Epidemiological Associations Between Chronic  Cough and Diarrhea among Adults in the United States: A Population-Based Analysis of NHANES Data

Epidemiological Associations Between Chronic Cough and Diarrhea among Adults in the United States: A Population-Based Analysis of NHANES Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Epidemiological Associations Between Chronic Cough and Diarrhea among Adults in the United States: A Population-Based Analysis of NHANES Data Kuang Du, Xiaojuan Tang, Yongheng He, Liang Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6456030/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 Based on data from the National Health and Nutrition Examination Survey (NHANES), this study analyzed the association between chronic cough and chronic diarrhea in 7,007 adults aged 40 years and older. Results showed that 8.81% of participants had chronic diarrhea, and after adjusting for confounders such as smoking and sleep by binary logistic regression, patients with chronic cough had a 50% increased risk of chronic diarrhea compared to those without cough (OR = 1.50).9 Subgroup analyses showed that none of the covariates (e.g., age, gender) interacted with the outcome significantly (p > 0.05 by interaction test), suggesting that these factors influenced the outcome independently rather than through an interaction. This study demonstrated for the first time in a large population the positive correlation between chronic cough and chronic diarrhea, supporting the “lung-gut axis” theory and emphasizing the need for synergistic interventions for gastrointestinal symptoms in the management of chronic cough.9 The results of this study provide new perspectives for the integrated management of the two co-morbidities in the clinical setting, including the synergistic effects of psychosocial co-morbidities, therapeutic strategies, and the potential for microbiological interventions. synergistic effects and the potential for microbial intervention. Health sciences/Diseases Health sciences/Diseases/Gastrointestinal diseases Health sciences/Diseases/Respiratory tract diseases Epidemiological Associations Chronic cough chronic diarrhea NHANES Figures Figure 1 Introduction Around 5% of people worldwide suffer from chronic diarrhea [1] . Similarly, the prevalence of chronic cough is 7.9%, which is also a condition with a high incidence globally [2] . There is a potential bidirectional interaction between the gut and the lungs [3] . While the connection between the lungs and the gut is well-known, there is little information regarding the link between chronic cough and chronic diarrhea. So far, no large-scale population-based studies have assessed the correlation between chronic diarrhea and chronic cough while adjusting for other potential confounding variables such as sleep and smoking. Thus, our study seeks to explore the connection between chronic cough and chronic diarrhea, accounting for clinical and demographic variables, using the National Health and Nutrition Examination Survey (NHANES) in a representative sample of the US population. Method Research Cohort The data were obtained from the NHANES dataset covering the years 2005 to 2010. NHANES provides a publicly accessible, nationally representative sample of non-institutionalized individuals in the United States. The National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia, USA, is in charge of the NHANES survey program. A stratified multistage probability design was used to choose participants, and in order to enable sample-weighted conclusions about the US population, some racial and age categories were oversampled. Prior to completing the NHANES survey, all participants gave written informed consent, and no patient identities are included in the NHANES database, which is accessible to the public. This study utilized data from the NHANES survey cycles of 2005–2006, 2007–2008, and 2009–2010. During these cycles, a total of 31,034 participants were recruited. After excluding 16,415 participants lacking comprehensive BHQ information, 5,032 without Chronic cough information, 1,228 missing covariate data, 313 with a history of asthma attacks in the past year, 211 self-reporting emphysema, 179 with chronic bronchitis, 134 self-reporting colorectal and lung cancer and an additional 514 with chronic constipation, we included 7,007 participants in the study (Fig. 1 ). Exposure and outcomes In clinical practice, we are hindered by the inability to directly quantify the severity of diarrhea. But, Clinicians can rely on tools such as the stool form scale as a useful guide to intestinal transit time [4] .So, in this study, chronic diarrhea can be assessed through a personal gut health interview conducted at a mobile examination center (MEC). This assessment is identified by the variable name prefix "BHQ" and specifically utilizes question BHQ 060, which employs the Bristol Stool Form Scale (BSFS). The BSFS is a tool used to classify stool consistency, which is often used in clinical settings to assess gastrointestinal function and symptoms, including diarrhea. Participants are asked to identify their typical or common stool type by referring to the relevant numbers on a reference card that displays graphical images of seven BSFS types. Individuals whose typical or most common stool type is determined to be Type 1 (separate hard lumps, like nuts) or Type 2 (sausage-shaped but lumpy) are classified as having chronic constipation. Conversely, individuals identified as Type 6 (loose, fluffy pieces with ragged edges, mushy stool) or Type 7 (watery consistency, with no solid pieces) are considered to exhibit symptoms of chronic diarrhea. Previous research has also done this [5–7] . The chronic cough status is determined by healthcare professionals. Participants are asked, "Do you usually cough on most days for 3 months or more consecutively during the year?" The NHANES program has been approved by the CDC Ethics Review Board. Just like previous studies [8, 9] Study variables The covariates included in this study were: gender (male, female), age group (20–59 years, ≥ 60 years), ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other races), depression (No depression to mild depression, Moderate depression to sever depression) [10] , sleep disorders (Yes, No), BMI ( Underweight, Normal weight, Overweight, Obese), alcohol use ≥ 12/year(Yes, No), and Cotinine category (<0.05 ng/ml, ≥ 0.05 ng/mL) [11] , and the income index PIR is divided into three categories: the poor (PIR 4) [12] . Statistical analysis Statistical evaluations in this study were performed using R (External. http://www.r-project.org ) and STATA 14.2 (College Station, TX, U.S.A.) with a significance threshold of p ≤ 0.05, and all estimates were calculated using sample weights according to the National Center for Health Statistics (NCHS) analytic guidelines. Because NHANES is designed to generate data representative of the U.S. civilian noninstitutionalized population, these sample weights ensure that the study results are generalizable to the broader U.S. population. Weighted multiple linear regression analysis was used to evaluate the linear relationship between chronic cough and