The Association between METS-IR Index and Obstructive Sleep Apnea-Hypopnea Syndrome: A Cross-Sectional Study Based on the National Health and Nutrition Examination Survey from 2015 to 2018

The Association between METS-IR Index and Obstructive Sleep Apnea-Hypopnea Syndrome: A Cross-Sectional Study Based on the National Health and Nutrition Examination Survey from 2015 to 2018 | 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 Research Article The Association between METS-IR Index and Obstructive Sleep Apnea-Hypopnea Syndrome: A Cross-Sectional Study Based on the National Health and Nutrition Examination Survey from 2015 to 2018 Yisen Hou, Rui Li, Zhen Xu, Wenhao Chen, Zhiwen Li, Weirong Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5322269/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder closely associated with metabolic syndrome. The metabolic score for insulin resistance (METS-IR) is a new indicator used to assess insulin resistance. However, evidence on the association between METS-IR and OSAHS remains limited. Objective This study aimed to analyze the association between METS-IR and OSAHS in American adults. Methods This study utilized cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2015 and 2018. We analyzed METS-IR and the prevalence of OSAHS in adult participants. Individuals aged 20 years and older were included, while those without available BMI, fasting blood glucose (FBG), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) data were excluded. Logistic regression analysis, subgroup analysis, and restricted cubic spline analysis were employed to evaluate the association between METS-IR and OSAHS, adjusting for potential confounders including sex, age, race/ethnicity, education level, income, smoking status, alcohol consumption, diabetes status, and lipid levels. Results: A total of 4274 adult participants were included in the study. Participants were categorized into quartiles based on METS-IR levels, with ranges of 19.98–33.94, 33.94–41.60, 41.60-51.15, and 51.15-124.47, respectively. After adjusting for age, sex, race/ethnicity, education level, smoking status, alcohol consumption status, hypertension status, diabetes status, and dyslipidemia status, METS-IR was positively associated with the risk of OSAHS (OR = 1.05, 95% CI: 1.03, 1.07). Specifically, each one-unit increase in METS-IR was associated with a 5% increase in the risk of OSAHS. Subgroup analysis revealed a significant positive correlation between METS-IR and the incidence of OSAHS in the highest METS-IR quartile. This association was particularly pronounced among Mexican Americans (OR = 6.33, 95% CI: 2.13, 23.67) and non-Hispanic Black individuals (OR = 12.22, 95% CI: 5.89, 26.62). Additionally, after controlling for potential confounders, the association between METS-IR and OSAHS remained significant. Notably, individuals with diabetes, hypertension, and hypertriglyceridemia were at a greater risk of OSAHS. Conclusion: The results of this study demonstrated a significant positive association between METS-IR and the incidence of OSAHS, which persisted after adjusting for various confounders. This suggests that METS-IR may be a potential risk factor for OSAHS. In clinical practice, the management of metabolic syndrome should be emphasized to prevent the occurrence of OSAHS. METS-IR obstructive sleep apnea-hypopnea syndrome NHANES cross-sectional study metabolic syndrome Figures Figure 1 Figure 2 Figure 3 1. Introduction Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder characterized by recurrent upper airway obstruction during sleep, leading to partial or complete apnea and hypopnea. [ 1 ] OSAHS is primarily manifested by symptoms such as nocturnal snoring, intermittent apnea, and excessive daytime sleepiness. [ 2 ] It significantly impacts patients' quality of life and is closely associated with various metabolic and cardiovascular diseases, including hypertension, type 2 diabetes, and cardiovascular disease. [ 3 – 6 ] Therefore, exploring the pathogenesis of OSAHS and its related factors is crucial for its prevention and management of this condition. Metabolic syndrome (MetS) is a cluster of metabolic abnormalities, including abdominal obesity, insulin resistance, hyperglycemia, hypertension, and dyslipidemia. [ 7 ] In recent years, the association between OSAHS and MetS has garnered extensive attention, especially regarding insulin resistance (IR), a core feature of MetS that is closely linked to OSAHS. [ 8 – 10 ] However, traditional methods for assessing IR, such as the homeostasis model assessment of insulin resistance (HOMA-IR), are limited in large-scale epidemiological studies due to the need for fasting glucose and insulin measurements. [ 11 , 12 ] To overcome these limitations, researchers have proposed the Metabolic Syndrome Insulin Resistance Index (METS-IR), a simplified calculation method based on MetS components that does not require insulin measurements, providing a novel approach for evaluating IR. [ 13 – 15 ] Although studies [ 9 , 16 – 18 ] have shown an association between OSAHS and MetS and its components, the specific relationships between METS-IR index, a novel IR evaluation metric, and OSAHS have not been sufficiently investigated. Therefore, this study aimed to fill this gap by utilizing cross-sectional data from the 2015—2018 National Health and Nutrition Examination Survey (NHANES) to explore the association between the METS-IR index and OSAHS. We hypothesize that the METS-IR index can serve as an independent risk factor for predicting OSAHS and assess its predictive efficacy. If validated, the METS-IR index could become a simple, cost-effective, and efficient screening tool for the early identification of high-risk OSAHS patients, guiding clinical practice and public health interventions to reduce the incidence of OSAHS and its related complications. 2. Materials and methods 2.1 Study Design and Population This study utilized data from the 2015–2018 National Health and Nutrition Examination Survey to investigate the relationship between the METS-IR index and the risk of OSAHS. The NHANES is an ongoing survey conducted by the National Center for Health Statistics (NCHS) aimed at assessing the health and nutritional status of the U.S. population. The data were collected using a multistage probability sampling method to ensure national representativeness. Initially, a total of participants were included in the study. Due to the potential confounding effect of smoking (SMQ020 - Smoked at least 100 cigarettes in life), which requires participants to be older than 20 years, those under 20 years of age were excluded. Additionally, participants lacking METS-IR index data or OSAHS data were excluded. After rigorous screening, a total of 4,274 eligible participants were included, among whom 1,384 reported a history of OSA. The exclusion criteria were as follows (Fig. 1 ). 2.2 METS-IR Data Collection In this study, the METS-IR index was designated as the exposure variable. METS-IR was calculated using the following formula: Ln[(2 × fasting blood glucose (mg/dL)) + fasting triglycerides (mg/dL)] × body mass index (kg/m²) / [Ln(high-density lipoprotein cholesterol (mg/dL))]. Fasting triglycerides, fasting blood glucose, and high-density lipoprotein cholesterol levels were measured enzymatically using an automatic biochemical analyzer. Serum triglyceride and high-density lipoprotein cholesterol concentrations were determined using a Roche Cobas 6000 biochemical analyzer and a Roche Modular P. Body mass index (BMI) was calculated by directly measuring the height and weight of the participants, with the specific formula being BMI = weight (kg) / (height (m))^2. 2.3 OSAHS Data Collection The OSAHS data were collected through a questionnaire survey. Based on previous studies [ 17 , 19 ] , we defined OSAHS using three questions from the Sleep Disorder Questionnaire (SLQ). The questions included the following: 1. SLQ030: "How often do you snore?"; 2. SLQ040: "How often do you snort or stop breathing?"; and 3. SLQ120: "How often do you feel overly sleepy during the day?". Participants were classified as having OSAHS symptoms if they reported snoring three or more times per week, or snorting/stopping breathing three or more times per week, or feeling excessively sleepy during daily activities frequently (16–30 times per month). 2.4 Covariate Selection In this study, we selected a range of covariates to control for potential confounding factors. These covariates included age, sex, race, marital status, education level, family income-to-poverty ratio (PIR), smoking status, alcohol consumption, diabetes status, hypertension status, and dyslipidemia status. These variables were chosen based on a comprehensive review of the literature and their potential impact on the relationship between the METS-IR index and OSAHS. [ 1 , 12 , 20 ] Age was categorized into three groups: 20–40 years, 41–60 years, and > 60 years. The PIR was defined as non-poor (PIR ≥ 1) or poor (PIR < 1). Alcohol consumption was determined based on responses to the questions "Had at least 12 alcohol drinks/lifetime?" (ALQ110) and "Ever had a drink of any kind of alcohol?" (ALQ111), with participants who answered "yes" classified as drinkers. Smoking status was assessed using the question "Smoked at least 100 cigarettes in life?", Those who answered "yes" to the SMQ020 were classified as smokers. Hypertension was defined as a systolic blood pressure (SBP) ≥ 130 mmHg or a diastolic blood pressure (DBP) ≥ 80 mmHg. [ 21 ] Diabetes was defined as a fasting glucose level ≥ 126 mg/dL, HbA1c ≥ 6.5%, or a history of insulin or oral hypoglycemic medication use. [ 22 ] Hyperlipidemia was defined as total cholesterol > 200 mg/dL, triglycerides > 150 mg/dL, low-density lipoprotein cholesterol ≥ 130 mg/dL, or high-density lipoprotein cholesterol < 40 mg/dL (for men) or < 50 mg/dL (for women). [ 23 ] 2.5 Statistical analysis To investigate the association between the METS-IR index and the occurrence of OSAHS, this study leveraged the large-scale, complex, multistage sampling design of the NHANES. We strictly adhered to the NHANES principles of sampling weights, stratification, and clustering adjustments to ensure the robustness and validity of our statistical results. First, we conducted detailed descriptive statistical analyses of the baseline characteristics of the participants. Continuous variables are described using weighted means and their standard errors, while categorical variables are presented as weighted percentages to show the distribution within the sample. To examine the relationship between the METS-IR index and OSAHS, we used logistic regression analysis to construct three progressively detailed covariate adjustment models. Model 1 served as the baseline model with no adjustments; Model 2 included preliminary adjustments for age, sex, and race; and Model 3 was comprehensively adjusted for all covariates, focusing on the trend effects of the METS-IR quartiles on OSAHS risk. We quantified the association strength between METS-IR levels and OSAHS risk by calculating odds ratios (ORs) and their 95% confidence intervals (CIs). Next, we employed restricted cubic spline analysis to explore the specific relationship shape between the METS-IR index and OSAHS incidence, capturing potential nonlinear associations. Additionally, we conducted subgroup analyses of key factors, such as sex, age, race, diabetes status, hypertension status, and hyperlipidemia status, to reveal the heterogeneity in the METS-IR index and OSAHS risk relationships across different subgroups. Interaction tests were performed to assess significant interactions between the METS-IR index and these subgroup characteristics, providing a deeper understanding of the pathways and mechanisms by which METS-IR influences OSAHS risk. All the statistical analyses were performed using R software version 4.4.0. 