Exploring the Link Between Sleep characteristics and Osteoarthritis: Evidence from NHANES and MR

Exploring the Link Between Sleep characteristics and Osteoarthritis: Evidence from NHANES and MR | 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 Exploring the Link Between Sleep characteristics and Osteoarthritis: Evidence from NHANES and MR Dongdong Cao, Jixin Chen, Weijie Yu, Jialin Yang, Tianci Guo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4756644/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 Epidemiological studies indicate that sleep disturbances are risk factors for osteoarthritis (OA). Obstructive sleep apnea (OSA) is a prevalent sleep disorder, yet its causal relationship with OA remains unclear. Therefore, this study investigates the causal relationship between three typical sleep characteristics of OSA and OA, aiming to provide theoretical support for clinical prevention and treatment strategies. Methods We used information from the National Health and Nutrition Examination Survey (NHANES) for 2015–2018 to conduct a cross-sectional study. Multivariate logistic regression was employed to evaluate the association between OSA and OA. We obtained genetic instruments from publicly available genome-wide association study (GWAS) databases for MR studies, with inverse variance weighting (IVW) as the primary method. Results After controlling for all confounding variables, multivariate logistic regression revealed an adjusted odds ratio (OR) of 1.67 (95% CI: 1.44, 1.95) for OSA about OA, supporting the positive connection between the two conditions established in the cross-sectional analysis. MR analysis further suggested a causal link between snoring and daytime sleepiness, two primary OSA symptoms, and an increased risk of OA, with OR of 1.059 (95% CI: 1.020, 1.099) and 1.052 (95% CI: 1.013, 1.094), respectively. Conclusion Our study found that OSA may be a risk factor for the development or progression of OA. Therefore, we believe that OSA may be a new target for the prevention and treatment of OA. Future studies should focus on confirming these findings in different populations and elucidating the exact biological mechanisms behind the OSA-OA relationship. Obstructive Sleep Apnea Osteoarthritis Mendelian Randomization NHANES Figures Figure 1 Figure 2 Figure 3 1. Introduction Osteoarthritis (OA) is a degenerative joint disease that causes damage to articular cartilage and structural abnormalities in the joints [ 1 ]. It is one of the most frequent joint illnesses in orthopaedics, causing pain, swelling, and movement limitations that, in extreme cases, can result in disability [ 2 , 3 ]. Epidemiological studies show that the prevalence of OA ranges from 12.3–21.6% [ 4 , 5 ]. Furthermore, the prevalence of OA is likely to increase further due to the aging population and the prevalence of overweight and obesity [ 6 ]. OA is influenced by several known risk factors, including aging, being postmenopausal in women, genetic factors, metabolic conditions like obesity and type 2 diabetes, and abnormal mechanical stress resulting from joint instability, overuse, or injury [ 7 – 9 ]. These risk factors contribute to the onset and progression of OA through inflammatory processes, mechanical stimulation, and metabolic pathways, either singly or in combination [ 10 , 11 ]. Therefore, exploring the risk factors for OA is crucial for the prevention and treatment of this disease. Patients with OA, especially those in the advanced stages, often experience sleep disorders due to pain, with obstructive sleep apnea (OSA) being one such disorder [ 12 , 13 ]. OSA is typically characterized by recurrent intermittent obstruction of the upper airway during sleep, with temporary interruption of airflow. These repeated episodes of breathing interruption led to periods of deoxygenation/reoxygenation (i.e., intermittent hypoxia), sympathetic nervous system activation, and frequent awakenings and micro-arousals during sleep (i.e., sleep fragmentation) [ 14 ]. As a result, sleep is usually exceedingly fragmented and non-restorative in those with OSA. Although some patients may not exhibit obvious clinical symptoms, the vast majority present with snoring, choking, gasping during sleep, daytime sleepiness, decreased attention, fatigue, and/or impaired cognitive function [ 15 ]. OSA is also highly prevalent worldwide. An epidemiological study reported that approximately one billion people globally have varying degrees of OSA, with China being the most affected, followed by the United States [ 16 ]. In fact, the prevalence of OSA is also increasing annually, closely related to the aging population and the prevalence of obesity. Recent research has found that OSA and OA share similar risk factors to some extent, such as aging, obesity, metabolic disorders, and sarcopenia [ 17 , 18 ]. Currently, scholars have partially elucidated the potential link between OSA and OA through mechanistic studies, epidemiological research, and cross-sectional studies [ 18 , 19 ]. However, determining a causal relationship between the two remains challenging due to potential confounding factors (e.g., obesity, depression, diabetes) and reverse causality bias [ 20 – 22 ]. Therefore, OSA remains an under-recognized target in the clinical management of OA. High-quality randomized controlled trials (RCTs) are the gold standard for testing causal relationships. However, RCTs require substantial time and financial investment, and they are often difficult to conduct due to ethical issues and financial constraints. Mendelian randomization (MR) is an alternate technique for examining causal relationships. It effectively evaluates the causal relationship between exposure and result by using genetic variations associated with exposure as instrumental variables (IVs) [ 23 , 24 ]. The National Health and Nutrition Examination Survey (NHANES) collects and analyzes health and nutrition data from the U.S. population to identify health trends and guide public health interventions. The NHANES database, when combined with MR, can partially complement the limitations of each method, elucidating the intrinsic link between exposure and outcome from both correlation and causation perspectives. This combined research approach has been widely used [ 25 – 27 ]. Thus, in this study, we first explored the correlation between OSA and OA using a cross-sectional analysis from the NHANES database, and we then used the MR approach to further evaluate the causal relationship between the two. 2. Materials and Methods 2.1 NHANES Study The National Center for Health Statistics (NCHS) conducts the NHANES, a national study aimed at the non-institutionalized civilian population in the US. The organization uses a sampling technique that combines probability-based clustering, multistage, and stratification to provide a representative sample of study participants. The first step in the data-collecting process is in-person interviews, where participants give comprehensive health and demographic data to NCHS-trained personnel at their homes. The in-home interviews are followed by an invitation for candidates to have physical and laboratory examinations at a mobile examination center. 2.1.1 Study Population Considering the completeness of the OSA data, we used data from the NHANES 2015–2016 and 2017–2018 cycles, which included a total of 19,225 participants. We excluded 7,937 participants who lacked a physician diagnosis of arthritis. Additionally, 28 participants who either refused or were unsure about their arthritis diagnosis were excluded. We also excluded 1,838 participants whose type of arthritis was not OA or who refused to specify the type of arthritis (Fig. 1 ). 2.1.2 Exposure Variables The frequency of snoring, the frequency of breathing pauses, gasps, or stops during sleep, and the frequency of feeling overly sleepy throughout the day were the three questions used to identify OSA [ 28 ]. Participants were defined as having OSA symptoms if they reported snoring three times or more a week, breathing disruptions, gasping, or stopping breathing three times or more a week, or feeling extremely drowsy sixteen to thirty times a month. 2.1.3 Outcome Variables In epidemiological research, self-reported OA is frequently used to identify cases. Self-reported OA and clinically verified OA diagnoses agreed by 85%, according to research by March et al. [ 29 ]. Participants were asked, "Have you ever been told by a doctor or other health professional that you have arthritis?" A "No" response indicated the absence of OA. If the response was "Yes," they were then asked, "What kind of arthritis is it?" Those who identified their condition as "osteoarthritis" were considered to have OA. 2.1.4 Covariant Variables Covariant variables included in this study included age, gender, race/ethnicity, educational attainment, household poverty-to-income ratio, body mass index, diabetes, cancer, smoking status, and physical activity. Supplementary Table 1 provides detailed information on these covariates. 2.2 MR Study 2.2.1 Data Sources The exposure variables utilized Genome-Wide Association Study (GWAS) data from three typical phenotypes of OSA. Instruments for daytime sleepiness/sleep and sleep apnea were derived from large-scale meta-analyses of GWAS data, including 9,851,867 single nucleotide polymorphisms (SNPs) from the UK Biobank. The snoring GWAS data originated from the Nepal laboratory, comprising 152,302 cases and 256,015 healthy controls, totaling 10,707,662 SNPs. Similarly, outcome data about 462,933 European-ancestry individuals (38,472 cases and 424,461 healthy controls) and 9,851,867 SNPs were obtained from the UK Biobank. The study involved a secondary review of data from existing publicly available databases, and therefore did not require additional ethical approval. A detailed breakdown of the GWAS data for exposure and outcome factors is provided in Table 1 . Table 1 Summary of GWASs databases for exposures and outcomes Exposure or outcome GWAS ID Casw/Control Sample size Number of SNPs Diagnosis Population Sex Author Consortium Year Unit Snoring ebi-a-GCST009760 152,302/256,015 408,317 10,707,662 self-reported European NA Campos AI NA 2020 NA Daytime dozing or sleeping ukb-b-5776 NA 460,913 9,851,867 self-reported European Males and Females Ben Elsworth MRC-IEU 2018 SD Sleep apnoea ukb-b-16781 2,320/460,690 463,010 9,851,867 main ICD10: G47.3 European Males and Females Ben Elsworth MRC-IEU 2018 SD Osteoarthritis ukb-b-14486 38,472/424,461 462,933 9,851,867 Non-cancer illness code, self-reported European Males and Females Ben Elsworth MRC-IEU 2018 SD 2.2.2 Selection of IVs An effective IV should fullfil the following three crucial assumptions (Fig. 2 ):1. Relevance Assumption: IVs must be directly associated with the exposure of interest; 2. Independence Assumption: The selected IVs are unrelated to any confounding variables between the exposure and outcome; 3. Exclusion Restriction Assumption: The selected IVs affect the outcome only through the exposure [ 30 – 32 ]. First, SNP selection was initially based on traditional GWAS significance thresholds (P < 5 × 10 − 8 ) and linkage disequilibrium (r^2 < 0.001, 10000kb). However, to obtain a certain number of SNPs, this criterion was adjusted to (P < 1 × 10 − 6 , r2 < 0.001, 10,000 kb) [ 33 ]. Second, to reduce the weak instrumental variable bias, the F-statistic was calculated separately for each SNP, and then weak instruments with an F-statistic < 10 were filtered. Third, to improve our IVs, SNPs linked to possible confounding factors were discovered and eliminated using the PhenoScannerV2 database. 