chronic diarrhea. The study included three models: model 1 required no variable adjustment; model 2 accounted for age, sex, and race; and model 3 adjusted for all covariates. In addition, subgroup analyses and interaction tests were performed. Results 3.1. Participant characteristics After applying the inclusion and exclusion criteria, 7,007 participants aged over 40 were enrolled in the study. The sample consisted of 50.57% males and 49.43% females, with 77.42% being Non-Hispanic White, 9.23% Non-Hispanic Black, 6.01% Mexican American, 3.08% Other Hispanic, and 4.25% from other racial backgrounds. The Chronic coughing and non-Chronic coughing populations accounted for 9% and 91% of the total population, respectively. Table 1 summarizes the clinical characteristics of the participants. The proportion of individuals reporting chronic cough in the general population was 8.65%. However, this proportion increased significantly to 13.48% in the chronic diarrhea population. (P < 0.05). Mexican American and Non-Hispanic Black women with diarrhea were more likely to cough for more than three months, be older, have a lower socioeconomic status, have a higher BMI, have higher smoke exposure, abuse alcohol, have sleep disorders, and suffer from depression (all p < 0.05). Detailed demographic data for all survey respondents are presented in Table 1 . Table 1 Baseline characteristics of participants according to the chronic diarrhea Characteristic Mean ± SD (n = 7007) Normal(n = 6390) Chronic diarrhea(n = 617) Standardize diff. P value Cough % 0.000 No 91.00 91.35 86.516 0.155 (0.154, 0.155) Yes 9.00 8.65 13.484 0.155 (0.154, 0.155) Gender % 0.016 Male 50.57 50.98 45.45 0.111 (0.110, 0.112) Female 49.43 49.02 54.55 0.111 (0.110, 0.112) Age group % 0.002 40-59years 64.01 64.50 57.838 0.137 (0.136, 0.138) ≥ 60years 35.99 35.50 42.162 0.137 (0.136, 0.138) RACE % 0.003 Mexican American 6.01 5.86 7.89 0.080 (0.079, 0.081) Other Hispanic 3.08 3.07 3.271 0.012 (0.011, 0.013) Non-Hispanic White 77.42 77.89 71.461 0.148 (0.148, 0.149) Non-Hispanic Black 9.23 8.90 13.401 0.143 (0.142, 0.144) Others 4.25 4.28 3.977 0.015 (0.014, 0.016) PIR % 0.000 The poor 8.52 8.14 13.418 0.171 (0.170, 0.172) The middle class 47.29 46.96 51.488 0.091 (0.090, 0.092) The wealthy 44.19 44.91 35.094 0.201 (0.201, 0.202) Drinking status % 0.002 No 74.83 75.29 69.077 0.139 (0.138, 0.140) Yes 25.17 24.71 30.923 0.139 (0.138, 0.140) BMI (kg/m2) % 0.000 Underweight 1.19 1.16 1.565 0.035 (0.034, 0.036) Normal weight 25.23 25.47 22.188 0.077 (0.076, 0.078) Overweight 35.90 36.39 29.645 0.144 (0.143, 0.145) Obese 37.68 36.97 46.603 0.196 (0.195, 0.197) Cotinine category % 0.030 < 0.05 ng/mL 54.70 55.07 50.12 0.099 (0.098, 0.100) ≥ 0.05 ng/mL 45.30 44.94 49.88 0.099 (0.098, 0.100) Depression Severity, % 0.000 None and mild 94.41 94.97 87.264 0.274 (0.273, 0.274) Moderately to sever 5.59 5.03 12.736 0.274 (0.273, 0.274) Sleep disorders % 0.006 Yes 27.41 27.00 32.577 0.122 (0.121, 0.123) No 72.59 73.00 67.423 0.122 (0.121, 0.123) 3.2. The relationship between chronic cough and chronic diarrhea Table 2 demonstrates the relationship between chronic cough and chronic diarrhea. All models indicate a positive correlation between chronic cough and chronic diarrhea. After adjusting for all confounding variables, the incidence of chronic diarrhea among individuals with Chronic cough was 1.503 times higher than that among those without Chronic cough [odds ratio (OR) = 1.503, 95% confidence interval (95% CI): 1.065–2.120, p = 0.020]. Table 2 The relationship between chronic cough and chronic diarrhea OR (95% CI) Crude model (model 1) Minimally adjusted model (model 2) Fully adjusted model (model 3) Cough 1.646(1.181,2.294)0.003 1.694(1.213,2.368)0.002 1.503(1.065, 2.120)0.020 OR, odds ratio; CI, confidence intervals; Model 1: Unadjusted; Model 2: Adjusted for age, race, and gender; Model 3: Adjusted for age, race, gender, body mass index, the family poverty income ratio, cotinine category, drinking status, depression severity, sleep disorders. 3.3. Subgroup analysis As presented in Table 3 , the analysis showed a significantly lower risk of chronic diarrhea in females (OR = 0.685, P = 0.043), non-Hispanic whites (OR = 0.658, P = 0.021), overweight group (OR = 0.482, P = 0.001), and those with no sleep disorders (OR = 0.627, P = 0.004) suggesting that these factors may have a protective effect; while other variables (e.g., other classifications in race, drinking status, obese group in BMI, etc.) did not show significant associations. In the interaction analysis, only the interaction effect of sleep disorder approached significance (P = 0.057), suggesting that it may modify the effects of other variables, but the interactions of the remaining covariates (sex, race, BMI, etc.) were not significant (P > 0.05). Overall, this suggests that outcome risk is more independently influenced by gender, race, weight status, and sleep health. Table 3 The relationship between chronic cough and chronic diarrhea in various subgroups Covariate Covariate Level OR p value P for interaction Gender 0.56237433 Male 0.808756 0.295027496 Female 0.684957 0.042818991 RACE 0.73547248 Mexican American 0.8550163 0.680954876 Other Hispanic 0.4488919 0.143629221 Non-Hispanic White 0.6578299 0.021097224 Non-Hispanic Black 0.9720833 0.931983862 Others 0.4503292 0.231896297 Drinking status 0.92468081 No 0.7581871 0.086541779 Yes 0.6952265 0.161432946 BMI (kg/m2) 0.34356398 Underweight 1.4000849 0.822890938 Normal weight 1.6306116 0.201367047 Overweight 0.4823259 0.00113377 Obese 0.77486 0.207031198 Depression Severity 0.84069805 None and mild 0.740586 0.047289333 Moderately to sever 0.7692326 0.413335772 Cotinine category 0.41199348 < 0.05 ng/mL 0.6811301 0.074733505 ≥ 0.05 ng/mL 0.800522 0.210235834 Sleep disorders 0.05711638 Yes 1.0854018 0.750261082 No 0.627194 0.003839421 Age group 0.42103437 40-59years 0.6729283 0.047777398 ≥ 60years 0.804697 0.247177185 PIR 0.89504614 The poor 0.672925 0.146852441 The middle class 0.7659791 0.143709309 The wealthy 0.7422463 0.352109551 Discussion Our cross-sectional survey revealed a significant positive correlation between chronic cough and chronic diarrhea among adults in the United States, which persisted across all adjusted models. Subgroup analyses and interaction evaluations showed that different covariates did not have a significant impact on the relationship between chronic cough and chronic diarrhea. To our knowledge, this constitutes the first cross-sectional investigation exploring the association between chronic cough and chronic diarrhea. The findings of this study demonstrate a significant independent association between chronic cough and chronic diarrhea in U.S. adults, even after adjusting for smoking, sleep disorders, and socioeconomic factors. This aligns with evidence supporting the gut-lung axis hypothesis, which posits bidirectional communication between the gastrointestinal and respiratory systems through shared immune, neural, and microbial pathways [13] . Mechanistic Insights from the Gut-Lung Axis This study discusses the finding that the association between chronic cough and chronic diarrhea is stable across different populations. This