The relationship between sleep disorders and the risk of colorectal cancer among United States adults in the NHANES from 2005 to 2016

The relationship between sleep disorders and the risk of colorectal cancer among United States adults in the NHANES from 2005 to 2016 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The relationship between sleep disorders and the risk of colorectal cancer among United States adults in the NHANES from 2005 to 2016 Gongming Cheng, Jianbo Han, Liuqun Shan, Wenjie Shi, Xucheng Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6648199/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 Introduction Sleep disorders (SD) may disrupt immune function and hormonal balance, potentially increasing colorectal cancer (CRC) risk. However, the specific relationship between SD and CRC remains underexplored in large-scale studies. This study investigates this relationship using data from the National Health and Nutrition Examination Survey (NHANES). Methods Participants from NHANES (2005–2016) were included, with 30,755 subjects after exclusions. Baseline statistics were detailed in tables. Three multivariate generalized linear models (GLMs) were constructed: an unadjusted model, a minimally adjusted model (for age, race, and gender), and a fully adjusted model (including additional covariates like smoking and comorbidities). Stratified analyses and ROC/smoothing curves were used to explore the impact of SD and other covariates on CRC. Results The study included 30,755 subjects (30,528 controls, 227 CRC cases). Significant differences were observed in SD, age, race, smoking, and comorbidities between groups. SD was consistently associated with CRC across models: Model 1 [Odds ratios (OR) = 2.17, 95% confidence intervals (CI):1.63–2.88, P = 4.40E-07, Model 2 (OR = 1.80, 95% CI:1.36–2.39, P = 7.72E-05), and Model 3 (OR = 1.53, 95% CI:1.15–2.03, P = 3.73E-03). Risk stratification confirmed SD as a CRC risk factor (OR = 1.53, 95% CI:1.15–2.03, P < 0.01). The ROC curve [Area Under the Curve(AUC) = 0.87%] and smoothing curves showed a positive correlation between SD and CRC incidence. Conclusion This study confirms a significant correlation between SD and CRC, identifying SD as a risk factor for CRC. It underscores the importance of monitoring and managing CRC risk in patients with SD. Colorectal cancer Sleep disorder NHANES Association analysis Risk stratification analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction Colorectal cancer (CRC) ranks among the most prevalent gastrointestinal malignancies globally. Roughly 40% of cases are found in the proximal colon, while 20% − 30% occur in the distal rectum or colon [ 1 , 2 ]. As the fourth leading cause of cancer-related deaths, the overall incidence trend of CRC has been declining in recent years, but the age of onset is becoming increasingly younger[ 3 ]. According to statistics, the incidence of CRC in individuals under 50 has increased by 2%, and the growth rate in those under 35 is nearly 100%[ 4 ]. The pathogenesis of CRC is not yet fully understood, but known risk factors include environment, gender, genetics, and lifestyle[ 5 ]. Clinically, early-stage CRC may be asymptomatic, but as the disease progresses, patients may develop symptoms such as anemia, abdominal pain, weight loss, rectal bleeding, and bowel obstruction[ 6 ]. Despite the widespread application of treatments such as surgical resection, radiotherapy, chemotherapy, or targeted therapy, the 10-year survival rate for CRC remains below 60%[ 7 ]. Therefore, patients with metastatic CRC have a particularly poor prognosis, characterized by low survival rates[ 8 ]. The long-term follow-up and postoperative care for CRC patients require high medical costs, placing a significant burden on both the patients' families and society[ 9 ]. To better address these challenges, further in-depth research and exploration of the etiologies associated with CRC remain of significant importance. Sleep disorder (SD) refers to a series of conditions that affect sleep quality and duration, including circadian rhythm disturbances, hypersomnia, insomnia, and sleep breathing disorders[ 10 ]. The etiology of SD is complex and multifactorial, involving psychological stress, environmental factors, and underlying physical diseases. Long-term SD not only impairs quality of life but also increases the risk of various diseases, such as cardiovascular diseases and metabolic disorders[ 11 , 12 ]. As the global prevalence of SD is on the rise, an increasing number of studies are delving into the potential associations between SD and various diseases. Several scholars have pointed out that SD is associated with the development and progression of various cancers, notably including gastrointestinal malignancies[ 13 , 14 ]. Chronic sleep deprivation is linked to an increased risk of cancer, particularly showing a significant positive correlation with CRC[ 15 ]. Another study has reported that sleep apnea syndrome is also linked to an elevated risk of CRC[ 16 ]. Despite existing studies exploring the relationship between CRC and SD, systematic research based on the National Health and Nutrition Examination Survey (NHANES) database remains relatively scarce. Utilizing large-scale data analysis from the NHANES database can reduce bias due to insufficient sample size and enhance the accuracy of measurements and statistical power[ 17 ]. Many studies have already utilized the NHANES database to report potential unknown risk factors for various cancers[ 18 , 19 ], for example, the correlation between CRC and the body roundness index[ 20 ]. Therefore, exploring the correlation between SD and CRC based on the NHANES database can further provide strong evidence for their association. This study’s protocol complied with the Declaration of Helsinki.The study is based on adult questionnaire data collected from the NHANES database between 2005 and 2016, systematically exploring the potential association between SD and CRC. The research content includes detailed stratified analyses of participants with varying demographic characteristics and health statuses, evaluating the strength of the association between SD and CRC, and validating the consistency and reliability of this association through multiple statistical models. The study aims to provide new management evidence for the early diagnosis and prevention of CRC, especially for healthcare providers like doctors and nurses, and it holds significant public health implications. 2. Materials and methods 2.1 Study design and participants Initiated by the Centers for Disease Control and Prevention in 1971, the NHANES) ( https://www.cdc.gov/nchs/nhanes ) stands as a pivotal program aimed at evaluating the health and nutritional status of the US populace, distinctly set apart from studies targeting patients with cancer. Utilizing a sophisticated sampling method that is complex, stratified, and involves multiple stages, NHANES has been consistently gathering data through both interviews and physical assessments from around 10,000 participants every two years. Since 1999, this extensive database has been accessible to the public, offering critical insights for research and the formulation of health policies. Participation in NHANES is predicated on obtaining informed consent from all participants, ensuring that involvement in the survey is both knowledgeable and voluntary[ 21 ]. This study included 60,936 participants from the NHANES surveys conducted between 2005 and 2016. The exclusion criteria were as follows: 1. Participants who refused to answer or responded "Don't know" to whether they had been diagnosed with a (SD by a doctor, or those with missing data on this question. 2. Participants who refused to answer or responded "Don't know" to whether they had been diagnosed with CRC by a doctor, or those with missing data on this question. 3. Participants lacking data on the following covariates: age, race, gender, education, smoking, high blood pressure, asthma, weak kidneys, arthritis, heart failure, angina, heart attack, and stroke. 4. Participants under 18 years of age. After applying these criteria, 30,755 participants were finally included in the study (Table 1 ). 