variables were expressed as weighted frequencies and percentages.Continuous variables were analyzed using weighted t-tests or Wilcoxon rank-sum tests, depending on the distribution of the data, which was assessed using the Shapiro-Wilk test for normality. Categorical variables were compared using weighted chi-square tests or Fisher’s exact tests, where appropriate. Statistical significance was set at P < 0.05 for all analyses. The associations between METS-IR and OSA were investigated via multivariate logistic regression models in four different models. The crude model was not adjusted for covariates, whereas Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was adjusted for the variables in Model 1, in addition to education level, marital status, physical activity, smoking status, and alcohol status. Model 3 was adjusted for the variables in Model 2, in addition to PIR, BMI and metabolic syndrome. A further assessment of the heterogeneity between METS-IR and OSA was conducted through subgroup analysis, which included the following variables: age, sex, race/ethnicity, smoking status, alcohol consumption, marital status, physical activity and metabolic syndrome. The indirect effect of METS-IR on the relationship between METS-IR and OSA was further explored through mediation analysis. Using causal mediation analysis, the indirect and direct effects were evaluated to determine the proportion of the total effect of METS-IR on OSA that could be explained by four mediators: PLF, HSI, SII and OBS. The indirect effect quantified how much of the association between METS-IR and OSA was mediated by these markers, while the direct effect measured the remaining effect not attributed to mediators. The non-parametric bootstrap re-sampling method was employed to estimate confidence intervals and significance levels for the mediation and direct effects. The proportion of mediation for each variable was calculated to identify the pathways through which METS-IR influences OSA. 3. Results 3.1 Baseline characteristics The weighted baseline characteristics of the participants in the study are shown in Table 1 . A total of 12,655 participants were included in the analysis, with a mean age of 48.08 years (95% CI: 47.47, 48.68). Among them, 49.43% were male and 50.57% were female. The majority of participants identified as non-Hispanic white (66.39%), followed by non-Hispanic black (10.80%), Mexican American (8.52%), other Hispanic origin (5.84%), and other or multi-racial groups (8.45%) . In total, 31.04% of the participants were categorized as having OSA. Across the four METS-IR groups, all variables showed statistical significance. Compared to participants in the lower METS-IR group, those in the highest quartile (Q4) were more likely to be male, older, and non-Hispanic white. They also tended to have higher PLF, HSI, SII, and BMI levels, were more likely to be never-smokers or mild drinkers, and had lower levels of physical activity, poverty income ratio (PIR) and OBS. Additionally, these participants exhibited higher educational attainment, were more often widowed, divorced, separated, or never married, and had a greater prevalence of metabolic syndrome. [Table 1 near here] 3.2 Association between METS-IR and OSA In the restricted cubic spline (RCS) analysis, METS-IR was positively associated with OSA (Fig. 1 ). Table 2 presents the associations between METS-IR and the risk of obstructive sleep apnea (OSA) across different models. Whether confounding factors were adjusted or not, METS-IR consistently showed a significant positive correlation with OSA in all participants. In the multivariate regression analysis, METS-IR was divided into quartiles, using the Q1 group as the reference to assess the relationship with OSA. After adjusting for age, gender, race, education level, marital status, physical activity, smoking, drinking status, PIR, BMI, and metabolic syndrome, compared to the Q1 reference group, the Q2 group (OR: 1.48; 95% CI: 1.25, 1.77), Q3 group (OR: 1.70; 95% CI: 1.34, 2.16), and Q4 group (OR: 2.35; 95% CI: 1.72, 3.21) all exhibited a significantly increased risk, showing a clear upward trend (P for trend < 0.0001). This trend was consistent across all models, indicating that the positive association between METS-IR and the risk of OSA remains stable regardless of adjustments for confounding factors. [Table 2 near here] 3.3 Subgroup analysis Table 3 Subgroup analyses were conducted across various factors, including age (≤ 40 years, 40–60 years, > 60 years), sex (male, female), race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, and other races), education level (High School), smoking status (former smoker, never smoked, current smoker), alcohol consumption (former drinker, heavy drinker, mild drinker, moderate drinker, never drank), marital status (Married/Living with Partner, Widowed/Divorced/Separated/Never married), physical activity (inactive, moderate activity, both moderate and vigorous, vigorous), and the presence of metabolic syndrome (yes or no) to evaluate the stability of the association between METS-IR and OSA. Interaction tests were performed to assess whether these factors influenced the strength of the association between METS-IR and OSA. The results revealed significant interactions for race/ethnicity (p-interaction = 0.002), smoking status (p-interaction = 0.016), alcohol consumption (p-interaction = 0.035), and metabolic syndrome (p-interaction = 0.004), indicating that these factors significantly modified the relationship between METS-IR and OSA. However, despite the significant interactions (p < 0.05), METS-IR remained positively correlated with OSA within these subgroups. No significant interactions were observed for age (p-interaction = 0.07), sex (p-interaction = 0.443), education level (p-interaction = 0.023), marital status (p-interaction = 0.857), or physical activity (p-interaction = 0.419), suggesting that the association between METS-IR and OSA remained stable within these subgroups. [Table 3 near here] 3.4 Mediation Analysis In this study, four key metabolic markers—PLF, HSI, SII and OBS—were used as mediating variables to explore the relationship between METS-IR and obstructive sleep apnea (OSA). Figures 2 – 5 showed the mediation analysis results. The mediating effect of PLF was 0.00348 (p < 0.0001), accounting for 11.22% of the total effect, while the mediating effect of HSI was 0.00706 (p < 0.0001), accounting for 22.78%. In contrast, SII (-0.01203, p = 0.984) and OBS (-0.00021, p = 0.18) did not show significant mediating effects, so no meaningful mediating ratios were calculated.These findings suggest that PLF and HSI play a key mediating role in the relationship between METS-IR and OSA, emphasizing the critical role of liver fat accumulation in this pathway. Conversely, the mediating effects of SII and OBS were not significant, indicating that systemic inflammation and oxidative stress may have a minimal impact on the association between insulin resistance and OSA. Furthermore, the direct effect of METS-IR on OSA remains significant, highlighting the need to consider insulin resistance when assessing OSA risk. Addressing liver health through PLF and HSI may be an effective strategy to mitigate the risk of OSA associated with insulin resistance. 4. Discussion This study is the first comprehensive analysis of how liver fat accumulation, systemic inflammation and oxidative stress mediate the relationship between METS-IR and OSA. We found that in a nationally representative sample, higher METS-IR scores were significantly associated with increased OSA risk, and liver fat accumulation estimated by the index PLF and HSI played a key mediating role. This suggests that insulin resistance captured by METS-IR promotes OSA mainly through liver metabolic dysfunction. Interestingly, SII and OBS did not show a significant mediating effect in this pathway, suggesting that although inflammation and oxidative stress are associated with OSA [ 23 ][ 24 ], they may not be the core mechanisms linking METS-IR and OSA. This highlights the importance of addressing liver health issues in managing OSA risk in people with high insulin resistance. METS-IR combines fasting blood glucose, triglycerides and BMI to form a comprehensive index of insulin resistance, which plays a key role in the pathophysiology of OSA. Insulin resistance exacerbates OSA through a combination of metabolic disorders driven primarily by intermittent hypoxia (a hallmark of OSA) [25]. This intermittent hypoxia activates the sympathetic nervous system, leading to oxidative stress and systemic inflammation, which in turn deteriorates insulin sensitivity [ 26 ]. The resulting increased visceral fat deposition and lipid metabolism dysfunction further aggravate the severity of OSA [ 27 ]. Visceral fat, especially liver and abdominal fat, due to its dual role, is the main trigger factor for OSA: mechanical compression of the upper airway, airway collapse deterioration, and secretion of inflammatory cytokines (such as TNF-α and IL-6) [ 28 ]. These pro-inflammatory signals interfere with insulin signaling, enhance insulin resistance and promote further fat accumulation [ 29 ]. This forms a vicious cycle, and metabolic dysfunction not only aggravates OSA, but also accelerates other cardiovascular risks [ 30 ]. In addition, osa-induced hypoxia-induced oxidative stress leads to the production of reactive oxygen species (ROS), which destroys the insulin receptor signaling pathway and impairs endothelial function [ 31 ]. This vascular health dysfunction is common in OSA patients, which can aggravate their overall metabolic status and promote cardiovascular complications [ 32 ]. Therefore, METS-IR can effectively capture these metabolic disorders and is of great significance in assessing OSA risk. Through mediation analysis, PLF and HSI play a key role in assessing the relationship between liver fat content, METS-IR and OSA. PLF directly estimates liver fat content, reflects the actual fat accumulation in liver tissue, and is closely related to metabolic dysfunction such as insulin resistance and nonalcoholic fatty liver disease. Increased liver fat directly affects insulin signal transduction, aggravates insulin resistance, and promotes common metabolic disorders in OSA patients [33]. This link underscores the central role of liver fat in OSA progression and the need for clinical interventions aimed at reducing liver fat to reduce OSA risk, especially in individuals with high insulin resistance.On the other hand, HSI can be used as an indirect measurement of liver fat, calculated by