3. Results 3.1 General Characteristics of the Study Population The baseline demographic characteristics of the study population, categorized by METS-IR quartiles, are presented in Table 1, comprising a total of 4,274 participants. The METS-IR index ranges for Quartiles 1 to 4 were 19.98-33.94, 33.94-41.60, 41.60-51.15, and 51.15-124.47, respectively. Significant differences were observed across the METS-IR quartiles in terms of age, sex, race, alcohol consumption, hypertension, diabetes, and dyslipidemia. Compared to Quartile 1 participants in Quartile 4 had greater proportions of males, Mexican Americans, and those with hypertension, diabetes, and hyperlipidemia, while the proportion of those consuming alcohol decreased. Additionally, there was a marked increase in the prevalence of diabetes, hypertension, and hyperlipidemia among participants in quartiles 2 to 4 compared to quartile 1. Table 1: Weighted Baseline Characteristics of the Participants Characteristic Q1 [19.98，33.94] Q2 [33.94，41.60] Q3 [41.60，51.15] Q1 [51.15，124.47] P-value N 1006 1090 1122 1056 METS-IR 28.97(3.26) 37.66(2.24) 45.96(2.82) 61.28(9.28) 60 (%) 21.2% 38.4% 24.8% 28.8% Gender (%) <0.001 Male 35.5% 49.1% 56.5% 53.2% Female 64.5% 50.9% 43.5% 46.8% Race (%) 0.005 Mexican American 3.6% 8.4% 10.8% 9.1% Non-Hispanic White 66.9% 66.6% 62.2% 65.9% Non-Hispanic Black 9.3% 11.2% 8.5% 11.3% Other Race 20.2% 13.9% 18.5% 13.6% Married/live with partner (%) 0.197 Yes 65.6% 67.3% 72.5% 70.9% No 34.4% 32.7% 27.5% 29.1% Education level (%) 0.112 Below high school 10.2% 15.3% 15.9% 12.8% High School or above 89.8% 84.7% 84.1% 87.2% Poverty income ratio (%) 0.087 Poor 10.7% 12.1% 16.3% 13.7% Not poor 89.3% 87.9% 83.7% 86.3% Smoking (%) 0.490 Yes 19.5% 18.8% 20.0% 16.6% No 80.5% 81.2% 80.0% 83.4% Alcohol (%) 0.003 Yes 77.0% 76.3% 73.7% 72.2% No 23.0% 23.7% 26.3% 27.8% Diabetes (%) <0.001 Yes 3.5% 8.8% 16.8% 12.6% No 96.5% 91.2% 83.2% 67.4% Hypertension (%) <0.001 Yes 29.3% 43.2% 55.1% 66.6% No 70.7% 56.8% 44.9% 33.4% Hyperlipidemia (%) <0.001 Yes 50.4% 71.6% 78.7% 86.9% No 49.6% 28.4% 21.3% 13.1% 3.2 Relationship between the METS-IR Index and OSAHS Table 2 presents the association between the METS-IR index and the risk of developing OSAHS. Participants were divided into quartiles based on their METS-IR index, with the lowest quartile (quartile 1) designated as the reference group. Across all adjustment models, a positive correlation was observed between the METS-IR index and the incidence of OSAHS. According to the fully adjusted model, each once-unit increase in the METS-IR index was associated with a 5% increase in the risk of OSAHS (OR = 1.05, 95% CI: 1.03, 1.07). Additionally, sensitivity analysis was performed by converting the METS-IR index from a continuous variable to a categorical variable (Q1-Q4) using quartiles. In Model 3, adjustments were made for multiple covariates, including age, sex, race, marital status, education level, PIR, smoking status, alcohol consumption, hypertension, hyperlipidemia, and diabetes status. Compared to quartile 1, participants in quartile 4 had a 4.14-fold greater risk of OSAHS (OR = 5.14, 95% CI: 1.36-19.35). The multivariable-adjusted spline curve of the relationship between the METS-IR index and the incidence of OSAHS is shown in Figure 2, indicating a positive linear correlation between higher METS-IR index and OSAHS incidence (nonlinearity p value > 0.05). Table:2 Weighted Association between METS-IR Index and OSAHS Characteristic OR (95% CI), P-value Model 1 Model 2 Model 3 METS-IR 1.05(1.03, 1.06) <0.001 1.05(1.03, 1.06) <0.001 1.05(1.03, 1.07) 0.004 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) Quartile 2 1.71 (0.93,3.15) 0.080 1.60(0.82,3.11), 0.145 1.58(0.45,5.56), 0.258 Quartile 3 2.53 (1.57,4.06) 0.001 2.37(1.45,3.89), 0.003 2.43(0.92,6.41), 0.059 Quartile 4 5.46(3.12,9.55) <0.001 5.15(2.85,9.31), <0.001 5.14(1.36,19.35), 0.034 p for trend 1.75(1.46, 2.08) <0.001 1.72(1.43, 2.08) <0.001 1.72(1.29, 2.34) 0.007 OR, odds ratio; CI, confidence interval. Model 1: crude model. Model 2: adjusted for age, sex and race. Model 3: adjusted for age, sex, race, educational level, poverty income ratio, smoking status, alcohol consumption, and history of hypertension, diabetes, and hyperlipidemia. 3.3 Subgroup Analysis Subgroup analyses were conducted to evaluate whether the relationship between the METS-IR index and OSAHS incidence was influenced by age, sex, race, diabetes status, hypertension status, and hyperlipidemia status. As shown in Figure 3, which details the statistical results of these stratified factors, race significantly modified the association between METS-IR and OSAHS (p values< 0.05). Specifically, the association was stronger among Mexican American and non-Hispanic Black populations. Conversely, age, sex, diabetes status, hypertension status, and dyslipidemia status did not significantly influence the METS-IR index or the incidence of OSAHS (p values> 0.05). This indicated that the association between METS-IR and OSAHS remained robust after controlling for potential confounders. 4. Discussion In this study, we utilized data from the National Health and Nutrition Examination Survey from 2015 to 2018 to evaluate the association between the METS-IR index and OSAHS. Our primary findings indicated that individuals with higher METS-IR scores were more likely to exhibit symptoms of OSAHS, suggesting that insulin resistance may play a significant role in the pathogenesis of OSAHS. 4.1 Key Findings First, our study revealed a significant association between the METS-IR index and OSAHS symptoms. Specifically, the fully adjusted model indicated that for each unit increase in the METS-IR index, the risk of OSAHS increased by 5% (OR = 1.05, 95% CI: 1.03, 1.07). Participants in the highest quartile (Q4) of the METS-IR index had a 4.14-fold greater risk of OSAHS than did those in the lowest quartile (Q1) (OR = 5.14, 95% CI: 1.36–19.35). This finding aligns with existing research, such as the study by Tianyi Huang et al., [ 24 ] which suggested that insulin resistance may play a more critical role than hyperglycemia in the pathogenesis of OSA. In their study, the odds ratio for OSA events in the highest quintile of fasting insulin was 3.59 (95% CI: 2.67–4.82; P-trend < 0.0001). Among the 6,342 participants with HbA1c data, 715 (11.3%) developed OSA, with an odds ratio for HbA1c of 2.21 (95% CI: 1.69–2.89; P-trend < 0.0001). Another study [ 25 ] revealed that the ZJU index, which incorporates BMI, triglycerides, fasting glucose, and the ratio of alanine aminotransferase to aspartate aminotransferase, is an indicator of obesity and insulin resistance. At the highest levels of the ZJU index, the risk of OSA was significantly greater (OR = 2.046 [95% CI: 1.057, 3.964]). Additionally, Ruobing Wei et al. [ 26 ] found that LAP and TyG are significant predictors of insulin resistance in OSA patients with normal weight and those who are overweight/obese. Among normal-weight OSA patients, the TyG index had a greater ability to predict IR than traditional indices such as BMI and WC. Currently, research on the relationship between the METS-IR index and OSAHS is limited, with only one study evaluating the role of the METS-IR index in predicting cardiovascular disease and its subtypes among hypertensive and OSA patients, without specifically addressing the association between the METS-IR and OSAHS risk. Second, the METS-IR index, as a novel assessment metric for insulin resistance, offers the advantage of not requiring insulin level measurements, thereby better reflecting the comprehensive impact of metabolic syndrome. Our study validated the effectiveness of the METS-IR in assessing the risk of OSAHS, suggesting that the METS-IR could be a valuable tool in clinical practice for screening high-risk populations for OSAHS. 