Unraveling the Novel Associations of Sleep Apnea with Glycosylated Hemoglobin: Insights from NHANES and Mendelian Randomization Analysis

Unraveling the Novel Associations of Sleep Apnea with Glycosylated Hemoglobin: Insights from NHANES and Mendelian Randomization Analysis | 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 Unraveling the Novel Associations of Sleep Apnea with Glycosylated Hemoglobin: Insights from NHANES and Mendelian Randomization Analysis zhen ma, min zhao, huanghong zhao, Nan Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3853490/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 While sleep apnea (SA) has been identified as a risk factor for metabolic dysfunction in diabetes, further research is required to establish a causal relationship between alterations in glycosylated hemoglobin(HbA1C) and the presence of sleep apnea. Methods We utilized data from the National Health and Nutrition Examination Survey (NHANES) 2015–2018 and employed logistic regression models to analyze the association,Based on the questionnaire data, sleep apnea (SA) is categorized into three levels: Rarely−1−2 nights a week, Occasionally−3−4 nights a week, and Frequently−5 or more nights a week. Additionally, a two-sample Mendelian randomization (MR) study was conducted using genome-wide association study (GWAS) summary statistics to assess the causal relationship between sleep apnea and HbA1C. The primary analysis utilized the inverse variance weighted (IVW) method. Sensitivity analyses were also performed to ensure the robustness of our findings. Results In our cross-sectional analysis, after adjusting for multiple covariates, we observed an increased risk of HbA1C ratio for both "Occasionally−3−4 nights a week" (OR = 0.08, 95% CI = 0.00 ~ 0.15, P = 0.036) and "Frequently−5 or more nights a week" (OR = 0.09, 95% CI = 0.00 ~ 0.17, P = 0.045). Utilizing the IVW technique, we calculated the risk of sleep apnea on HbA1C to be (OR = 1.086, 95% CI = 0.89 ~ 0.96, P = 0.035). The MR sensitivity analysis generated consistent findings. Conclusion Sleep apnea is linked to a higher risk of elevated HbA1c. The MR analysis supports a potential causal effect of sleep apnea on HbA1c. Sleep apnea Glycosylated hemoglobin NHANES survey Mendelian randomization analysis Figures Figure 1 Figure 2 Introduction Sleep apnea (SA) is a common sleep disorder characterized by repeated episodes of breathing pauses and reduced airflow during sleep. This sleep disorder can lead to inadequate oxygen supply and decreased sleep quality. Research suggests a close relationship between SA and diabetes(1).Specifically, there is a significant association between SA and HbA1c ratio (HbA1c) level. Studies have shown that as the severity of SA increases, insulin levels tend to decrease, indicating a potential link between SA and insulin resistance(2). Furthermore, SA is positively correlated with elevated levels of HbA1c, a long-term indicator of blood glucose control(3). Further investigations have revealed an increased risk of diabetes among individuals with SA. A historical cohort study involving a large sample of adults in the United States found SA to be a potential risk factor for diabetes(4).Therefore, improving sleep quality may contribute to the prevention and management of diabetes. In-depth studies have also demonstrated that SA may exacerbate insulin resistance by affecting insulin levels and metabolic function(5).In conclusion, numerous studies have supported the relationship between SA and the risk of diabetes. These findings suggest that SA may be a contributing factor to the development of diabetes, highlighting the importance of enhancing sleep quality as a preventive measure for diabetes(6,7). To investigate the association between SA, snoring and HbA1c ratio, we utilized the National Health and Nutrition Examination Survey (NHANES) data from 2015-2018. NHANES provides a valuable resource for examining the relationship between sleep-related disorders and glycemic features, offering large-scale access to high-quality, nationally representative data on the U.S. population's nutritional status and emerging public health conditions(8). To assess causal effects and mitigate confounding issues, we employed a two-sample Mendelian randomization (MR) analysis ( Fig. 1) . This approach utilizes genetic variants that are randomly assigned during meiosis, making them independent of environmental factors and reducing the potential for reverse causation(9,10). By combining the strengths of MR analysis and a large observational study, we aimed to provide a robust assessment of the potential association between sleep-related disorders and HbA1C ratio. The use of NHANES data and Mendelian randomization analysis allows us to comprehensively explore the associations between SA and glycemic characteristics. Further research, including longitudinal studies, is needed to establish causality and understand the metabolic benefits of SA treatment. This knowledge has the potential to significantly impact the management and prevention of metabolic diseases like type 2 diabetes. Fig. 1 Overview of the MR study design. Methods Epidemiological Observation Analyses The National Health and Nutrition Examination Survey(NHANES) is a cross-sectional national survey that utilizes a stratified multistage probability design to select participants, ensuring a representative sample of the U.S. population( 11). The focus of this study is the association between SA and HbA1c. The subjects of the study were individuals who participated in the NHANES survey from 2015 to 2018, totaling 11,759 people. Information was collected through questionnaires, including data on sociodemographic characteristics, the severity of SA and HbA1c. The definition of SA was based on the "SLQ040" information from the "SLQ" questionnaire in the NHANES data, specifically labeled "In the past 12 months, how often did {you/SP} snort, gasp, or stop breathing while {you were/s/he was} asleep?". The HbA1c ratio is determined through specific laboratory tests and often requires the combined analysis of