metabolic factors such as liver enzyme levels (AST/ALT), BMI and the presence of diabetes. The increase of HSI value indicates hepatic steatosis, which aggravates insulin resistance and is common in OSA patients. Since HSI is also associated with liver function, strategies to improve liver health such as optimizing liver enzyme levels and addressing NAFLD are crucial [ 34 ]. While reducing liver fat, improving liver function may further enhance insulin sensitivity and metabolic regulation [35]. This shows that by changing lifestyle, diet adjustment and drug intervention, not only focusing on lipid reduction, but also focusing on clinical strategies to improve liver function can more effectively reduce the impact of insulin resistance on OSA and improve the clinical outcomes of patients with metabolic disorders [ 36 ][ 37 ]. Although systemic inflammation and oxidative stress are known to play a role in the pathology of OSA, the results of this study show that SII and OBS do not mediate the METS-IR-OSA relationship [38]. This suggests that although chronic low-grade inflammation and oxidative stress are elevated in OSA patients, their effects may not be significant in the case of insulin resistance. Nevertheless, future research should further explore whether SII and OBS play a greater role in specific subpopulations, where the interaction between insulin resistance, inflammation, and oxidative stress may be more pronounced. Moreover, even after considering the mediating effect of liver fat through PLF and HSI, METS-IR still showed a significant direct association with OSA. This suggests that insulin resistance itself is an independent driver of OSA. Clinicians should give priority to insulin resistance when assessing the risk of OSA.Improving insulin sensitivity by changing lifestyle (such as weight control, physical activity and diet adjustment) can be used as an effective strategy to reduce the risk of OSA. This study has several advantages. First of all,The use of NHANES data provides a nationally representative large sample and enhances the general applicability of the research results. In addition, by controlling various confounding factors and conducting mediation analysis between METS-IR, PLF, HSI, SII, OBS and OSA, we can gain a deeper understanding of the metabolic pathways involved in OSA. However, the cross-sectional design limits the ability to establish a causal relationship, underscoring the need for future longitudinal studies to validate these findings and further study of the long-term effects of insulin resistance and liver health on OSA. This can provide important insights into how targeted interventions to improve metabolic health can help reduce the risk of OSA. 5. Conclusion This study shows that the METS-IR is significantly associated with the risk of OSA, in which PLF and HSI play a key mediating role. As scoring systems, PLF and HSI capture liver metabolic abnormalities and play a crucial role in connecting insulin resistance and OSA. The results suggest that improving liver health and managing insulin resistance may be effective strategies to reduce OSA risk in people with high METS-IR scores. Future research should focus on improving these scoring systems in order to better predict and manage OSA risks and apply them to clinical practice. Abbreviations NHANES: National Health and Nutrition Examination Survey; CDC: Centers for Disease Control and Prevention; METS-IR: Metabolic Score for Insulin Resistance, PLF: Percentage of liver fat, HSI: Hepatic Steatosis Index, SII: Systemic Immunity-inflammation Index; OBS: Oxidative Balance Score. NAFLD: non-alcoholic fatty liver disease; BMI: body mass index; PIR: a ratio of family income to the poverty threshold. Declarations Ethics approval and consent to participate: The National Center for Health Statistics Research Ethics Review Board authorized the NHANES study protocols in compliance with the revised Declaration of Helsinki. All participants provided informed consent. Consent for publication: Not applicable. Availability of data and materials: The data used in this study are available on the National Health and Nutrition Examination Survey website: https://www.cdc.gov/nchs/nhanes/index.htm Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by Key Project of Shandong Province Traditional Chinese Medicine Science and Technology Program (No. Z–2023019) and Shandong Medical system staff science and Technology Innovation Program project (SDYWZGKCJH2023020). Authors’ contributions: SYS participated in the literature search, study design, data collection, data analysis, data interpretation and wrote the manuscript. XHL,YCL,XXW and WHZ conceived the study and participated in its design, coordination, data collection and analysis. JGY and YXL participated in the study design and provided critical revision. All the authors read and approved the final manuscript. Acknowledgment: Special thanks to all of the NHANES participants who freely gave their time to make this and other studies possible. References Kapur VK, Auckley DH, Chowdhuri S, Kuhlmann DC, Mehra R, Ramar K, Harrod CG. 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Tables Table 1.Baseline characteristics of participants in the NHANES 2005-2008 and 2015-2020 (n=12,655) Characteristic Overall METS-IR Quartiles p value Q1(<34.596) Q2(34.596to<41.919) Q3(41.919to<50.674) Q4(≥50.674) Age, years, mean(95% CI) 48.08(47.47,48.68) 45.13(44.19,46.06) 49.63(48.57,50.70) 49.87(48.80,50.93) 48.06(47.31,48.81) < 0.0001 PIR,mean(95% CI) 3.05(2.98,3.11) 3.18(3.09,3.27) 3.13(3.03,3.22) 3.01(2.92,3.10) 2.85(2.74,2.96) < 0.0001 BMI,mean(95% CI) 29.19(28.96,29.41) 22.30(22.20,22.40) 26.76(26.65,26.87) 30.43(30.29,30.58) 38.29(37.97,38.61) < 0.0001 PLF,mean(95% CI) 4.88(4.77,4.99) 2.07(2.01,2.13) 3.53(3.41,3.66) 5.24(5.08,5.41) 9.11(8.85,9.37) < 0.0001 HSI,mean(95% CI) 38.42(38.16,38.69) 30.28(30.13,30.43) 35.56(35.38,35.73) 40.01(39.82,40.20) 49.06(48.71,49.42) < 0.0001 OBS,mean(95% CI) 17.90(17.50,18.30) 19.65(19.09,20.21) 17.73(17.19,18.26) 17.02(16.54,17.50) 16.96(16.46,17.45) < 0.0001 SII,mean(95% CI) 536.47(526.25,546.69) 525.43(505.21,545.65) 512.22(497.06,527.37) 532.42(517.38,547.46) 577.85(563.82,591.89) < 0.0001 Sex, n (%) < 0.0001 Female 6395(50.57) 1853(62.35) 1477(46.61) 1445(43.07) 1620(48.67) Male 6260(49.43) 1312(37.65) 1685(53.39) 1719(56.93) 1544(51.33) Race/ethnicity, n (%) < 0.0001 Non-Hispanic White 4967(66.39) 1341(69.51) 1213(66.48) 1207(64.42) 1206(64.70) Other Race - Including Multi-Racial 1636( 8.45) 587(10.76) 448( 8.45) 339( 7.80) 262( 6.48) Non-Hispanic Black 2831(10.80) 692( 9.85) 683(10.89) 685(10.44) 771(12.14) Mexican American 1962( 8.52) 305( 5.25) 507( 8.49) 565(10.17) 585(10.62) Other Hispanic 1259( 5.84) 240(4.62) 311(5.69) 368(7.17) 340(6.07) Educational level, n (%) < 0.0001 High school 6681(60.08) 1825(65.78) 1684(60.25) 1577(57.68) 1595(55.79) Marital status, n (%) < 0.1 Married/Living with partner 5394(50.41) 1269(48.34) 1368(49.86) 1446(52.82) 1311(50.91) Widowed/Divorced/Separated/Never married 7254(49.55) 1894(51.63) 1792(50.13) 1717(47.09) 1851(49.07) Alcohol consumption, n (%) < 0.0001 Former 1229( 8.77) 221( 6.00) 302( 8.65) 356( 9.87) 350(10.95) Heavy 2203(18.82) 550(19.79) 532(18.07) 516(18.72) 605(18.57) Mild 4001(35.21) 1030(34.60) 1047(35.54) 981(35.76) 943(35.02) Moderate 1804(16.09) 524(20.05) 418(15.62) 427(14.10) 435(14.01) Never 1403( 8.87) 372(9.49) 355(9.02) 362(8.72) 314(8.18) Smoking status, n (%) < 0.0001 Never smoking 6898(53.53) 1784(55.91) 1742(53.35) 1757(53.16) 1615(51.35) Former smoker 3195(26.59) 641(22.46) 780(25.47) 842(28.54) 932(30.50) Current smoker 2549(19.83) 737(21.59) 635(21.12) 563(18.23) 614(18.11) Physical activity, n (%) < 0.0001 Inactive 5771(37.67) 1233(30.03) 1375(34.80) 1515(41.09) 1648(45.90) Moderate 2600(22.43) 638(21.35) 660(23.29) 639(21.52) 663(23.70) Both moderate and vigorous 1653(15.64) 570(22.08) 443(17.05) 373(13.95) 267( 8.52) Vigorous 743( 6.37) 224(7.60) 191(6.29) 174(6.61) 154(4.81) Metabolic Syndrome, n (%) < 0.0001 No 7830(65.08) 3054(97.25) 2456(80.64) 1555(53.03) 765(24.58) Yes 4825(34.92) 111( 2.75) 706(19.36) 1609(46.97) 2399(75.42) OSA, n (%) < 0.0001 No 8679(68.96) 2586(82.80) 2287(72.22) 2125(66.33) 1681(52.47) Yes 3976(31.04) 579(17.20) 875(27.78) 1039(33.67) 1483(47.53) Abbreviations: NHANES, National Health and Nutrition Examination Survey; Q, quantile; PIR, ratio of family income to poverty; BMI, body mass index; PLF, Percentage of Liver Fat; HSI, Hepatic Steatosis Index; SII, Systemic Inflammatory Index; OBS, Oxidative Balance Score; OSA, Obstructive Sleep Apnea; METS-IR, metabolic Score for Insulin Resistance. Table 2.Multivariable logistic regression models for the association between METS-IR and OSA in adults:NHANES 2005–2008 and 2015-2020 METS-IR Crude model a Model 1 b Model 2 c Model 3 d OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value Continuous(per 10 units) 1.50(1.44,1.56) <0.0001 1.49(1.44,1.55) <0.0001 1.49(1.43, 1.55) <0.0001 1.30(1.10, 1.53) 0.002 Quartiles Q1(<34.596) Reference Reference Reference Reference Q2(34.596to<41.919) 1.85(1.56,2.20) <0.0001 1.75(1.47,2.08) <0.0001 1.75(1.47, 2.08) <0.0001 1.48(1.25, 1.77) <0.0001 Q3(41.919to<50.674) 2.44(2.04,2.93) <0.0001 2.30(1.91,2.77) <0.0001 2.30(1.91, 2.77) <0.0001 1.70(1.34, 2.16) <0.0001 Q4(≥50.674) 4.36(3.73,5.10) <0.0001 4.24(3.63,4.94) <0.0001 4.18(3.56, 4.90) <0.0001 2.35(1.72, 3.21) <0.0001 p for trend <0.0001 <0.0001 <0.0001 <0.0001 Abbreviations: CI, confidence interval; OR, odds ratio; Q, quantile; METS-IR, metabolic Score for Insulin Resistance; OSA, Obstructive Sleep Apnea. In multivariate regression, samples with missing values for covariates in the model were encoded as dummy variables. The missing values were categorized as "Unknown" and included as a separate category in the analysis. a Crude Model: Unadjusted. b Model 1: Adjusted for age, sex, and race/ethnicity. c Model 2: Adjusted for the variables in Model 1 plus education level, marital status, physical activity,smoking status, and alcohol status. d Model 3: Adjusted for the variables in Model 2 plus ratio of family income to poverty,body mass index and metabolic syndrome. Table 3. Associations of METS-IR with OSA by different factors character 95% CI p p for interaction Age 0.07 ≤40 1.045(1.038,1.051) 40,≤60 1.042(1.034,1.050) 60 1.032(1.025,1.040) <0.0001 Sex 0.443 Female 1.042(1.038,1.047) <0.0001 Male 1.039(1.033,1.046) <0.0001 Race/ethnicity 0.002 Non-Hispanic White 1.042(1.036,1.047) <0.0001 Other Race - Including Multi-Racial 1.050(1.038,1.062) <0.0001 Non-Hispanic Black 1.038(1.032,1.044) <0.0001 Mexican American 1.038(1.029,1.048) <0.0001 Other Hispanic 1.045(1.031,1.059) <0.0001 Education level 0.023 < High school 1.034(1.022,1.047) <0.0001 Completed high school 1.035(1.029,1.041) High school 1.045(1.039,1.052) <0.0001 Smoking status 0.016 Former smoker 1.041(1.034,1.049) <0.0001 Never smoking 1.046(1.041,1.052) <0.0001 Current smoker 1.032(1.024,1.041) <0.0001 Alcohol status 0.035 Former 1.037(1.027,1.048) <0.0001 Heavy 1.031(1.020,1.043) <0.0001 Mild 1.049(1.041,1.057) <0.0001 Moderate 1.045(1.034,1.056) <0.0001 Never 1.029(1.015,1.042) <0.0001 Marital status 0.857 Married/Living with Partner 1.042(1.034,1.049) <0.0001 Widowed/Divorced/Separated/Never married 1.041(1.036,1.046) <0.0001 Physical activity 0.419 Inactive 1.039(1.032,1.046) <0.0001 Moderate 1.037(1.029,1.045) <0.0001 Both moderate and igorous 1.051(1.037,1.065) <0.0001 Vigorous 1.044(1.024,1.065) <0.0001 Metabolic Syndrome 0.004 No 1.045(1.038,1.052) <0.0001 Yes 1.031(1.025,1.037) <0.0001 Additional Declarations No competing interests reported. 