phenomenon may be mediated through various interactive mechanisms of the gut-lung axis. First, microbe-mediated immune regulation may be the core pathway: dysbiosis of the gut microbiota can trigger a Th2-type immune shift, activating both intestinal and respiratory mast cells through circulating cytokines, leading to mucosal barrier disruption and increased neural sensitivity [14–17] . Secondly, the vagus nerve pathway may play a bridging role. Microbial metabolites in the gut-brain axis, such as short-chain fatty acids, can regulate the activity of the enteric nervous plexus and enhance the sensitivity of the cough reflex arc through afferent fibers of the vagus nerve [18, 19] . It is noteworthy that a research [20] confirms that fecal microbiota transplantation can improve pulmonary immune responses by restoring short-chain fatty acid levels, suggesting that microbial metabolites may be key mediators of bidirectional regulation. Limitations and Future Directions This study has several limitations. First, its cross-sectional design precludes causal inference. Longitudinal studies are needed to determine whether chronic cough precedes diarrhea or vice versa. Second, reliance on self-reported symptoms may introduce recall bias, though NHANES’ standardized questionnaires mitigate this concern. Third, while we adjusted for key confounders, unmeasured variables (e.g., dietary habits, specific medications) could influence the association. Future research should incorporate biomarkers of inflammation (e.g., fecal calprotectin, serum IL-6) and gut microbiome profiling to elucidate mechanistic links. In clinical practice, this study offers a new perspective on comorbidity management: 1) Priority of psychological comorbidity intervention: Research [21–23] has shown that the depression rate in chronic cough patients can reach as high as 61.2%. Similarly, depressive states also have an impact on the gut. [24] This suggests that psychological assessment should be integrated into routine diagnosis and treatment. 2)The synergy of treatment strategies: A study [25] found that chronic cough patients with comorbid psychological conditions had a poorer response to proton pump inhibitors, suggesting the need for combined neuroregulatory treatments. 3) The potential value of microbial intervention: Study [26] pointed out that regulating the gut microbiota may improve both respiratory and digestive symptoms by restoring immune homeostasis. Conclusions This study confirms the independent association between chronic cough and chronic diarrhea, with mechanisms potentially involving abnormal vagus nerve regulation and the interaction of systemic inflammation. Although the cross-sectional design limits causal inference, it provides population-level evidence for the "lung-gut axis" theory. Future research should focus on verifying specific pathways through mechanistic studies and developing targeted therapeutic strategies to achieve a shift from symptom control to etiological intervention. Declarations Author Contribution Author contributionsAll authors made significant contributions to this research. Kuang Du(KD) was responsible for data collection, investigation, and drafting the manuscript. Xiao-Juan Tang (XT) focused on the study design, performed the statistical analyses, and contributed to manuscript writing. Liang Zhao (LZ) supervised the project, offered critical guidance, and refined the manuscript through detailed review and editing. Yong-Heng He (YH) supervised the project, offered critical guidance, and refined the manuscript through detailed review and editing. All authors have reviewed and approved the final version of the manuscript. Data Availability The NHANES dataset used for this study is publicly available and can be found at: https://wwwn.cdc.gov/nchs/nhanes. References Schiller, L.R., D.S. Pardi, and J.H. Sellin, Chronic Diarrhea: Diagnosis and Management. Clin Gastroenterol Hepatol, 2017. 15 (2): p. 182-193 e3. Song, W.J., et al., The global epidemiology of chronic cough in adults: a systematic review and meta-analysis. Eur Respir J, 2015. 45 (5): p. 1479-81. Xiaofan, S., et al., The role and mechanism of gut-lung axis mediated bidirectional communication in the occurrence and development of chronic obstructive pulmonary disease. Gut Microbes, 2024. Lewis, S.J. and K.W. Heaton, Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol, 1997. 32 (9): p. 920-4. Wang, C., L. Zhang, and L. Li, Association Between Selenium Intake with Chronic Constipation and Chronic Diarrhea in Adults: Findings from the National Health and Nutrition Examination Survey. Biol Trace Elem Res, 2021. 199 (9): p. 3205-3212. Singh, P., et al., Demographic and Dietary Associations of Chronic Diarrhea in a Representative Sample of Adults in the United States. Am J Gastroenterol, 2018. 113 (4): p. 593-600. Sommers, T., et al., Prevalence of Chronic Constipation and Chronic Diarrhea in Diabetic Individuals in the United States. Am J Gastroenterol, 2019. 114 (1): p. 135-142. Jiang, M. and H. Zhao, Association of chronic cough with exposure to polycyclic aromatic hydrocarbons in the US population. Heliyon, 2024. 10 (1): p. e23413. Wu, W., et al., Association between exposure to per- and polyfluoroalkyl substances (PFAS) and chronic cough in American adults: Results from NHANES 2003-2012. Ecotoxicol Environ Saf, 2025. 291 : p. 117901. Kroenke, K., R.L. Spitzer, and J.B. Williams, The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med, 2001. 16 (9): p. 606-13. Nwosu, B.U. and P. Kum-Nji, Tobacco smoke exposure is an independent predictor of vitamin D deficiency in US children. PLoS One, 2018. 13 (10): p. e0205342. Zhao, Y., et al., Association between family income to poverty ratio and HPV infection status among U.S. women aged 20 years and older: a study from NHANES 2003-2016. Front Oncol, 2023. 13 : p. 1265356. Budden, K.F., et al., Emerging pathogenic links between microbiota and the gut-lung axis. Nat Rev Microbiol, 2017. 15 (1): p. 55-63. De Nuccio, F., P. Piscitelli, and D.M. Toraldo, Gut-lung Microbiota Interactions in Chronic Obstructive Pulmonary Disease (COPD): Potential Mechanisms Driving Progression to COPD and Epidemiological Data. Lung, 2022. 200 (6): p. 773-781. Baldi, S., et al., First Exploration of the Altered Microbial Gut-Lung Axis in the Pathogenesis of Human Refractory Chronic Cough. Lung, 2024. 202 (2): p. 107-118. Guo, Y., et al., Sodium houttuyfonate modulates the lung Th1/Th2 balance and gut microbiota to protect against pathological changes in lung of ovalbumin-induced asthmatic mice. J Asthma, 2024. 61 (12): p. 1759-1771. Sultan, M., et al., Endocannabinoid Anandamide Attenuates Acute Respiratory Distress Syndrome through Modulation of Microbiome in the Gut-Lung Axis. Cells, 2021. 10 (12). Cheng, Y., et al., Therapeutic role of gut microbiota in lung injury-related cognitive impairment. Front Nutr, 2024. 11 : p. 1521214. Yata, V.K., Ex vivo and miniaturized in vitro models to study microbiota-gut-brain axis. 3 Biotech, 2024. 14 (11): p. 280. Le Guern, R., et al., Gut colonisation with multidrug-resistant Klebsiella pneumoniae worsens Pseudomonas aeruginosa lung infection. Nat Commun, 2023. 