other indicators, such as fasting insulin test, fasting blood glucose test, insulin resistance test, and glucose tolerance test( 12 ). The NHANES analysis adhered to rigorous methodologies, incorporating sampling weights, stratification, and clustering techniques to ensure robust estimates and standard errors based on the PSU and stratified data within the dataset( 13 ). Approval from the CDC's National Center for Health Statistics was obtained, and informed consent was obtained from all study participants.To mitigate potential confounding factors, we meticulously controlled for various demographic characteristics, including gender, age, race, education level, BMI, physical activity, blood pressure status, HS-CRP, and total cholesterol. These variables were selected based on their established associations with the outcome of interest or if their inclusion resulted in a significant (≥ 10%) impact on the estimated effect( 14 ). GenomeWide Association Studies (GWAS) Summary Data We obtained the GWAS summary statistics for SA from FinnGen ( https://www.finngen.fi/en ). The study included 372,657 European individuals (Ncase = 38,998, Ncontrol = 333,659) for GWAS analysis. Additionally, we acquired HbA1c data (GWAS ID: GCST90161187) from the GWAS Catalog ( https://www.ebi.ac.uk/gwas/).W e conducted quality control procedures to select reliable single nucleotide polymorphisms (SNP) and then performed a genome-wide association analysis. We identified 21 significant SNP associated with obstructive SA and 16 SNP significantly related to HbA1c proportion ( P < 5×10 − 8 )(Supplementary Table 1–2)( 15 ). Furthermore, in accordance with the conventional process of Mendelian randomization analysis, we selected instrumental variables (IVs) based on version v1.90( 16 ). These IVs were used to modify the SNP and were chosen with a chain disequilibrium LD r2 threshold of less than 0.1 within a distance of 500 kb. The LD r2 calculation was performed using the 1000 Genomes Project reference panel( 17 ). To account for potential confounding effects on HbA1c ratio, we controlled for several potential confounders, including BMI, blood pressure, and lipid abnormalities( 18 ). Statistical Analyses Multivariable regression analyses In our NHANES study, we conducted a population-based epidemiological analysis to gain insights into the prevalence, distribution, and potential risk factors associated with SA in the population. Building upon this, we employed a multivariable-adjusted logistic regression model to explore the relationship between SA and HbA1c ratio( 19). We evaluated three models, each adjusted for different covariates: Model 1, the unadjusted model; Model 2, including factors such as gender, age, race, education level, and body mass indexl; and Model 3, further adjusting for variables such as systolic and diastolic blood pressure, HbA1c ratio levels, C-reactive protein, fasting insulin, HOMA-IR, and OGTT. The results were presented as odds ratios or β coefficients with 95% confidence intervals. Our statistical analysis accounted for the complex probability clustering structure in NHANES and incorporated appropriate survey weights( 20 ). This analytical approach enabled us to investigate the impact of SA on HbA1c ratio while controlling for potential confounding factors.Ultimately, our goal is to derive clinically meaningful conclusions regarding the influence of SA on HbA1c ratio. Mendelian randomization analyses This study utilized a robust two-sample Mendelian randomization research (MR) approach to investigate potential causal associations inferred from genetic indicators. The analysis was anchored on the highly reliable inverse variance weighted (IVW) technique, which was further supported by a comprehensive set of complementary MR methods: MR Egger, weighted median, weighted mode, and Simple mode, ensuring the robustness of the IVW results( 21). To address potential bias arising from pleiotropic effects of the instrumental variables, we conducted meticulous sensitivity analyses.The Cochrane's Q test was used to assess heterogeneity in the data ( P < 0.05) and correct for significant heterogeneity( 22 ).The MR Egger intercept and the MR-PRESSO test were utilized to assess the presence of pleiotropy, which refers to the phenomenon where a single genetic variant affects traits. A significant MR Egger intercept ( P < 0.05) suggests the potential influence of pleiotropy. Furthermore, the MR-PRESSO test was employed to thoroughly evaluate pleiotropy by comparing the observed residual sum of squares with the expected sum( 23 ). To enhance the reliability of our findings, we conducted a rigorous leave-one-out analysis to confirm that no single variant unduly influenced the results. Results Population Characteristics Based on the clinical and laboratory data obtained from participants in the NHSNES study, the individuals were divided into two groups: 2,750 individuals with SA and 9,009 individuals without SA(Table 1). The SA group tended to be older, with no significant gender disparity, and predominantly comprised non-Hispanic white individuals. They exhibited lower levels of education and higher smoking rates. The severity of SA was positively correlated with body mass index and negatively correlated with physical activity levels. In terms of laboratory indicators, BMI, DBP, SBP, fasting glucose, insulin, HOMA-IR, HbA1c, and HS-CRP all demonstrated statistically significant differences between the groups with varying degrees of SA severity. These indicators also exhibited a trend of increasing levels with the worsening of SA severity. However, OGTT did not show a significant difference between the groups, but it did exhibit a trend of increasing levels with the worsening of SA severity. On the other hand, albumin and urine did not show a significant difference between the groups and did not demonstrate a clear trend with the worsening of SA severity. Observational Associations Between SA and Glycosylated hemoglobin The data before adjustment revealed significant associations between different degrees of SA and the HbA1c ratio(Table 