2.3 Statistical Methods In the NHANES study, categorical variables are reported as percentages, and group differences are tested using the chi-square test; continuous variables are presented as mean ± standard deviation, and group differences are assessed using the Student's t-test. The relationship between OSA and OA is explored using both univariate and multivariate logistic regression models. In multivariate logistic regression analyses, Model 1 was not adjusted for any variables; Model 2 was adjusted for age, gender, and race/ethnicity; and Model 3 was adjusted for all covariates included in this study. We proposed to assess the relationship between OSA symptoms and OA by subgroup analyses for potential effect modification. Stratified logistic regression models will be used for subgroup analyses based on gender, education level, race/ethnicity, BMI, cancer, diabetes, and other categorical variables. Interaction tests will determine if there are significant interactions between these variables. In the MR analyses, we mainly used the Inverse Variance Weighted (IVW) method for the assessment, which assumes that all genetic variants are valid instrumental variables. Additionally, we employ four complementary methods to address horizontal pleiotropy: Weighted Median Estimate, Weighted Mode, MR-Egger regression, and Simple Mode. We consider that when the p-value of the IVW method is ≤ 0.05, and the OR of the alternative method is in the same direction as the IVW method, it indicates a significant result [ 34 ]. In MR, the exclusion restriction assumption may be violated in cases of horizontal pleiotropy, where genetic variants can affect both exposure and outcomes through multiple pathways. To address this, we assess horizontal pleiotropy using MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) tests, correcting for outlier SNP effects to reduce estimation heterogeneity. A p-value > 0.05 for the MR-PRESSO global test indicates no pleiotropy. Cochran's Q test assesses SNP heterogeneity (P > 0.05 suggests no heterogeneity). Without heterogeneity among instrument variables, we primarily use the fixed-effects IVW model to explore causal relationships. Conversely, the random-effects model is used when heterogeneity exists. Sensitivity analyses employ leave-one-out methods to evaluate the influence of SNPs on causal association estimates. Statistical analyses were conducted using EmpowerStats 2.0 ( http://www.empowerstats.com ), the Mendelian Randomisation 0.7.0, TwoSampleMR 0.5.6 packages, and R version 4.2.3. 3 Results 3.1 NHANES Study Results Table 2 presents detailed characteristics of 9,422 participants stratified by OSA status. The mean age of participants was 48.33 ± 17.69 years, with 48.87% male and 51.13% female. The prevalence of OA was higher among participants with OSA compared to those without OSA (17.65% vs. 10.72%). Individuals in the OSA group were predominantly male and non-Hispanic white compared to the non-OSA group. Additionally, individuals with OSA were more likely to be older, obese, diabetic, former smokers, and not engage in moderate physical exercise. Supplementary Table 2 displays the basic characteristics of individuals stratified by their 0A status. Table 2 Baseline characteristics of participants stratified by OSA status Characteristic Total NO-OSA OSA P - Value N=(9422) N=(4851) N=(5471) Age 48.33 ± 17.69 46.66 ± 18.46 50.09 ± 16.66 < 0.001 Gender < 0.001 Male 4605 (48.87) 2139 (44.09%) 2466 (53.95%) Female 4817 (51.13) 2712 (55.91%) 2105 (46.05%) Race < 0.001 Mexican America 1489 (15.80) 689 (14.20%) 800 (17.50%) Other Hispanic 1039 (11.03) 491 (10.12%) 548 (11.99%) Non-Hispanic White 3107 (32.98) 1630 (33.60%) 1477 (32.31%) Non-Hispanic Black 2011 (21.34) 1036 (21.36%) 975 (21.33%) Other Race 1776 (18.85) 1005 (20.72%) 771 (16.87%) Education level (%) 0.261 Less than high school 1943 (20.62) 977 (20.14%) 966 (21.13%) High school graduate/GED 2108 (22.37) 1070 (22.06%) 1038 (22.71%) College or above 5371 (57.01) 2804 (57.80%) 2567 (56.16%) PIR 2.54 ± 1.61 2.53 ± 1.62 2.54 ± 1.60 0.714 BMI < 0.001 Normal 2511 (26.65) 1659 (36.85%) 852 (19.64%) Overweight 2869 (30.45) 1486 (33.01%) 1383 (31.88%) Obese 3460 (36.72) 1357 (30.14%) 2103 (48.48%) Un 582 (6.18) Diabetes < 0.001 Yes 1670 (17.72) 681 (14.04%) 989 (21.64%) No 7752 (82.28) 4170 (85.96%) 3582 (78.36%) Cancer 0.332 Yes 823 (8.83) 415 (8.55%) 417 (9.12%) No 8590 (91.17) 4436 (91.45%) 4154 (90.88%) Smoked status < 0.001 Past 2054 (21.8) 931 (19.19%) 1123 (24.57%) Current 1693 (17.97) 793 (16.35%) 900 (19.69%) Never 5675 (60.23) 3127 (64.46%) 2548 (55.74%) Moderate activities < 0.001 Yes 3921 (41.62) 2120 (43.70%) 1801 (39.40%) No 5501 (58.38) 2731 (56.30%) 2770 (60.60%) OA < 0.001 No 8095 (85.92) 4331 (89.28%) 3764 (82.35%) Yes 1327 (14.08) 520 (10.72%) 807 (17.65%) OSA, Obstructive Sleep Apnea; PIR, ratio of family income to poverty; BMI, body mass index; OA, Osteoarthritis. In univariate logistic regression, all variables showed association with OA (Supplementary Table 3). As a result, the multivariable logistic regression analysis considered all factors, as indicated in Table 3 . All models showed a positive correlation between OSA and OA. From Model 1 to Model 3, the ORs and 95% CIs were 1.79 (1.59, 2.01), 2.01 (1.76, 2.30), 1.67 (1.44, 1.95), respectively. Table 3 Association between OSA and OA in weighted multivariable logistic regression Variable Model 1 OR (95% CI ) Model 2 OR (95% CI ) Model 3 OR (95% CI ) P value P value P value OSA no 1 (Reference) 1 (Reference) 1 (Reference) OSA yes 1.79 (1.59, 2.01) 2.01 (1.76, 2.30) 1.67 (1.44, 1.95) Model 1: no covariates were adjusted. Model 2: age, gender, and race were adjusted. Model 3: age, sex, race, education, PIR, BMI, diabetes, cancer, moderate activities, and smoked status were adjusted. OSA, Obstructive Sleep Apnea; OA, Osteoarthritis, PIR, ratio of family income to poverty; BMI, body mass index. OSA was positively associated with OA in all subgroups, with no significant interactions observed across all subgroups (Supplementary Table 4). 3.2 MR Study Results We explored the causal relationship between three typical phenotypes of OSA - snoring, sleep apnea, and daytime sleepiness - and OA. Following selection criteria, the number of IVs extracted for snoring, sleep apnea, and daytime sleepiness were 25, 4, and 30, respectively. All SNPs had F-statistics greater than 10, indicating that weak instrumental variables did not introduce bias. For detailed information on the IVs for the three OSA phenotypes, please refer to Supplementary Tables 4–6. The IVW results show positive causal associations between snoring, daytime sleepiness, and sleep apnea with OA. Specifically, the results are as follows: snoring (OR = 1.059, 95% CI = 1.020–1.099), daytime sleepiness (OR = 1.052, 95% CI = 1.013–1.094), and sleep apnea (OR = 1.052, 95% CI = 1.013–1.094). For snoring and daytime sleepiness phenotypes, the results from all five statistical methods were similar, showing consistent directions of the overall effect estimates. Therefore, we consider snoring and daytime sleepiness as potential risk factors for OA (Fig. 3 ). As snoring and daytime sleepiness increase, the risk of developing OA will also increase. Nevertheless, this link was disregarded in the instance of sleep apnea and OA since the findings from MR Egger and IVW approaches revealed the total impact estimates to be pointing in different directions. For the complete MR analysis results, please refer to Table 4 . Table 4 The complete results of MR analysis Phenotype Method SNP β lo_ci up_ci SE OR OR_lci95 0R_uci95 P- value Snoring MR Egger 25 0.071 -0.116 0.257 0.095 1.073 0.890 1.293 0.466 Weighted median 25 0.058 0.008 0.107 0.025 1.059 1.008 1.113 0.023 Inverse variance weighted 25 0.057 0.020 0.094 0.019 1.059 1.020 1.099 0.002 Simple mode 25 0.068 -0.033 0.170 0.052 1.071 0.967 1.185 0.200 Weighted mode 25 0.047 -0.054 0.149 0.052 1.049 0.947 1.161 0.370 Daytime dozing / sleeping MR Egger 30 0.075 -0.099 0.250 0.089 1.078 0.905 1.285 0.405 Weighted median 30 0.031 -0.015 0.077 0.024 1.031 0.985 1.080 0.189 Inverse variance weighted 30 0.051 0.012 0.090 0.020 1.052 1.013 1.094 0.010 Simple mode 30 0.007 -0.088 0.103 0.049 1.007 0.916 1.108 0.882 Weighted mode 30 0.007 -0.068 0.083 0.039 1.007 0.934 1.087 0.852 Sleep apnoea MR Egger 4 -1.200 -6.787 4.387 2.851 0.301 0.001 80.406 0.715 Weighted median 4 1.460 0.444 2.476 0.518 4.307 1.560 11.895 0.005 Inverse variance weighted 4 1.539 0.733 2.345 0.411 4.660 2.081 10.437 0.0001 Simple mode 4 1.918 0.445 3.392 0.752 6.810 1.561 29.718 0.084 Weighted mode 4 1.176 -0.181 2.533 0.692 3.242 0.835 12.588 0.188 We conducted the MR-Egger intercept, Cochran's Q test, and MR-PRESSO global test to assess the robustness of the results (Table 5 ). There was no indication of horizontal pleiotropy, as indicated by MR Egger intercepts for daytime sleepiness and snoring being near 0 with P > 0.05. The findings of Cochran's Q test showed some variability in the relationship between OA outcomes and daytime drowsiness, with snoring and OA showing less constancy. We infer that horizontal pleiotropy does not affect the causal link between the selected IVs and OA since the MR-PRESSO pleiotropy test revealed no outlier SNPs. Leave-one-out sensitivity analysis showed that removing any single SNP did not significantly affect the causal