The interaction between CCR and disability on the risk of chronic obstructive pulmonary disease: evidence from the China Health and Retirement Longitudinal Study Database

The interaction between CCR and disability on the risk of chronic obstructive pulmonary disease: evidence from the China Health and Retirement Longitudinal Study Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The interaction between CCR and disability on the risk of chronic obstructive pulmonary disease: evidence from the China Health and Retirement Longitudinal Study Database Chao Cheng, Xiaoyu Wang, Min Huang, Jiafu He, Yan Li, Yunhua Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7413461/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Although previous studies have separately examined the association between the serum creatinine to cystatin C ratio (CCR) or disability and chronic obstructive pulmonary disease (COPD), the interaction between these factors and their combined impact on COPD prevalence remains insufficiently explored. Methods Self-reported physician-diagnosed COPD, as measured by the validated CHARLS baseline questionnaire, served as the primary outcome. Logistic regression was utilized to assess the association between CCR, disability, and COPD, as well as to evaluate the interaction between these factors and COPD prevalence. Results. Among the 9,668 participants, 1,835 (19.0%) had a COPD diagnosis. The risk of COPD was significantly higher in the low-level CCR group compared to the high-level CCR group (OR = 1.15, 95% CI: 1.03–1.29, P = 0.014). Similarly, individuals with disability exhibited a significantly higher risk of COPD compared to non-disabled individuals (OR = 1.19, 95% CI: 1.04–1.37, P = 0.013). The co-presence of low-level CCR and disability substantially increased the risk of COPD (OR = 1.60, 95% CI: 1.34–1.91, P < 0.001). Conclusions This study identified low-level CCR and disability as independent risk factors for COPD in middle-aged and older adults in China and demonstrated their synergistic effect in increasing COPD risk. A comprehensive prevention and control strategy that integrates nutritional support, muscle function maintenance, and functional ability interventions is recommended for the clinical management of patients with COPD. COPD serum creatinine to cystatin C ratio disability Figures Figure 1 Introduction Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition marked by chronic respiratory symptoms such as dyspnea, cough, and sputum production, alongside persistent and progressively worsening airflow limitation. This limitation arises due to airway abnormalities (bronchitis, bronchiolitis) and/or alveolar damage (emphysema) [ 1 ]. The prevalence and mortality rates of COPD remain substantial globally [ 2 ]. According to the World Health Organization (WHO), COPD is the third leading cause of death worldwide, affecting over 380 million individuals [ 3 , 4 ]. Traditionally, the risk factors for COPD have centered on environmental influences like smoking, air pollution, and exposure to occupational dust [ 3 ]. In recent years, research has increasingly highlighted that systemic pathophysiological changes (e.g., sarcopenia) and reduced functional capacity (e.g., disability) further contribute to the heightened risk of COPD[ 5 , 6 ]. The serum creatinine to cystatin C ratio (CCR) has recently emerged as a potential marker for muscle mass. Cystatin C (CysC) is a stable, non-glycated, low-molecular-weight protein that is less influenced by factors such as age, gender, and muscle mass. Blood creatinine (Cr), primarily produced through a slow, non-enzymatic cyclization of creatine in the liver, is then transported to muscles, where 98% of creatine is stored, and subsequently released into the bloodstream to be excreted by the kidneys [ 7 , 8 ]. As Cr levels are closely tied to muscle content, the CCR ratio has been proposed as a tool for assessing muscle mass. Kashani et al. [ 9 ] reported lower CCR levels in patients with sarcopenia, suggesting its potential utility in evaluating sarcopenia[ 10 , 11 ]. Sarcopenia, a syndrome characterized by progressive loss of skeletal muscle mass, strength, and function, has been closely linked to various chronic conditions, including cardiovascular disease and diabetes [ 12 ]. Skeletal muscle dysfunction is particularly pronounced in patients with COPD, potentially due to mechanisms such as chronic hypoxia, systemic inflammation, oxidative stress, and reduced physical activity [ 13 , 14 ]. The prevalence of sarcopenia among patients with COPD ranges from 8.4–52.1%[ 15 ] and is strongly associated with worsened lung function, reduced exercise capacity, and an increased risk of hospitalization. Disability, a critical challenge in healthy aging, is notably prevalent in patients with COPD[ 16 ]. Beyond physical immobility, it encompasses multidimensional functional deficits, including reduced respiratory muscle function, energy metabolism imbalances, and psychosocial disorders [ 17 , 18 ]. Epidemiological data consistently indicate a significantly higher incidence of disability in patients with COPD compared to healthy populations [ 19 – 21 ]. While the individual associations between CCR, disability, and COPD have been investigated in prior studies [ 20 , 22 , 23 ], the combined effects of their interactions on COPD prevalence remain insufficiently explored. This interaction may arise from a confluence of biological mechanisms, such as inflammatory cytokine-driven muscle-lung tissue interactions[ 24 ], and behavioral factors, like systemic metabolic dysregulation due to diminished physical activity[ 25 ]. However, the precise pathways are yet to be fully defined. Thus, understanding the synergistic impact of disability and CCR