2). After the first round of adjustment, the associations between SA and HbA1C ratio became non-significant for “Rarely−1−2 nights a week” (OR = 0.10, 95% CI = 0.03 ~ 0.16, P < 0.0033), but remained significant for “Occasionally−3−4 nights a week” (OR = 0.24, 95% CI = 0.15 ~ 0.32, P < 0.0001) and “Frequently−5 or more nights a week” (OR = 0.43, 95% CI = 0.33 ~ 0.53, P < 0.0001). In the second round of adjustment, the associations between “Frequently−5 or more nights a week” and HbA1C ratio remained non-significant for “Rarely−1−2 nights a week” (OR = 0.02,95% CI=−0.04 ~ 0.07, P = 0.5027), while remaining significant for “Occasionally−3−4 nights a week” (OR = 0.08, 95% CI = 0.00 ~ 0.15, P = 0.0362) and “Frequently−5 or more nights a week” (OR = 0.09, 95% CI = 0.00 ~ 0.17, P = 0.0448), with the latter showing a significant association with HbA1C ratio even after adjustment. Causal Relationships Between SA and Glycosylated hemoglobin We conducted a Mendelian randomization (MR) analysis to investigate the causal relationship and directionality between SA and HbA1c ratio. For MR analysis, 16 instrumental SNP were selected for genetic predicting glycosylated hemoglobin. By applying the IVW technique, SA on glycosylated hemoglobin risk was calculated to be(OR = 1.086, 95% CI = 0.89 ~ 0.96, P = 0.035). The results were similar for weighted mode (OR = 0.944, 95% CI = 0.89 ~ 1.00, P = 0.057), weighted median (OR = 1.058, 95% CI = 0.89 ~ 1.25, P = 0.529),and MR-Egger (OR = 1.289,95%CI = 0.95 ~ 1.75, P = 0.134)(Fig. 2a).The stability of the data was further demonstrated using scatterplots and leave-one-out (Fig. 2b-c). In addition, for all four associations, the MR-Egger intercept and MR-PRESSO global tests excluded the notion of horizontally collapsed products(Table 3). Sensitivity analyses provided comprehensive details that validated the strength of the causal relationships found (Supplementary Table 3). Figure 2. Mendelian randomization analyses were performed to investigate the influence of Sleep Apnea (SA) on the HbA1C ratio, employing a range of visual tools including forest plots, scatter plots, and leave-one-out plots for a comprehensive evaluation. Discussion In this study, we conducted an integrated analysis using the NHANES 2015-2018 cohort and a two-sample Mendelian randomization (MR) approach to examine the relationship between SA and HbA1c ratio. Our findings revealed that SA patients had higher HbA1c ratios compared to non-SA patients. Moreover, the MR analysis provided further evidence supporting the causal effect of SA on HbA1c ratio, suggesting that SA may be a modifiable factor influencing glycemic features. Although the pathological mechanisms of SA remain unclear, it is closely associated with metabolic disorders, including obesity, insulin resistance, and T2DM(24), For example, intermittent hypoxia and sleep fragmentation can exacerbate insulin resistance(25). Moreover, some scholars believe that inflammation, oxidative stress, and sympathetic nervous system activity potentially play a role in the process of sleep apnea(26). The potential mechanisms for glucose dysregulation in SA may be related to chronic metabolic changes and specific cytokine stressors linked to nocturnal oxygen desaturation, Moderate to severe SA can lead to the depletion of pancreatic B cell function and a decrease in secretory capacity over time(27). A study investigating sleep-disordered breathing(SDB) and insomnia among the Hispanic/Latino population in the United States found that over a six-year follow-up period, SA was associated with an increased risk of incident hypertension and diabetes, while insomnia was only associated with incident hypertension, not diabetes. These findings emphasize themportance of considering SDB as an independent factor in the risk assessment for diabetes(28). Another previous historical cohort study in Canada investigated the link between SA and the risk of developing new-onset diabetes(29). Adults without diabetes underwent SA evaluations from 1994 to 2010, and their health records were followed until May 2011. After a median follow-up of 67 months, 1,017 out of 8,678 patients (11.7%) developed diabetes, with a 5-year cumulative incidence rate of 9.1% (95%CI, 8.4~9.8%). This convergence of evidence accentuates the need for clinicians to consider the initial severity of SA as a critical component in predicting and managing diabetes risk. It underscores the imperative for early diagnosis and intervention, advocating for a holistic approach to patient care. In this integrated model, sleep disorders are evaluated as part of a comprehensive health risk assessment, ensuring that the potential implications of SA on metabolic health are neither underestimated nor overlooked(30). Such an approach could significantly improve patient outcomes by facilitating the early identification of individuals at heightened risk for diabetes and enabling timely, targeted interventions. Our study is strengthened by the integration of a large-scale observational study from NHANES with the MR approach, allowing for a comprehensive assessment of confounding factors and adjustment for multiple covariates(31).Using observational studies alone can be susceptible to unmeasured confounding factors and reverse causality(32). Using MR alone, although it controls for confounding factors, may have a higher false positive rate. By combining these two methods, our results are mutually corroborated, making the findings more reliable(33). Furthermore, our MR analysis is based on a large dataset, providing sufficient statistical evidence to estimate the causal relationship between SA and HbA1c ratio. However, our study also has some limitations. Firstly, the diagnosis of SA was based on self-reporting, which may introduce measurement errors. Secondly, considering the exclusion of a significant number of participants lacking data on SA and SLQ040 in our observational analysis, there may be potential selection bias. However, the overall similarity in participant distribution in NHANES contradicts the presence of such bias. Thirdly, despite