14 (1): p. 78. Arinze, J.T., et al., The interrelationship of chronic cough and depression: a prospective population-based study. ERJ Open Res, 2022. 8 (2). Chronic diarrhea. Am Fam Physician, 2011. 84 (10): p. 1133-4. Li, Y., et al., Anxiety and depression are associated with reduced quality of life and increased cough severity in chronic cough. Asian Pac J Allergy Immunol, 2022. Ballou, S., et al., Chronic Diarrhea and Constipation Are More Common in Depressed Individuals. Clin Gastroenterol Hepatol, 2019. 17 (13): p. 2696-2703. Zhang, T., et al., Psychological morbidity and chronic cough: which is predominant? A comparison of clinical characteristics. Ther Adv Chronic Dis, 2023. 14 : p. 20406223231173628. Brister, D., et al., Emerging drugs in the treatment of chronic cough. Expert Opin Emerg Drugs, 2023. 28 (2): p. 67-77. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6456030","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":470173626,"identity":"7486612a-4024-4103-a558-7ed19908f9a6","order_by":0,"name":"Kuang Du","email":"","orcid":"","institution":"Hunan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kuang","middleName":"","lastName":"Du","suffix":""},{"id":470173627,"identity":"38b978cd-d319-4c9a-b234-453848c4197b","order_by":1,"name":"Xiaojuan Tang","email":"","orcid":"","institution":"Hunan Province Integrated Traditional Chinese and Western Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Tang","suffix":""},{"id":470173628,"identity":"a7f70be0-2124-43fd-9e18-7ad9cb7fdc93","order_by":2,"name":"Yongheng He","email":"","orcid":"","institution":"Hunan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yongheng","middleName":"","lastName":"He","suffix":""},{"id":470173629,"identity":"185cff34-a2b8-4958-9195-20f51ce86cab","order_by":3,"name":"Liang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACAyBmBmI5fvbmAwc+/CBOC2MzkDaW7DmWeHBmDwlaEjfcyDE+zMFGhBZz9ubnjwsq7oG0fDjMwMMgzy92AL8Wy55jhs0zzhQbzzzzdsPhAgsGw5mzEwg47EaCYTNvW4Js3/HcDYdn8DAkGNwmpOX+84/NvP8SGBsO5Dw4zMNGjJYbPEBbGhIUJ5zIYSBSy5mcwtk8xxJAgWwADGQJIvxy/PiGzzw1CaCofPzhww8beX5pAlrQgQRpykfBKBgFo2AUYAcA5oFN2LplQgcAAAAASUVORK5CYII=","orcid":"","institution":"Hunan Province Integrated Traditional Chinese and Western Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-04-15 14:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6456030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6456030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84689679,"identity":"eaa96d0e-d9f1-4e91-8f75-946f0e9eddc0","added_by":"auto","created_at":"2025-06-16 09:31:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140033,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart showing the selection of the studied population\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6456030/v1/f20e2008e3de1af1f507fd35.png"},{"id":92848199,"identity":"52ffef39-3faa-4b46-9a02-8fcf2dc49136","added_by":"auto","created_at":"2025-10-06 10:02:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":967867,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6456030/v1/8cac6543-a308-4e21-9a98-8ed34b8fd95d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiological Associations Between Chronic Cough and Diarrhea among Adults in the United States: A Population-Based Analysis of NHANES Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAround 5% of people worldwide suffer from chronic diarrhea\u003csup\u003e[1]\u003c/sup\u003e. Similarly, the prevalence of chronic cough is 7.9%, which is also a condition with a high incidence globally\u003csup\u003e[2]\u003c/sup\u003e. There is a potential bidirectional interaction between the gut and the lungs\u003csup\u003e[3]\u003c/sup\u003e. While the connection between the lungs and the gut is well-known, there is little information regarding the link between chronic cough and chronic diarrhea.\u003c/p\u003e \u003cp\u003eSo far, no large-scale population-based studies have assessed the correlation between chronic diarrhea and chronic cough while adjusting for other potential confounding variables such as sleep and smoking. Thus, our study seeks to explore the connection between chronic cough and chronic diarrhea, accounting for clinical and demographic variables, using the National Health and Nutrition Examination Survey (NHANES) in a representative sample of the US population.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Cohort\u003c/h2\u003e \u003cp\u003eThe data were obtained from the NHANES dataset covering the years 2005 to 2010. NHANES provides a publicly accessible, nationally representative sample of non-institutionalized individuals in the United States. The National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia, USA, is in charge of the NHANES survey program. A stratified multistage probability design was used to choose participants, and in order to enable sample-weighted conclusions about the US population, some racial and age categories were oversampled. Prior to completing the NHANES survey, all participants gave written informed consent, and no patient identities are included in the NHANES database, which is accessible to the public.\u003c/p\u003e \u003cp\u003eThis study utilized data from the NHANES survey cycles of 2005\u0026ndash;2006, 2007\u0026ndash;2008, and 2009\u0026ndash;2010. During these cycles, a total of 31,034 participants were recruited. After excluding 16,415 participants lacking comprehensive BHQ information, 5,032 without Chronic cough information, 1,228 missing covariate data, 313 with a history of asthma attacks in the past year, 211 self-reporting emphysema, 179 with chronic bronchitis, 134 self-reporting colorectal and lung cancer and an additional 514 with chronic constipation, we included 7,007 participants in the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure and outcomes\u003c/h3\u003e\n\u003cp\u003eIn clinical practice, we are hindered by the inability to directly quantify the severity of diarrhea. But, Clinicians can rely on tools such as the stool form scale as a useful guide to intestinal transit time\u003csup\u003e[4]\u003c/sup\u003e.So, in this study, chronic diarrhea can be assessed through a personal gut health interview conducted at a mobile examination center (MEC). This assessment is identified by the variable name prefix \"BHQ\" and specifically utilizes question BHQ 060, which employs the Bristol Stool Form Scale (BSFS). The BSFS is a tool used to classify stool consistency, which is often used in clinical settings to assess gastrointestinal function and symptoms, including diarrhea. Participants are asked to identify their typical or common stool type by referring to the relevant numbers on a reference card that displays graphical images of seven BSFS types. Individuals whose typical or most common stool type is determined to be Type 1 (separate hard lumps, like nuts) or Type 2 (sausage-shaped but lumpy) are classified as having chronic constipation. Conversely, individuals identified as Type 6 (loose, fluffy pieces with ragged edges, mushy stool) or Type 7 (watery consistency, with no solid pieces) are considered to exhibit symptoms of chronic diarrhea. Previous research has also done this\u003csup\u003e[5\u0026ndash;7]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe chronic cough status is determined by healthcare professionals. Participants are asked, \"Do you usually cough on most days for 3 months or more consecutively during the year?