2.2 The definition of variables and building baseline profiles In this study, CRC, SD, and confounding factors were defined based on questionnaire data ( Additional file 1 ). Specifically, in the questionnaire section containing questions MCQ230A, MCQ230B, MCQ230C, or MCQ230D: "Has a doctor ever told you that you had any type of cancer?" participants who answered "16: CRC" considered as having been diagnosed with CRC and thus form the disease group. Participants who responded with "9 to 15" or "17 to 39" (other cancers) to question MCQ230A: "What kind of cancer was it?", or "No" to question MCQ220: "Ever told you had cancer or malignancy" were classified as the control group without CRC. Regarding SD, according to the survey question SLQ050: "Has a doctor ever told you that you have a SD?" participants who answered "Yes" were categorized as having been diagnosed with a SD, forming the SD group. Those who answered "No" in SLQ050 were classified as the control group without SD. Additionally, to assess the impact of potential confounding factors, several key covariates were selected: age, race, gender, education, smoking status, high blood pressure, asthma, weak kidneys, arthritis, heart failure, angina, heart attack, and stroke. Age was categorized into four groups: 18–44 years, 45–59 years, 60–75 years, and over 75 years. Gender was divided into two categories: male and female. Race was categorized into five groups: Mexican American, non-Hispanic white, other Hispanic, non-Hispanic black, and other races. Education was divided into three categories: less than high school grad, high school grad, and more than high school grad. Smoke was divided into two categories: smoker (Yes) and never smoked (No). While high blood pressure, asthma, weak kidneys, arthritis, heart failure, angina, heart attack, and stroke were all categorized similarly based on whether participants had been diagnosed by a doctor or health professional. Each condition was divided into two groups: those diagnosed with the condition (Yes) and those without (No). Next, to investigate potential differences in variables between the CRC group and the control group, the baseline characteristics of the subjects in both groups were analyzed employing a tableone package (v 0.13.2)[ 22 ]. Weighted chi-square tests were employed to assess differences in the baseline characteristics between the groups ( P < 0.05), and categorical variables were expressed as percentages. 2.3 Analysis of association and risk stratification To delve deeper into how covariates influenced the relationship between SD and CRC, and to assess whether there was a notable variance in odds ratio (OR) between SD and CRC, two adjusted models were developed with the assumption that all covariates interacted with CRC. Hence, utilizing the survey package (v 4.4.1)[ 23 ], three consecutive multivariate generalized linear models (GLMs) were set up to compute adjusted ORs along with 95% confidence intervals (CIs). In detail, Model 1, which was unadjusted, explored the direct association between SD and CRC without any covariate adjustments. Model 2, building on Model 1, offers minimal adjustment, controlled for age, race, and gender. Extending upon this, Model 3, the fully adjusted model, included adjustments for education, smoke, high blood pressure, asthma, weak kidneys, arthritis, heart failure, angina, heart attack, and stroke. Following that, to validate the consistency of the association between SD and the risk of CRC across diverse populations, this study incorporated SD and various covariates, and the interactions with CRC were evaluated leveraging weighted logistic regression. For a visual representation, a forest plot was created applying a forestplot package (v 3.1.1)[ 24 ], providing a clear visual summary of the findings. 2.4 Development of Receiver Operating Characteristic (ROC) and Smoothing Curves Among Model 3, the diagnostic capability for CRC was evaluated by incorporating all these variables into ROC analysis through the pROC package (v 1.18.0)[ 25 ]. The area under the curve (AUC) was calculated as a quantitative measure of predictive validity. An AUC greater than 0.7 was considered indicative of favorable predictive capability. Moreover, to further analyze the impact of SD on CRC, based on Model 1, the smooth curve was plotted making use of ggplot2 (v 3.7.4)[ 26 ] to illustrate the relationship between SD and CRC incidence. 2.5 Statistical analysis All statistical analyses were conducted using the nhanesR package in the R programming language (v 4.2.2). P < 0.05 was considered statistically significant. 3. Results 3.1 Demographic differences between CRC patients and controls After excluding certain samples and defining the variables, 30,755 participants were included in the study, consisting of 30,528 in the control group and 227 in the CRC group. The baseline characteristics table detailed the differences between variables in the two groups (Table 2 ). Of these, 88 individuals had both SD and CRC, 7,143 had SD but not CRC, 139 had CRC but no SD, and 23,385 had neither condition, indicating a significant effect of sleep disorders on CRC ( P < 0.001). In addition to SD, significant demographic differences were observed between CRC and control groups in terms of age, race, smoking, high blood pressure, weak kidneys, arthritis, heart failure, heart attack, and stroke, suggesting that these covariates also exhibited significant impacts on CRC ( P < 0.05). 3.2 Identified SD as a contributing exposure factor for CRC Subsequent association analysis was conducted, involving the construction of three models: an unadjusted model and two adjusted models (minimally adjusted and fully adjusted). The results indicated a consistent and significant correlation with CRC regardless of the adjustments made to the covariates ( P < 0.05), among them, Model 1 displayed an OR of 2.17 (95% CI: 1.63–2.88 and P = 4.40E-07), Model 2 showed an OR of 1.80 (95% CI: 1.36–2.39 and P = 7.72E-05), and Model 3 reported an OR of 1.53 (95% CI: 1.15–2.03 and P = 3.73E-03) (Table 3 ). These results suggested that the impact of SD on CRC was not significantly influenced by other covariates, highlighting the potential importance of monitoring and managing CRC in patients with or at risk for SD. 3.3 Evaluating SD as a potential risk factor for CRC Further risk stratification analysis incorporated SD along with other covariates and was visualized using a forest plot (Fig. 1 ). It demonstrated that SD remained strongly associated with CRC, acting as a risk factor (OR = 1.53, 95% CI: 1.15–2.03 and P = 3.73E-03), suggesting that patients with SD had an elevated risk of developing CRC. Furthermore, we identified strong associations between CRC and several other variables: age (45–59, 60–75 and > 75), race (non-Hispanic white and non-Hispanic black), high blood pressure (No), weak kidneys (No), and angina (No). Except for 'high blood pressure (No)' and 'weak kidneys (No)' which appeared to be protective factors for CRC (OR < 1 and P 1 and P < 0.05). 3.4 Increased CRC risk in patients with SD A ROC analysis of Model 3 was performed, which revealed an AUC of 0.87, indicating that SD possessed strong diagnostic performance for the risk of CRC (Fig. 2 ). Subsequently, a smooth curve was plotted to clearly and unequivocally depict the relationship between SD and CRC prevalence (Fig. 3 ). The curve demonstrated a positive correlation, indicating that the occurrence of SD was closely linked to the incidence of CRC, and patients with SD typically had a higher probability of developing CRC. Table 1 Sample exclusion criteria and final sample sizes for each variable in the study Varible Excluding condition Number Colorectal cancer Refuse or Exclude missing values 31185 Age Exclude missing values 31185 Gender Exclude missing values 31185 Race Exclude missing values 31185 Education Exclude missing values 31144 Smoke Refuse or Exclude missing values 31124 High blood pressure Refuse or Exclude missing values 31080 Asthma Refuse or Exclude missing values 31057 Weak kidneys Refuse or Exclude missing values 31010 Arthritis Refuse or Exclude missing values 30956 Heart failure Refuse or Exclude missing values 30888 Angina Refuse or Exclude missing values 30829 Heart attack Refuse or Exclude missing values 30798 Stroke Refuse or Exclude missing values 30769 Sleep disorder Refuse or Exclude missing values 30755 Table 2 Baseline characteristics