controlling for many covariates that are considered relevant confounders, residual and unmeasured confounding factors (such as dietary factors) may still be present. Lastly, our study primarily focused on European and American populations, limiting the generalize ability of our results to other ethnic groups. Further research with larger sample sizes and diverse populations is needed to validate our findings. Conclusion Drawing from a comprehensive nationwide observational study using NHANES data and Mendelian randomization analysis, our findings indicate an elevated risk of HbA1c ratio associated with sleep apnea. These results necessitate validation through carefully crafted prospective cohort studies. Moreover, an in-depth exploration of the underlying mechanisms is warranted. Abbreviations SA sleep apnea HbA1C glycosylated hemoglobin NHANES the National Health and Nutrition Examination Survey GWAS genome-wide association study. Declarations Acknowledgments The authors would like to thank Dr. Jie Zhang and graduate student Zhen Wang for their valuable input in designing the study and interpreting the results. Authors contributions ZM assisted with planning, directing, and writing, as well as with editing and revising. HHZ helped with the first draft of the writing and the formal analysis. NQ helped with the data collection. MZ helped with the statistical analysis. The essay was written by all writers, who also gave their approval to the final draft. Funding This study was financially supported by National Administration of Traditional Chinese Medicine Science and Technology Project (GZY-KJS-2022-038-4). Availability of data and materials The GWAS data for SA was obtained from FinnGen (https://www.finngen.fi/en). The summary datasets of glycosylated hemoglobin data (GWAS ID: GCST90161187) were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/). Ethics approval and consent to participate As the data used in this study is publicly accessible and de-identified, it does not involve individual consent or require additional ethical approval. All data-sets used in this data obtained fully informed consent from participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Association between obstructive SAand diabetic Leong WB, Jadhakhan F, Taheri S, Chen YF, Adab P, Thomas GN. 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Tables Table 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table2.xlsx Table3.xlsx SupplementaryTable1.csv SupplementaryTable2.csv SupplementaryTable3.csv 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":56165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOverview of the MR study design.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/f9d6e5af8d2afff158f00d9d.png"},{"id":49863348,"identity":"8ada1668-1cbe-4a05-9ea0-a793b3b0d441","added_by":"auto","created_at":"2024-01-19 09:31:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1225914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMendelian randomization analyses were performed to investigate the influence of Sleep Apnea (SA) on the HbA1C ratio, employing a range of visual tools including forest plots, scatter plots, and leave-one-out plots for a comprehensive evaluation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/3769aa615c05a4a6a17d9f1c.png"},{"id":65725145,"identity":"d3628483-d208-48ff-a60b-d99625c5742d","added_by":"auto","created_at":"2024-10-01 18:02:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":688265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/db4f8cce-1abc-4bfd-95f7-4cf585968288.pdf"},{"id":49863346,"identity":"b8d12df2-08db-482c-a0e3-55b79ed4beb0","added_by":"auto","created_at":"2024-01-19 09:31:22","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14290,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/b92dece1fc39f33311a6923c.xlsx"},{"id":49863350,"identity":"ff00eaec-802b-4cb0-9773-64abb753c3d6","added_by":"auto","created_at":"2024-01-19 09:31:22","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11129,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/64e002af20302bf16d798c43.xlsx"},{"id":49863343,"identity":"a18020d4-b161-4e3c-82ba-b52b5fcadeab","added_by":"auto","created_at":"2024-01-19 09:31:22","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11156,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/d10c06bc3aa200509228ff1a.xlsx"},{"id":49863345,"identity":"7bacf10c-fb36-4d3c-8c5d-457eb7856e8d","added_by":"auto","created_at":"2024-01-19 09:31:22","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3935,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.csv","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/7a6321ff3e8bfbbe1a9ea5bd.csv"},{"id":49863349,"identity":"88e546e9-7500-4b7d-a327-c8a59e76a2cf","added_by":"auto","created_at":"2024-01-19 09:31:22","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2687,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/881e6c58d3b310e9d715eb99.csv"},{"id":49863347,"identity":"e9f2eac0-600d-4596-84cf-ff1ff6b428ac","added_by":"auto","created_at":"2024-01-19 09:31:22","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1154,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.csv","url":"https://assets-eu.researchsquare.com/files/rs-3853490/v1/f912cc2d7dc3e09b98986ff7.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unraveling the Novel Associations of Sleep Apnea with Glycosylated Hemoglobin: Insights from NHANES and Mendelian Randomization Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSleep apnea\u0026nbsp;(SA) is a common sleep disorder characterized by repeated episodes of breathing pauses and reduced airflow during sleep. This sleep disorder can lead to inadequate oxygen supply and decreased sleep quality. Research suggests a close relationship between SA and diabetes(1).Specifically, there is a significant association between SA and HbA1c ratio (HbA1c) level. Studies have shown that as the severity of SA increases, insulin levels tend to decrease, indicating a potential link between SA and insulin resistance(2). Furthermore, SA is positively correlated with elevated levels of HbA1c, a long-term indicator of blood glucose control(3).