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Age, sex, race/ethnicity, education level, marital status, physical activity, smoking status, alcohol status, the ratio of family income to poverty, body mass index and metabolic syndrome were adjusted for. The solid line represents the line of best fit, and the pale pink area represents the 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5330011/v1/3b4e255f8149fc4db0391479.png"},{"id":71051842,"identity":"2c704fca-fe2e-4135-83c6-963283ab2a06","added_by":"auto","created_at":"2024-12-10 15:49:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43295,"visible":true,"origin":"","legend":"\u003cp\u003ePLF as a Mediator between METS-IR and OSA\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5330011/v1/12a9dfb18c043cadd9b20264.jpeg"},{"id":71051846,"identity":"2c3b5e14-873c-4d4f-936d-4077b6e93d65","added_by":"auto","created_at":"2024-12-10 15:49:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43254,"visible":true,"origin":"","legend":"\u003cp\u003eHSI as a Mediator between METS-IR and OSA\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5330011/v1/070ae0ce0a0cbaa26f2801d5.jpeg"},{"id":71051841,"identity":"a50a3bd0-b2c4-4342-939e-f9a68917ce63","added_by":"auto","created_at":"2024-12-10 15:49:55","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42499,"visible":true,"origin":"","legend":"\u003cp\u003eSII as a Mediator between METS-IR and OSA\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5330011/v1/578fe7593d6c338193cce06e.jpeg"},{"id":71054509,"identity":"726fd7dc-8db5-436e-b027-71d9bea07277","added_by":"auto","created_at":"2024-12-10 16:05:56","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":43798,"visible":true,"origin":"","legend":"\u003cp\u003eOBS as a Mediator between METS-IR and OSA\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5330011/v1/e8def5ae5b2e4542292ebbbd.jpeg"},{"id":91358937,"identity":"88b050f5-e64a-4e63-9700-6405c43c3e3c","added_by":"auto","created_at":"2025-09-15 16:00:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1199990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5330011/v1/d0a2cfd9-c66f-495a-a631-0c7c7e0fc120.pdf"},{"id":71051845,"identity":"bb5421ba-6bf6-4b66-a077-50b5a0df7c72","added_by":"auto","created_at":"2024-12-10 15:49:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32722,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5330011/v1/e347c125cfa817b8ef8b07d5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The metabolic score for insulin resistance as a Predictor of Obstructive Sleep Apnea: The Mediating Effects of Liver Fat and Steatosis","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eObstructive sleep apnea syndrome (OSA) is a common sleep disorder characterized by the repeated partial or complete blockage of the upper airway during sleep, resulting in intermittent breathing interruptions and hypoxemia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These recurring episodes of apnea not only severely disrupt the patient's nighttime sleep quality, but also lead to excessive daytime sleepiness, cognitive impairment, and a significant reduction in overall quality of life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. More critically, OSA is closely linked to a range of metabolic disorders and cardiovascular diseases, including type 2 diabetes, hypertension, coronary artery disease, and stroke [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In recent years, the global rise in obesity rates has contributed to a rapid increase in OSA prevalence, posing significant challenges for public health [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough a large number of studies have revealed the correlation between OSA and cardiovascular and metabolic diseases, there is still a lack of in-depth understanding of its potential metabolic mechanisms [6]. Insulin resistance is considered to be one of the core mechanisms linking OSA and metabolic syndrome. Insulin resistance is not only closely related to metabolic syndrome, but also interacts with pathological processes such as visceral fat accumulation, chronic inflammation and oxidative stress, which may play a key role in the pathogenesis of OSA [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e7\u003c/span\u003e].The excessive activation of sympathetic nerve and oxidative stress induced by intermittent hypoxemia in OSA patients are important causes of insulin resistance, and insulin resistance further aggravates metabolic disorders..\u003c/p\u003e \u003cp\u003eIn recent years, the metabolic insulin resistance score (METS-IR), as a new metabolic index, can effectively evaluate the individual 's insulin resistance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. METS-IR combines a variety of metabolic-related parameters, such as waist circumference, triglyceride ( TG ) and fasting blood glucose, and is a reliable alternative indicator of metabolic syndrome and metabolic dysfunction. In view of the important role of insulin resistance in the pathogenesis of OSA, METS-IR provides a new perspective to understand how metabolic disorders affect OSA.\u003c/p\u003e \u003cp\u003eHepatic steatosis involves abnormal liver fat accumulation, often linked to obesity, insulin resistance, and metabolic syndrome. Percentage of liver fat (PLF) and hepatic steatosis index (HSI) are key markers to assess liver fat buildup and functional impairment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In OSA patients, liver fat accumulation is common and correlates with heightened insulin resistance and metabolic disturbances [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Over time, excessive liver fat can lead to non-alcoholic fatty liver disease (NAFLD), further disrupting metabolic balance and elevating cardiovascular risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Liver dysfunction may also exacerbate OSA by worsening insulin resistance and systemic inflammation.\u003c/p\u003e \u003cp\u003eSystemic inflammation plays a significant role in OSA's metabolic consequences. OSA often triggers sympathetic overactivation, driving a chronic inflammatory state. The systemic immunity-inflammation index (SII), based on neutrophil, platelet, and lymphocyte ratios, measures systemic inflammation. Elevated SII in OSA patients suggests ongoing low-grade inflammation, which not only aggravates insulin resistance but may also directly contribute to OSA progression by affecting the respiratory system's inflammatory state [13].\u003c/p\u003e \u003cp\u003eOxidative stress arises when the body\u0026rsquo;s antioxidant defenses are overwhelmed by free radicals, resulting in cellular damage [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. OSA-induced intermittent hypoxia increases oxidative stress, particularly in the cardiovascular and respiratory systems [15]. This stress impairs endothelial function, promoting vascular sclerosis and increasing cardiovascular event risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Elevated oxidative balance score (OBS) in OSA patients indicate persistent oxidative stress, highlighting its role as another key mechanism linking OSA with metabolic dysfunction [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e][\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on this, the purpose of this study is to systematically evaluate the association between METS-IR and OSA by analyzing large-scale data from the National Health and Nutrition Survey ( NHANES ), and to focus on the mediating role of PLF, HSI, SII and OBS in this association. By revealing the mediating effects of these metabolic markers, this study provides a new clinical perspective for the early identification and intervention of OSA.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is an ongoing national program designed to gather comprehensive data regarding the dietary patterns and overall health of the U.S. population. Before beginning data collection, participants provided written informed consent, and all study procedures received approval from the ethical review board of the National Center for Health Statistics. Additional details about the NHANES program are available on its official website.