4.2 Existing Research Analysis Current research suggests a bidirectional interaction between OSAHS and IR. [ 27 – 29 ] An analysis of OSA patients versus primary snorers showed that OSA, rather than short sleep duration, is the primary driver of the insulin resistance (IR) association. Furthermore, the combination of OSA with very short sleep duration exacerbates the risk of IR. [ 27 ] Additionally, studies using HOMA-IR to measure insulin resistance have shown an independent association between IR and the apnea index during REM sleep (adjusted odds ratio [aOR] 1.09; 95% CI, 1.03 to 1.16; p = 0.004). [ 30 ] Recent studies have highlighted various body fat indices as effective tools for predicting IR in OSAHS patients. [ 12 , 26 , 31 ] Body mass index, waist circumference, waist-to-height ratio, and waist-to-hip ratio have been used to estimate IR in OSA patients. More refined indices, such as the lipid accumulation product (LAP), visceral adiposity index (VAI), and triglyceride-glucose index (TyG), have shown strong correlations with visceral obesity and IR. These indices combine anthropometric and metabolic parameters, providing a more comprehensive assessment of metabolic risk. [ 14 ] Conversely, OSA is also considered an independent risk factor for IR. For instance, studies have shown that insulin concentrations and HOMA-IR values are significantly elevated in patients grouped by OSA severity and reported sleepiness, with an AHI ≥ 15 and an AHI ≥ 30. [ 32 ] Similarly, among Japanese OSA patients without diabetes and cardiovascular disease, the severity of OSA (AHI, lowest oxygen saturation, %TST SO2 < 90%) was positively correlated with IR (HOMA-IR), even after adjusting for age, sex, and BMI. [ 33 ] Furthermore, correcting OSA symptoms can improve insulin resistance. A study comparing 12 weeks of conservative treatment (CT) versus continuous positive airway pressure (CPAP) therapy in OSA patients revealed that glucose tolerance impairment significantly improved in the CPAP group. [ 34 ] Despite providing initial evidence of the association between OSAHS and insulin resistance, existing studies have several limitations. First, many studies have small sample sizes and limited representativeness and are often derived from single regions or specific populations. Second, most studies employ cross-sectional designs, which can only reveal associations between variables but cannot establish causality. Additionally, inadequate control of confounding factors may affect the accuracy of the results. Existing research primarily relies on traditional metrics such as HOMA-IR to assess insulin resistance, which may not fully capture the complexity of IR. There is also a lack of uniform diagnostic criteria for OSAHS and insufficient long-term follow-up data, making it challenging to understand the dynamic relationship between OSAHS and IR. Finally, there is a shortage of subgroup analyses and a lack of in-depth examination of different clinical and metabolic characteristics. 4.3 Mechanistic Insights Insulin resistance influences the onset and progression of obstructive sleep apnea-hypopnea syndrome (OSAHS) through multiple mechanisms, including obesity, the inflammatory response, autonomic dysfunction, oxidative stress, and sleep fragmentation. Obesity is a shared risk factor for both OSAHS and IR. Fat deposition in the upper airway due to obesity increases the risk of OSAHS by causing mechanical obstruction. [ 28 ] Additionally, systemic inflammation and oxidative stress induced by obesity affect insulin sensitivity and metabolic function, exacerbating both IR and OSAHS symptoms. [ 35 , 36 ] In OSAHS patients, intermittent hypoxemia and recurrent arousal during sleep activate the sympathetic nervous system, increasing catecholamine release and further aggravating IR. [ 27 , 30 ] Moreover, hypoxemia and inflammation in OSAHS patients elevate the levels of inflammatory markers such as C-reactive protein, tumor necrosis factor-α, and interleukin-6, which interfere with insulin signaling pathways. Furthermore, adipocyte dysfunction in OSAHS patients, characterized by increased leptin and resistin and decreased adiponectin levels, impacts insulin sensitivity. [ 37 ] Oxidative stress generates free radicals that damage cell membranes and insulin receptors, disrupting insulin signal transduction and leading to IR. Finally, sleep fragmentation caused by OSAHS disrupts normal metabolic rhythms, affecting glucose metabolism and insulin secretion and thus increasing the risk of IR. [ 32 , 38 ] The complex interplay between insulin resistance and obstructive sleep apnea-hypopnea syndrome involves these interconnected mechanisms, collectively heightening the risk of both conditions. Future research should aim to further explore these mechanisms, particularly focusing on breaking this vicious cycle to improve patient outcomes. 4.4 Clinical Significance The findings of this study have significant clinical implications. First, the METS-IR serves as a convenient and practical marker for screening high-risk populations for obstructive sleep apnea-hypopnea syndrome (OSAHS) in clinical practice. Early identification and intervention for insulin resistance and metabolic syndrome may lower the risk of developing OSAHS and improve patient prognosis. Second, this study suggested that systematic assessment of metabolic syndrome and insulin resistance in OSAHS patients is important. Comprehensive management strategies, including weight reduction, lifestyle improvements, and pharmacological treatments, may help alleviate OSAHS symptoms and reduce the risk of associated complications in these patients. 4.5 Strengths and Limitations This study has several strengths: first, we utilized a large national sample, enhancing the representativeness and generalizability of our results. Second, we employed multivariable adjustment models to minimize the influence of confounding factors. However, the study also has limitations. First, due to its cross-sectional design, causal relationships cannot be determined. Second, the data relied on self-reports and questionnaires, potentially introducing information bias. Additionally, the calculation of the METS-IR index involves multiple metabolic indicators that may be influenced by various factors, thereby increasing variability in METS-IR measurements. Last, the study sample predominantly comprised participants from the United States, limiting the generalizability of the findings to other populations. Future research should further explore the causal relationships and underlying mechanisms between METS-IR and OSAHS. Longitudinal studies and interventional trials can help elucidate the role of insulin resistance in OSAHS pathophysiology. Furthermore, investigating the applicability and predictive value of the METS-IR in diverse populations is crucial for advancing clinical understanding and management strategies. 5. Conclusion In conclusion, our study revealed a significant association between the METS-IR index and OSAHS, suggesting that insulin resistance may play a pivotal role in the pathogenesis of OSAHS. Specifically, we observed that for each unit increase in METS-IR, there was a 5% increase in the risk of OSAHS (OR = 1.05, 95% CI: 1.03, 1.07). The METS-IR, as a straightforward tool for assessing insulin resistance, holds potential clinical value, particularly in screening and early intervention among high-risk populations for OSAHS. Future research should further explore the causal relationship and underlying mechanisms of this association, focusing on factors such as obesity, inflammatory responses, and autonomic nervous system dysfunction, to deepen our understanding of OSAHS pathophysiology. Such insights could pave the way for personalized treatment strategies. Declarations Ethical Statement Human subjects involved in the studies were granted ethical approval by the Ethical Review Board of the National Center for Health Statistics. The studies were conducted in compliance with local laws and institutional guidelines. Informed consent was obtained from the legal guardians or next of kin of the study participants. Consent for publication Not Applicable Conflict of interest The authors declare no competing interests Funding The study did not receive any funding sponsorship. Author Contribution Study Design and Data Acquisition: Hou Yisen, Li Rui, Chen Wenhao, Xu Zhen, Li Zhiwen, Jiang Weirong, Meng Yong and Jianli Han collectively conceived the study design and obtained publicly available NHANES summary statistics. Data Analysis and Interpretation: Li Rui, Xu Zhen, Chen Wenhao, Li Zhiwen, and Jiang Weirong were responsible for the statistical analysis and interpretation of the data. Drafting of the manuscript: Hou Yisen drafted the initial manuscript. Manuscript Revision and Approval: Hou Yisen, Li Rui, Chen Wenhao, Xu Zhen, Li Zhiwen, Jiang Weirong, Meng Yong and Jianli Han collaborated in revising the manuscript and approving the final article. Acknowledgement The authors wish to extend their gratitude to the participants and researchers of the National Health and Nutrition Examination Survey (NHANES) for their contributions to the study, and they also express their appreciation to NHANES for providing the essential, open-source data that were utilized in this research. Data Availability This study is based on data from the National Health and Nutrition Examination Survey (NHANES), which is a publicly available dataset. All data used in this study can be accessed through the NHANES repository at https://www.cdc.gov/nchs/nhanes/index.htm. No additional data were generated or analyzed in this study, and there are no ethical, privacy, or security concerns that restrict data sharing. References GOTTLIEB D J, PUNJABI NM. Diagnosis and Management of Obstructive Sleep Apnea: A Review [J]. JAMA. 2020;323(14):1389–400. PATEL SR. Obstructive Sleep Apnea [J]. Annals of internal medicine, 2019, 171(11): Itc81-itc96. 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YANG S, GUO X, LIU W, et al. Alcohol as an independent risk factor for obstructive sleep apnea [J]. Ir J Med Sci. 2022;191(3):1325–30. FLACK JM. Blood pressure and the new ACC/AHA hypertension guidelines [J]. Trends Cardiovasc Med. 2020;30(3):160–4. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021 [J]. Diabetes Care. 2021;44(Suppl 1):S15–33. ZHAO J, FU S, CHEN Q. Association between the serum vitamin D level and prevalence of obesity/abdominal obesity in women with infertility: a cross-sectional study of the National Health and Nutrition Examination Survey data [J]. Gynecol Endocrinol, 2023, 39(1). HUANG T, SANDS S A, STAMPFER M J, et al. Insulin Resistance, Hyperglycemia, and Risk of Developing Obstructive Sleep Apnea in Men and Women in the United States [J]. Annals Am Thorac Soc. 2022;19(10):1740–9. WANG L, NIE G, YAN F, et al. The ZJU index is associated with the risk of obstructive sleep apnea syndrome in Chinese middle-aged and older people: a cross-sectional study [J]. Lipids Health Dis. 2023;22(1):207. WEI R, GAO Z, XU H, et al. Body Fat Indices as Effective Predictors of Insulin Resistance in Obstructive Sleep Apnea: Evidence from a Cross-Sectional and Longitudinal Study: BFI as Predictors of IR in OSA [J]. Obes Surg. 2021;31(5):2219–30. XU H, LIANG C, ZOU J et al. Interaction between obstructive sleep apnea and short sleep duration on insulin resistance: a large-scale study [J]. Respir Res, 2020, 21(1). RYAN S. Adipose tissue inflammation by intermittent hypoxia: mechanistic link between obstructive sleep apnoea and metabolic dysfunction [J]. J Physiol. 