\u0026nbsp;Further investigations have revealed an increased risk of diabetes among individuals with SA. A historical cohort study involving a large sample of adults in the United States found SA to be a potential risk factor for diabetes(4).Therefore, improving sleep quality may contribute to the prevention and management of diabetes. In-depth studies have also demonstrated that SA may exacerbate insulin resistance by affecting insulin levels and metabolic function(5).In conclusion, numerous studies have supported the relationship between SA and the risk of diabetes. These findings suggest that SA may be a contributing factor to the development of diabetes, highlighting the importance of enhancing sleep quality as a preventive measure for diabetes(6,7).\u003c/p\u003e\n\u003cp\u003eTo investigate the association between SA, snoring and HbA1c ratio, we utilized the National Health and Nutrition Examination Survey (NHANES) data from 2015-2018. NHANES provides a valuable resource for examining the relationship between sleep-related disorders and glycemic features, offering large-scale access to high-quality, nationally representative data on the U.S. population\u0026apos;s nutritional status and emerging public health conditions(8). To assess causal effects and mitigate confounding issues, we employed a two-sample Mendelian randomization (MR) analysis (\u003cem\u003eFig. 1)\u003c/em\u003e. This approach utilizes genetic variants that are randomly assigned during meiosis, making them independent of environmental factors and reducing the potential for reverse causation(9,10). By combining the strengths of MR analysis and a large observational study, we aimed to provide a robust assessment of the potential association between sleep-related disorders and HbA1C ratio.\u003c/p\u003e\n\u003cp\u003eThe use of NHANES data and Mendelian randomization analysis allows us to comprehensively explore the associations between SA and glycemic characteristics. Further research, including longitudinal studies, is needed to establish causality and understand the metabolic benefits of SA treatment. This knowledge has the potential to significantly impact the management and prevention of metabolic diseases like type 2 diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFig. 1 \u0026nbsp;Overview of the MR study design.\u003c/em\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eEpidemiological Observation Analyses\u003c/b\u003e\u003cp\u003eThe National Health and Nutrition Examination Survey(NHANES) is a cross-sectional national survey that utilizes a stratified multistage probability design to select participants, ensuring a representative sample of the U.S. population(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e\u003c/span\u003e11). The focus of this study is the association between SA and HbA1c. The subjects of the study were individuals who participated in the NHANES survey from 2015 to 2018, totaling 11,759 people. Information was collected through questionnaires, including data on sociodemographic characteristics, the severity of SA and HbA1c. The definition of SA was based on the \"SLQ040\" information from the \"SLQ\" questionnaire in the NHANES data, specifically labeled \"In the past 12 months, how often did {you/SP} snort, gasp, or stop breathing while {you were/s/he was} asleep?\". The HbA1c ratio is determined through specific laboratory tests and often requires the combined analysis of other indicators, such as fasting insulin test, fasting blood glucose test, insulin resistance test, and glucose tolerance test(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e\u003c/span\u003e12\u003cspan\u003e). The NHANES analysis adhered to rigorous methodologies, incorporating sampling weights, stratification, and clustering techniques to ensure robust estimates and standard errors based on the PSU and stratified data within the dataset(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e\u003c/span\u003e13\u003c/span\u003e). Approval from the CDC's National Center for Health Statistics was obtained, and informed consent was obtained from all study participants.To mitigate potential confounding factors, we meticulously controlled for various demographic characteristics, including gender, age, race, education level, BMI, physical activity, blood pressure status, HS-CRP, and total cholesterol. These variables were selected based on their established associations with the outcome of interest or if their inclusion resulted in a significant (≥ 10%) impact on the estimated effect(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e\u003c/span\u003e14\u003cspan\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenomeWide Association Studies (GWAS) Summary Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe obtained the GWAS summary statistics for SA from FinnGen (\u003cspan class=\"ExternalRef\"\u003e\u003c/span\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003ehttps://www.finngen.fi/en\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003cspan\u003e\u003c/span\u003e). The study included 372,657 European individuals (Ncase = 38,998, Ncontrol = 333,659) for GWAS analysis. Additionally, we acquired HbA1c data (GWAS ID: GCST90161187) from the GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003c/span\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003ehttps://www.ebi.ac.uk/gwas/).W\u003cspan\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/).W\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan\u003ee conducted quality control procedures to select reliable single nucleotide polymorphisms (SNP) and then performed a genome-wide association analysis. We identified 21 significant SNP associated with obstructive SA and 16 SNP significantly related to HbA1c proportion (\u003cem\u003eP\u003c/em\u003e \u0026lt; 5×10 \u003csup\u003e− 8\u003c/sup\u003e)(Supplementary Table\u0026nbsp;1–2)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e\u003c/span\u003e15\u003c/span\u003e). Furthermore, in accordance with the conventional process of