\u003c/p\u003e \u003cp\u003eThis cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2005\u0026ndash;2008 and 2015\u0026ndash;2020, including participants aged 20 years and older (n\u0026thinsp;=\u0026thinsp;29,763). Participants with missing data required to calculate the Metabolic Score for Insulin Resistance (METS-IR) and the four mediators \u0026mdash; percentage of liver fat (PLF), hepatic steatosis index (HSI), systemic immunity-inflammation index (SII) and oxidative balance score (OBS) were excluded (n\u0026thinsp;=\u0026thinsp;17,105). Additionally, data from participants with missing or incomplete information required for an obstructive sleep apnea syndrome (OSA) diagnosis were excluded from the analysis (n\u0026thinsp;=\u0026thinsp;3). The final sample size included in the analysis was 12,655 participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definitions of the exposure variable, mediating variable and outcome variable\u003c/h2\u003e \u003cp\u003eThe exposure variable in this study is the Metabolic Score for Insulin Resistance (METS-IR), which serves as a predictor for insulin resistance and metabolic health. Data for calculating METS-IR were obtained from the NHANES database. The formula used is:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{M}\\text{E}\\text{T}\\text{S}-\\text{I}\\text{R}=\\frac{Ln(2\\times\\:FPG+TG)}{Ln(BMI\\times\\:HDL-\\text{C})}\\)\u003c/span\u003e\u003c/span\u003e, where FPG stands for fasting plasma glucose, TG stands for triglycerides, BMI is the body mass index, and HDL-C refers to high-density lipoprotein cholesterol [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mediating variables in this study were percentage of liver fat (PLF), hepatic steatosis index (HSI), systemic immunity-inflammation index (SII) and oxidative balance score (OBS). These markers are linked to liver fat accumulation, inflammation, and oxidative stress, which are key metabolic processes associated with OSA. The formulas for calculating each of these mediators are as follows:\u003c/p\u003e \u003cp\u003ePLF (%)\u0026thinsp;=\u0026thinsp;10\u003csup\u003e(\u0026minus;0.805+0.282\u0026times;metabolic syndrome+0.078\u0026times;type 2 diabetes+0.525\u0026times;log(insulin)+0.521\u0026times;log(AST)\u0026minus;0.454\u0026times;log(AST/ALT))\u003c/sup\u003e. AST stands for aspartate aminotransferase (a liver enzyme indicating liver damage when elevated), ALT stands for alanine aminotransferase (another enzyme indicating liver injury), and insulin represents fasting insulin levels. This formula incorporates metabolic syndrome and type 2 diabetes as binary variables (yes/no) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHSI\u0026thinsp;=\u0026thinsp;8\u0026times;ALT/AST\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;2\u0026times;(if diabetes)\u0026thinsp;+\u0026thinsp;2\u0026times;(if female). BMI is body mass index, and ALT and AST are the liver enzymes. Diabetes and female sex are included as binary variables [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\text{I}\\text{I}=\\frac{Platelet\\:count\\times\\:Neutropℎil\\:count}{Lympℎocyte\\:\\:\\text{c}\\text{o}\\text{u}\\text{n}\\text{t}}\\)\u003c/span\u003e \u003c/span\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe OBS combines 16 dietary components and four lifestyle factors, with higher scores indicating greater antioxidant exposure. Tobacco use was assessed via the cotinine test, and alcohol consumption was categorized into nondrinkers, nonheavy drinkers, and heavy drinkers, with scores of 2, 1, and 0, respectively. Antioxidants were scored higher in upper tertiles, while pro-oxidants were inversely scored [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn accordance with earlier studies, the outcome variable OSA is diagnosed when a person answers 'yes' to at least one of the following three NHANES questions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e]: (1) feeling excessively sleepy during the day despite getting at least 7 hours of sleep per night, as reported 16\u0026ndash;30 times; (2) experiencing episodes of gasping, snorting, or stopping their breath on three or more occasions per week; (3) snoring on three or more occasions every week.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Potential covariates\u003c/h2\u003e \u003cp\u003eThe self-reported sociodemographic characteristics included age, sex (male/female), race/ethnicity (non-Hispanic White, Mexican American, non-Hispanic Black, other Hispanic, or other Race/multiple Races), education level (less than high school, completed high school, or more than high school), marital status (married/living with a partner, never married, widowed, divorced, or separated) and family poverty index ratio (PIR, a ratio of family income to the poverty threshold). BMI was calculated as weight (kg) divided by height (m) squared. Physical activity was assessed by asking participants to report the types of physical activities they engaged in regularly, categorized into vigorous physical activities (e.g., running, playing basketball) and moderate physical activities (e.g., brisk walking, swimming, cycling). Alcohol consumption and smoking status were also treated as categorical variables.\u003c/p\u003e \u003cp\u003eThe participants' alcohol consumption status was categorized into five groups: never (fewer than 12 drinks in a lifetime), former (12 or more drinks in a lifetime but none in the previous year), mild (1\u0026thinsp;+\u0026thinsp;drinks/day for women, 2\u0026thinsp;+\u0026thinsp;drinks/day for men), moderate (2\u0026thinsp;+\u0026thinsp;drinks/day for women, 3\u0026thinsp;+\u0026thinsp;drinks/day for men), and heavy (5\u0026thinsp;+\u0026thinsp;drinks/day for women, 4\u0026thinsp;+\u0026thinsp;drinks/day for men). This categorization was based on the number of alcoholic beverages consumed per day, with the following thresholds applied: \u0026ge;2 drinks/day for women and \u0026ge;\u0026thinsp;3 drinks/day for men, binge drinking\u0026thinsp;\u0026ge;\u0026thinsp;2 days/month, and heavy drinking (\u0026ge;\u0026thinsp;3 drinks/day for women and \u0026ge;\u0026thinsp;4 drinks/day for men, or binge drinking\u0026thinsp;\u0026ge;\u0026thinsp;5 days/month). The participants were divided into three smoking categories: never smokers (smoked fewer than 100 cigarettes), former smokers (smoked at least 100 cigarettes but not currently smoking), and current smokers (smoked at least 100 cigarettes and currently smoking daily or some days). The definition of metabolic syndrome (MetS) was in accordance with the updated National Cholesterol Education Program/Adult Treatment Panel III criteria for Americans [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll missing values in this study were handled using appropriate imputation methods: continuous variables were imputed with mean values, while categorical variables were assigned dummy variables.The confounding variables and their detailed definitions are collated in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for reference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e \u003cp\u003eThe statistical analyses for this study were performed using the R statistical computing environment (version 4.4.0). All analyses accounted for the complex, multistage survey design of NHANES by applying sample weights, stratification, and clustering to ensure the results are representative of the U.S. population. Baseline characteristics were summarized using weighted means and 95% confidence intervals (CI) for continuous variables, while categorical variables were expressed as weighted frequencies and percentages.Continuous variables were analyzed using weighted t-tests or Wilcoxon rank-sum tests, depending on the distribution of the data, which was assessed using the Shapiro-Wilk test for normality. Categorical variables were compared using weighted chi-square tests or Fisher\u0026rsquo;s exact tests, where appropriate. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e \u003cp\u003eThe associations between METS-IR and OSA were investigated via multivariate logistic regression models in four different models. The crude model was not adjusted for covariates, whereas Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was adjusted for the variables in Model 1, in addition to education level, marital status, physical activity, smoking status, and alcohol status. Model 3 was adjusted for the variables in Model 2, in addition to PIR, BMI and metabolic syndrome. A further assessment of the heterogeneity between METS-IR and OSA was conducted through subgroup analysis, which included the following variables: age, sex, race/ethnicity, smoking status, alcohol consumption, marital status, physical activity and metabolic syndrome.\u003c/p\u003e \u003cp\u003eThe indirect effect of METS-IR on the relationship between METS-IR and OSA was further explored through mediation analysis. Using causal mediation analysis, the indirect and direct effects were evaluated to determine the proportion of the total effect of METS-IR on OSA that could be explained by four mediators: PLF, HSI, SII and OBS. The indirect effect quantified how much of the association between METS-IR and OSA was mediated by these markers, while the direct effect measured the remaining effect not attributed to mediators. The non-parametric bootstrap re-sampling method was employed to estimate confidence intervals and significance levels for the mediation and direct effects. The proportion of mediation for each variable was calculated to identify the pathways through which METS-IR influences OSA.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e\n \u003cp\u003eThe weighted baseline characteristics of the participants in the study are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 12,655 participants were included in the analysis, with a mean age of 48.08 years (95% CI: 47.47, 48.68). Among them, 49.43% were male and 50.57% were female. The majority of participants identified as non-Hispanic white (66.39%), followed by non-Hispanic black (10.80%), Mexican American (8.52%), other Hispanic origin (5.84%), and other or multi-racial groups (8.45%) .\u003c/p\u003e\n \u003cp\u003eIn total, 31.04% of the participants were categorized as having OSA. Across the four METS-IR groups, all variables showed statistical significance. Compared to participants in the lower METS-IR group, those in the highest quartile (Q4) were more likely to be male, older, and non-Hispanic white. They also tended to have higher PLF, HSI, SII, and BMI levels, were more likely to be never-smokers or mild drinkers, and had lower levels of physical activity, poverty income ratio (PIR) and OBS. Additionally, these participants exhibited higher educational attainment, were more often widowed, divorced, separated, or never married, and had a greater prevalence of metabolic syndrome.\u003c/p\u003e\n \u003cp\u003e[Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e near here]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Association between METS-IR and OSA\u003c/h2\u003e\n \u003cp\u003eIn the restricted cubic spline (RCS) analysis, METS-IR was positively associated with OSA (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations between METS-IR and the risk of obstructive sleep apnea (OSA) across different models. Whether confounding factors were adjusted or not, METS-IR consistently showed a significant positive correlation with OSA in all participants. In the multivariate regression analysis, METS-IR was divided into quartiles, using the Q1 group as the reference to assess the relationship with OSA.