estimates, indicating the robustness of the MR analysis (Supplementary Fig. 1). The funnel plot displayed a distribution of causal effects that were largely symmetric, indicating no significant bias (Supplementary Fig. 2). Table 5 Sensitivity analysis of Mendelian randomization studies Outcome Cochran Q test Egger_intercept MR-PRESSO text MR Egger IVW Intercept P -value Outlier P -value Snoring OA P = 0.159 P = 0.194 -0.0001 0.887 NA 0.222 Daytime dozing / sleeping OA P = 0.010 P = 0.013 -0.0002 0.781 NA 0.017 4 Discussion Firstly, this study utilized the NHANES database to establish the association between OSA and OA, and subsequently, through MR, demonstrated the potential causal relationship between OSA characteristics and OA. After adjusting for all confounding variables, the OR (95% CI) for OSA was 1.67 (1.44, 1.95). IVW results indicated a potential causal relationship between snoring and daytime sleepiness with OA, with ORs of 1.059 (1.020, 1.099) and 1.052 (1.013, 1.094), respectively, suggesting that snoring and daytime sleepiness may be risk factors for OA. OSA and OA are both highly prevalent diseases, and epidemiological research has identified a link between them. For example, a study involving 300 retired veterans with OA found that 66% also had OSA [ 35 ]. Kanbay et al. studied the inherent link between OSA and OA using a cross-sectional study and revealed that there was a strong positive correlation between OSA and the severity of OA, especially in the severe forms of both disorders and that this association was independent of BMI [ 36 ]. Additionally, a clinical study found that early-stage knee OA patients with concomitant OSA experienced more severe pain, stiffness, and impaired physical function compared to OA patients without OSA [ 13 ]. These previous studies have established the correlation between OSA and OA through observational and retrospective methods. However, due to methodological limitations, these studies struggled to fully account for unmeasured confounding factors that could influence the results. Our study employed a large-scale cross-sectional design and rigorously adjusted for relevant confounders, confirming the positive correlation between OSA and OA. On this basis, a causal effect of OSA on the risk of developing OA at the gene level was observed by MR methods, excluding unmeasured confounders and reverse causality. Therefore, OSA may represent a novel target for preventing and managing OA, although further exploration in clinical practice is warranted. As previously mentioned, the onset and progression of OA are driven by various risk factors that trigger inflammation, mechanical stress, and metabolic signals. These signals activate several key pathways, including the master regulator of inflammation, nuclear factor-kappa B (NF-κB), and members of the mitogen-activated protein kinase family, which drive inflammation, alter metabolic gene expression, and inhibit cartilage matrix gene expression [ 37 , 38 ]. Other contributing processes and pathways include mitochondrial dysfunction, impaired autophagy, and altered growth factor signaling via SMAD proteins, affecting chondrocytes, synovial cells, osteoblasts, and other joint tissue cells [ 39 ]. While there is no direct evidence showing which signaling pathways OSA influences in the progression of OA, it is evident that the mechanisms affecting OA are closely related to the pathological characteristics of OSA. The main pathological features of OSA, according to recent research, are fragmented sleep, sympathetic nervous system activation, and intermittent hypoxia linked to hypercapnia [ 40 ]. Intermittent hypoxia is known as a potent inflammatory stimulus that can selectively activate the NF-κB inflammatory signaling pathway and promote the secretion of circulating inflammatory cytokines such as IL-6, IL-1β, IL-17, and tumor necrosis factor-alpha (TNF-α), leading to systemic chronic low-grade inflammation [ 41 – 43 ]. Current studies have demonstrated that plasma leukocytes as well as inflammatory factors (e.g., TNF-α, IL-6, CRP, and IL-8) are positively associated with the severity of OSA [ 44 ]. An inflammatory environment can disrupt the metabolic balance of chondrocytes, increasing the production of tissue-degrading enzymes such as matrix metalloproteinase-13 and a disintegrin and metalloproteinase with thrombospondin motifs-5. This disruption in chondrocyte metabolism induces oxidative stress and damages cartilage, resulting in joint structural damage and pain, thereby accelerating the progression of OA [ 45 ]. On the other hand, it has been shown that IL-17 also stimulates human OA synovial fibroblasts, increases the production of vascular cell adhesion molecule, and enhances monocyte adhesion, thereby inducing the development of OA [ 46 , 47 ]. Additionally, intermittent hypoxia has been shown by Zhang et al. to cause the senescence-associated secretory phenotype in synovial tissues and cartilage, which in turn speeds up cellular senescence and causes or worsens OA [ 48 ]. In patients with OSA, fragmented sleep is considered a primary pathological mechanism leading to increased fatigue, heightened pain sensitivity, and increased likelihood of depression [ 15 ]. Consequently, clinical studies have found that patients with both OSA and OA experience increased pain and more severe depressive symptoms compared to those with OA alone [ 13 ]. Fragmented sleep disrupts circadian rhythms and alters central and peripheral clock systems. Disruption of circadian rhythms is closely linked to melatonin secretion [ 49 ]. Research has shown that melatonin plays roles in chondrocytes including anti-inflammatory, anti-catabolic, anti-apoptotic effects, and promotes metabolism [ 50 ]. Lim et al. demonstrated that melatonin exerts cellular protection and anti-inflammatory effects in oxidative stress-induced chondrocyte models and rabbit OA models, mediated largely by NAD+-dependent protein deacetylase SIRT1 [ 51 ]. On the other hand, an overabundance of sympathetic nervous system (SNS) activity is also linked to changes in circadian cycles. Overactivation of the SNS can induce the production of inflammatory cytokines and increase pain sensitivity. Existing evidence also indicates a link between SNS and the pathophysiology of OA and pain [ 52 ]. Studies by Suri et al. showed that sympathetic nerve fibers invade normal neural joint cartilage in both mild and severe OA cases [ 53 ]. These findings suggest that disrupted circadian rhythms and sympathetic nervous system overactivity in OSA may contribute to the exacerbation of OA symptoms through various mechanisms involving inflammation, pain sensitivity, and joint tissue integrity. Obesity is a common risk factor for both OSA and OA, suggesting shared mechanisms in OA progression due to OSA, potentially involving lipid metabolism [ 54 , 55 ]. Transcriptomic studies by Weng et al. highlight significant enrichment of lipid metabolism-related genes in both conditions [ 56 ]. Intermittent hypoxia, a hallmark of OSA, plays a pivotal role in exacerbating oxidative stress within the body. This condition triggers a significant increase in reactive oxygen specie, which in turn activate critical cellular pathways affecting lipid metabolism and contributing to disease progression [ 57 ]. Proteomic analysis further suggests that changes in lipid metabolism may contribute to OA onset or progression, with lipid factors emerging as key regulatory elements in OA pathogenesis [ 58 ]. Obviously, our study has several strengths. Initially, we employed MR techniques to merge data from the NHANES cross-sectional study. The use of nationally representative data enabled a thorough evaluation with a sizable sample size and took into account several variables, meaning that our findings were more reliable. Additionally, utilizing robust causal inference methods, we assessed the independent causal impact of OSA on OA, addressing issues of reverse causation and residual confounding. Notably, both analytical approaches produced nearly consistent results, enhancing the credibility of our findings. However, several limitations need to be acknowledged. First, the diagnosis of OSA in this study primarily relied on sleep questionnaires from the NHANES database, which mainly assessed typical clinical symptoms of OSA such as snoring, breathing pauses, and daytime sleepiness. However, NHANES did not capture other common symptoms like morning headaches or driving accidents. Additionally, participants did not undergo laboratory sleep tests or home sleep apnea testing, which could lead to underdiagnosis or misclassification of OSA. Additionally, the diagnosis of OA was based on self-reports, which may introduce inaccuracies. Although research has shown that self-reported OA diagnoses have an 85% agreement with clinically verified OA diagnoses, there is still a risk of non-differential misclassification, potentially biasing the results. Third, the MR study primarily relied on data from individuals of European ancestry due to a lack of GWAS data from other races and ethnicities. This limits the generalizability of our findings to non-European populations, and the results may not be directly applicable to individuals of other racial and ethnic backgrounds. Finally, although the Mendelian randomization approach effectively reduces confounding bias and avoids reverse causation, it still relies on the validity and representativeness of the selected genetic variants. If the genetic variants are related to other potential confounders, the results might still be affected [ 59 ]. 5 Conclusion Our research underscores the significance of considering OSA as a potential risk factor in the clinical management of OA. Future studies should aim to validate these findings across diverse populations and delve deeper into the underlying biological mechanisms linking OSA and OA. This could pave the way for novel preventive and therapeutic strategies for OA by targeting OSA symptoms. Declarations Ethics statement In the cross-sectional study, the NCHS Ethics Committee granted approval. The MR study, involving a secondary review of data from existing publicly available databases, did not require additional ethical approval. Consent for publication Not applicable. Availability of data and materials This study analyzed publicly available datasets. These data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm and https://gwas.mrcieu.ac.uk/. Competing interests Dongdong Cao, Jixin Chen, Weijie Yu, Jialin Yang, Tianci Guo, Yu Zhang, and Aifeng Liu declare that they have no conflict of interest. Funding This research was supported by the Tianjin Municipal Health Commission Jinmen Medical Excellence Programme (TJSJMYXYC-D2-028). The funding source did not play any role in the research design, data collection, analysis and interpretation, writing of the manuscript and the decision to submit the manuscript for this study. Author contributions: DC and JC contributed to study design, data acquisition, analysis, and interpretation, and drafted the manuscript. AF contributed to the interpretation of the data and revised the manuscript. WY, JY, TG, and YZ contribute to statistical analysis and visualisation of data. All authors read critically reviewed and approved the final manuscript as submitted. Acknowledgements None. References Glyn-Jones S, Palmer AJ, Agricola R, et al. Osteoarthr Lancet. 