\" The NHANES program has been approved by the CDC Ethics Review Board. Just like previous studies\u003csup\u003e[8, 9]\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eThe covariates included in this study were: gender (male, female), age group (20\u0026ndash;59 years, \u0026ge; 60 years), ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other races), depression (No depression to mild depression, Moderate depression to sever depression)\u003csup\u003e[10]\u003c/sup\u003e, sleep disorders (Yes, No), BMI ( Underweight, Normal weight, Overweight, Obese), alcohol use\u0026thinsp;\u0026ge;\u0026thinsp;12/year(Yes, No), and Cotinine category (\u0026lt;0.05 ng/ml, \u0026ge;\u0026thinsp;0.05 ng/mL)\u003csup\u003e[11]\u003c/sup\u003e, and the income index PIR is divided into three categories: the poor (PIR\u0026thinsp;\u0026lt;\u0026thinsp;1), the middle class (PIR 1\u0026ndash;4), and the wealthy (PIR\u0026thinsp;\u0026gt;\u0026thinsp;4)\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical evaluations in this study were performed using R (External. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and STATA 14.2 (College Station, TX, U.S.A.) with a significance threshold of p\u0026thinsp;\u0026le;\u0026thinsp;0.05, and all estimates were calculated using sample weights according to the National Center for Health Statistics (NCHS) analytic guidelines. Because NHANES is designed to generate data representative of the U.S. civilian noninstitutionalized population, these sample weights ensure that the study results are generalizable to the broader U.S. population. Weighted multiple linear regression analysis was used to evaluate the linear relationship between chronic cough and chronic diarrhea. The study included three models: model 1 required no variable adjustment; model 2 accounted for age, sex, and race; and model 3 adjusted for all covariates. In addition, subgroup analyses and interaction tests were performed.\u003c/p\u003e "},{"header":"Results","content":"\u003cp\u003e3.1. Participant characteristics\u003c/p\u003e\n\u003cp\u003eAfter applying the inclusion and exclusion criteria, 7,007 participants aged over 40 were enrolled in the study. The sample consisted of 50.57% males and 49.43% females, with 77.42% being Non-Hispanic White, 9.23% Non-Hispanic Black, 6.01% Mexican American, 3.08% Other Hispanic, and 4.25% from other racial backgrounds. The Chronic coughing and non-Chronic coughing populations accounted for 9% and 91% of the total population, respectively. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the clinical characteristics of the participants. The proportion of individuals reporting chronic cough in the general population was 8.65%. However, this proportion increased significantly to 13.48% in the chronic diarrhea population. (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Mexican American and Non-Hispanic Black women with diarrhea were more likely to cough for more than three months, be older, have a lower socioeconomic status, have a higher BMI, have higher smoke exposure, abuse alcohol, have sleep disorders, and suffer from depression (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Detailed demographic data for all survey respondents are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of participants according to the chronic diarrhea\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (n\u0026thinsp;=\u0026thinsp;7007)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal(n\u0026thinsp;=\u0026thinsp;6390)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChronic diarrhea(n\u0026thinsp;=\u0026thinsp;617)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandardize diff.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP 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\u003eCough %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155 (0.154, 0.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155 (0.154, 0.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\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\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111 (0.110, 0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111 (0.110, 0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\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\u003e40-59years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137 (0.136, 0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e\u0026ge;\u0026thinsp;60years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137 (0.136, 0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\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\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080 (0.079, 0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012 (0.011, 0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148 (0.148, 0.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.143 (0.142, 0.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015 (0.014, 0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\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\u003eThe poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.171 (0.170, 0.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eThe middle class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091 (0.090, 0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eThe wealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201 (0.201, 0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking status %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.139 (0.138, 0.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e25.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.139 (0.138, 0.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m2) %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\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\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035 (0.034, 0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077 (0.076, 0.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.144 (0.143, 0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.196 (0.195, 0.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCotinine category %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\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\u003e\u0026lt;\u0026thinsp;0.05 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099 (0.098, 0.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e\u0026ge;\u0026thinsp;0.05 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099 (0.098, 0.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression Severity, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\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\u003eNone and mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274 (0.273, 0.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eModerately to sever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274 (0.273, 0.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disorders %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\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\u003e27.