\u003c/p\u003e\n \u003cp\u003eAfter adjusting for age, gender, race, education level, marital status, physical activity, smoking, drinking status, PIR, BMI, and metabolic syndrome, compared to the Q1 reference group, the Q2 group (OR: 1.48; 95% CI: 1.25, 1.77), Q3 group (OR: 1.70; 95% CI: 1.34, 2.16), and Q4 group (OR: 2.35; 95% CI: 1.72, 3.21) all exhibited a significantly increased risk, showing a clear upward trend (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This trend was consistent across all models, indicating that the positive association between METS-IR and the risk of OSA remains stable regardless of adjustments for confounding factors.\u003c/p\u003e\n \u003cp\u003e[Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e near here]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Subgroup analysis\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e Subgroup analyses were conducted across various factors, including age (\u0026le;\u0026thinsp;40 years, 40\u0026ndash;60 years, \u0026gt;\u0026thinsp;60 years), sex (male, female), race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, and other races), education level (\u0026lt;\u0026thinsp;High School, Completed High School, \u0026gt;High School), smoking status (former smoker, never smoked, current smoker), alcohol consumption (former drinker, heavy drinker, mild drinker, moderate drinker, never drank), marital status (Married/Living with Partner, Widowed/Divorced/Separated/Never married), physical activity (inactive, moderate activity, both moderate and vigorous, vigorous), and the presence of metabolic syndrome (yes or no) to evaluate the stability of the association between METS-IR and OSA. Interaction tests were performed to assess whether these factors influenced the strength of the association between METS-IR and OSA.\u003c/p\u003e\n \u003cp\u003eThe results revealed significant interactions for race/ethnicity (p-interaction\u0026thinsp;=\u0026thinsp;0.002), smoking status (p-interaction\u0026thinsp;=\u0026thinsp;0.016), alcohol consumption (p-interaction\u0026thinsp;=\u0026thinsp;0.035), and metabolic syndrome (p-interaction\u0026thinsp;=\u0026thinsp;0.004), indicating that these factors significantly modified the relationship between METS-IR and OSA. However, despite the significant interactions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), METS-IR remained positively correlated with OSA within these subgroups. No significant interactions were observed for age (p-interaction\u0026thinsp;=\u0026thinsp;0.07), sex (p-interaction\u0026thinsp;=\u0026thinsp;0.443), education level (p-interaction\u0026thinsp;=\u0026thinsp;0.023), marital status (p-interaction\u0026thinsp;=\u0026thinsp;0.857), or physical activity (p-interaction\u0026thinsp;=\u0026thinsp;0.419), suggesting that the association between METS-IR and OSA remained stable within these subgroups.\u003c/p\u003e\n \u003cp\u003e[Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e near here]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Mediation Analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, four key metabolic markers\u0026mdash;PLF, HSI, SII and OBS\u0026mdash;were used as mediating variables to explore the relationship between METS-IR and obstructive sleep apnea (OSA). Figures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e showed the mediation analysis results.\u003c/p\u003e\n \u003cp\u003eThe mediating effect of PLF was 0.00348 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), accounting for 11.22% of the total effect, while the mediating effect of HSI was 0.00706 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), accounting for 22.78%. In contrast, SII (-0.01203, p\u0026thinsp;=\u0026thinsp;0.984) and OBS (-0.00021, p\u0026thinsp;=\u0026thinsp;0.18) did not show significant mediating effects, so no meaningful mediating ratios were calculated.These findings suggest that PLF and HSI play a key mediating role in the relationship between METS-IR and OSA, emphasizing the critical role of liver fat accumulation in this pathway. Conversely, the mediating effects of SII and OBS were not significant, indicating that systemic inflammation and oxidative stress may have a minimal impact on the association between insulin resistance and OSA.\u003c/p\u003e\n \u003cp\u003eFurthermore, the direct effect of METS-IR on OSA remains significant, highlighting the need to consider insulin resistance when assessing OSA risk. Addressing liver health through PLF and HSI may be an effective strategy to mitigate the risk of OSA associated with insulin resistance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first comprehensive analysis of how liver fat accumulation, systemic inflammation and oxidative stress mediate the relationship between METS-IR and OSA. We found that in a nationally representative sample, higher METS-IR scores were significantly associated with increased OSA risk, and liver fat accumulation estimated by the index PLF and HSI played a key mediating role. This suggests that insulin resistance captured by METS-IR promotes OSA mainly through liver metabolic dysfunction. Interestingly, SII and OBS did not show a significant mediating effect in this pathway, suggesting that although inflammation and oxidative stress are associated with OSA [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e][\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], they may not be the core mechanisms linking METS-IR and OSA. This highlights the importance of addressing liver health issues in managing OSA risk in people with high insulin resistance.\u003c/p\u003e \u003cp\u003eMETS-IR combines fasting blood glucose, triglycerides and BMI to form a comprehensive index of insulin resistance, which plays a key role in the pathophysiology of OSA. Insulin resistance exacerbates OSA through a combination of metabolic disorders driven primarily by intermittent hypoxia (a hallmark of OSA) [25]. This intermittent hypoxia activates the sympathetic nervous system, leading to oxidative stress and systemic inflammation, which in turn deteriorates insulin sensitivity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The resulting increased visceral fat deposition and lipid metabolism dysfunction further aggravate the severity of OSA [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVisceral fat, especially liver and abdominal fat, due to its dual role, is the main trigger factor for OSA: mechanical compression of the upper airway, airway collapse deterioration, and secretion of inflammatory cytokines (such as TNF-α and IL-6) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These pro-inflammatory signals interfere with insulin signaling, enhance insulin resistance and promote further fat accumulation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This forms a vicious cycle, and metabolic dysfunction not only aggravates OSA, but also accelerates other cardiovascular risks [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, osa-induced hypoxia-induced oxidative stress leads to the production of reactive oxygen species (ROS), which destroys the insulin receptor signaling pathway and impairs endothelial function [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This vascular health dysfunction is common in OSA patients, which can aggravate their overall metabolic status and promote cardiovascular complications [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, METS-IR can effectively capture these metabolic disorders and is of great significance in assessing OSA risk.\u003c/p\u003e \u003cp\u003eThrough mediation analysis, PLF and HSI play a key role in assessing the relationship between liver fat content, METS-IR and OSA. PLF directly estimates liver fat content, reflects the actual fat accumulation in liver tissue, and is closely related to metabolic dysfunction such as insulin resistance and nonalcoholic fatty liver disease. Increased liver fat directly affects insulin signal transduction, aggravates insulin resistance, and promotes common metabolic disorders in OSA patients [33]. This link underscores the central role of liver fat in OSA progression and the need for clinical interventions aimed at reducing liver fat to reduce OSA risk, especially in individuals with high insulin resistance.On the other hand, HSI can be used as an indirect measurement of liver fat, calculated by metabolic factors such as liver enzyme levels (AST/ALT), BMI and the presence of diabetes. The increase of HSI value indicates hepatic steatosis, which aggravates insulin resistance and is common in OSA patients. Since HSI is also associated with liver function, strategies to improve liver health such as optimizing liver enzyme levels and addressing NAFLD are crucial [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. While reducing liver fat, improving liver function may further enhance insulin sensitivity and metabolic regulation [35]. This shows that by changing lifestyle, diet adjustment and drug intervention, not only focusing on lipid reduction, but also focusing on clinical strategies to improve liver function can more effectively reduce the impact of insulin resistance on OSA and improve the clinical outcomes of patients with metabolic disorders [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e][\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough systemic inflammation and oxidative stress are known to play a role in the pathology of OSA, the results of this study show that SII and OBS do not mediate the METS-IR-OSA relationship [38]. This suggests that although chronic low-grade inflammation and oxidative stress are elevated in OSA patients, their effects may not be significant in the case of insulin resistance. Nevertheless, future research should further explore whether SII and OBS play a greater role in specific subpopulations, where the interaction between insulin resistance, inflammation, and oxidative stress may be more pronounced.\u003c/p\u003e \u003cp\u003eMoreover, even after considering the mediating effect of liver fat through PLF and HSI, METS-IR still showed a significant direct association with OSA. This suggests that insulin resistance itself is an independent driver of OSA. Clinicians should give priority to insulin resistance when assessing the risk of OSA.Improving insulin sensitivity by changing lifestyle (such as weight control, physical activity and diet adjustment) can be used as an effective strategy to reduce the risk of OSA.\u003c/p\u003e \u003cp\u003eThis study has several advantages. First of all,The use of NHANES data provides a nationally representative large sample and enhances the general applicability of the research results. In addition, by controlling various confounding factors and conducting mediation analysis between METS-IR, PLF, HSI, SII, OBS and OSA, we can gain a deeper understanding of the metabolic pathways involved in OSA. However, the cross-sectional design limits the ability to establish a causal relationship, underscoring the need for future longitudinal studies to validate these findings and further study of the long-term effects of insulin resistance and liver health on OSA. This can provide important insights into how targeted interventions to improve metabolic health can help reduce the risk of OSA.