2015;386:376–87. Prieto-Alhambra D, Judge A, Javaid MK, et al. Incidence and risk factors for clinically diagnosed knee, hip and hand osteoarthritis: influences of age, gender and osteoarthritis affecting other joints. Ann Rheum Dis. 2014;73:1659–64. Jang S, Lee K, Ju JH. 2021. Recent Updates of Diagnosis, Pathophysiology, and Treatment on Osteoarthritis of the Knee. Int J Mol Sci 22. Palazzo C, Ravaud JF, Papelard A, et al. The burden of musculoskeletal conditions. PLoS ONE. 2014;9:e90633. Helmick CG, Felson DT, Lawrence RC, et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part I. Arthritis Rheum. 2008;58:15–25. Bannuru RR, Osani MC, Vaysbrot EE, et al. OARSI guidelines for the non-surgical management of knee, hip, and polyarticular osteoarthritis. Osteoarthritis Cartilage. 2019;27:1578–89. Xie J, Wang Y, Lu L, et al. Cellular senescence in knee osteoarthritis: molecular mechanisms and therapeutic implications. Ageing Res Rev. 2021;70:101413. Yao Q, Wu X, Tao C, et al. Osteoarthritis: pathogenic signaling pathways and therapeutic targets. Signal Transduct Target Ther. 2023;8:56. Tong L, Yu H, Huang X, et al. Current understanding of osteoarthritis pathogenesis and relevant new approaches. Bone Res. 2022;10:60. Millerand M, Berenbaum F, Jacques C. Danger signals and inflammaging in osteoarthritis. Clin Exp Rheumatol 37 Suppl. 2019;120:48–56. Silverwood V, Blagojevic-Bucknall M, Jinks C, et al. Current evidence on risk factors for knee osteoarthritis in older adults: a systematic review and meta-analysis. Osteoarthritis Cartilage. 2015;23:507–15. Taylor-Gjevre RM, Gjevre JA, Nair B, et al. Components of sleep quality and sleep fragmentation in rheumatoid arthritis and osteoarthritis. Musculoskelet Care. 2011;9:152–9. Silva A, Mello MT, Serrão PR, et al. Influence of Obstructive Sleep Apnea in the Functional Aspects of Patients With Osteoarthritis. J Clin Sleep Med. 2018;14:265–70. Bonsignore MR, Mazzuca E, Baiamonte P et al. 2024. REM sleep obstructive sleep apnoea. Eur Respir Rev 33. Gottlieb DJ, Punjabi NM. Diagnosis and Management of Obstructive Sleep Apnea: A Review. JAMA. 2020;323:1389–400. Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med. 2019;7:687–98. Ryan S, Cummins EP, Farre R et al. 2020. Understanding the pathophysiological mechanisms of cardiometabolic complications in obstructive sleep apnoea: towards personalised treatment approaches. Eur Respir J 56. Gaspar LS, Sousa C, Álvaro AR, et al. Common risk factors and therapeutic targets in obstructive sleep apnea and osteoarthritis: An unexpectable link? Pharmacol Res. 2021;164:105369. Jacob L, Smith L, Konrad M, et al. Association between sleep disorders and osteoarthritis: A case-control study of 351,932 adults in the UK. J Sleep Res. 2021;30:e13367. Xing X, Wang Y, Pan F, et al. Osteoarthritis and risk of type 2 diabetes: A two-sample Mendelian randomization analysis. J Diabetes. 2023;15:987–93. Zhang L, Zhang W, Wu X, et al. A sex- and site-specific relationship between body mass index and osteoarthritis: evidence from observational and genetic analyses. Osteoarthritis Cartilage. 2023;31:819–28. Barowsky S, Jung JY, Nesbit N, et al. Cross-Disorder Genomics Data Analysis Elucidates a Shared Genetic Basis Between Major Depression and Osteoarthritis Pain. Front Genet. 2021;12:687687. Nazarzadeh M, Pinho-Gomes AC, Bidel Z, et al. Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study. Eur Heart J. 2020;41:3913–20. Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:1133–63. Sekula P, Del Greco MF, Pattaro C, et al. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016;27:3253–65. Li W, Zheng Q, Xu M, et al. Association between circulating 25-hydroxyvitamin D metabolites and periodontitis: Results from the NHANES 2009–2012 and Mendelian randomization study. J Clin Periodontol. 2023;50:252–64. Huang G, Qian D, Liu Y, et al. The association between frailty and osteoarthritis based on the NHANES and Mendelian randomization study. Arch Med Sci. 2023;19:1545–50. Cavallino V, Rankin E, Popescu A, et al. Antimony and sleep health outcomes: NHANES 2009–2016. Sleep Health. 2022;8:373–9. March LM, Schwarz JM, Carfrae BH, et al. Clinical validation of self-reported osteoarthritis. Osteoarthritis Cartilage. 1998;6:87–93. Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26:2333–55. Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186. Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326:1614–21. Palmer TM, Lawlor DA, Harbord RM, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21:223–42. Howard DM, Adams MJ, Clarke TK, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22:343–52. Taylor SS, Hughes JM, Coffman CJ, et al. Prevalence of and characteristics associated with insomnia and obstructive sleep apnea among veterans with knee and hip osteoarthritis. BMC Musculoskelet Disord. 2018;19:79. Kanbay A, Köktürk O, Pıhtılı A, et al. Obstructive sleep apnea is a risk factor for osteoarthritis. Tuberk Toraks. 2018;66:304–11. Charlier E, Deroyer C, Ciregia F, et al. Chondrocyte dedifferentiation and osteoarthritis (OA). Biochem Pharmacol. 2019;165:49–65. Liu-Bryan R. Synovium and the innate inflammatory network in osteoarthritis progression. Curr Rheumatol Rep. 2013;15:323. Rosenberg JH, Rai V, Dilisio MF, et al. Damage-associated molecular patterns in the pathogenesis of osteoarthritis: potentially novel therapeutic targets. Mol Cell Biochem. 2017;434:171–9. Lévy P, Kohler M, McNicholas WT, et al. Obstructive sleep apnoea syndrome. Nat Rev Dis Primers. 2015;1:15015. Unnikrishnan D, Jun J, Polotsky V. Inflammation in sleep apnea: an update. Rev Endocr Metab Disord. 2015;16:25–34. Ryan S, Taylor CT, McNicholas WT. Selective activation of inflammatory pathways by intermittent hypoxia in obstructive sleep apnea syndrome. Circulation. 2005;112:2660–7. Maniaci A, Iannella G, Cocuzza S et al. 2021. Oxidative Stress and Inflammation Biomarker Expression in Obstructive Sleep Apnea Patients. J Clin Med 10. Fiedorczuk P, Olszewska E, Polecka A et al. 2023. Investigating the Role of Serum and Plasma IL-6, IL-8, IL-10, TNF-alpha, CRP, and S100B Concentrations in Obstructive Sleep Apnea Diagnosis. Int J Mol Sci 24. Zhao Y, Yang X, Li S, et al. sTNFRII-Fc modification protects human UC-MSCs against apoptosis/autophagy induced by TNF-α and enhances their efficacy in alleviating inflammatory arthritis. Stem Cell Res Ther. 2021;12:535. Liu B, Xian Y, Chen X, et al. Inflammatory Fibroblast-Like Synoviocyte-Derived Exosomes Aggravate Osteoarthritis via Enhancing Macrophage Glycolysis. Adv Sci (Weinh). 2024;11:e2307338. Lee KT, Lin CY, Liu SC, et al. IL-17 promotes IL-18 production via the MEK/ERK/miR-4492 axis in osteoarthritis synovial fibroblasts. Aging. 2024;16:1829–44. Zhang Y, Zhou S, Cai W, et al. Hypoxia/reoxygenation activates the JNK pathway and accelerates synovial senescence. Mol Med Rep. 2020;22:265–76. Hernández C, Abreu J, Abreu P, et al. Nocturnal melatonin plasma levels in patients with OSAS: the effect of CPAP. Eur Respir J. 2007;30:496–500. Jahanban-Esfahlan R, Mehrzadi S, Reiter RJ, et al. Melatonin in regulation of inflammatory pathways in rheumatoid arthritis and osteoarthritis: involvement of circadian clock genes. Br J Pharmacol. 2018;175:3230–8. Lim HD, Kim YS, Ko SH, et al. Cytoprotective and anti-inflammatory effects of melatonin in hydrogen peroxide-stimulated CHON-001 human chondrocyte cell line and rabbit model of osteoarthritis via the SIRT1 pathway. J Pineal Res. 2012;53:225–37. Grässel S, Muschter D. 2017. Peripheral Nerve Fibers and Their Neurotransmitters in Osteoarthritis Pathology. Int J Mol Sci 18. Suri S, Gill SE, Massena de Camin S, et al. Neurovascular invasion at the osteochondral junction and in osteophytes in osteoarthritis. Ann Rheum Dis. 2007;66:1423–8. Lam JC, Mak JC, Ip MS. Obesity, obstructive sleep apnoea and metabolic syndrome. Respirology. 2012;17:223–36. Meszaros M, Bikov A. 2022. Obstructive Sleep Apnoea and Lipid Metabolism: The Summary of Evidence and Future Perspectives in the Pathophysiology of OSA-Associated Dyslipidaemia. Biomedicines 10. Weng L, Luo X, Luo Y, et al. Association Between Sleep Apnea Syndrome and Osteoarthritis: Insights from Bidirectional Mendelian Randomization and Bioinformatics Analysis. Nat Sci Sleep. 2024;16:473–87. Li D, Xu N, Hou Y, et al. Abnormal lipid droplets accumulation induced cognitive deficits in obstructive sleep apnea syndrome mice via JNK/SREBP/ACC pathway but not through PDP1/PDC pathway. Mol Med. 2022;28:3. Gkretsi V, Simopoulou T, Tsezou A. Lipid metabolism and osteoarthritis: lessons from atherosclerosis. Prog Lipid Res. 2011;50:133–40. Birney E. 2022. Mendelian Randomization. Cold Spring Harb Perspect Med 12. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tif SupplementaryFigure2.tif SupplementaryTable1.doc SupplementaryTable2.doc SupplementaryTable3.doc SupplementaryTable4.doc SupplementaryTable5.doc SupplementaryTable6.doc SupplementaryTable7.doc SupplementaryTable8.