2017;595(8):2423–30. SANAPO L, BUBLITZ M H, BAI A et al. Association between sleep disordered breathing in early pregnancy and glucose metabolism [J]. Sleep, 2022, 45(4). MANGAS-MORO A, CASITAS R, SáNCHEZ-SáNCHEZ B, et al. Characteristics of obstructive sleep apnea related to insulin resistance [J]. Sleep & breathing = Schlaf & Atmung; 2024. BEHNOUSH A H, KHALAJI A. GHONDAGHSAZ E, Triglyceride-glucose index and obstructive sleep apnea: a systematic review and meta-analysis [J]. Lipids Health Dis, 2024, 23(1). MICHALEK-ZRABKOWSKA M, MACEK P, MARTYNOWICZ H, et al. Obstructive Sleep Apnea as a Risk Factor of Insulin Resistance in Nondiabetic Adults [J]. Life. 2021;11(1):50. TOMO Y, NAITO R, TOMITA Y, et al. The Correlation between the Severity of Obstructive Sleep Apnea and Insulin Resistance in a Japanese Population [J]. J Clin Med. 2024;13(11):3135. SALORD N, FORTUNA A M, MONASTERIO C, et al. A Randomized Controlled Trial of Continuous Positive Airway Pressure on Glucose Tolerance in Obese Patients with Obstructive Sleep Apnea [J]. Sleep. 2016;39(1):35–41. GABRYELSKA A, KARUGA F F, SZMYD B et al. HIF-1α as a Mediator of Insulin Resistance, T2DM, and Its Complications: Potential Links With Obstructive Sleep Apnea [J]. Front Physiol, 2020, 11. PAPACHRISTOFOROU E, LAMBADIARI V et al. MARATOU E,. Association of Glycemic Indices (Hyperglycemia, Glucose Variability, and Hypoglycemia) with Oxidative Stress and Diabetic Complications [J]. J Diabetes Res, 2020, 2020: 7489795. KHALYFA A, GOZAL D, MASA JF, et al. Sleep-disordered breathing, circulating exosomes, and insulin sensitivity in adipocytes [J]. Int J Obes (Lond). 2018;42(6):1127–39. FERREIRA C B, SCHOORLEMMER G H, ROCHA A A et al. Increased sympathetic responses induced by chronic obstructive sleep apnea are caused by sleep fragmentation [J]. Journal of applied physiology (Bethesda, Md: 1985), 2020, 129(1): 163 – 72. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5322269","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376311800,"identity":"b0620702-295e-4cac-8056-6d10cc71db19","order_by":0,"name":"Yisen Hou","email":"","orcid":"","institution":"Xi’an No.3 Hospital, the Afliated Hospital of Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Yisen","middleName":"","lastName":"Hou","suffix":""},{"id":376311801,"identity":"3b4add1c-6237-4b05-b413-e6db292b4bd8","order_by":1,"name":"Rui Li","email":"","orcid":"","institution":"Xi’an No.3 Hospital, the Afliated Hospital of Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Li","suffix":""},{"id":376311802,"identity":"e1cd4750-8733-41ff-9718-fa02564aea66","order_by":2,"name":"Zhen Xu","email":"","orcid":"","institution":"Xi’an No.3 Hospital, the Afliated Hospital of Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Xu","suffix":""},{"id":376311804,"identity":"c0cc05b5-a0c9-48bc-8a54-df215b5e43de","order_by":3,"name":"Wenhao Chen","email":"","orcid":"","institution":"Xi’an No.3 Hospital, the Afliated Hospital of Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Chen","suffix":""},{"id":376311807,"identity":"df56f302-7f64-4848-9043-0bf1d74d12ed","order_by":4,"name":"Zhiwen Li","email":"","orcid":"","institution":"Xi’an No.3 Hospital, the Afliated Hospital of Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwen","middleName":"","lastName":"Li","suffix":""},{"id":376311809,"identity":"d13d4d49-9873-49b9-9841-7ce5a0f6b333","order_by":5,"name":"Weirong Jiang","email":"","orcid":"","institution":"Xi’an No.3 Hospital, the Afliated Hospital of Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Weirong","middleName":"","lastName":"Jiang","suffix":""},{"id":376311811,"identity":"b622a70f-cf98-4af6-8811-6ec878ac39b0","order_by":6,"name":"Yong Meng","email":"","orcid":"","institution":"Xi’an No.3 Hospital, the Afliated Hospital of Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Meng","suffix":""},{"id":376311813,"identity":"4d0352b7-f9f4-41db-a06b-f002c62ea05e","order_by":7,"name":"Jianli Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3RPWrDMBTAcT0E6vLcrE8kc2dnaQmYpEcJGDyZ0tHdFAzO4gM4pNBblG6VENSL264uWXIEe8/QELqVyhk76DdK+qMvxjzvHwqZVabLaA5Ptu7kIcLRhRpKzMpSE8W8EgmbqWQiSz2UQK6DIoF1hVfsQdkobG/dyQ0YpamxPNiWYv/18omsZdD16d/JTB2T+8xeysf3erppdghbxeXm2XEw/bPLVN0txyR2yCda8GAoCQoLrzoNxwfxgYKWZyUJrKr0mmShEc9JTo8M5VscUhMjocndd2mt7U9fuc7NnrL5YlHnpusdCSP9awiUY/3RaGDe8zzPY9+Ll2Pu8LR4XAAAAABJRU5ErkJggg==","orcid":"","institution":"Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jianli","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-10-24 03:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5322269/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5322269/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69441392,"identity":"ae5d77b4-a49a-4afb-b944-056755c78c9b","added_by":"auto","created_at":"2024-11-20 11:22:22","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69134,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection. A total of 19,942 participants were initially included, with 8,504 participants under the age of 20 being excluded, leaving 11,438 participants. Next, 6,624 participants were excluded due to missing METS-IR information, including fasting blood glucose (FBG), HDL-C, BMI, and triglycerides (TG), leaving 4,814 participants. Subsequently, 540 participants were excluded due to missing OSAHS information, resulting in a final sample of 4,274 participants. Based on OSAHS status, the participants were further divided into the OSAHS group (N=1,384) and the non-OSAHS group (N=2,890).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5322269/v1/fe5bce0c31226dd5dd768bee.jpeg"},{"id":69441390,"identity":"b6bac495-a9cf-49de-b9a6-fa1635846a6a","added_by":"auto","created_at":"2024-11-20 11:22:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12730,"visible":true,"origin":"","legend":"\u003cp\u003eWe conducted a restricted cubic spline analysis to assess the association between the METS-IR index and the risk of developing OSAHS. The model was adjusted for all covariates. Odds ratios (ORs) were depicted using a solid orange line, while the 95% confidence intervals are represented by a lightly shaded yellow area.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5322269/v1/656a62b3b6dc44b8fa21a585.png"},{"id":69441393,"identity":"de127285-4470-4b4f-bee8-61c291e73a57","added_by":"auto","created_at":"2024-11-20 11:22:22","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171480,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of the METS-IR Index and OSAHS. This figure presents the results of subgroup analysis evaluating the influence of demographic and health factors on the relationship between the METS-IR index and the incidence of OSAHS. Stratification factors included age, sex, race, diabetes status, hypertension status, and hyperlipidemia status. Race significantly modified the association between METS-IR index and OSAHS (p-values \u0026lt; 0.05), with this association being more pronounced among Mexican American and non-Hispanic Black populations.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5322269/v1/07e60208b7217e13991300a3.jpeg"},{"id":93802462,"identity":"144fa364-73e0-47ff-a53e-bbab8d207834","added_by":"auto","created_at":"2025-10-17 17:16:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1118604,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5322269/v1/d53b1b47-5ece-48ab-9e67-c0185a12cb98.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association between METS-IR Index and Obstructive Sleep Apnea-Hypopnea Syndrome: A Cross-Sectional Study Based on the National Health and Nutrition Examination Survey from 2015 to 2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eObstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder characterized by recurrent upper airway obstruction during sleep, leading to partial or complete apnea and hypopnea.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e OSAHS is primarily manifested by symptoms such as nocturnal snoring, intermittent apnea, and excessive daytime sleepiness.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e It significantly impacts patients' quality of life and is closely associated with various metabolic and cardiovascular diseases, including hypertension, type 2 diabetes, and cardiovascular disease.\u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e Therefore, exploring the pathogenesis of OSAHS and its related factors is crucial for its prevention and management of this condition.\u003c/p\u003e \u003cp\u003eMetabolic syndrome (MetS) is a cluster of metabolic abnormalities, including abdominal obesity, insulin resistance, hyperglycemia, hypertension, and dyslipidemia.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e In recent years, the association between OSAHS and MetS has garnered extensive attention, especially regarding insulin resistance (IR), a core feature of MetS that is closely linked to OSAHS.\u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e However, traditional methods for assessing IR, such as the homeostasis model assessment of insulin resistance (HOMA-IR), are limited in large-scale epidemiological studies due to the need for fasting glucose and insulin measurements.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo overcome these limitations, researchers have proposed the Metabolic Syndrome Insulin Resistance Index (METS-IR), a simplified calculation method based on MetS components that does not require insulin measurements, providing a novel approach for evaluating IR.\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e Although studies\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e have shown an association between OSAHS and MetS and its components, the specific relationships between METS-IR index, a novel IR evaluation metric, and OSAHS have not been sufficiently investigated.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to fill this gap by utilizing cross-sectional data from the 2015\u0026mdash;2018 National Health and Nutrition Examination Survey (NHANES) to explore the association between the METS-IR index and OSAHS. We hypothesize that the METS-IR index can serve as an independent risk factor for predicting OSAHS and assess its predictive efficacy. If validated, the METS-IR index could become a simple, cost-effective, and efficient screening tool for the early identification of high-risk OSAHS patients, guiding clinical practice and public health interventions to reduce the incidence of OSAHS and its related complications.