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study shows that the METS-IR is significantly associated with the risk of OSA, in which PLF and HSI play a key mediating role. As scoring systems, PLF and HSI capture liver metabolic abnormalities and play a crucial role in connecting insulin resistance and OSA. The results suggest that improving liver health and managing insulin resistance may be effective strategies to reduce OSA risk in people with high METS-IR scores. Future research should focus on improving these scoring systems in order to better predict and manage OSA risks and apply them to clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHANES: National Health and Nutrition Examination Survey; CDC: Centers for Disease Control and Prevention; METS-IR: Metabolic Score for Insulin Resistance, PLF: Percentage of liver fat, HSI: Hepatic Steatosis Index, SII: Systemic Immunity-inflammation Index; OBS: Oxidative Balance Score. NAFLD: non-alcoholic fatty liver disease; BMI: body mass index; PIR: a ratio of family income to the poverty threshold.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe National Center for Health Statistics Research Ethics Review Board authorized the NHANES study protocols in compliance with the revised Declaration of Helsinki. All participants provided informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data used in this study are available on the National Health and Nutrition Examination Survey website: https://www.cdc.gov/nchs/nhanes/index.htm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis work was supported by\u0026nbsp;Key Project of Shandong Province Traditional Chinese Medicine Science and Technology Program (No. Z\u0026ndash;2023019) and\u0026nbsp;Shandong Medical system staff science and Technology Innovation Program project\u0026nbsp;(SDYWZGKCJH2023020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSYS\u0026nbsp;participated in the literature search, study design, data collection, data analysis,\u0026nbsp;data interpretation and wrote the manuscript.\u0026nbsp;XHL,YCL,XXW and WHZ\u0026nbsp;conceived the study and participated in its design, coordination, data collection and analysis.\u0026nbsp;JGY and YXL\u0026nbsp;participated in the study design and provided critical revision. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eSpecial thanks to all of the NHANES participants who freely gave their time to make this and other studies possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKapur VK, Auckley DH, Chowdhuri S, Kuhlmann DC, Mehra R, Ramar K, Harrod CG. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline. J Clin Sleep Med. 2017 Mar 15;13(3):479-504.\u003c/li\u003e\n\u003cli\u003eSomers VK, White DP, Amin R, Abraham WT, Costa F, Culebras A, Daniels S, Floras JS, Hunt CE, Olson LJ, Pickering TG, Russell R, Woo M, Young T. 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Physiol Rep. 2021 Mar;9(5):e14738.\u003c/li\u003e\n\u003cli\u003eXu, X., Lu, L., Dong, Q. et al. Research advances in the relationship between nonalcoholic fatty liver disease and atherosclerosis. Lipids Health Dis 14, 158 (2015). \u003c/li\u003e\n\u003cli\u003eG\u0026uuml;neş ZY, G\u0026uuml;naydın FM. The relationship between the systemic immune-inflammation index and obstructive sleep apnea. Sleep Breath. 2024 Mar;28(1):311-317.\u003c/li\u003e\n\u003cli\u003eBirben, E., Sahiner, U.M., Sackesen, C. et al. Oxidative Stress and Antioxidant Defense. World Allergy Organ J 5, 9\u0026ndash;19 (2012). \u003c/li\u003e\n\u003cli\u003eGabryelska A, Łukasik ZM, Makowska JS, Białasiewicz P. Obstructive Sleep Apnea: From Intermittent Hypoxia to Cardiovascular Complications via Blood Platelets. Front Neurol. 2018 Aug 3;9:635. \u003c/li\u003e\n\u003cli\u003eLiu J, He L, Wang A, Lv Y, He H, Wang C, Xiong K, Zhao L. 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Dig Liver Dis. 2010 Jul;42(7):503-8. \u003c/li\u003e\n\u003cli\u003eHu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, Zhang X, Wang WM, Qiu SJ, Zhou J, Fan J. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014 Dec 1;20(23):6212-22.\u003c/li\u003e\n\u003cli\u003eCavallino V, Rankin E, Popescu A, Gopang M, Hale L, Meliker JR. Antimony and sleep health outcomes: NHANES 2009\u0026ndash;2016. Sleep Health. 2022;8:373\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eGrundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA,et al. Diagnosis and Management of the Metabolic Syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112:2735\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eBirben E, Sahiner UM, Sackesen C, Erzurum S, Kalayci O. Oxidative stress and antioxidant defense. World Allergy Organ J. 2012 Jan;5(1):9-19. \u003c/li\u003e\n\u003cli\u003eGabryelska A, Łukasik ZM, Makowska JS, Białasiewicz P. Obstructive Sleep Apnea: From Intermittent Hypoxia to Cardiovascular Complications via Blood Platelets. Front Neurol. 2018 Aug 3;9:635. \u003c/li\u003e\n\u003cli\u003eKohler M, Stradling JR. Mechanisms of vascular damage in obstructive sleep apnea. Nat Rev Cardiol. 2010 Dec;7(12):677-85. \u003c/li\u003e\n\u003cli\u003eHarsch IA, Schahin SP, Radespiel-Tr\u0026ouml;ger M, Weintz O, Jahreiss H, Fuchs FS, Wiest GH, Hahn EG, Lohmann T, Konturek PC, Ficker JH. Continuous positive airway pressure treatment rapidly improves insulin sensitivity in patients with obstructive sleep apnea syndrome. Am J Respir Crit Care Med. 2004 Jan 15;169(2):156-62. \u003c/li\u003e\n\u003cli\u003eDeng H, Duan X, Huang J, Zheng M, Lao M, Weng F, Su QY, Zheng ZF, Mei Y, Huang L, Yang WH, Xing X, Ma X, Zhao W, Liu X. Association of adiposity with risk of obstructive sleep apnea: a population-based study. BMC Public Health. 2023 Sep 21;23(1):1835. \u003c/li\u003e\n\u003cli\u003eCarey DG. Abdominal obesity. Curr Opin Lipidol. 1998 Feb;9(1):35-40. \u003c/li\u003e\n\u003cli\u003eUnamuno X, G\u0026oacute;mez-Ambrosi J, Rodr\u0026iacute;guez A, Becerril S, Fr\u0026uuml;hbeck G, Catal\u0026aacute;n V. Adipokine dysregulation and adipose tissue inflammation in human obesity. Eur J Clin Invest. 2018 Sep;48(9):e12997. \u003c/li\u003e\n\u003cli\u003eJavaheri S, Barbe F, Campos-Rodriguez F, Dempsey JA, Khayat R, Javaheri S, Malhotra A, Martinez-Garcia MA, Mehra R, Pack AI, Polotsky VY, Redline S, Somers VK. Sleep Apnea: Types, Mechanisms, and Clinical Cardiovascular Consequences. J Am Coll Cardiol. 2017 Feb 21;69(7):841-858. \u003c/li\u003e\n\u003cli\u003eZhou L, Chen P, Peng Y, Ouyang R. Role of Oxidative Stress in the Neurocognitive Dysfunction of Obstructive Sleep Apnea Syndrome. Oxid Med Cell Longev. 2016;2016:9626831. \u003c/li\u003e\n\u003cli\u003eNarkiewicz K, Somers VK. Sympathetic nerve activity in obstructive sleep apnoea. Acta Physiol Scand. 2003 Mar;177(3):385-90. \u003c/li\u003e\n\u003cli\u003eMesarwi OA, Loomba R, Malhotra A. Obstructive Sleep Apnea, Hypoxia, and Nonalcoholic Fatty Liver Disease. Am J Respir Crit Care Med. 2019 Apr 1;199(7):830-841. \u003c/li\u003e\n\u003cli\u003eLee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, Kim YJ, Yoon JH, Cho SH, Sung MW, Lee HS. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis. 2010 Jul;42(7):503-8. \u003c/li\u003e\n\u003cli\u003eTilg H, Moschen AR. Evolution of inflammation in nonalcoholic fatty liver disease: the multiple parallel hits hypothesis. Hepatology. 2010 Nov;52(5):1836-46. \u003c/li\u003e\n\u003cli\u003eNg SSS, Tam WWS, Lee RWW, Chan TO, Yiu K, Yuen BTY, Wong KT, Woo J, Ma RCW, Chan KKP, Ko FWS, Cistulli PA, Hui DS. Effect of Weight Loss and Continuous Positive Airway Pressure on Obstructive Sleep Apnea and Metabolic Profile Stratified by Craniofacial Phenotype: A Randomized Clinical Trial. Am J Respir Crit Care Med. 2022 Mar 15;205(6):711-720. \u003c/li\u003e\n\u003cli\u003eMurillo R, Reid KJ, Arredondo EM, Cai J, Gellman MD, Gotman NM, Marquez DX, Penedo FJ, Ramos AR, Zee PC, Daviglus ML. Association of self-reported physical activity with obstructive sleep apnea: Results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Prev Med. 2016 Dec;93:183-188. \u003c/li\u003e\n\u003cli\u003eGabryelska A, Łukasik ZM, Makowska JS, Białasiewicz P. Obstructive Sleep Apnea: From Intermittent Hypoxia to Cardiovascular Complications via Blood Platelets. Front Neurol. 2018 Aug 3;9:635. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1.Baseline characteristics of participants in the NHANES 2005-2008 and 2015-2020 (n=12,655)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eMETS-IR Quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ1(\u0026lt;34.596)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQ2(34.596to\u0026lt;41.919)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQ3(41.919to\u0026lt;50.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQ4(\u0026ge;50.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, years, mean(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48.08(47.47,48.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45.13(44.19,46.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49.63(48.57,50.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49.87(48.80,50.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48.06(47.31,48.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePIR,mean(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.05(2.98,3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.18(3.09,3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.13(3.03,3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.01(2.92,3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.85(2.74,2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBMI,mean(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.19(28.96,29.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.30(22.20,22.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26.76(26.65,26.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.43(30.29,30.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.29(37.97,38.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePLF,mean(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.88(4.77,4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.07(2.01,2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.53(3.41,3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.24(5.08,5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.11(8.85,9.