of the study population, stratified by colorectal cancer (CRC) status. Varible Level Control (n = 30528) Colorectal cancer (n = 227) P -value Sleep disorder (%) No 23385 (76.6) 139 (61.2) < 0.001 Yes 7143 (23.4) 88 (38.8) Age (%) 18–44 14869 (48.7) 12 (5.3) 75 2118 (6.9) 98 (43.2) Gender (%) Male 14809 (48.5) 111 (48.9) 0.96 Female 15719 (51.5) 116 (51.1) Race (%) Mexican American 5168 (16.9) 9 ( 4.0) < 0.001 Other Hispanic 3060 (10.0) 17 ( 7.5) Non-Hispanic White 12241 (40.1) 147 (64.8) Non-Hispanic Black 6775 (22.2) 45 (19.8) Other Race 3284 (10.8) 9 (4.0) Education (%) Less Than High School Grad 3469 (11.4) 33 (14.5) 0.193 High School Grad 11554 (37.8) 90 (39.6) More Than High School Grad 15505 (50.8) 104 (45.8) Smoke (%) Yes 13322 (43.6) 117 (51.5) 0.02 No 17206 (56.4) 110 (48.5) High blood pressure (%) Yes 10039 (32.9) 160 (70.5) < 0.001 No 20489 (67.1) 67 (29.5) Asthma (%) Yes 4217 (13.8) 42 (18.5) 0.052 No 26311 (86.2) 185 (81.5) Weak kidneys (%) Yes 843 (2.8) 23 (10.1) < 0.001 No 29685 (97.2) 204 (89.9) Arthritis (%) Yes 7202 (23.6) 135 (59.5) < 0.001 No 23326 (76.4) 92 (40.5) Heart failure (%) Yes 885 (2.9) 25 (11.0) < 0.001 No 29643 (97.1) 202 (89.0) Angina (%) Yes 672 (2.2) 9 (4.0) 0.116 No 29856 (97.8) 218 (96.0) Heart attack (%) Yes 1066 (3.5) 33 (14.5) < 0.001 No 29462 (96.5) 194 (85.5) Stroke (%) Yes 994 (3.3) 29 (12.8) < 0.001 No 29534 (96.7) 198 (87.2) Table 3 Odds ratios (OR) and 95% confidence intervals (CI) for the association between sleep disorders and colorectal cancer across three adjusted models. Odds Ratio (OR) OR_lci95 OR_uci95 P -value Sleep disorder Model 1 2.17 1.63 2.88 4.40E-07 Model 2 1.8 1.36 2.39 7.72E-05 Model 3 1.53 1.15 2.03 3.73E-03 4. Discussion CRC is one of the leading cancers globally, characterized by a complex etiology that involves multiple factors[ 27 ]. In this study, we harnessed the NHANES data to explore the association between SD and CRC. By analyzing baseline characteristics, applying multivariate generalized linear models, and conducting ROC analysis, we determined that SD is an independent risk factor for CRC. Other significant covariates, including age, race, hypertension, and angina, were also found to be associated with CRC. These findings underscore the importance of monitoring and managing CRC in patients with SD or at risk for SD and provide clinicians with additional strategies for CRC management. Employing the NHANES database to explore the relationship between SD as an exposure factor and CRC as an outcome variable brings about multiple benefits. The large sample size and wide generalizability of NHANES ensure high representativeness and replicability of the results[ 28 ]. Furthermore, the original data in NHANES are collected and managed through standardized procedures by professional personnel[ 29 ], enhancing the robustness and reliability of this study's findings. Next, the significance of several CRC risk factors defined in this study will be further discussed in conjunction with existing evidence. A recent systematic review of CRC identified hypertension and chronic kidney disease as potential CRC risk factors, but the evidence is limited by the small number of studies[ 30 ]. Another study found that hypertension in males is associated with the development and progression of CRC[ 31 ]. Additionally, a study that controlled for confounding factors such as obesity, high waist circumference, and diabetes reported that diastolic blood pressure (DBP) greater than 90 mm Hg or systolic blood pressure (SBP) greater than 140 mm Hg is associated with an increased risk of incident CRC[ 32 ]. The results of this study further provide data supporting hypertension as a risk factor for CRC, thereby supplementing the existing evidence. However, future research should delve into whether hypertension impacts CRC differently based on gender and explore its underlying mechanisms. For several other covariates, research on the kidneys has reported an increased prevalence of CRC in patients with chronic renal insufficiency[ 33 ], which complements our finding that weak kidneys are a risk factor for CRC. Statistical data indicate that the incidence and mortality of CRC increase rapidly in individuals aged 50 and above[ 34 ]. The age covariate analysis in this study aligns with this evidence, indicating an increase in CRC incidence with age. The incidence of CRC differs across various regions. In general, the incidence in developed countries is higher compared to economically transitioning countries[ 9 ]. North America and Europe report relatively lower incidence rates, while the United States as part of North America, has a relatively higher incidence within this context[ 34 , 35 ]. This study contributes to the existing evidence on the incidence of CRC among different population groups, as observed in the NHANES database. Specifically, non-Hispanic Whites and non-Hispanic Blacks have a higher incidence of CRC compared to Mexican Americans, other Hispanic groups, and other racial populations. Our analysis, after adjusting for confounding variables in three different models, consistently demonstrated that SD poses an independent risk for the development of CRC. These models accounted for a variety of potential factors, further strengthening the observed correlation. As a risk factor for CRC, SD may contribute to the pathophysiological mechanisms of CRC through immune-inflammatory states and hormonal levels. Specifically, changes in immune cell counts and functions are associated with sleep cycles, such as the involvement of sleep and circadian rhythms in the generation of undifferentiated naive T cells and pro-inflammatory cytokines[ 36 ]. The immune surveillance functions of T cells and natural killer (NK) cells play a crucial role in determining the development and progression of CRC[ 37 ]. Therefore, the potential effect of SD on immune cells may further influence CRC. It is widely acknowledged that during sleep, the blood concentrations of prolactin[ 36 , 38 , 39 ], melatonin[ 40 ], and [ 41 ] rise rapidly, and all three hormones exert synergistic effects within the immune system. Studies have found that these hormone levels effectively regulate immune cell activation, proliferation, and the production of pro-inflammatory cytokines such as IL-1, IL-12, and TNF-α[ 42 – 45 ]. Chronic inflammation is a recognized factor in CRC development[ 46 ]. Consequently, sleep deprivation may result in unstable or persistent inflammation, creating conditions that are favorable for the development of CRC. Another area of evidence that deserves attention is the impact of sleep or circadian rhythms on human DNA repair. It is currently believed that mechanisms related to DNA damage and the cell cycle are involved in the onset and progression of tumors[ 47 ]. The specific mechanisms may involve the interaction of certain circadian clock proteins with DNA damage checkpoint factors, where dysregulated clock proteins lead to the accumulation of DNA damage, thereby promoting tumor development[ 48 ]. While multiple hypotheses exist about the relationship between SD and diseases, we discuss here the evidence-based potential mechanisms by which SD may influence CRC, such as through immune-inflammatory status, hormone levels, and DNA-related processes. Future research could explore the specific factors mediating the effects of SD on CRC in greater depth, thus providing more substantial evidence. In summary, this study revealed a significant association between SD and CRC through large-scale sample analysis, contributing to the clinical management of CRC. However, several limitations of the study require further exploration. Firstly, although the NHANES database has a large sample size, it may still suffer from inadequate representation of certain specific populations, potentially limiting the generalizability of the results. Secondly, there may be biases in the variable selection process, and future studies should consider more potential confounding variables to comprehensively control for confounders. Additionally, the current study is primarily based on observational data and lacks in-depth investigation into the biological mechanisms. To further validate the relationship between SD and CRC, future research should include specific cellular experiments and animal studies to