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4756644","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339110822,"identity":"0eda16b3-5075-41cc-a717-c2c844e16cd1","order_by":0,"name":"Dongdong Cao","email":"","orcid":"","institution":"First Teaching Hospital of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dongdong","middleName":"","lastName":"Cao","suffix":""},{"id":339110823,"identity":"cd5a0045-0bb1-4ed2-bfe7-951c84fb27d6","order_by":1,"name":"Jixin Chen","email":"","orcid":"","institution":"Shaoxing Hospital 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Introduction","content":"\u003cp\u003eOsteoarthritis (OA) is a degenerative joint disease that causes damage to articular cartilage and structural abnormalities in the joints [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is one of the most frequent joint illnesses in orthopaedics, causing pain, swelling, and movement limitations that, in extreme cases, can result in disability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Epidemiological studies show that the prevalence of OA ranges from 12.3\u0026ndash;21.6% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, the prevalence of OA is likely to increase further due to the aging population and the prevalence of overweight and obesity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. OA is influenced by several known risk factors, including aging, being postmenopausal in women, genetic factors, metabolic conditions like obesity and type 2 diabetes, and abnormal mechanical stress resulting from joint instability, overuse, or injury [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These risk factors contribute to the onset and progression of OA through inflammatory processes, mechanical stimulation, and metabolic pathways, either singly or in combination [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, exploring the risk factors for OA is crucial for the prevention and treatment of this disease.\u003c/p\u003e \u003cp\u003ePatients with OA, especially those in the advanced stages, often experience sleep disorders due to pain, with obstructive sleep apnea (OSA) being one such disorder [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. OSA is typically characterized by recurrent intermittent obstruction of the upper airway during sleep, with temporary interruption of airflow. These repeated episodes of breathing interruption led to periods of deoxygenation/reoxygenation (i.e., intermittent hypoxia), sympathetic nervous system activation, and frequent awakenings and micro-arousals during sleep (i.e., sleep fragmentation) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As a result, sleep is usually exceedingly fragmented and non-restorative in those with OSA. Although some patients may not exhibit obvious clinical symptoms, the vast majority present with snoring, choking, gasping during sleep, daytime sleepiness, decreased attention, fatigue, and/or impaired cognitive function [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. OSA is also highly prevalent worldwide. An epidemiological study reported that approximately one billion people globally have varying degrees of OSA, with China being the most affected, followed by the United States [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In fact, the prevalence of OSA is also increasing annually, closely related to the aging population and the prevalence of obesity. Recent research has found that OSA and OA share similar risk factors to some extent, such as aging, obesity, metabolic disorders, and sarcopenia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Currently, scholars have partially elucidated the potential link between OSA and OA through mechanistic studies, epidemiological research, and cross-sectional studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, determining a causal relationship between the two remains challenging due to potential confounding factors (e.g., obesity, depression, diabetes) and reverse causality bias [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, OSA remains an under-recognized target in the clinical management of OA.\u003c/p\u003e \u003cp\u003eHigh-quality randomized controlled trials (RCTs) are the gold standard for testing causal relationships. However, RCTs require substantial time and financial investment, and they are often difficult to conduct due to ethical issues and financial constraints. Mendelian randomization (MR) is an alternate technique for examining causal relationships. It effectively evaluates the causal relationship between exposure and result by using genetic variations associated with exposure as instrumental variables (IVs) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The National Health and Nutrition Examination Survey (NHANES) collects and analyzes health and nutrition data from the U.S. population to identify health trends and guide public health interventions. The NHANES database, when combined with MR, can partially complement the limitations of each method, elucidating the intrinsic link between exposure and outcome from both correlation and causation perspectives. This combined research approach has been widely used [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, in this study, we first explored the correlation between OSA and OA using a cross-sectional analysis from the NHANES database, and we then used the MR approach to further evaluate the causal relationship between the two.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 NHANES Study\u003c/h2\u003e \u003cp\u003eThe National Center for Health Statistics (NCHS) conducts the NHANES, a national study aimed at the non-institutionalized civilian population in the US. The organization uses a sampling technique that combines probability-based clustering, multistage, and stratification to provide a representative sample of study participants. The first step in the data-collecting process is in-person interviews, where participants give comprehensive health and demographic data to NCHS-trained personnel at their homes. The in-home interviews are followed by an invitation for candidates to have physical and laboratory examinations at a mobile examination center.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Study Population\u003c/h2\u003e \u003cp\u003eConsidering the completeness of the OSA data, we used data from the NHANES 2015\u0026ndash;2016 and 2017\u0026ndash;2018 cycles, which included a total of 19,225 participants. We excluded 7,937 participants who lacked a physician diagnosis of arthritis. Additionally, 28 participants who either refused or were unsure about their arthritis diagnosis were excluded. We also excluded 1,838 participants whose type of arthritis was not OA or who refused to specify the type of arthritis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Exposure Variables\u003c/h2\u003e \u003cp\u003eThe frequency of snoring, the frequency of breathing pauses, gasps, or stops during sleep, and the frequency of feeling overly sleepy throughout the day were the three questions used to identify OSA [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Participants were defined as having OSA symptoms if they reported snoring three times or more a week, breathing disruptions, gasping, or stopping breathing three times or more a week, or feeling extremely drowsy sixteen to thirty times a month.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Outcome Variables\u003c/h2\u003e \u003cp\u003eIn epidemiological research, self-reported OA is frequently used to identify cases. Self-reported OA and clinically verified OA diagnoses agreed by 85%, according to research by March et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Participants were asked, \"Have you ever been told by a doctor or other health professional that you have arthritis?\" A \"No\" response indicated the absence of OA. If the response was \"Yes,\" they were then asked, \"What kind of arthritis is it?\" Those who identified their condition as \"osteoarthritis\" were considered to have OA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4 Covariant Variables\u003c/h2\u003e \u003cp\u003eCovariant variables included in this study included age, gender, race/ethnicity, educational attainment, household poverty-to-income ratio, body mass index, diabetes, cancer, smoking status, and physical activity. Supplementary Table\u0026nbsp;1 provides detailed information on these covariates.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2 MR Study\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Data Sources\u003c/h2\u003e \u003cp\u003eThe exposure variables utilized Genome-Wide Association Study (GWAS) data from three typical phenotypes of OSA. Instruments for daytime sleepiness/sleep and sleep apnea were derived from large-scale meta-analyses of GWAS data, including 9,851,867 single nucleotide polymorphisms (SNPs) from the UK Biobank. The snoring GWAS data originated from the Nepal laboratory, comprising 152,302 cases and 256,015 healthy controls, totaling 10,707,662 SNPs.\u003c/p\u003e \u003cp\u003eSimilarly, outcome data about 462,933 European-ancestry individuals (38,472 cases and 424,461 healthy controls) and 9,851,867 SNPs were obtained from the UK Biobank. The study involved a secondary review of data from existing publicly available databases, and therefore did not require additional ethical approval. A detailed breakdown of the GWAS data for exposure and outcome factors is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of GWASs databases for exposures and outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure or outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCasw/Control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eConsortium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSnoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST009760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152,302/256,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e408,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,707,662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eself-reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCampos AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaytime dozing or sleeping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-5776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e460,913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eself-reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBen Elsworth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep apnoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-16781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,320/460,690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e463,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emain ICD10: G47.