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThis study utilized data from the 2015\u0026ndash;2018 National Health and Nutrition Examination Survey to investigate the relationship between the METS-IR index and the risk of OSAHS. The NHANES is an ongoing survey conducted by the National Center for Health Statistics (NCHS) aimed at assessing the health and nutritional status of the U.S. population. The data were collected using a multistage probability sampling method to ensure national representativeness. Initially, a total of participants were included in the study. Due to the potential confounding effect of smoking (SMQ020 - Smoked at least 100 cigarettes in life), which requires participants to be older than 20 years, those under 20 years of age were excluded. Additionally, participants lacking METS-IR index data or OSAHS data were excluded. After rigorous screening, a total of 4,274 eligible participants were included, among whom 1,384 reported a history of OSA. The exclusion criteria were as follows (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 METS-IR Data Collection\u003c/h2\u003e \u003cp\u003eIn this study, the METS-IR index was designated as the exposure variable. METS-IR was calculated using the following formula: Ln[(2 \u0026times; fasting blood glucose (mg/dL))\u0026thinsp;+\u0026thinsp;fasting triglycerides (mg/dL)] \u0026times; body mass index (kg/m\u0026sup2;) / [Ln(high-density lipoprotein cholesterol (mg/dL))]. Fasting triglycerides, fasting blood glucose, and high-density lipoprotein cholesterol levels were measured enzymatically using an automatic biochemical analyzer. Serum triglyceride and high-density lipoprotein cholesterol concentrations were determined using a Roche Cobas 6000 biochemical analyzer and a Roche Modular P. Body mass index (BMI) was calculated by directly measuring the height and weight of the participants, with the specific formula being BMI\u0026thinsp;=\u0026thinsp;weight (kg) / (height (m))^2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 OSAHS Data Collection\u003c/h2\u003e \u003cp\u003eThe OSAHS data were collected through a questionnaire survey. Based on previous studies\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, we defined OSAHS using three questions from the Sleep Disorder Questionnaire (SLQ). The questions included the following: 1. SLQ030: \"How often do you snore?\"; 2. SLQ040: \"How often do you snort or stop breathing?\"; and 3. SLQ120: \"How often do you feel overly sleepy during the day?\". Participants were classified as having OSAHS symptoms if they reported snoring three or more times per week, or snorting/stopping breathing three or more times per week, or feeling excessively sleepy during daily activities frequently (16\u0026ndash;30 times per month).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariate Selection\u003c/h2\u003e \u003cp\u003eIn this study, we selected a range of covariates to control for potential confounding factors. These covariates included age, sex, race, marital status, education level, family income-to-poverty ratio (PIR), smoking status, alcohol consumption, diabetes status, hypertension status, and dyslipidemia status. These variables were chosen based on a comprehensive review of the literature and their potential impact on the relationship between the METS-IR index and OSAHS.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e Age was categorized into three groups: 20\u0026ndash;40 years, 41\u0026ndash;60 years, and \u0026gt;\u0026thinsp;60 years. The PIR was defined as non-poor (PIR\u0026thinsp;\u0026ge;\u0026thinsp;1) or poor (PIR\u0026thinsp;\u0026lt;\u0026thinsp;1). Alcohol consumption was determined based on responses to the questions \"Had at least 12 alcohol drinks/lifetime?\" (ALQ110) and \"Ever had a drink of any kind of alcohol?\" (ALQ111), with participants who answered \"yes\" classified as drinkers. Smoking status was assessed using the question \"Smoked at least 100 cigarettes in life?\", Those who answered \"yes\" to the SMQ020 were classified as smokers. Hypertension was defined as a systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or a diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e Diabetes was defined as a fasting glucose level\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or a history of insulin or oral hypoglycemic medication use.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e Hyperlipidemia was defined as total cholesterol\u0026thinsp;\u0026gt;\u0026thinsp;200 mg/dL, triglycerides\u0026thinsp;\u0026gt;\u0026thinsp;150 mg/dL, low-density lipoprotein cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dL, or high-density lipoprotein cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL (for men) or \u0026lt;\u0026thinsp;50 mg/dL (for women).\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo investigate the association between the METS-IR index and the occurrence of OSAHS, this study leveraged the large-scale, complex, multistage sampling design of the NHANES. We strictly adhered to the NHANES principles of sampling weights, stratification, and clustering adjustments to ensure the robustness and validity of our statistical results.\u003c/p\u003e \u003cp\u003eFirst, we conducted detailed descriptive statistical analyses of the baseline characteristics of the participants. Continuous variables are described using weighted means and their standard errors, while categorical variables are presented as weighted percentages to show the distribution within the sample. To examine the relationship between the METS-IR index and OSAHS, we used logistic regression analysis to construct three progressively detailed covariate adjustment models. Model 1 served as the baseline model with no adjustments; Model 2 included preliminary adjustments for age, sex, and race; and Model 3 was comprehensively adjusted for all covariates, focusing on the trend effects of the METS-IR quartiles on OSAHS risk. We quantified the association strength between METS-IR levels and OSAHS risk by calculating odds ratios (ORs) and their 95% confidence intervals (CIs).\u003c/p\u003e \u003cp\u003eNext, we employed restricted cubic spline analysis to explore the specific relationship shape between the METS-IR index and OSAHS incidence, capturing potential nonlinear associations. Additionally, we conducted subgroup analyses of key factors, such as sex, age, race, diabetes status, hypertension status, and hyperlipidemia status, to reveal the heterogeneity in the METS-IR index and OSAHS risk relationships across different subgroups. Interaction tests were performed to assess significant interactions between the METS-IR index and these subgroup characteristics, providing a deeper understanding of the pathways and mechanisms by which METS-IR influences OSAHS risk. All the statistical analyses were performed using R software version 4.4.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 General Characteristics of the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline demographic characteristics of the study population, categorized by METS-IR quartiles, are presented in Table 1, comprising a total of 4,274 participants. The METS-IR index ranges for Quartiles 1 to 4 were 19.98-33.94, 33.94-41.60, 41.60-51.15, and 51.15-124.47, respectively. Significant differences were observed across the METS-IR quartiles in terms of age, sex, race, alcohol consumption, hypertension, diabetes, and dyslipidemia. Compared to Quartile 1 participants in Quartile 4 had greater proportions of males, Mexican Americans, and those with hypertension, diabetes, and hyperlipidemia, while the proportion of those consuming alcohol decreased. Additionally, there was a marked increase in the prevalence of diabetes, hypertension, and hyperlipidemia among participants in quartiles 2 to 4 compared to quartile 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Weighted Baseline Characteristics of the Participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003eQ1 [19.98，33.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003eQ2 [33.94，41.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003eQ3 [41.60，51.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003eQ1 [51.15，124.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e1006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e1122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e1056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eMETS-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e28.97(3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e37.66(2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e45.96(2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e61.28(9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e45.52(17.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e51.96 (17.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e48.65(16.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e49.87(15.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003e20-40 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e43.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e31.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e35.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e29.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003e41-60 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e35.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e30.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e40.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e41.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003e\u0026gt;60 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e21.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e38.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e24.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e28.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e35.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e49.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e56.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e53.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e64.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e50.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e43.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e46.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e8.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e10.