Mendelian randomization analysis, we selected instrumental variables (IVs) based on version v1.90(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e\u003c/span\u003e16\u003cspan\u003e). These IVs were used to modify the SNP and were chosen with a chain disequilibrium LD r2 threshold of less than 0.1 within a distance of 500 kb. The LD r2 calculation was performed using the 1000 Genomes Project reference panel(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e\u003c/span\u003e17\u003c/span\u003e). To account for potential confounding effects on HbA1c ratio, we controlled for several potential confounders, including BMI, blood pressure, and lipid abnormalities(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e\u003c/span\u003e18\u003cspan\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariable regression analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn our NHANES study, we conducted a population-based epidemiological analysis to gain insights into the prevalence, distribution, and potential risk factors associated with SA in the population. Building upon this, we employed a multivariable-adjusted logistic regression model to explore the relationship between SA and HbA1c ratio(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e\u003c/span\u003e19). We evaluated three models, each adjusted for different covariates: Model 1, the unadjusted model; Model 2, including factors such as gender, age, race, education level, and body mass indexl; and Model 3, further adjusting for variables such as systolic and diastolic blood pressure, HbA1c ratio levels, C-reactive protein, fasting insulin, HOMA-IR, and OGTT. The results were presented as odds ratios or β coefficients with 95% confidence intervals. Our statistical analysis accounted for the complex probability clustering structure in NHANES and incorporated appropriate survey weights(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e\u003c/span\u003e20\u003cspan\u003e\u003c/span\u003e). This analytical approach enabled us to investigate the impact of SA on HbA1c ratio while controlling for potential confounding factors.Ultimately, our goal is to derive clinically meaningful conclusions regarding the influence of SA on HbA1c ratio.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMendelian randomization analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study utilized a robust two-sample Mendelian randomization research (MR) approach to investigate potential causal associations inferred from genetic indicators. The analysis was anchored on the highly reliable inverse variance weighted (IVW) technique, which was further supported by a comprehensive set of complementary MR methods: MR Egger, weighted median, weighted mode, and Simple mode, ensuring the robustness of the IVW results(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e\u003c/span\u003e21). To address potential bias arising from pleiotropic effects of the instrumental variables, we conducted meticulous sensitivity analyses.The Cochrane's Q test was used to assess heterogeneity in the data (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and correct for significant heterogeneity(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e\u003c/span\u003e22\u003cspan\u003e).The MR Egger intercept and the MR-PRESSO test were utilized to assess the presence of pleiotropy, which refers to the phenomenon where a single genetic variant affects traits. A significant MR Egger intercept (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) suggests the potential influence of pleiotropy. Furthermore, the MR-PRESSO test was employed to thoroughly evaluate pleiotropy by comparing the observed residual sum of squares with the expected sum(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e\u003c/span\u003e23\u003c/span\u003e). To enhance the reliability of our findings, we conducted a rigorous leave-one-out analysis to confirm that no single variant unduly influenced the results.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePopulation Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the clinical and laboratory data obtained from participants in the NHSNES study, the individuals were divided into two groups: 2,750 individuals with SA and 9,009 individuals without SA(Table\u0026nbsp;1). The SA group tended to be older, with no significant gender disparity, and predominantly comprised non-Hispanic white individuals. They exhibited lower levels of education and higher smoking rates. The severity of SA was positively correlated with body mass index and negatively correlated with physical activity levels. In terms of laboratory indicators, BMI, DBP, SBP, fasting glucose, insulin, HOMA-IR, HbA1c, and HS-CRP all demonstrated statistically significant differences between the groups with varying degrees of SA severity. These indicators also exhibited a trend of increasing levels with the worsening of SA severity. However, OGTT did not show a significant difference between the groups, but it did exhibit a trend of increasing levels with the worsening of SA severity. On the other hand, albumin and urine did not show a significant difference between the groups and did not demonstrate a clear trend with the worsening of SA severity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObservational Associations Between SA and Glycosylated hemoglobin\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data before adjustment revealed significant associations between different degrees of SA and the HbA1c ratio(Table\u0026nbsp;2). After the first round of adjustment, the associations between SA and HbA1C ratio became non-significant for “Rarely−1−2 nights a week” (OR = 0.10, 95% CI = 0.03 ~ 0.16, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0033), but remained significant for “Occasionally−3−4 nights a week” (OR = 0.24, 95% CI = 0.15 ~ 0.32, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001) and “Frequently−5 or more nights a week” (OR = 0.43, 95% CI = 0.33 ~ 0.53, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). In the second round of adjustment, the associations between “Frequently−5 or more nights a week” and HbA1C ratio remained non-significant for “Rarely−1−2 nights a week” (OR = 0.02,95% CI=−0.04 ~ 0.07,\u003cem\u003eP\u003c/em\u003e = 0.5027), while remaining significant for “Occasionally−3−4 nights a week” (OR = 0.08, 95% CI = 0.00 ~ 0.15, \u003cem\u003eP\u003c/em\u003e = 0.0362) and “Frequently−5 or more nights a week” (OR = 0.09, 95% CI = 0.00 ~ 0.17, \u003cem\u003eP\u003c/em\u003e = 0.0448), with the latter showing a significant association with HbA1C ratio even after adjustment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCausal Relationships Between SA and Glycosylated hemoglobin\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted a Mendelian randomization (MR) analysis to investigate the causal relationship and directionality between SA and HbA1c ratio. For MR analysis, 16 instrumental SNP were selected for genetic predicting glycosylated hemoglobin. By applying the IVW technique, SA on glycosylated hemoglobin risk was calculated to be(OR = 1.086, 95% CI = 0.89 ~ 0.96, \u003cem\u003eP\u003c/em\u003e = 0.035). The results were similar for weighted mode (OR = 0.944, 95% CI = 0.89 ~ 1.00, \u003cem\u003eP\u003c/em\u003e = 0.057), weighted median (OR = 1.058, 95% CI = 0.89 ~ 1.25, \u003cem\u003eP\u003c/em\u003e = 0.529),and MR-Egger (OR = 1.289,95%CI = 0.95 ~ 1.75, \u003cem\u003eP\u003c/em\u003e = 0.134)(Fig.\u0026nbsp;2a).The stability of the data was further demonstrated using scatterplots and \u003cem\u003eleave-one-out\u003c/em\u003e(Fig.\u0026nbsp;2b-c). In addition, for all four associations, the MR-Egger intercept and MR-PRESSO global tests excluded the notion of horizontally collapsed products(Table\u0026nbsp;3). Sensitivity analyses provided comprehensive details that validated the strength of the causal relationships found (Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003cem\u003eFigure 2. Mendelian randomization analyses were performed to investigate the influence of Sleep Apnea (SA) on the HbA1C ratio, employing a range of visual tools including forest plots, scatter plots, and leave-one-out plots for a comprehensive evaluation.\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted an integrated analysis using the NHANES 2015-2018 cohort and a two-sample Mendelian randomization (MR) approach to examine the relationship between SA and HbA1c ratio. Our findings revealed that SA patients had higher HbA1c ratios compared to non-SA patients. Moreover, the MR analysis provided further evidence supporting the causal effect of SA on HbA1c ratio, suggesting that SA may be a modifiable factor influencing glycemic features.\u003c/p\u003e\n\u003cp\u003eAlthough the pathological mechanisms of SA remain unclear, it is closely associated with metabolic disorders, including obesity, insulin resistance, and T2DM(24),\u0026nbsp;For example, intermittent hypoxia and sleep fragmentation can exacerbate insulin resistance(25). Moreover, some scholars believe that inflammation, oxidative stress, and sympathetic nervous system activity potentially play a role in the process of sleep apnea(26).\u0026nbsp;The potential mechanisms for glucose dysregulation in SA may be related to chronic metabolic changes and specific cytokine stressors linked to nocturnal oxygen desaturation, Moderate to severe SA can lead to the depletion of pancreatic B cell function and a decrease in secretory capacity over time(27).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA study investigating sleep-disordered breathing(SDB) and insomnia among the Hispanic/Latino population in the United States found that over a six-year follow-up period, SA was associated with an increased risk of incident hypertension and diabetes, while insomnia was only associated with incident hypertension, not diabetes. These findings emphasize themportance of considering SDB as an independent factor in the risk assessment for diabetes(28). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother previous historical cohort study in Canada investigated the link between SA and the risk of developing new-onset diabetes(29). Adults without diabetes underwent SA evaluations from 1994 to 2010, and their health records were followed until May 2011. After a median follow-up of 67 months, 1,017 out of 8,678 patients (11.7%) developed diabetes, with a 5-year cumulative incidence rate of 9.1% (95%CI, 8.4~9.8%). This convergence of evidence accentuates the need for clinicians to consider the initial severity of SA as a critical component in predicting and managing diabetes risk. It underscores the imperative for early diagnosis and intervention, advocating for a holistic approach to patient care. In this integrated model, sleep disorders are evaluated as part of a comprehensive health risk assessment, ensuring that the potential implications of SA on metabolic health are neither underestimated nor overlooked(30). Such an approach could significantly improve patient outcomes by facilitating the early identification of individuals at heightened risk for diabetes and enabling timely, targeted interventions.\u003c/p\u003e\n\u003cp\u003eOur study is strengthened by the integration of a large-scale observational study from NHANES with the MR approach, allowing for a comprehensive assessment of confounding factors and adjustment for multiple covariates(31).Using observational studies alone can be susceptible to unmeasured confounding factors and reverse causality(32).\u0026nbsp;Using MR alone, although it controls for confounding factors, may have a higher false positive rate. By combining these two methods, our results are mutually corroborated, making the findings more reliable(33). Furthermore, our MR analysis is based on a large dataset, providing sufficient statistical evidence to estimate the causal relationship between SA and HbA1c ratio.