elucidate the underlying biological mechanisms and pathways. Our team will continue to monitor the effects of these mechanisms and strive to advance related research, aiming to provide more scientific evidence for the prevention and treatment of colorectal cancer. Conclusion This study confirms a significant correlation between SD and CRC through large-scale data analysis and identifies SD as an independent risk factor for CRC using multivariate generalized linear models. ROC curve analysis demonstrates that SD has high diagnostic performance in CRC risk assessment. The findings suggest that SD may influence CRC development through various mechanisms, providing new insights into the relationship between SD and CRC. Abbreviations Abbreviation Full Form CRC Colorectal Cancer SD Sleep Disorders NHANES National Health and Nutrition Examination Survey GLMs Generalized Linear Models OR Odds Ratio CI Confidence Interval AUC Area Under the Curve ROC Receiver Operating Characteristic TNF-α Tumor Necrosis Factor-alpha IL-1 Interleukin-1 IL-12 Interleukin-12 NK cells Natural Killer cells DBP Diastolic Blood Pressure SBP Systolic Blood Pressure Declarations Ethics approval and consent to participate The NHANS data has all been approved by the NCHS Institutional Review Board, and all participants have provided informed consent forms. Consent for publication Not applicable Availability of data and materials The datasets generated and analysed during the current study are available in the [NHANES] repository, [https://www.cdc.gov/nchs/nhanes] Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contributions GMC: Conceptualization, Data curation, Validation, Visualization, Writing–original draft, Writing–review & editing. LQS: Data curation, Validation, Visualization, Writing–review & editing. WJS: Validation, Writing–review & editing. XCL: Visualization, Writing–review & editing. XWT: Conceptualization, Supervision, Writing–review & editing. JBH: Conceptualization, Project administration, Supervision, Writing–review & editing. 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Emerging trends in colorectal cancer: Dysregulated signaling pathways (Review). Int J Mol Med. 2021;47:14. Mármol I, Sánchez-de-Diego C, Pradilla Dieste A, Cerrada E, Rodriguez Yoldi MJ. Colorectal Carcinoma: A General Overview and Future Perspectives in Colorectal Cancer. Int J Mol Sci. 2017;18:197. Besedovsky L, Lange T, Born J. Sleep and immune function. Pflugers Arch. 2012;463:121–37. Wozniakova M, Skarda J, Raska M. The Role of Tumor Microenvironment and Immune Response in Colorectal Cancer Development and Prognosis. Pathol Oncol Res. 2022;28:1610502. Born J, Fehm HL. Hypothalamus-pituitary-adrenal activity during human sleep: a coordinating role for the limbic hippocampal system. Exp Clin Endocrinol Diabetes. 1998;106:153–63. Spiegel K, Follenius M, Simon C, Saini J, Ehrhart J, Brandenberger G. Prolactin secretion and sleep. Sleep. 1994;17:20–7. Haus E. Chronobiology in the endocrine system. Adv Drug Deliv Rev. 2007;59:985–1014. Arvin P, Ghafouri S, Bavarsad K, Hajipour S, Khoshnam SE, Sarkaki A, Farbood Y. Administration of growth hormone ameliorates adverse effects of total sleep deprivation. Metab Brain Dis. 2023;38:1671–81. Blask DE. Melatonin, sleep disturbance and cancer risk. Sleep Med Rev. 2009;13:257–64. Hattori N. Expression, regulation and biological actions of growth hormone (GH) and ghrelin in the immune system. Growth Horm IGF Res. 2009;19:187–97. Kelley KW, Weigent DA, Kooijman R. Protein hormones and immunity. Brain Behav Immun. 2007;21:384–92. Radogna F, Diederich M, Ghibelli L. Melatonin: a pleiotropic molecule regulating inflammation. Biochem Pharmacol. 2010;80:1844–52. Lichtenstern CR, Ngu RK, Shalapour S, Karin M, Immunotherapy. Inflamm Colorectal Cancer Cells. 2020;9:618. Ton M, Watson NF, Sillah A, Malen RC, Labadie JD, Reedy AM, Cohen SA, Burnett-Hartman AN, Newcomb PA, Phipps AI. Colorectal Cancer Anat Site Sleep Qual Cancers (Basel). 2021;13:2578. Collis SJ, Boulton SJ. Emerging links between the biological clock and the DNA damage response. Chromosoma. 2007;116:331–9. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx 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. 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-6648199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467927899,"identity":"5456fbc5-5507-40d7-a5e0-50b6afe36045","order_by":0,"name":"Gongming Cheng","email":"","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Gongming","middleName":"","lastName":"Cheng","suffix":""},{"id":467927903,"identity":"1569a2b3-1bf0-4832-af51-a76a09d5e06d","order_by":1,"name":"Jianbo Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYDACHh4GY4YKCTl5ErWcsTA2bCBFCzNjW0UiwwFidfDznD1QXDhPIoGxgfnhoxvEaJHs7UswnrlNIo+dgc3YOIcYLQbneQyMebdJFDM28LBJk6BljkRiwwGitZztAWppIEWLZM8ZA+MZxySMDZuJ9Qs/T46ZcUFNnZw8e/PDx0RpAQI2AzDFTKRysNoHJCgeBaNgFIyCkQgAHgMpQ1amuwkAAAAASUVORK5CYII=","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Han","suffix":""},{"id":467927904,"identity":"5d8e6596-2a79-4fda-b9e9-54fa8facd6b7","order_by":2,"name":"Liuqun Shan","email":"","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liuqun","middleName":"","lastName":"Shan","suffix":""},{"id":467927905,"identity":"5263bdde-30a1-4c79-acb2-753983d94b9f","order_by":3,"name":"Wenjie Shi","email":"","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Shi","suffix":""},{"id":467927906,"identity":"ac64042a-bfcc-404b-848b-1fc6a14f4c40","order_by":4,"name":"Xucheng Liu","email":"","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xucheng","middleName":"","lastName":"Liu","suffix":""},{"id":467927907,"identity":"77b85789-4469-4f4e-a120-0487a4b03c49","order_by":5,"name":"Xuewen Tao","email":"","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xuewen","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2025-05-12 15:38:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6648199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6648199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84380936,"identity":"d767a830-b7d5-48ed-b908-574546eeec11","added_by":"auto","created_at":"2025-06-11 09:15:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4625725,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the association between sleep disorders and colorectal cancer risk, stratified by demographic and health factors.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6648199/v1/3a03d800ff0783daab29dcb6.png"},{"id":84380934,"identity":"15ff1bd4-a42c-42eb-b474-3bc41bff3d71","added_by":"auto","created_at":"2025-06-11 09:15:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":623460,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curve for the diagnostic performance of sleep disorders in predicting colorectal cancer risk.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6648199/v1/1b9770426b5ce2cfd4499435.png"},{"id":84380935,"identity":"5d9e502f-d299-435e-b391-4685ec090e1b","added_by":"auto","created_at":"2025-06-11 09:15:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":706616,"visible":true,"origin":"","legend":"\u003cp\u003eSmooth curve illustrating the relationship between sleep disorders and colorectal cancer incidence.