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHSI,mean(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.42(38.16,38.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.28(30.13,30.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.56(35.38,35.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.01(39.82,40.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49.06(48.71,49.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOBS,mean(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.90(17.50,18.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19.65(19.09,20.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.73(17.19,18.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.02(16.54,17.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.96(16.46,17.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSII,mean(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e536.47(526.25,546.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e525.43(505.21,545.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e512.22(497.06,527.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e532.42(517.38,547.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e577.85(563.82,591.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6395(50.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1853(62.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1477(46.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1445(43.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1620(48.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6260(49.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1312(37.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1685(53.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1719(56.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1544(51.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRace/ethnicity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4967(66.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1341(69.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1213(66.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1207(64.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1206(64.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1636( 8.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e587(10.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e448( 8.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e339( 7.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e262( 6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2831(10.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e692( 9.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e683(10.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e685(10.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e771(12.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1962( 8.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e305( 5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e507( 8.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e565(10.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e585(10.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1259( 5.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e240(4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e311(5.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e368(7.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e340(6.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducational level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1290( 5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e204(3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e357(5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e391(6.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e338(5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCompleted high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4676(34.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1135(30.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1116(33.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1194(35.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1231(38.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6681(60.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1825(65.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1684(60.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1577(57.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1595(55.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried/Living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5394(50.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1269(48.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1368(49.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1446(52.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1311(50.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWidowed/Divorced/Separated/Never married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7254(49.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1894(51.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1792(50.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1717(47.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1851(49.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1229( 8.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e221( 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e302( 8.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e356( 9.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e350(10.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHeavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2203(18.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e550(19.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e532(18.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e516(18.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e605(18.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4001(35.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1030(34.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1047(35.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e981(35.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e943(35.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1804(16.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e524(20.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e418(15.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e427(14.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e435(14.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1403( 8.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e372(9.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e355(9.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e362(8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e314(8.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6898(53.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1784(55.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1742(53.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1757(53.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1615(51.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3195(26.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e641(22.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e780(25.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e842(28.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e932(30.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2549(19.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e737(21.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e635(21.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e563(18.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e614(18.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5771(37.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1233(30.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1375(34.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1515(41.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1648(45.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2600(22.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e638(21.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e660(23.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e639(21.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e663(23.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBoth moderate and vigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1653(15.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e570(22.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e443(17.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e373(13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e267( 8.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e743( 6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e224(7.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e191(6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e174(6.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e154(4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMetabolic Syndrome, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7830(65.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3054(97.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2456(80.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1555(53.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e765(24.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4825(34.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e111( 2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e706(19.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1609(46.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2399(75.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOSA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8679(68.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2586(82.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2287(72.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2125(66.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1681(52.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3976(31.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e579(17.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e875(27.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1039(33.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1483(47.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: NHANES, National Health and Nutrition Examination Survey; Q, quantile; PIR, ratio of family income to poverty; BMI, body mass index; PLF, Percentage of Liver Fat; HSI, Hepatic Steatosis Index; SII, Systemic Inflammatory Index; OBS, Oxidative Balance Score; OSA, Obstructive Sleep Apnea; METS-IR, metabolic Score for Insulin Resistance.