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBen Elsworth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eukb-b-14486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38,472/424,461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e462,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-cancer illness code, self-reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBen Elsworth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Selection of IVs\u003c/h2\u003e \u003cp\u003eAn effective IV should fullfil the following three crucial assumptions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):1. Relevance Assumption: IVs must be directly associated with the exposure of interest; 2. Independence Assumption: The selected IVs are unrelated to any confounding variables between the exposure and outcome; 3. Exclusion Restriction Assumption: The selected IVs affect the outcome only through the exposure [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. First, SNP selection was initially based on traditional GWAS significance thresholds (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) and linkage disequilibrium (r^2\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 10000kb). However, to obtain a certain number of SNPs, this criterion was adjusted to (P\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 10,000 kb) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Second, to reduce the weak instrumental variable bias, the F-statistic was calculated separately for each SNP, and then weak instruments with an F-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 were filtered. Third, to improve our IVs, SNPs linked to possible confounding factors were discovered and eliminated using the PhenoScannerV2 database.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Methods\u003c/h2\u003e \u003cp\u003eIn the NHANES study, categorical variables are reported as percentages, and group differences are tested using the chi-square test; continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and group differences are assessed using the Student's t-test. The relationship between OSA and OA is explored using both univariate and multivariate logistic regression models. In multivariate logistic regression analyses, Model 1 was not adjusted for any variables; Model 2 was adjusted for age, gender, and race/ethnicity; and Model 3 was adjusted for all covariates included in this study. We proposed to assess the relationship between OSA symptoms and OA by subgroup analyses for potential effect modification. Stratified logistic regression models will be used for subgroup analyses based on gender, education level, race/ethnicity, BMI, cancer, diabetes, and other categorical variables. Interaction tests will determine if there are significant interactions between these variables.\u003c/p\u003e \u003cp\u003eIn the MR analyses, we mainly used the Inverse Variance Weighted (IVW) method for the assessment, which assumes that all genetic variants are valid instrumental variables. Additionally, we employ four complementary methods to address horizontal pleiotropy: Weighted Median Estimate, Weighted Mode, MR-Egger regression, and Simple Mode. We consider that when the p-value of the IVW method is \u0026le;\u0026thinsp;0.05, and the OR of the alternative method is in the same direction as the IVW method, it indicates a significant result [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In MR, the exclusion restriction assumption may be violated in cases of horizontal pleiotropy, where genetic variants can affect both exposure and outcomes through multiple pathways. To address this, we assess horizontal pleiotropy using MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) tests, correcting for outlier SNP effects to reduce estimation heterogeneity. A p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for the MR-PRESSO global test indicates no pleiotropy. Cochran's Q test assesses SNP heterogeneity (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 suggests no heterogeneity). Without heterogeneity among instrument variables, we primarily use the fixed-effects IVW model to explore causal relationships. Conversely, the random-effects model is used when heterogeneity exists. Sensitivity analyses employ leave-one-out methods to evaluate the influence of SNPs on causal association estimates.\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted using EmpowerStats 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Mendelian Randomisation 0.7.0, TwoSampleMR 0.5.6 packages, and R version 4.2.3.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 NHANES Study Results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents detailed characteristics of 9,422 participants stratified by OSA status. The mean age of participants was 48.33\u0026thinsp;\u0026plusmn;\u0026thinsp;17.69 years, with 48.87% male and 51.13% female. The prevalence of OA was higher among participants with OSA compared to those without OSA (17.65% vs. 10.72%). Individuals in the OSA group were predominantly male and non-Hispanic white compared to the non-OSA group. Additionally, individuals with OSA were more likely to be older, obese, diabetic, former smokers, and not engage in moderate physical exercise. Supplementary Table\u0026nbsp;2 displays the basic characteristics of individuals stratified by their 0A status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants stratified by OSA status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO-OSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e- Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN=(9422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN=(4851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN=(5471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.33\u0026thinsp;\u0026plusmn;\u0026thinsp;17.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.66\u0026thinsp;\u0026plusmn;\u0026thinsp;18.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.09\u0026thinsp;\u0026plusmn;\u0026thinsp;16.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4605 (48.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2139 (44.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2466 (53.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4817 (51.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2712 (55.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2105 (46.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1489 (15.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e689 (14.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e800 (17.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1039 (11.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e491 (10.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e548 (11.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3107 (32.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1630 (33.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1477 (32.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2011 (21.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1036 (21.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e975 (21.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1776 (18.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1005 (20.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e771 (16.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1943 (20.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e977 (20.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e966 (21.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2108 (22.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1070 (22.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1038 (22.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5371 (57.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2804 (57.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2567 (56.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2511 (26.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1659 (36.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e852 (19.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2869 (30.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1486 (33.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1383 (31.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3460 (36.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1357 (30.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2103 (48.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e582 (6.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1670 (17.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e681 (14.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e989 (21.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7752 (82.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4170 (85.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3582 (78.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e823 (8.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e415 (8.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e417 (9.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8590 (91.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4436 (91.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4154 (90.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoked status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2054 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e931 (19.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1123 (24.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1693 (17.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e793 (16.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e900 (19.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5675 (60.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3127 (64.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2548 (55.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate activities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3921 (41.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2120 (43.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1801 (39.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5501 (58.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2731 (56.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2770 (60.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8095 (85.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4331 (89.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3764 (82.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1327 (14.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520 (10.