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6648199/v1/bf1a2c42201aaa3fc7a626e0.png"},{"id":90335136,"identity":"98bf9908-f367-4ad6-8b97-9b4a18d7eb08","added_by":"auto","created_at":"2025-09-01 14:02:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6657717,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6648199/v1/0fdcef6d-d8b3-4a5a-98bb-d9b26ec836a7.pdf"},{"id":84380939,"identity":"4b332a57-88ac-4e61-ae7d-09b7894984ba","added_by":"auto","created_at":"2025-06-11 09:15:09","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11287,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6648199/v1/edaa07250ae5549c4c09806d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship between sleep disorders and the risk of colorectal cancer among United States adults in the NHANES from 2005 to 2016","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) ranks among the most prevalent gastrointestinal malignancies globally. Roughly 40% of cases are found in the proximal colon, while 20% \u0026minus;\u0026thinsp;30% occur in the distal rectum or colon [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As the fourth leading cause of cancer-related deaths, the overall incidence trend of CRC has been declining in recent years, but the age of onset is becoming increasingly younger[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to statistics, the incidence of CRC in individuals under 50 has increased by 2%, and the growth rate in those under 35 is nearly 100%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The pathogenesis of CRC is not yet fully understood, but known risk factors include environment, gender, genetics, and lifestyle[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Clinically, early-stage CRC may be asymptomatic, but as the disease progresses, patients may develop symptoms such as anemia, abdominal pain, weight loss, rectal bleeding, and bowel obstruction[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite the widespread application of treatments such as surgical resection, radiotherapy, chemotherapy, or targeted therapy, the 10-year survival rate for CRC remains below 60%[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, patients with metastatic CRC have a particularly poor prognosis, characterized by low survival rates[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The long-term follow-up and postoperative care for CRC patients require high medical costs, placing a significant burden on both the patients' families and society[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To better address these challenges, further in-depth research and exploration of the etiologies associated with CRC remain of significant importance.\u003c/p\u003e \u003cp\u003eSleep disorder (SD) refers to a series of conditions that affect sleep quality and duration, including circadian rhythm disturbances, hypersomnia, insomnia, and sleep breathing disorders[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The etiology of SD is complex and multifactorial, involving psychological stress, environmental factors, and underlying physical diseases. Long-term SD not only impairs quality of life but also increases the risk of various diseases, such as cardiovascular diseases and metabolic disorders[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As the global prevalence of SD is on the rise, an increasing number of studies are delving into the potential associations between SD and various diseases. Several scholars have pointed out that SD is associated with the development and progression of various cancers, notably including gastrointestinal malignancies[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Chronic sleep deprivation is linked to an increased risk of cancer, particularly showing a significant positive correlation with CRC[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Another study has reported that sleep apnea syndrome is also linked to an elevated risk of CRC[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite existing studies exploring the relationship between CRC and SD, systematic research based on the National Health and Nutrition Examination Survey (NHANES) database remains relatively scarce. Utilizing large-scale data analysis from the NHANES database can reduce bias due to insufficient sample size and enhance the accuracy of measurements and statistical power[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Many studies have already utilized the NHANES database to report potential unknown risk factors for various cancers[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], for example, the correlation between CRC and the body roundness index[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, exploring the correlation between SD and CRC based on the NHANES database can further provide strong evidence for their association.\u003c/p\u003e \u003cp\u003e This study\u0026rsquo;s protocol complied with the Declaration of Helsinki.The study is based on adult questionnaire data collected from the NHANES database between 2005 and 2016, systematically exploring the potential association between SD and CRC. The research content includes detailed stratified analyses of participants with varying demographic characteristics and health statuses, evaluating the strength of the association between SD and CRC, and validating the consistency and reliability of this association through multiple statistical models. The study aims to provide new management evidence for the early diagnosis and prevention of CRC, especially for healthcare providers like doctors and nurses, and it holds significant public health implications.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e \u003cp\u003eInitiated by the Centers for Disease Control and Prevention in 1971, the NHANES) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) stands as a pivotal program aimed at evaluating the health and nutritional status of the US populace, distinctly set apart from studies targeting patients with cancer. Utilizing a sophisticated sampling method that is complex, stratified, and involves multiple stages, NHANES has been consistently gathering data through both interviews and physical assessments from around 10,000 participants every two years. Since 1999, this extensive database has been accessible to the public, offering critical insights for research and the formulation of health policies. Participation in NHANES is predicated on obtaining informed consent from all participants, ensuring that involvement in the survey is both knowledgeable and voluntary[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study included 60,936 participants from the NHANES surveys conducted between 2005 and 2016. The exclusion criteria were as follows: 1. Participants who refused to answer or responded \"Don't know\" to whether they had been diagnosed with a (SD by a doctor, or those with missing data on this question. 2. Participants who refused to answer or responded \"Don't know\" to whether they had been diagnosed with CRC by a doctor, or those with missing data on this question. 3. Participants lacking data on the following covariates: age, race, gender, education, smoking, high blood pressure, asthma, weak kidneys, arthritis, heart failure, angina, heart attack, and stroke. 4. Participants under 18 years of age. After applying these criteria, 30,755 participants were finally included in the study (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The definition of variables and building baseline profiles\u003c/h2\u003e \u003cp\u003eIn this study, CRC, SD, and confounding factors were defined based on questionnaire data (\u003cb\u003eAdditional file 1\u003c/b\u003e). Specifically, in the questionnaire section containing questions MCQ230A, MCQ230B, MCQ230C, or MCQ230D: \"Has a doctor ever told you that you had any type of cancer?\" participants who answered \"16: CRC\" considered as having been diagnosed with CRC and thus form the disease group. Participants who responded with \"9 to 15\" or \"17 to 39\" (other cancers) to question MCQ230A: \"What kind of cancer was it?\", or \"No\" to question MCQ220: \"Ever told you had cancer or malignancy\" were classified as the control group without CRC.\u003c/p\u003e \u003cp\u003eRegarding SD, according to the survey question SLQ050: \"Has a doctor ever told you that you have a SD?\" participants who answered \"Yes\" were categorized as having been diagnosed with a SD, forming the SD group. Those who answered \"No\" in SLQ050 were classified as the control group without SD.\u003c/p\u003e \u003cp\u003eAdditionally, to assess the impact of potential confounding factors, several key covariates were selected: age, race, gender, education, smoking status, high blood pressure, asthma, weak kidneys, arthritis, heart failure, angina, heart attack, and stroke. Age was categorized into four groups: 18\u0026ndash;44 years, 45\u0026ndash;59 years, 60\u0026ndash;75 years, and over 75 years. Gender was divided into two categories: male and female. Race was categorized into five groups: Mexican American, non-Hispanic white, other Hispanic, non-Hispanic black, and other races. Education was divided into three categories: less than high school grad, high school grad, and more than high school grad. Smoke was divided into two categories: smoker (Yes) and never smoked (No). While high blood pressure, asthma, weak kidneys, arthritis, heart failure, angina, heart attack, and stroke were all categorized similarly based on whether participants had been diagnosed by a doctor or health professional. Each condition was divided into two groups: those diagnosed with the condition (Yes) and those without (No).