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e66.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e66.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e62.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e65.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e8.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e20.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e18.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eMarried/live with partner (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e65.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e67.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e72.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e70.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e34.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e32.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e27.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e10.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e15.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e15.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e12.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eHigh School or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e89.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e84.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e84.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e87.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003ePoverty income ratio\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e12.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e16.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNot poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e89.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e87.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e83.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e86.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e19.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e16.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e80.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e81.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e80.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e83.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eAlcohol (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e77.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e76.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e73.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e72.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e23.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e23.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e26.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e27.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e8.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e16.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e12.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e96.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e91.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e83.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e67.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e29.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e43.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e55.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e66.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e70.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e56.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e44.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e33.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eHyperlipidemia \u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e50.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e71.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e78.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e86.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0459%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e49.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2257%;\"\u003e\n \u003cp\u003e28.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e21.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6966%;\"\u003e\n \u003cp\u003e13.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8148%;\"\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\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Relationship between the METS-IR Index and OSAHS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the association between the METS-IR index and the risk of developing OSAHS. Participants were divided into quartiles based on their METS-IR index, with the lowest quartile (quartile 1) designated as the reference group. Across all adjustment models, a positive correlation was observed between the METS-IR index and the incidence of OSAHS. According to the fully adjusted model, each once-unit increase in the METS-IR index was associated with a 5% increase in the risk of OSAHS (OR = 1.05, 95% CI: 1.03, 1.07). Additionally, sensitivity analysis was performed by converting the METS-IR index from a continuous variable to a categorical variable (Q1-Q4) using quartiles. In Model 3, adjustments were made for multiple covariates, including age, sex, race, marital status, education level, PIR, smoking status, alcohol consumption, hypertension, hyperlipidemia, and diabetes status. Compared to quartile 1, participants in quartile 4 had a 4.14-fold greater risk of OSAHS (OR = 5.14, 95% CI: 1.36-19.35). The multivariable-adjusted spline curve of the relationship between the METS-IR index and the incidence of OSAHS is shown in Figure 2, indicating a positive linear correlation between higher METS-IR index and OSAHS incidence (nonlinearity p value \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable:2 Weighted Association between METS-IR Index and OSAHS\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 437px;\"\u003e\n \u003cp\u003eOR (95% CI), P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eMETS-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e1.05(1.03, 1.06)\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.05(1.03, 1.06)\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.05(1.03, 1.07)\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e1.71 (0.93,3.15)\u003c/p\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.60(0.82,3.11),\u0026nbsp;0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.58(0.45,5.56), 0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e2.53 (1.57,4.06)\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e2.37(1.45,3.89),\u0026nbsp;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e2.43(0.92,6.41),\u0026nbsp;0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e5.46(3.12,9.55)\u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5.15(2.85,9.31), \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5.14(1.36,19.35),\u0026nbsp;0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e1.75(1.46,\u0026nbsp;2.08)\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.72(1.43,\u0026nbsp;2.08)\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.72(1.29,\u0026nbsp;2.34)\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1:\u003c/strong\u003e crude model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2:\u0026nbsp;\u003c/strong\u003eadjusted for age, sex and race.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3:\u003c/strong\u003e adjusted for age, sex, race, educational level, poverty income ratio, smoking status, alcohol consumption, and history of hypertension, diabetes, and hyperlipidemia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Subgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analyses were conducted to evaluate whether the relationship between the METS-IR index and OSAHS incidence was influenced by age, sex, race, diabetes status, hypertension status, and hyperlipidemia status. As shown in Figure 3, which details the statistical results of these stratified factors, race significantly modified the association between METS-IR and OSAHS (p values\u0026lt; 0.05). Specifically, the association was stronger among Mexican American and non-Hispanic Black populations. Conversely, age, sex, diabetes status, hypertension status, and dyslipidemia status did not significantly influence the METS-IR index or the incidence of OSAHS (p values\u0026gt; 0.05). This indicated that the association between METS-IR and OSAHS remained robust after controlling for potential confounders.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we utilized data from the National Health and Nutrition Examination Survey from 2015 to 2018 to evaluate the association between the METS-IR index and OSAHS. Our primary findings indicated that individuals with higher METS-IR scores were more likely to exhibit symptoms of OSAHS, suggesting that insulin resistance may play a significant role in the pathogenesis of OSAHS.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Key Findings\u003c/h2\u003e \u003cp\u003eFirst, our study revealed a significant association between the METS-IR index and OSAHS symptoms. Specifically, the fully adjusted model indicated that for each unit increase in the METS-IR index, the risk of OSAHS increased by 5% (OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.03, 1.07). Participants in the highest quartile (Q4) of the METS-IR index had a 4.14-fold greater risk of OSAHS than did those in the lowest quartile (Q1) (OR\u0026thinsp;=\u0026thinsp;5.14, 95% CI: 1.36\u0026ndash;19.35). This finding aligns with existing research, such as the study by Tianyi Huang et al.,\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e which suggested that insulin resistance may play a more critical role than hyperglycemia in the pathogenesis of OSA. In their study, the odds ratio for OSA events in the highest quintile of fasting insulin was 3.59 (95% CI: 2.67\u0026ndash;4.82; P-trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Among the 6,342 participants with HbA1c data, 715 (11.3%) developed OSA, with an odds ratio for HbA1c of 2.21 (95% CI: 1.69\u0026ndash;2.89; P-trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Another study\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e revealed that the ZJU index, which incorporates BMI, triglycerides, fasting glucose, and the ratio of alanine aminotransferase to aspartate aminotransferase, is an indicator of obesity and insulin resistance. At the highest levels of the ZJU index, the risk of OSA was significantly greater (OR\u0026thinsp;=\u0026thinsp;2.046 [95% CI: 1.057, 3.964]). Additionally, Ruobing Wei et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e found that LAP and TyG are significant predictors of insulin resistance in OSA patients with normal weight and those who are overweight/obese. Among normal-weight OSA patients, the TyG index had a greater ability to predict IR than traditional indices such as BMI and WC. Currently, research on the relationship between the METS-IR index and OSAHS is limited, with only one study evaluating the role of the METS-IR index in predicting cardiovascular disease and its subtypes among hypertensive and OSA patients, without specifically addressing the association between the METS-IR and OSAHS risk.