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.122 (0.121, 0.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.122 (0.121, 0.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e3.2. The relationship between chronic cough and chronic diarrhea\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the relationship between chronic cough and chronic diarrhea. All models indicate a positive correlation between chronic cough and chronic diarrhea. After adjusting for all confounding variables, the incidence of chronic diarrhea among individuals with Chronic cough was 1.503 times higher than that among those without Chronic cough [odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.503, 95% confidence interval (95% CI): 1.065\u0026ndash;2.120, p\u0026thinsp;=\u0026thinsp;0.020].\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe relationship between chronic cough and chronic diarrhea\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003cp\u003eCrude model (model 1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimally adjusted model (model 2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFully adjusted model (model 3)\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\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.646(1.181,2.294)0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.694(1.213,2.368)0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.503(1.065, 2.120)0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eOR, odds ratio; CI, confidence intervals;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eModel 1: Unadjusted; Model 2: Adjusted for age, race, and gender; Model 3: Adjusted for age, race, gender, body mass index, the family poverty income ratio, cotinine category, drinking status, depression severity, sleep disorders.\u003c/p\u003e\n\u003cp\u003e3.3. Subgroup analysis\u003c/p\u003e\n\u003cp\u003eAs presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the analysis showed a significantly lower risk of chronic diarrhea in females (OR\u0026thinsp;=\u0026thinsp;0.685, P\u0026thinsp;=\u0026thinsp;0.043), non-Hispanic whites (OR\u0026thinsp;=\u0026thinsp;0.658, P\u0026thinsp;=\u0026thinsp;0.021), overweight group (OR\u0026thinsp;=\u0026thinsp;0.482, P\u0026thinsp;=\u0026thinsp;0.001), and those with no sleep disorders (OR\u0026thinsp;=\u0026thinsp;0.627, P\u0026thinsp;=\u0026thinsp;0.004) suggesting that these factors may have a protective effect; while other variables (e.g., other classifications in race, drinking status, obese group in BMI, etc.) did not show significant associations. In the interaction analysis, only the interaction effect of sleep disorder approached significance (P\u0026thinsp;=\u0026thinsp;0.057), suggesting that it may modify the effects of other variables, but the interactions of the remaining covariates (sex, race, BMI, etc.) were not significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Overall, this suggests that outcome risk is more independently influenced by gender, race, weight status, and sleep health.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe relationship between chronic cough and chronic diarrhea in various subgroups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCovariate Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP for interaction\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\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56237433\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\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.808756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.295027496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.684957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042818991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73547248\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\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8550163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.680954876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4488919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.143629221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6578299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021097224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9720833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.931983862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4503292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231896297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92468081\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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7581871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086541779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e0.6952265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.161432946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34356398\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\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4000849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.822890938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6306116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201367047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4823259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00113377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.207031198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression Severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84069805\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\u003eNone and mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.740586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047289333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eModerately to sever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7692326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.413335772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCotinine category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41199348\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\u003e\u0026lt;\u0026thinsp;0.05 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6811301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074733505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e\u0026ge;\u0026thinsp;0.05 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.210235834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05711638\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\u003e1.0854018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.750261082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.627194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003839421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42103437\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\u003e40-59years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6729283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047777398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e\u0026ge;\u0026thinsp;60years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.804697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.247177185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89504614\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\u003eThe poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.672925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146852441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eThe middle class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7659791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.143709309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003eThe wealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7422463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.352109551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur cross-sectional survey revealed a significant positive correlation between chronic cough and chronic diarrhea among adults in the United States, which persisted across all adjusted models. Subgroup analyses and interaction evaluations showed that different covariates did not have a significant impact on the relationship between chronic cough and chronic diarrhea. To our knowledge, this constitutes the first cross-sectional investigation exploring the association between chronic cough and chronic diarrhea.\u003c/p\u003e\n\u003cp\u003eThe findings of this study demonstrate a significant independent association between chronic cough and chronic diarrhea in U.S. adults, even after adjusting for smoking, sleep disorders, and socioeconomic factors. This aligns with evidence supporting the gut-lung axis hypothesis, which posits bidirectional communication between the gastrointestinal and respiratory systems through shared immune, neural, and microbial pathways \u003csup\u003e[13]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eMechanistic Insights from the Gut-Lung Axis\u003c/h3\u003e\n\u003cp\u003eThis study discusses the finding that the association between chronic cough and chronic diarrhea is stable across different populations. This phenomenon may be mediated through various interactive mechanisms of the gut-lung axis. First, microbe-mediated immune regulation may be the core pathway: dysbiosis of the gut microbiota can trigger a Th2-type immune shift, activating both intestinal and respiratory mast cells through circulating cytokines, leading to mucosal barrier disruption and increased neural sensitivity \u003csup\u003e[14\u0026ndash;17]\u003c/sup\u003e. Secondly, the vagus nerve pathway may play a bridging role. Microbial metabolites in the gut-brain axis, such as short-chain fatty acids, can regulate the activity of the enteric nervous plexus and enhance the sensitivity of the cough reflex arc through afferent fibers of the vagus nerve\u003csup\u003e[18, 19]\u003c/sup\u003e. It is noteworthy that a research\u003csup\u003e[20]\u003c/sup\u003e confirms that fecal microbiota transplantation can improve pulmonary immune responses by restoring short-chain fatty acid levels, suggesting that microbial metabolites may be key mediators of bidirectional regulation.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\n\u003cp\u003eThis study has several limitations. First, its cross-sectional design precludes causal inference. Longitudinal studies are needed to determine whether chronic cough precedes diarrhea or vice versa. Second, reliance on self-reported symptoms may introduce recall bias, though NHANES\u0026rsquo; standardized questionnaires mitigate this concern. Third, while we adjusted for key confounders, unmeasured variables (e.g., dietary habits, specific medications) could influence the association. Future research should incorporate biomarkers of inflammation (e.g., fecal calprotectin, serum IL-6) and gut microbiome profiling to elucidate mechanistic links.\u003c/p\u003e\n\u003cp\u003eIn clinical practice, this study offers a new perspective on comorbidity management: 1) Priority of psychological comorbidity intervention: Research\u003csup\u003e[21\u0026ndash;23]\u003c/sup\u003e has shown that the depression rate in chronic cough patients can reach as high as 61.2%. Similarly, depressive states also have an impact on the gut. \u003csup\u003e[24]\u003c/sup\u003eThis suggests that psychological assessment should be integrated into routine diagnosis and treatment. 2)The synergy of treatment strategies: A study\u003csup\u003e[25]\u003c/sup\u003e found that chronic cough patients with comorbid psychological conditions had a poorer response to proton pump inhibitors, suggesting the need for combined neuroregulatory treatments. 3) The potential value of microbial intervention: Study\u003csup\u003e[26]\u003c/sup\u003e pointed out that regulating the gut microbiota may improve both respiratory and digestive symptoms by restoring immune homeostasis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study confirms the independent association between chronic cough and chronic diarrhea, with mechanisms potentially involving abnormal vagus nerve regulation and the interaction of systemic inflammation. Although the cross-sectional design limits causal inference, it provides population-level evidence for the \"lung-gut axis\" theory. Future research should focus on verifying specific pathways through mechanistic studies and developing targeted therapeutic strategies to achieve a shift from symptom control to etiological intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributionsAll authors made significant contributions to this research. Kuang Du(KD) was responsible for data collection, investigation, and drafting the manuscript. Xiao-Juan Tang (XT) focused on the study design, performed the statistical analyses, and contributed to manuscript writing. Liang Zhao (LZ) supervised the project, offered critical guidance, and refined the manuscript through detailed review and editing. Yong-Heng He (YH) supervised the project, offered critical guidance, and refined the manuscript through detailed review and editing. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe NHANES dataset used for this study is publicly available and can be found at: https://wwwn.cdc.gov/nchs/nhanes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSchiller, L.R., D.S. Pardi, and J.H. 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Li, \u003cem\u003eAssociation Between Selenium Intake with Chronic Constipation and Chronic Diarrhea in Adults: Findings from the National Health and Nutrition Examination Survey.\u003c/em\u003e Biol Trace Elem Res, 2021. \u003cstrong\u003e199\u003c/strong\u003e(9): p. 3205-3212.\u003c/li\u003e\n\u003cli\u003eSingh, P., et al., \u003cem\u003eDemographic and Dietary Associations of Chronic Diarrhea in a Representative Sample of Adults in the United States.\u003c/em\u003e Am J Gastroenterol, 2018. \u003cstrong\u003e113\u003c/strong\u003e(4): p. 593-600.\u003c/li\u003e\n\u003cli\u003eSommers, T., et al., \u003cem\u003ePrevalence of Chronic Constipation and Chronic Diarrhea in Diabetic Individuals in the United States.\u003c/em\u003e Am J Gastroenterol, 2019. \u003cstrong\u003e114\u003c/strong\u003e(1): p. 135-142.\u003c/li\u003e\n\u003cli\u003eJiang, M. and H. Zhao, \u003cem\u003eAssociation of chronic cough with exposure to polycyclic aromatic hydrocarbons in the US population.\u003c/em\u003e Heliyon, 2024. \u003cstrong\u003e10\u003c/strong\u003e(1): p. e23413.