\u003c/p\u003e \u003cp\u003eNext, to investigate potential differences in variables between the CRC group and the control group, the baseline characteristics of the subjects in both groups were analyzed employing a tableone package (v 0.13.2)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Weighted chi-square tests were employed to assess differences in the baseline characteristics between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and categorical variables were expressed as percentages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of association and risk stratification\u003c/h2\u003e \u003cp\u003eTo delve deeper into how covariates influenced the relationship between SD and CRC, and to assess whether there was a notable variance in odds ratio (OR) between SD and CRC, two adjusted models were developed with the assumption that all covariates interacted with CRC. Hence, utilizing the survey package (v 4.4.1)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], three consecutive multivariate generalized linear models (GLMs) were set up to compute adjusted ORs along with 95% confidence intervals (CIs). In detail, Model 1, which was unadjusted, explored the direct association between SD and CRC without any covariate adjustments. Model 2, building on Model 1, offers minimal adjustment, controlled for age, race, and gender. Extending upon this, Model 3, the fully adjusted model, included adjustments for education, smoke, high blood pressure, asthma, weak kidneys, arthritis, heart failure, angina, heart attack, and stroke.\u003c/p\u003e \u003cp\u003eFollowing that, to validate the consistency of the association between SD and the risk of CRC across diverse populations, this study incorporated SD and various covariates, and the interactions with CRC were evaluated leveraging weighted logistic regression. For a visual representation, a forest plot was created applying a forestplot package (v 3.1.1)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], providing a clear visual summary of the findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Development of Receiver Operating Characteristic (ROC) and Smoothing Curves\u003c/h2\u003e \u003cp\u003eAmong Model 3, the diagnostic capability for CRC was evaluated by incorporating all these variables into ROC analysis through the pROC package (v 1.18.0)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The area under the curve (AUC) was calculated as a quantitative measure of predictive validity. An AUC greater than 0.7 was considered indicative of favorable predictive capability. Moreover, to further analyze the impact of SD on CRC, based on Model 1, the smooth curve was plotted making use of ggplot2 (v 3.7.4)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] to illustrate the relationship between SD and CRC incidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using the nhanesR package in the R programming language (v 4.2.2). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Demographic differences between CRC patients and controls\u003c/h2\u003e\n \u003cp\u003eAfter excluding certain samples and defining the variables, 30,755 participants were included in the study, consisting of 30,528 in the control group and 227 in the CRC group. The baseline characteristics table detailed the differences between variables in the two groups (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Of these, 88 individuals had both SD and CRC, 7,143 had SD but not CRC, 139 had CRC but no SD, and 23,385 had neither condition, indicating a significant effect of sleep disorders on CRC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition to SD, significant demographic differences were observed between CRC and control groups in terms of age, race, smoking, high blood pressure, weak kidneys, arthritis, heart failure, heart attack, and stroke, suggesting that these covariates also exhibited significant impacts on CRC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Identified SD as a contributing exposure factor for CRC\u003c/h2\u003e\n \u003cp\u003eSubsequent association analysis was conducted, involving the construction of three models: an unadjusted model and two adjusted models (minimally adjusted and fully adjusted). The results indicated a consistent and significant correlation with CRC regardless of the adjustments made to the covariates (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), among them, Model 1 displayed an OR of 2.17 (95% CI: 1.63\u0026ndash;2.88 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.40E-07), Model 2 showed an OR of 1.80 (95% CI: 1.36\u0026ndash;2.39 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.72E-05), and Model 3 reported an OR of 1.53 (95% CI: 1.15\u0026ndash;2.03 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.73E-03) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These results suggested that the impact of SD on CRC was not significantly influenced by other covariates, highlighting the potential importance of monitoring and managing CRC in patients with or at risk for SD.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Evaluating SD as a potential risk factor for CRC\u003c/h2\u003e\n \u003cp\u003eFurther risk stratification analysis incorporated SD along with other covariates and was visualized using a forest plot (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). It demonstrated that SD remained strongly associated with CRC, acting as a risk factor (OR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1.15\u0026ndash;2.03 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.73E-03), suggesting that patients with SD had an elevated risk of developing CRC. Furthermore, we identified strong associations between CRC and several other variables: age (45\u0026ndash;59, 60\u0026ndash;75 and \u0026gt;\u0026thinsp;75), race (non-Hispanic white and non-Hispanic black), high blood pressure (No), weak kidneys (No), and angina (No). Except for \u0026apos;high blood pressure (No)\u0026apos; and \u0026apos;weak kidneys (No)\u0026apos; which appeared to be protective factors for CRC (OR\u0026thinsp;\u0026lt;\u0026thinsp;1 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the other variables all contribute to an increased risk of CRC (OR\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Increased CRC risk in patients with SD\u003c/h2\u003e\n \u003cp\u003eA ROC analysis of Model 3 was performed, which revealed an AUC of 0.87, indicating that SD possessed strong diagnostic performance for the risk of CRC (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequently, a smooth curve was plotted to clearly and unequivocally depict the relationship between SD and CRC prevalence (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The curve demonstrated a positive correlation, indicating that the occurrence of SD was closely linked to the incidence of CRC, and patients with SD typically had a higher probability of developing CRC.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample exclusion criteria and final sample sizes for each variable in the study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVarible\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExcluding condition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeak kidneys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart attack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefuse or Exclude missing values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"char\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics of the study population, stratified by colorectal cancer (CRC) status.