\u003c/p\u003e \u003cp\u003eSecond, the METS-IR index, as a novel assessment metric for insulin resistance, offers the advantage of not requiring insulin level measurements, thereby better reflecting the comprehensive impact of metabolic syndrome. Our study validated the effectiveness of the METS-IR in assessing the risk of OSAHS, suggesting that the METS-IR could be a valuable tool in clinical practice for screening high-risk populations for OSAHS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Existing Research Analysis\u003c/h2\u003e \u003cp\u003eCurrent research suggests a bidirectional interaction between OSAHS and IR.\u003csup\u003e[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e An analysis of OSA patients versus primary snorers showed that OSA, rather than short sleep duration, is the primary driver of the insulin resistance (IR) association. Furthermore, the combination of OSA with very short sleep duration exacerbates the risk of IR.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e Additionally, studies using HOMA-IR to measure insulin resistance have shown an independent association between IR and the apnea index during REM sleep (adjusted odds ratio [aOR] 1.09; 95% CI, 1.03 to 1.16; p\u0026thinsp;=\u0026thinsp;0.004).\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e Recent studies have highlighted various body fat indices as effective tools for predicting IR in OSAHS patients.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e Body mass index, waist circumference, waist-to-height ratio, and waist-to-hip ratio have been used to estimate IR in OSA patients. More refined indices, such as the lipid accumulation product (LAP), visceral adiposity index (VAI), and triglyceride-glucose index (TyG), have shown strong correlations with visceral obesity and IR. These indices combine anthropometric and metabolic parameters, providing a more comprehensive assessment of metabolic risk.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConversely, OSA is also considered an independent risk factor for IR. For instance, studies have shown that insulin concentrations and HOMA-IR values are significantly elevated in patients grouped by OSA severity and reported sleepiness, with an AHI\u0026thinsp;\u0026ge;\u0026thinsp;15 and an AHI\u0026thinsp;\u0026ge;\u0026thinsp;30.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e Similarly, among Japanese OSA patients without diabetes and cardiovascular disease, the severity of OSA (AHI, lowest oxygen saturation, %TST SO2\u0026thinsp;\u0026lt;\u0026thinsp;90%) was positively correlated with IR (HOMA-IR), even after adjusting for age, sex, and BMI.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e Furthermore, correcting OSA symptoms can improve insulin resistance. A study comparing 12 weeks of conservative treatment (CT) versus continuous positive airway pressure (CPAP) therapy in OSA patients revealed that glucose tolerance impairment significantly improved in the CPAP group.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite providing initial evidence of the association between OSAHS and insulin resistance, existing studies have several limitations. First, many studies have small sample sizes and limited representativeness and are often derived from single regions or specific populations. Second, most studies employ cross-sectional designs, which can only reveal associations between variables but cannot establish causality. Additionally, inadequate control of confounding factors may affect the accuracy of the results. Existing research primarily relies on traditional metrics such as HOMA-IR to assess insulin resistance, which may not fully capture the complexity of IR. There is also a lack of uniform diagnostic criteria for OSAHS and insufficient long-term follow-up data, making it challenging to understand the dynamic relationship between OSAHS and IR. Finally, there is a shortage of subgroup analyses and a lack of in-depth examination of different clinical and metabolic characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Mechanistic Insights\u003c/h2\u003e \u003cp\u003eInsulin resistance influences the onset and progression of obstructive sleep apnea-hypopnea syndrome (OSAHS) through multiple mechanisms, including obesity, the inflammatory response, autonomic dysfunction, oxidative stress, and sleep fragmentation. Obesity is a shared risk factor for both OSAHS and IR. Fat deposition in the upper airway due to obesity increases the risk of OSAHS by causing mechanical obstruction.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e Additionally, systemic inflammation and oxidative stress induced by obesity affect insulin sensitivity and metabolic function, exacerbating both IR and OSAHS symptoms.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e In OSAHS patients, intermittent hypoxemia and recurrent arousal during sleep activate the sympathetic nervous system, increasing catecholamine release and further aggravating IR.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e Moreover, hypoxemia and inflammation in OSAHS patients elevate the levels of inflammatory markers such as C-reactive protein, tumor necrosis factor-α, and interleukin-6, which interfere with insulin signaling pathways. Furthermore, adipocyte dysfunction in OSAHS patients, characterized by increased leptin and resistin and decreased adiponectin levels, impacts insulin sensitivity.\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e Oxidative stress generates free radicals that damage cell membranes and insulin receptors, disrupting insulin signal transduction and leading to IR. Finally, sleep fragmentation caused by OSAHS disrupts normal metabolic rhythms, affecting glucose metabolism and insulin secretion and thus increasing the risk of IR.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe complex interplay between insulin resistance and obstructive sleep apnea-hypopnea syndrome involves these interconnected mechanisms, collectively heightening the risk of both conditions. Future research should aim to further explore these mechanisms, particularly focusing on breaking this vicious cycle to improve patient outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Clinical Significance\u003c/h2\u003e \u003cp\u003eThe findings of this study have significant clinical implications. First, the METS-IR serves as a convenient and practical marker for screening high-risk populations for obstructive sleep apnea-hypopnea syndrome (OSAHS) in clinical practice. Early identification and intervention for insulin resistance and metabolic syndrome may lower the risk of developing OSAHS and improve patient prognosis.\u003c/p\u003e \u003cp\u003eSecond, this study suggested that systematic assessment of metabolic syndrome and insulin resistance in OSAHS patients is important. Comprehensive management strategies, including weight reduction, lifestyle improvements, and pharmacological treatments, may help alleviate OSAHS symptoms and reduce the risk of associated complications in these patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study has several strengths: first, we utilized a large national sample, enhancing the representativeness and generalizability of our results. Second, we employed multivariable adjustment models to minimize the influence of confounding factors. However, the study also has limitations. First, due to its cross-sectional design, causal relationships cannot be determined. Second, the data relied on self-reports and questionnaires, potentially introducing information bias. Additionally, the calculation of the METS-IR index involves multiple metabolic indicators that may be influenced by various factors, thereby increasing variability in METS-IR measurements. Last, the study sample predominantly comprised participants from the United States, limiting the generalizability of the findings to other populations. Future research should further explore the causal relationships and underlying mechanisms between METS-IR and OSAHS. Longitudinal studies and interventional trials can help elucidate the role of insulin resistance in OSAHS pathophysiology. Furthermore, investigating the applicability and predictive value of the METS-IR in diverse populations is crucial for advancing clinical understanding and management strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study revealed a significant association between the METS-IR index and OSAHS, suggesting that insulin resistance may play a pivotal role in the pathogenesis of OSAHS. Specifically, we observed that for each unit increase in METS-IR, there was a 5% increase in the risk of OSAHS (OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.03, 1.07). The METS-IR, as a straightforward tool for assessing insulin resistance, holds potential clinical value, particularly in screening and early intervention among high-risk populations for OSAHS. Future research should further explore the causal relationship and underlying mechanisms of this association, focusing on factors such as obesity, inflammatory responses, and autonomic nervous system dysfunction, to deepen our understanding of OSAHS pathophysiology. Such insights could pave the way for personalized treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eEthical Statement\u003c/h2\u003e \u003cp\u003e Human subjects involved in the studies were granted ethical approval by the Ethical Review Board of the National Center for Health Statistics. The studies were conducted in compliance with local laws and institutional guidelines. Informed consent was obtained from the legal guardians or next of kin of the study participants.