\u003c/li\u003e\n\u003cli\u003eWu, W., et al., \u003cem\u003eAssociation between exposure to per- and polyfluoroalkyl substances (PFAS) and chronic cough in American adults: Results from NHANES 2003-2012.\u003c/em\u003e Ecotoxicol Environ Saf, 2025. \u003cstrong\u003e291\u003c/strong\u003e: p. 117901.\u003c/li\u003e\n\u003cli\u003e Kroenke, K., R.L. Spitzer, and J.B. Williams, \u003cem\u003eThe PHQ-9: validity of a brief depression severity measure.\u003c/em\u003e J Gen Intern Med, 2001. \u003cstrong\u003e16\u003c/strong\u003e(9): p. 606-13.\u003c/li\u003e\n\u003cli\u003e Nwosu, B.U. and P. Kum-Nji, \u003cem\u003eTobacco smoke exposure is an independent predictor of vitamin D deficiency in US children.\u003c/em\u003e PLoS One, 2018. \u003cstrong\u003e13\u003c/strong\u003e(10): p. e0205342.\u003c/li\u003e\n\u003cli\u003e Zhao, Y., et al., \u003cem\u003eAssociation between family income to poverty ratio and HPV infection status among U.S. women aged 20 years and older: a study from NHANES 2003-2016.\u003c/em\u003e Front Oncol, 2023. \u003cstrong\u003e13\u003c/strong\u003e: p. 1265356.\u003c/li\u003e\n\u003cli\u003e Budden, K.F., et al., \u003cem\u003eEmerging pathogenic links between microbiota and the gut-lung axis.\u003c/em\u003e Nat Rev Microbiol, 2017. \u003cstrong\u003e15\u003c/strong\u003e(1): p. 55-63.\u003c/li\u003e\n\u003cli\u003e De Nuccio, F., P. Piscitelli, and D.M. Toraldo, \u003cem\u003eGut-lung Microbiota Interactions in Chronic Obstructive Pulmonary Disease (COPD): Potential Mechanisms Driving Progression to COPD and Epidemiological Data.\u003c/em\u003e Lung, 2022. \u003cstrong\u003e200\u003c/strong\u003e(6): p. 773-781.\u003c/li\u003e\n\u003cli\u003e Baldi, S., et al., \u003cem\u003eFirst Exploration of the Altered Microbial Gut-Lung Axis in the Pathogenesis of Human Refractory Chronic Cough.\u003c/em\u003e Lung, 2024. \u003cstrong\u003e202\u003c/strong\u003e(2): p. 107-118.\u003c/li\u003e\n\u003cli\u003e Guo, Y., et al., \u003cem\u003eSodium houttuyfonate modulates the lung Th1/Th2 balance and gut microbiota to protect against pathological changes in lung of ovalbumin-induced asthmatic mice.\u003c/em\u003e J Asthma, 2024. \u003cstrong\u003e61\u003c/strong\u003e(12): p. 1759-1771.\u003c/li\u003e\n\u003cli\u003e Sultan, M., et al., \u003cem\u003eEndocannabinoid Anandamide Attenuates Acute Respiratory Distress Syndrome through Modulation of Microbiome in the Gut-Lung Axis.\u003c/em\u003e Cells, 2021. \u003cstrong\u003e10\u003c/strong\u003e(12).\u003c/li\u003e\n\u003cli\u003e Cheng, Y., et al., \u003cem\u003eTherapeutic role of gut microbiota in lung injury-related cognitive impairment.\u003c/em\u003e Front Nutr, 2024. \u003cstrong\u003e11\u003c/strong\u003e: p. 1521214.\u003c/li\u003e\n\u003cli\u003e Yata, V.K., \u003cem\u003eEx vivo and miniaturized in vitro models to study microbiota-gut-brain axis.\u003c/em\u003e 3 Biotech, 2024. \u003cstrong\u003e14\u003c/strong\u003e(11): p. 280.\u003c/li\u003e\n\u003cli\u003e Le Guern, R., et al., \u003cem\u003eGut colonisation with multidrug-resistant Klebsiella pneumoniae worsens Pseudomonas aeruginosa lung infection.\u003c/em\u003e Nat Commun, 2023. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 78.\u003c/li\u003e\n\u003cli\u003e Arinze, J.T., et al., \u003cem\u003eThe interrelationship of chronic cough and depression: a prospective population-based study.\u003c/em\u003e ERJ Open Res, 2022. \u003cstrong\u003e8\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003e \u003cem\u003eChronic diarrhea.\u003c/em\u003e Am Fam Physician, 2011. \u003cstrong\u003e84\u003c/strong\u003e(10): p. 1133-4.\u003c/li\u003e\n\u003cli\u003e Li, Y., et al., \u003cem\u003eAnxiety and depression are associated with reduced quality of life and increased cough severity in chronic cough.\u003c/em\u003e Asian Pac J Allergy Immunol, 2022.\u003c/li\u003e\n\u003cli\u003e Ballou, S., et al., \u003cem\u003eChronic Diarrhea and Constipation Are More Common in Depressed Individuals.\u003c/em\u003e Clin Gastroenterol Hepatol, 2019. \u003cstrong\u003e17\u003c/strong\u003e(13): p. 2696-2703.\u003c/li\u003e\n\u003cli\u003e Zhang, T., et al., \u003cem\u003ePsychological morbidity and chronic cough: which is predominant? A comparison of clinical characteristics.\u003c/em\u003e Ther Adv Chronic Dis, 2023. \u003cstrong\u003e14\u003c/strong\u003e: p. 20406223231173628.\u003c/li\u003e\n\u003cli\u003e Brister, D., et al., \u003cem\u003eEmerging drugs in the treatment of chronic cough.\u003c/em\u003e Expert Opin Emerg Drugs, 2023. \u003cstrong\u003e28\u003c/strong\u003e(2): p. 67-77.\u003c/li\u003e\n\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":"Epidemiological, Associations, Chronic cough, chronic diarrhea, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6456030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6456030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on data from the National Health and Nutrition Examination Survey (NHANES), this study analyzed the association between chronic cough and chronic diarrhea in 7,007 adults aged 40 years and older. Results showed that 8.81% of participants had chronic diarrhea, and after adjusting for confounders such as smoking and sleep by binary logistic regression, patients with chronic cough had a 50% increased risk of chronic diarrhea compared to those without cough (OR\u0026thinsp;=\u0026thinsp;1.50).9 Subgroup analyses showed that none of the covariates (e.g., age, gender) interacted with the outcome significantly (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 by interaction test), suggesting that these factors influenced the outcome independently rather than through an interaction. This study demonstrated for the first time in a large population the positive correlation between chronic cough and chronic diarrhea, supporting the \u0026ldquo;lung-gut axis\u0026rdquo; theory and emphasizing the need for synergistic interventions for gastrointestinal symptoms in the management of chronic cough.9 The results of this study provide new perspectives for the integrated management of the two co-morbidities in the clinical setting, including the synergistic effects of psychosocial co-morbidities, therapeutic strategies, and the potential for microbiological interventions. synergistic effects and the potential for microbial intervention.\u003c/p\u003e","manuscriptTitle":"Epidemiological Associations Between Chronic Cough and Diarrhea among Adults in the United States: A Population-Based Analysis of NHANES Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 09:31:40","doi":"10.21203/rs.3.rs-6456030/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":"9fb237ea-0b60-4f11-a6ad-fe7383c8d1ba","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49925154,"name":"Health sciences/Diseases"},{"id":49925155,"name":"Health sciences/Diseases/Gastrointestinal diseases"},{"id":49925156,"name":"Health sciences/Diseases/Respiratory tract diseases"}],"tags":[],"updatedAt":"2025-10-06T09:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 09:31:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6456030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6456030","identity":"rs-6456030","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}