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e807 (17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOSA, Obstructive Sleep Apnea; PIR, ratio of family income to poverty; BMI, body mass index; OA, Osteoarthritis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn univariate logistic regression, all variables showed association with OA (Supplementary Table\u0026nbsp;3). As a result, the multivariable logistic regression analysis considered all factors, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All models showed a positive correlation between OSA and OA. From Model 1 to Model 3, the ORs and 95% CIs were 1.79 (1.59, 2.01), 2.01 (1.76, 2.30), 1.67 (1.44, 1.95), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between OSA and OA in weighted multivariable logistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 OR (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2 OR (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3 OR (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSA no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSA yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79 (1.59, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01 (1.76, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67 (1.44, 1.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no covariates were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: age, gender, and race were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: age, sex, race, education, PIR, BMI, diabetes, cancer, moderate activities, and smoked status were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eOSA, Obstructive Sleep Apnea; OA, Osteoarthritis, PIR, ratio of family income to poverty; BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOSA was positively associated with OA in all subgroups, with no significant interactions observed across all subgroups (Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 MR Study Results\u003c/h2\u003e \u003cp\u003eWe explored the causal relationship between three typical phenotypes of OSA - snoring, sleep apnea, and daytime sleepiness - and OA. Following selection criteria, the number of IVs extracted for snoring, sleep apnea, and daytime sleepiness were 25, 4, and 30, respectively. All SNPs had F-statistics greater than 10, indicating that weak instrumental variables did not introduce bias. For detailed information on the IVs for the three OSA phenotypes, please refer to Supplementary Tables\u0026nbsp;4\u0026ndash;6.\u003c/p\u003e \u003cp\u003eThe IVW results show positive causal associations between snoring, daytime sleepiness, and sleep apnea with OA. Specifically, the results are as follows: snoring (OR\u0026thinsp;=\u0026thinsp;1.059, 95% CI\u0026thinsp;=\u0026thinsp;1.020\u0026ndash;1.099), daytime sleepiness (OR\u0026thinsp;=\u0026thinsp;1.052, 95% CI\u0026thinsp;=\u0026thinsp;1.013\u0026ndash;1.094), and sleep apnea (OR\u0026thinsp;=\u0026thinsp;1.052, 95% CI\u0026thinsp;=\u0026thinsp;1.013\u0026ndash;1.094). For snoring and daytime sleepiness phenotypes, the results from all five statistical methods were similar, showing consistent directions of the overall effect estimates. Therefore, we consider snoring and daytime sleepiness as potential risk factors for OA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As snoring and daytime sleepiness increase, the risk of developing OA will also increase. Nevertheless, this link was disregarded in the instance of sleep apnea and OA since the findings from MR Egger and IVW approaches revealed the total impact estimates to be pointing in different directions. For the complete MR analysis results, please refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe complete results of MR analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elo_ci\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup_ci\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR_lci95\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0R_uci95\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSnoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInverse variance weighted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaytime dozing / sleeping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInverse variance weighted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep apnoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInverse variance weighted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe conducted the MR-Egger intercept, Cochran's Q test, and MR-PRESSO global test to assess the robustness of the results (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). There was no indication of horizontal pleiotropy, as indicated by MR Egger intercepts for daytime sleepiness and snoring being near 0 with P\u0026thinsp;\u0026gt;\u0026thinsp;0.05. The findings of Cochran's Q test showed some variability in the relationship between OA outcomes and daytime drowsiness, with snoring and OA showing less constancy. We infer that horizontal pleiotropy does not affect the causal link between the selected IVs and OA since the MR-PRESSO pleiotropy test revealed no outlier SNPs. Leave-one-out sensitivity analysis showed that removing any single SNP did not significantly affect the causal estimates, indicating the robustness of the MR analysis (Supplementary Fig.\u0026nbsp;1). The funnel plot displayed a distribution of causal effects that were largely symmetric, indicating no significant bias (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis of Mendelian randomization studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCochran Q test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eEgger_intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMR-PRESSO text\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOutlier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSnoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaytime dozing / sleeping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eFirstly, this study utilized the NHANES database to establish the association between OSA and OA, and subsequently, through MR, demonstrated the potential causal relationship between OSA characteristics and OA. After adjusting for all confounding variables, the OR (95% CI) for OSA was 1.67 (1.44, 1.95). IVW results indicated a potential causal relationship between snoring and daytime sleepiness with OA, with ORs of 1.059 (1.020, 1.099) and 1.052 (1.013, 1.094), respectively, suggesting that snoring and daytime sleepiness may be risk factors for OA.\u003c/p\u003e \u003cp\u003eOSA and OA are both highly prevalent diseases, and epidemiological research has identified a link between them. For example, a study involving 300 retired veterans with OA found that 66% also had OSA [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Kanbay et al. studied the inherent link between OSA and OA using a cross-sectional study and revealed that there was a strong positive correlation between OSA and the severity of OA, especially in the severe forms of both disorders and that this association was independent of BMI [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, a clinical study found that early-stage knee OA patients with concomitant OSA experienced more severe pain, stiffness, and impaired physical function compared to OA patients without OSA [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These previous studies have established the correlation between OSA and OA through observational and retrospective methods. However, due to methodological limitations, these studies struggled to fully account for unmeasured confounding factors that could influence the results. Our study employed a large-scale cross-sectional design and rigorously adjusted for relevant confounders, confirming the positive correlation between OSA and OA. On this basis, a causal effect of OSA on the risk of developing OA at the gene level was observed by MR methods, excluding unmeasured confounders and reverse causality. Therefore, OSA may represent a novel target for preventing and managing OA, although further exploration in clinical practice is warranted.\u003c/p\u003e \u003cp\u003eAs previously mentioned, the onset and progression of OA are driven by various risk factors that trigger inflammation, mechanical stress, and metabolic signals. These signals activate several key pathways, including the master regulator of inflammation, nuclear factor-kappa B (NF-κB), and members of the mitogen-activated protein kinase family, which drive inflammation, alter metabolic gene expression, and inhibit cartilage matrix gene expression [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Other contributing processes and pathways include mitochondrial dysfunction, impaired autophagy, and altered growth factor signaling via SMAD proteins, affecting chondrocytes, synovial cells, osteoblasts, and other joint tissue cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. While there is no direct evidence showing which signaling pathways OSA influences in the progression of OA, it is evident that the mechanisms affecting OA are closely related to the pathological characteristics of OSA.\u003c/p\u003e \u003cp\u003eThe main pathological features of OSA, according to recent research, are fragmented sleep, sympathetic nervous system activation, and intermittent hypoxia linked to hypercapnia [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Intermittent hypoxia is known as a potent inflammatory stimulus that can selectively activate the NF-κB inflammatory signaling pathway and promote the secretion of circulating inflammatory cytokines such as IL-6, IL-1β, IL-17, and tumor necrosis factor-alpha (TNF-α), leading to systemic chronic low-grade inflammation [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Current studies have demonstrated that plasma leukocytes as well as inflammatory factors (e.g., TNF-α, IL-6, CRP, and IL-8) are positively associated with the severity of OSA [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. An inflammatory environment can disrupt the metabolic balance of chondrocytes, increasing the production of tissue-degrading enzymes such as matrix metalloproteinase-13 and a disintegrin and metalloproteinase with thrombospondin motifs-5. This disruption in chondrocyte metabolism induces oxidative stress and damages cartilage, resulting in joint structural damage and pain, thereby accelerating the progression of OA [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. On the other hand, it has been shown that IL-17 also stimulates human OA synovial fibroblasts, increases the production of vascular cell adhesion molecule, and enhances monocyte adhesion, thereby inducing the development of OA [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Additionally, intermittent