\u003c/p\u003e\n\u003cp\u003eTable 2.Multivariable logistic regression models for the association between METS-IR and OSA in adults:NHANES 2005\u0026ndash;2008 and 2015-2020\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15%;\"\u003e\n \u003cp\u003eMETS-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21%;\"\u003e\n \u003cp\u003eCrude model\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21%;\"\u003e\n \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21%;\"\u003e\n \u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21%;\"\u003e\n \u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003eContinuous(per 10 units)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.50(1.44,1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.49(1.44,1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.49(1.43, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.30(1.10, 1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003eQuartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cpre\u003eQ1(\u0026lt;34.596)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cpre\u003eQ2(34.596to\u0026lt;41.919)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.85(1.56,2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.75(1.47,2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.75(1.47, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.48(1.25, 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cpre\u003eQ3(41.919to\u0026lt;50.674)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.44(2.04,2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.30(1.91,2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.30(1.91, 2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.70(1.34, 2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003eQ4(\u0026ge;50.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4.36(3.73,5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4.24(3.63,4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4.18(3.56, 4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.35(1.72, 3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval; OR, odds ratio; Q, quantile;\u0026nbsp;METS-IR, metabolic Score for Insulin Resistance; OSA, Obstructive Sleep Apnea.\u003c/p\u003e\n\u003cp\u003eIn multivariate regression, samples with missing values for covariates in the model were encoded as dummy variables. The missing values were categorized as \u0026quot;Unknown\u0026quot; and included as a separate category in the analysis.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eCrude Model: Unadjusted.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eModel 1: Adjusted for age, sex, and race/ethnicity.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eModel 2: Adjusted for the variables in Model 1 plus education level, marital status, physical activity,smoking status, and alcohol status.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eModel 3: Adjusted for the variables in Model 2 plus ratio of family income to poverty,body mass index and metabolic syndrome.\u003c/p\u003e\n\u003cp\u003eTable 3.\u0026nbsp;Associations\u0026nbsp;of METS-IR with OSA by different factors\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 257px;\"\u003e\n \u003cp\u003echaracter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003ep for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026le;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.045(1.038,1.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026gt;40,\u0026le;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.042(1.034,1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.032(1.025,1.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.042(1.038,1.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.039(1.033,1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRace/ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.042(1.036,1.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.050(1.038,1.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.038(1.032,1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.038(1.029,1.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.045(1.031,1.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.034(1.022,1.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCompleted high school\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.035(1.029,1.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026gt; High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.045(1.039,1.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.041(1.034,1.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNever smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.046(1.041,1.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.032(1.024,1.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAlcohol status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.037(1.027,1.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHeavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.031(1.020,1.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.049(1.041,1.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.045(1.034,1.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.029(1.015,1.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMarried/Living with Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.042(1.034,1.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWidowed/Divorced/Separated/Never married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.041(1.036,1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInactive \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.039(1.032,1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.037(1.029,1.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBoth moderate and igorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.051(1.037,1.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.044(1.024,1.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMetabolic Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.045(1.038,1.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.031(1.025,1.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\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":"metabolic score for Insulin resistance, obstructive sleep apnea, percentage of liver fat, hepatic steatosis index, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5330011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5330011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eObstructive sleep apnea (OSA) is associated with metabolic disorders such as insulin resistance and liver fat accumulation. However, the specific mediating role of liver-related metabolic indicators in this association has not been fully studied. The purpose of this study was to investigate the relationship between Metabolic Score for Insulin Resistance (METS-IR) and OSA, focusing on the mediating effects of liver fat percentage (PLF) and hepatic steatosis index (HSI). Understanding these mechanisms may provide insights into targeted interventions for OSA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 12,655 participants from the National Health and Nutrition Examination Survey (NHANES) were included in this analysis. Obstructive sleep apnea (OSA) was assessed using the NHANES questionnaire. Weighted multivariate logistic regression was employed to assess the relationship between METS-IR and OSA, with a mediation model constructed to explore the mediating roles of key liver and metabolic markers, including PLF, HSI, SII, and OBS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 12,655 subjects, 31.04% had OSA. METS-IR was closely related to the increased risk of OSA, and the highest quartile group of METS-IR had a significantly increased risk of OSA ( OR\u0026thinsp;=\u0026thinsp;2.35, 95% CI : 1.72\u0026ndash;3.21 ). Mediating effect analysis showed that PLF and HSI mediated 11.22% and 22.78% of the effects, respectively, while systemic immunity-inflammation index (SII) and oxidative balance score (OBS) had no significant mediating effect.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMETS-IR is an important predictor of OSA risk, primarily mediated by hepatic lipid accumulation. Addressing insulin resistance and hepatic metabolic health is crucial for the effective management of OSA and provides valuable guidance for clinical risk assessment in susceptible populations.\u003c/p\u003e","manuscriptTitle":"The metabolic score for insulin resistance as a Predictor of Obstructive Sleep Apnea: The Mediating Effects of Liver Fat and Steatosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-10 15:49:50","doi":"10.21203/rs.3.rs-5330011/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-20T09:12:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-20T00:26:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233315537944389181836305081988476429176","date":"2024-12-19T23:48:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-07T10:28:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238685442145347804769688915696275263121","date":"2024-12-04T08:36:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123888235713970190607034651088633001784","date":"2024-12-02T05:30:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-01T21:52:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-01T21:36:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-08T16:23:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-07T12:11:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-25T06:22:44+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":"d2652f48-8997-490b-9d21-a67c4349a749","owner":[],"postedDate":"December 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40582332,"name":"Health sciences/Diseases"},{"id":40582333,"name":"Health sciences/Endocrinology"},{"id":40582334,"name":"Health sciences/Pathogenesis"},{"id":40582335,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-09-15T15:58:00+00:00","versionOfRecord":{"articleIdentity":"rs-5330011","link":"https://doi.org/10.1038/s41598-025-89850-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-09 15:56:54","publishedOnDateReadable":"September 9th, 2025"},"versionCreatedAt":"2024-12-10 15:49:50","video":"","vorDoi":"10.1038/s41598-025-89850-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-89850-z","workflowStages":[]},"version":"v1","identity":"rs-5330011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5330011","identity":"rs-5330011","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}