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVarible\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;30528)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eColorectal cancer (n\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disorder (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23385 (76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7143 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAge (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14869 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7504 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6037 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2118 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14809 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15719 (51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5168 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 ( 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3060 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 ( 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12241 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147 (64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6775 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3284 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eEducation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess Than High School Grad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3469 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh School Grad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11554 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore Than High School Grad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15505 (50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSmoke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13322 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117 (51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17206 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHigh blood pressure (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10039 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20489 (67.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAsthma (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4217 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26311 (86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185 (81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eWeak kidneys (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29685 (97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204 (89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArthritis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7202 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135 (59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23326 (76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHeart failure (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e885 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29643 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202 (89.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAngina (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e672 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29856 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHeart attack (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1066 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29462 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194 (85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStroke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e994 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29534 (96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOdds ratios (OR) and 95% confidence intervals (CI) for the association between sleep disorders and colorectal cancer across three adjusted models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR_lci95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR_uci95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.40E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.72E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.73E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCRC is one of the leading cancers globally, characterized by a complex etiology that involves multiple factors[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, we harnessed the NHANES data to explore the association between SD and CRC. By analyzing baseline characteristics, applying multivariate generalized linear models, and conducting ROC analysis, we determined that SD is an independent risk factor for CRC. Other significant covariates, including age, race, hypertension, and angina, were also found to be associated with CRC. These findings underscore the importance of monitoring and managing CRC in patients with SD or at risk for SD and provide clinicians with additional strategies for CRC management.\u003c/p\u003e \u003cp\u003eEmploying the NHANES database to explore the relationship between SD as an exposure factor and CRC as an outcome variable brings about multiple benefits. The large sample size and wide generalizability of NHANES ensure high representativeness and replicability of the results[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, the original data in NHANES are collected and managed through standardized procedures by professional personnel[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], enhancing the robustness and reliability of this study's findings. Next, the significance of several CRC risk factors defined in this study will be further discussed in conjunction with existing evidence.\u003c/p\u003e \u003cp\u003eA recent systematic review of CRC identified hypertension and chronic kidney disease as potential CRC risk factors, but the evidence is limited by the small number of studies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Another study found that hypertension in males is associated with the development and progression of CRC[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, a study that controlled for confounding factors such as obesity, high waist circumference, and diabetes reported that diastolic blood pressure (DBP) greater than 90 mm Hg or systolic blood pressure (SBP) greater than 140 mm Hg is associated with an increased risk of incident CRC[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The results of this study further provide data supporting hypertension as a risk factor for CRC, thereby supplementing the existing evidence. However, future research should delve into whether hypertension impacts CRC differently based on gender and explore its underlying mechanisms.\u003c/p\u003e \u003cp\u003eFor several other covariates, research on the kidneys has reported an increased prevalence of CRC in patients with chronic renal insufficiency[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which complements our finding that weak kidneys are a risk factor for CRC. Statistical data indicate that the incidence and mortality of CRC increase rapidly in individuals aged 50 and above[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The age covariate analysis in this study aligns with this evidence, indicating an increase in CRC incidence with age. The incidence of CRC differs across various regions. In general, the incidence in developed countries is higher compared to economically transitioning countries[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. North America and Europe report relatively lower incidence rates, while the United States as part of North America, has a relatively higher incidence within this context[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This study contributes to the existing evidence on the incidence of CRC among different population groups, as observed in the NHANES database. Specifically, non-Hispanic Whites and non-Hispanic Blacks have a higher incidence of CRC compared to Mexican Americans, other Hispanic groups, and other racial populations.