hypoxia has been shown by Zhang et al. to cause the senescence-associated secretory phenotype in synovial tissues and cartilage, which in turn speeds up cellular senescence and causes or worsens OA [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn patients with OSA, fragmented sleep is considered a primary pathological mechanism leading to increased fatigue, heightened pain sensitivity, and increased likelihood of depression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consequently, clinical studies have found that patients with both OSA and OA experience increased pain and more severe depressive symptoms compared to those with OA alone [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Fragmented sleep disrupts circadian rhythms and alters central and peripheral clock systems. Disruption of circadian rhythms is closely linked to melatonin secretion [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Research has shown that melatonin plays roles in chondrocytes including anti-inflammatory, anti-catabolic, anti-apoptotic effects, and promotes metabolism [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Lim et al. demonstrated that melatonin exerts cellular protection and anti-inflammatory effects in oxidative stress-induced chondrocyte models and rabbit OA models, mediated largely by NAD+-dependent protein deacetylase SIRT1 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. On the other hand, an overabundance of sympathetic nervous system (SNS) activity is also linked to changes in circadian cycles. Overactivation of the SNS can induce the production of inflammatory cytokines and increase pain sensitivity. Existing evidence also indicates a link between SNS and the pathophysiology of OA and pain [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Studies by Suri et al. showed that sympathetic nerve fibers invade normal neural joint cartilage in both mild and severe OA cases [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. These findings suggest that disrupted circadian rhythms and sympathetic nervous system overactivity in OSA may contribute to the exacerbation of OA symptoms through various mechanisms involving inflammation, pain sensitivity, and joint tissue integrity.\u003c/p\u003e \u003cp\u003eObesity is a common risk factor for both OSA and OA, suggesting shared mechanisms in OA progression due to OSA, potentially involving lipid metabolism [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Transcriptomic studies by Weng et al. highlight significant enrichment of lipid metabolism-related genes in both conditions [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Intermittent hypoxia, a hallmark of OSA, plays a pivotal role in exacerbating oxidative stress within the body. This condition triggers a significant increase in reactive oxygen specie, which in turn activate critical cellular pathways affecting lipid metabolism and contributing to disease progression [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Proteomic analysis further suggests that changes in lipid metabolism may contribute to OA onset or progression, with lipid factors emerging as key regulatory elements in OA pathogenesis [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObviously, our study has several strengths. Initially, we employed MR techniques to merge data from the NHANES cross-sectional study. The use of nationally representative data enabled a thorough evaluation with a sizable sample size and took into account several variables, meaning that our findings were more reliable. Additionally, utilizing robust causal inference methods, we assessed the independent causal impact of OSA on OA, addressing issues of reverse causation and residual confounding. Notably, both analytical approaches produced nearly consistent results, enhancing the credibility of our findings.\u003c/p\u003e \u003cp\u003eHowever, several limitations need to be acknowledged. First, the diagnosis of OSA in this study primarily relied on sleep questionnaires from the NHANES database, which mainly assessed typical clinical symptoms of OSA such as snoring, breathing pauses, and daytime sleepiness. However, NHANES did not capture other common symptoms like morning headaches or driving accidents. Additionally, participants did not undergo laboratory sleep tests or home sleep apnea testing, which could lead to underdiagnosis or misclassification of OSA. Additionally, the diagnosis of OA was based on self-reports, which may introduce inaccuracies. Although research has shown that self-reported OA diagnoses have an 85% agreement with clinically verified OA diagnoses, there is still a risk of non-differential misclassification, potentially biasing the results. Third, the MR study primarily relied on data from individuals of European ancestry due to a lack of GWAS data from other races and ethnicities. This limits the generalizability of our findings to non-European populations, and the results may not be directly applicable to individuals of other racial and ethnic backgrounds. Finally, although the Mendelian randomization approach effectively reduces confounding bias and avoids reverse causation, it still relies on the validity and representativeness of the selected genetic variants. If the genetic variants are related to other potential confounders, the results might still be affected [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur research underscores the significance of considering OSA as a potential risk factor in the clinical management of OA. Future studies should aim to validate these findings across diverse populations and delve deeper into the underlying biological mechanisms linking OSA and OA. This could pave the way for novel preventive and therapeutic strategies for OA by targeting OSA symptoms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the cross-sectional study, the NCHS Ethics Committee granted approval. The MR study, involving a secondary review of data from existing publicly available databases, did not require additional ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed publicly available datasets. These data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm and https://gwas.mrcieu.ac.uk/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDongdong Cao, Jixin Chen, Weijie Yu, Jialin Yang, Tianci Guo, Yu Zhang, and Aifeng Liu declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Tianjin Municipal Health Commission Jinmen Medical Excellence Programme (TJSJMYXYC-D2-028). The funding source did not play any role in the research design, data collection, analysis and interpretation, writing of the manuscript and the decision to submit the manuscript for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDC and JC contributed to study design, data acquisition, analysis, and interpretation, and drafted the manuscript. AF contributed to the interpretation of the data and revised the manuscript. WY, JY, TG, and YZ\u0026nbsp;contribute to statistical analysis and visualisation of data. All authors read critically reviewed and approved the final manuscript as submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlyn-Jones S, Palmer AJ, Agricola R, et al. 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Abnormal lipid droplets accumulation induced cognitive deficits in obstructive sleep apnea syndrome mice via JNK/SREBP/ACC pathway but not through PDP1/PDC pathway. Mol Med. 2022;28:3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGkretsi V, Simopoulou T, Tsezou A. Lipid metabolism and osteoarthritis: lessons from atherosclerosis. Prog Lipid Res. 2011;50:133\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirney E. 2022. Mendelian Randomization. Cold Spring Harb Perspect Med 12.\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":"Obstructive Sleep Apnea, Osteoarthritis, Mendelian Randomization, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4756644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4756644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEpidemiological studies indicate that sleep disturbances are risk factors for osteoarthritis (OA). Obstructive sleep apnea (OSA) is a prevalent sleep disorder, yet its causal relationship with OA remains unclear. Therefore, this study investigates the causal relationship between three typical sleep characteristics of OSA and OA, aiming to provide theoretical support for clinical prevention and treatment strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used information from the National Health and Nutrition Examination Survey (NHANES) for 2015\u0026ndash;2018 to conduct a cross-sectional study. Multivariate logistic regression was employed to evaluate the association between OSA and OA. We obtained genetic instruments from publicly available genome-wide association study (GWAS) databases for MR studies, with inverse variance weighting (IVW) as the primary method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter controlling for all confounding variables, multivariate logistic regression revealed an adjusted odds ratio (OR) of 1.67 (95% CI: 1.44, 1.95) for OSA about OA, supporting the positive connection between the two conditions established in the cross-sectional analysis. MR analysis further suggested a causal link between snoring and daytime sleepiness, two primary OSA symptoms, and an increased risk of OA, with OR of 1.059 (95% CI: 1.020, 1.099) and 1.052 (95% CI: 1.013, 1.094), respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study found that OSA may be a risk factor for the development or progression of OA. Therefore, we believe that OSA may be a new target for the prevention and treatment of OA. Future studies should focus on confirming these findings in different populations and elucidating the exact biological mechanisms behind the OSA-OA relationship.\u003c/p\u003e","manuscriptTitle":"Exploring the Link Between Sleep characteristics and Osteoarthritis: Evidence from NHANES and MR","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-13 18:06:36","doi":"10.21203/rs.3.rs-4756644/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":"28f3ba0f-8255-4388-b2bb-c0ebb4d6d60b","owner":[],"postedDate":"August 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-29T00:38:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-13 18:06:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4756644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4756644","identity":"rs-4756644","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}