\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study did not receive any funding sponsorship.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy Design and Data Acquisition: Hou Yisen, Li Rui, Chen Wenhao, Xu Zhen, Li Zhiwen, Jiang Weirong, Meng Yong and Jianli Han collectively conceived the study design and obtained publicly available NHANES summary statistics. Data Analysis and Interpretation: Li Rui, Xu Zhen, Chen Wenhao, Li Zhiwen, and Jiang Weirong were responsible for the statistical analysis and interpretation of the data. Drafting of the manuscript: Hou Yisen drafted the initial manuscript. Manuscript Revision and Approval: Hou Yisen, Li Rui, Chen Wenhao, Xu Zhen, Li Zhiwen, Jiang Weirong, Meng Yong and Jianli Han collaborated in revising the manuscript and approving the final article.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors wish to extend their gratitude to the participants and researchers of the National Health and Nutrition Examination Survey (NHANES) for their contributions to the study, and they also express their appreciation to NHANES for providing the essential, open-source data that were utilized in this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study is based on data from the National Health and Nutrition Examination Survey (NHANES), which is a publicly available dataset. All data used in this study can be accessed through the NHANES repository at https://www.cdc.gov/nchs/nhanes/index.htm. No additional data were generated or analyzed in this study, and there are no ethical, privacy, or security concerns that restrict data sharing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGOTTLIEB D J, PUNJABI NM. Diagnosis and Management of Obstructive Sleep Apnea: A Review [J]. JAMA. 2020;323(14):1389\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePATEL SR. Obstructive Sleep Apnea [J]. Annals of internal medicine, 2019, 171(11): Itc81-itc96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLE-DONG N N MARTINOTJB. MALHOTRA A, Respiratory effort during sleep and prevalent hypertension in obstructive sleep apnoea [J]. Eur Respir J, 2023, 61(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDING Q, QIN L. Polysomnographic Phenotypes of Obstructive Sleep Apnea and Incident Type 2 Diabetes: Results from the DREAM Study [J]. 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Ir J Med Sci. 2022;191(3):1325\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFLACK JM. Blood pressure and the new ACC/AHA hypertension guidelines [J]. Trends Cardiovasc Med. 2020;30(3):160\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021 [J]. Diabetes Care. 2021;44(Suppl 1):S15\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHAO J, FU S, CHEN Q. Association between the serum vitamin D level and prevalence of obesity/abdominal obesity in women with infertility: a cross-sectional study of the National Health and Nutrition Examination Survey data [J]. Gynecol Endocrinol, 2023, 39(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHUANG T, SANDS S A, STAMPFER M J, et al. Insulin Resistance, Hyperglycemia, and Risk of Developing Obstructive Sleep Apnea in Men and Women in the United States [J]. Annals Am Thorac Soc. 2022;19(10):1740\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG L, NIE G, YAN F, et al. The ZJU index is associated with the risk of obstructive sleep apnea syndrome in Chinese middle-aged and older people: a cross-sectional study [J]. Lipids Health Dis. 2023;22(1):207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWEI R, GAO Z, XU H, et al. Body Fat Indices as Effective Predictors of Insulin Resistance in Obstructive Sleep Apnea: Evidence from a Cross-Sectional and Longitudinal Study: BFI as Predictors of IR in OSA [J]. Obes Surg. 2021;31(5):2219\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXU H, LIANG C, ZOU J et al. Interaction between obstructive sleep apnea and short sleep duration on insulin resistance: a large-scale study [J]. Respir Res, 2020, 21(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRYAN S. Adipose tissue inflammation by intermittent hypoxia: mechanistic link between obstructive sleep apnoea and metabolic dysfunction [J]. J Physiol. 2017;595(8):2423\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSANAPO L, BUBLITZ M H, BAI A et al. Association between sleep disordered breathing in early pregnancy and glucose metabolism [J]. Sleep, 2022, 45(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMANGAS-MORO A, CASITAS R, S\u0026aacute;NCHEZ-S\u0026aacute;NCHEZ B, et al. Characteristics of obstructive sleep apnea related to insulin resistance [J]. Sleep \u0026amp; breathing\u0026thinsp;=\u0026thinsp;Schlaf \u0026amp; Atmung; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBEHNOUSH A H, KHALAJI A. GHONDAGHSAZ E, Triglyceride-glucose index and obstructive sleep apnea: a systematic review and meta-analysis [J]. Lipids Health Dis, 2024, 23(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMICHALEK-ZRABKOWSKA M, MACEK P, MARTYNOWICZ H, et al. Obstructive Sleep Apnea as a Risk Factor of Insulin Resistance in Nondiabetic Adults [J]. Life. 2021;11(1):50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTOMO Y, NAITO R, TOMITA Y, et al. The Correlation between the Severity of Obstructive Sleep Apnea and Insulin Resistance in a Japanese Population [J]. J Clin Med. 2024;13(11):3135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSALORD N, FORTUNA A M, MONASTERIO C, et al. A Randomized Controlled Trial of Continuous Positive Airway Pressure on Glucose Tolerance in Obese Patients with Obstructive Sleep Apnea [J]. Sleep. 2016;39(1):35\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGABRYELSKA A, KARUGA F F, SZMYD B et al. HIF-1α as a Mediator of Insulin Resistance, T2DM, and Its Complications: Potential Links With Obstructive Sleep Apnea [J]. Front Physiol, 2020, 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePAPACHRISTOFOROU E, LAMBADIARI V et al. MARATOU E,. Association of Glycemic Indices (Hyperglycemia, Glucose Variability, and Hypoglycemia) with Oxidative Stress and Diabetic Complications [J]. J Diabetes Res, 2020, 2020: 7489795.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKHALYFA A, GOZAL D, MASA JF, et al. Sleep-disordered breathing, circulating exosomes, and insulin sensitivity in adipocytes [J]. Int J Obes (Lond). 2018;42(6):1127\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFERREIRA C B, SCHOORLEMMER G H, ROCHA A A et al. Increased sympathetic responses induced by chronic obstructive sleep apnea are caused by sleep fragmentation [J]. Journal of applied physiology (Bethesda, Md: 1985), 2020, 129(1): 163\u0026thinsp;\u0026ndash;\u0026thinsp;72.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"METS-IR, obstructive sleep apnea-hypopnea syndrome, NHANES, cross-sectional study, metabolic syndrome","lastPublishedDoi":"10.21203/rs.3.rs-5322269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5322269/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-hypopnea syndrome (OSAHS) is a common sleep disorder closely associated with metabolic syndrome. The metabolic score for insulin resistance (METS-IR) is a new indicator used to assess insulin resistance. However, evidence on the association between METS-IR and OSAHS remains limited.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to analyze the association between METS-IR and OSAHS in American adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2015 and 2018. We analyzed METS-IR and the prevalence of OSAHS in adult participants. Individuals aged 20 years and older were included, while those without available BMI, fasting blood glucose (FBG), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) data were excluded. Logistic regression analysis, subgroup analysis, and restricted cubic spline analysis were employed to evaluate the association between METS-IR and OSAHS, adjusting for potential confounders including sex, age, race/ethnicity, education level, income, smoking status, alcohol consumption, diabetes status, and lipid levels.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 4274 adult participants were included in the study. Participants were categorized into quartiles based on METS-IR levels, with ranges of 19.98\u0026ndash;33.94, 33.94\u0026ndash;41.60, 41.60-51.15, and 51.15-124.47, respectively. After adjusting for age, sex, race/ethnicity, education level, smoking status, alcohol consumption status, hypertension status, diabetes status, and dyslipidemia status, METS-IR was positively associated with the risk of OSAHS (OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.03, 1.07). Specifically, each one-unit increase in METS-IR was associated with a 5% increase in the risk of OSAHS. Subgroup analysis revealed a significant positive correlation between METS-IR and the incidence of OSAHS in the highest METS-IR quartile. This association was particularly pronounced among Mexican Americans (OR\u0026thinsp;=\u0026thinsp;6.33, 95% CI: 2.13, 23.67) and non-Hispanic Black individuals (OR\u0026thinsp;=\u0026thinsp;12.22, 95% CI: 5.89, 26.62). Additionally, after controlling for potential confounders, the association between METS-IR and OSAHS remained significant. Notably, individuals with diabetes, hypertension, and hypertriglyceridemia were at a greater risk of OSAHS.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThe results of this study demonstrated a significant positive association between METS-IR and the incidence of OSAHS, which persisted after adjusting for various confounders. This suggests that METS-IR may be a potential risk factor for OSAHS. In clinical practice, the management of metabolic syndrome should be emphasized to prevent the occurrence of OSAHS.\u003c/p\u003e","manuscriptTitle":"The Association between METS-IR Index and Obstructive Sleep Apnea-Hypopnea Syndrome: A Cross-Sectional Study Based on the National Health and Nutrition Examination Survey from 2015 to 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 11:22:17","doi":"10.21203/rs.3.rs-5322269/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d3a35e82-05c3-47d8-80d0-9546e0a3a90a","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T17:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 11:22:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5322269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5322269","identity":"rs-5322269","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}