\u003c/p\u003e \u003cp\u003eOur analysis, after adjusting for confounding variables in three different models, consistently demonstrated that SD poses an independent risk for the development of CRC. These models accounted for a variety of potential factors, further strengthening the observed correlation. As a risk factor for CRC, SD may contribute to the pathophysiological mechanisms of CRC through immune-inflammatory states and hormonal levels. Specifically, changes in immune cell counts and functions are associated with sleep cycles, such as the involvement of sleep and circadian rhythms in the generation of undifferentiated naive T cells and pro-inflammatory cytokines[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The immune surveillance functions of T cells and natural killer (NK) cells play a crucial role in determining the development and progression of CRC[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, the potential effect of SD on immune cells may further influence CRC. It is widely acknowledged that during sleep, the blood concentrations of prolactin[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], melatonin[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] rise rapidly, and all three hormones exert synergistic effects within the immune system. Studies have found that these hormone levels effectively regulate immune cell activation, proliferation, and the production of pro-inflammatory cytokines such as IL-1, IL-12, and TNF-α[\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Chronic inflammation is a recognized factor in CRC development[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Consequently, sleep deprivation may result in unstable or persistent inflammation, creating conditions that are favorable for the development of CRC. Another area of evidence that deserves attention is the impact of sleep or circadian rhythms on human DNA repair. It is currently believed that mechanisms related to DNA damage and the cell cycle are involved in the onset and progression of tumors[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The specific mechanisms may involve the interaction of certain circadian clock proteins with DNA damage checkpoint factors, where dysregulated clock proteins lead to the accumulation of DNA damage, thereby promoting tumor development[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. While multiple hypotheses exist about the relationship between SD and diseases, we discuss here the evidence-based potential mechanisms by which SD may influence CRC, such as through immune-inflammatory status, hormone levels, and DNA-related processes. Future research could explore the specific factors mediating the effects of SD on CRC in greater depth, thus providing more substantial evidence.\u003c/p\u003e \u003cp\u003eIn summary, this study revealed a significant association between SD and CRC through large-scale sample analysis, contributing to the clinical management of CRC. However, several limitations of the study require further exploration. Firstly, although the NHANES database has a large sample size, it may still suffer from inadequate representation of certain specific populations, potentially limiting the generalizability of the results. Secondly, there may be biases in the variable selection process, and future studies should consider more potential confounding variables to comprehensively control for confounders. Additionally, the current study is primarily based on observational data and lacks in-depth investigation into the biological mechanisms. To further validate the relationship between SD and CRC, future research should include specific cellular experiments and animal studies to elucidate the underlying biological mechanisms and pathways. Our team will continue to monitor the effects of these mechanisms and strive to advance related research, aiming to provide more scientific evidence for the prevention and treatment of colorectal cancer.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms a significant correlation between SD and CRC through large-scale data analysis and identifies SD as an independent risk factor for CRC using multivariate generalized linear models. ROC curve analysis demonstrates that SD has high diagnostic performance in CRC risk assessment. The findings suggest that SD may influence CRC development through various mechanisms, providing new insights into the relationship between SD and CRC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFull Form\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eColorectal Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSleep Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGLMs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneralized Linear Models\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTNF-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTumor Necrosis Factor-alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterleukin-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterleukin-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNK cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNatural Killer cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANS data has all been approved by the NCHS Institutional Review Board, and all participants have provided informed consent forms.\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\u003eThe datasets generated and analysed during the current study are available in the [NHANES] repository, [https://www.cdc.gov/nchs/nhanes]\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eGMC: Conceptualization, Data curation, Validation, Visualization, Writing\u0026ndash;original draft, Writing\u0026ndash;review \u0026amp; editing. LQS: Data curation, Validation, Visualization, Writing\u0026ndash;review \u0026amp; editing. WJS: Validation, Writing\u0026ndash;review \u0026amp; editing. XCL: Visualization, Writing\u0026ndash;review \u0026amp; editing. XWT: Conceptualization, Supervision, Writing\u0026ndash;review \u0026amp; editing. JBH: Conceptualization, Project administration, Supervision, Writing\u0026ndash;review \u0026amp; editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate the NHANES database for all the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePatel SG, Karlitz JJ, Yen T, Lieu CH, Boland CR. 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Chromosoma. 2007;116:331\u0026ndash;9.\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":"Colorectal cancer, Sleep disorder, NHANES, Association analysis, Risk stratification analysis","lastPublishedDoi":"10.21203/rs.3.rs-6648199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6648199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSleep disorders (SD) may disrupt immune function and hormonal balance, potentially increasing colorectal cancer (CRC) risk. However, the specific relationship between SD and CRC remains underexplored in large-scale studies. This study investigates this relationship using data from the National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants from NHANES (2005–2016) were included, with 30,755 subjects after exclusions. Baseline statistics were detailed in tables. Three multivariate generalized linear models (GLMs) were constructed: an unadjusted model, a minimally adjusted model (for age, race, and gender), and a fully adjusted model (including additional covariates like smoking and comorbidities). Stratified analyses and ROC/smoothing curves were used to explore the impact of SD and other covariates on CRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 30,755 subjects (30,528 controls, 227 CRC cases). Significant differences were observed in SD, age, race, smoking, and comorbidities between groups. SD was consistently associated with CRC across models: Model 1 [Odds ratios (OR) = 2.17, 95% confidence intervals (CI):1.63–2.88, P = 4.40E-07, Model 2 (OR = 1.80, 95% CI:1.36–2.39, P = 7.72E-05), and Model 3 (OR = 1.53, 95% CI:1.15–2.03, P = 3.73E-03). Risk stratification confirmed SD as a CRC risk factor (OR = 1.53, 95% CI:1.15–2.03, P \u0026lt; 0.01). The ROC curve [Area Under the Curve(AUC) = 0.87%] and smoothing curves showed a positive correlation between SD and CRC incidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study confirms a significant correlation between SD and CRC, identifying SD as a risk factor for CRC. It underscores the importance of monitoring and managing CRC risk in patients with SD.\u003c/p\u003e","manuscriptTitle":"The relationship between sleep disorders and the risk of colorectal cancer among United States adults in the NHANES from 2005 to 2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-11 09:15:04","doi":"10.21203/rs.3.rs-6648199/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":"a9aa3e24-0d8f-4f5a-a616-834a1c658148","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T13:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-11 09:15:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6648199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6648199","identity":"rs-6648199","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}