\u003c/p\u003e\n\u003cp\u003eHowever, our study also has some limitations. Firstly, the diagnosis of SA was based on self-reporting, which may introduce measurement errors. Secondly, considering the exclusion of a significant number of participants lacking data on SA and SLQ040 in our observational analysis, there may be potential selection bias. However, the overall similarity in participant distribution in NHANES contradicts the presence of such bias. Thirdly, despite controlling for many covariates that are considered relevant confounders, residual and unmeasured confounding factors (such as dietary factors) may still be present. Lastly, our study primarily focused on European and American populations, limiting the generalize ability of our results to other ethnic groups. Further research with larger sample sizes and diverse populations is needed to validate our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDrawing from a comprehensive nationwide observational study using NHANES data and Mendelian randomization analysis, our findings indicate an elevated risk of HbA1c ratio associated with sleep apnea. These results necessitate validation through carefully crafted prospective cohort studies. Moreover, an in-depth exploration of the underlying mechanisms is warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esleep apnea\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglycosylated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe National Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egenome-wide association study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Dr. Jie Zhang and graduate student Zhen Wang for their valuable input in designing the study and interpreting the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZM assisted with planning, directing, and writing, as well as with editing and revising. HHZ helped with the first draft of the writing and the formal analysis. NQ helped with the data collection. MZ helped with the statistical analysis. The essay was written by all writers, who also gave their approval to the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by National Administration of Traditional Chinese Medicine Science and Technology Project (GZY-KJS-2022-038-4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS data for SA was obtained from FinnGen (https://www.finngen.fi/en). The summary datasets of glycosylated hemoglobin data (GWAS ID: GCST90161187) were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the data used in this study is publicly accessible and de-identified, it does not involve individual consent or require additional ethical approval. All data-sets used in this data obtained fully informed consent from participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAssociation between obstructive SAand diabetic Leong WB, Jadhakhan F, Taheri S, Chen YF, Adab P, Thomas GN. Effect of obstructive sleep apnoea on diabetic retinopathy and maculopathy: a systematic review and meta-analysis. 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Arch Med Sci. 2023;19(5):1545\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5114/aoms/171270\u003c/span\u003e\u003cspan address=\"10.5114/aoms/171270\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 3 are available in the Supplementary Files section.\u003c/p\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":"Sleep apnea, Glycosylated hemoglobin, NHANES survey, Mendelian randomization analysis","lastPublishedDoi":"10.21203/rs.3.rs-3853490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3853490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile sleep apnea (SA) has been identified as a risk factor for metabolic dysfunction in diabetes, further research is required to establish a causal relationship between alterations in glycosylated hemoglobin(HbA1C) and the presence of sleep apnea.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilized data from the National Health and Nutrition Examination Survey (NHANES) 2015\u0026ndash;2018 and employed logistic regression models to analyze the association,Based on the questionnaire data, sleep apnea (SA) is categorized into three levels: Rarely\u0026minus;1\u0026minus;2 nights a week, Occasionally\u0026minus;3\u0026minus;4 nights a week, and Frequently\u0026minus;5 or more nights a week. Additionally, a two-sample Mendelian randomization (MR) study was conducted using genome-wide association study (GWAS) summary statistics to assess the causal relationship between sleep apnea and HbA1C. The primary analysis utilized the inverse variance weighted (IVW) method. Sensitivity analyses were also performed to ensure the robustness of our findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn our cross-sectional analysis, after adjusting for multiple covariates, we observed an increased risk of HbA1C ratio for both \"Occasionally\u0026minus;3\u0026minus;4 nights a week\" (OR\u0026thinsp;=\u0026thinsp;0.08, 95% CI\u0026thinsp;=\u0026thinsp;0.00\u0026thinsp;~\u0026thinsp;0.15, P\u0026thinsp;=\u0026thinsp;0.036) and \"Frequently\u0026minus;5 or more nights a week\" (OR\u0026thinsp;=\u0026thinsp;0.09, 95% CI\u0026thinsp;=\u0026thinsp;0.00\u0026thinsp;~\u0026thinsp;0.17, P\u0026thinsp;=\u0026thinsp;0.045). Utilizing the IVW technique, we calculated the risk of sleep apnea on HbA1C to be (OR\u0026thinsp;=\u0026thinsp;1.086, 95% CI\u0026thinsp;=\u0026thinsp;0.89\u0026thinsp;~\u0026thinsp;0.96, P\u0026thinsp;=\u0026thinsp;0.035). The MR sensitivity analysis generated consistent findings.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSleep apnea is linked to a higher risk of elevated HbA1c. The MR analysis supports a potential causal effect of sleep apnea on HbA1c.\u003c/p\u003e","manuscriptTitle":"Unraveling the Novel Associations of Sleep Apnea with Glycosylated Hemoglobin: Insights from NHANES and Mendelian Randomization Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 09:31:17","doi":"10.21203/rs.3.rs-3853490/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":"accc86fc-6904-417a-afbd-4bcd80b07e31","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-01T17:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 09:31:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3853490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3853490","identity":"rs-3853490","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}