on the onset and progression of COPD is essential for refining the multidimensional etiological model of the disease, and may also lay the groundwork for developing intervention strategies focused on muscle function and physical mobility. This study aims to elucidate the interaction between disability and CCR in relation to COPD prevalence through both epidemiological analysis and mechanism exploration, with particular emphasis on their combined effects in the pathophysiological process. The findings are anticipated to offer novel insights for the early prevention, risk stratification, and comprehensive management of COPD. Method Study design and populations Data from the CHARLS project were utilized in this analysis. In 2011, a baseline survey was conducted across 150 county-level and 450 village-level units in 28 provinces, collecting information on the socio-economic and health status of middle-aged and elderly individuals. Follow-up surveys have been conducted biennially since then [ 26 ]. The current analysis used data from the follow-up surveys carried out between July 1 and September 30, 2015, which included sociodemographic characteristics and health behavior factors. A total of 13,273 individuals participated in the 2015 survey, from which 1,904 participants lacking COPD diagnostic information were excluded. Additionally, 1,701 individuals with missing data on other variables (such as ability to perform daily living activities, CCR, gender, age, residence, alcohol consumption, and smoking) were further excluded. The final analysis included 9,668 participants (Fig. 1 ). The CHARLS survey was approved by the Ethics Review Board of Peking University (approval number: IRB0000105211015), and all participants provided written informed consent [ 26 ]. Definition of chronic obstructive pulmonary disease COPD diagnosis was assessed using the CHARLS baseline questionnaire, which included the question, "Have you ever been diagnosed with COPD by a doctor?"[ 27 ]. Participants who answered "yes" were classified as having COPD, and those who answered "no" were considered non-COPD. Definition of CCR CCR, defined as the ratio of Cr to CysC, was treated as a dichotomous variable. The median value of 0.928 was used as the cutoff point, with values ≥ 0.928 classified as high-level CCR (coded as 0) and values < 0.928 classified as low-level CCR (coded as 1)[ 28 – 30 ]. Definition of disability Impaired activities of daily living (IADL) are a primary cause of disability in the elderly. The IADL scale [ 31 ] was used to assess disability among participants. The scale evaluates the ability to live independently across eight domains: going out for activities, shopping, food preparation, housework, laundry, medication management, telephone use, and financial management. Each item is rated as "no difficulty," "difficulty, can complete independently," "difficulty, requires help," or "unable to complete." If any item indicates "difficulty requiring help" or "inability to complete," the participant is classified as having IADL dysfunction, indicative of disability. Relevant covariates Sociodemographic characteristics (age, gender, current residence, marital status, education level) and health behavior factors (body mass index [BMI], smoking, alcohol consumption, health status, and depression) were collected through face-to-face interviews. Sociodemographic characteristics were defined as follows: gender was categorized as male or female; current residence as urban or rural; marital status as married (if the participant is currently married, regardless of whether they live with their spouse) or unmarried (if divorced, widowed, or never married); education level was classified into "elementary school or below," "junior high school graduate," or "high school or above." For health behavior factors, BMI was calculated as weight (kg) divided by the square of height (m). Participants with a BMI < 18.5 kg/m 2 were classified as underweight, those with a BMI between 18.5 kg/m 2 and 24.9 kg/m 2 were categorized as normal weight, those with a BMI between 25.0 kg/m 2 and 29.9 kg/m 2 as overweight, and those with a BMI ≥ 30.0 kg/m 2 as obese, with normal weight serving as the reference category[ 32 ]. Smoking status was categorized as non-smokers (including those who had quit) and current smokers. Alcohol consumption was categorized as non-drinkers (including those abstaining) and current drinkers. Health status was assessed by the question, "How do you feel about your own health?" and responses were divided into three groups: "very good" or "good" as group 1, "average" as group 2, and "bad" or "very bad" as group 3. Depression was assessed using the 10-item Depression Self-Rating Scale (CESD-10), with participants scoring ≥ 10 points considered to have depressive symptoms, and those with < 10 points categorized as having no depressive symptoms [ 33 ]. Statistical analysis To compare characteristics between groups with and without COPD, continuous variables were expressed as means ± standard deviations (xˉ ± s) for normally distributed data, and the independent samples t-test was used for comparisons. Categorical variables were presented as proportions, and the chi-square test was applied to assess differences between groups. A logistic regression model was employed to evaluate factors associated with the prevalence of COPD, reporting odds ratios (OR) with 95% confidence intervals. The interaction between CCR and disability in relation to COPD prevalence was also analyzed. Statistical analyses were performed using IBM SPSS Statistics 26, with a two-tailed P value of < 0.05 considered statistically significant. Results Among the 9,668 participants (Table 1 ), 1,835 (19.0%) were diagnosed with COPD. The COPD group was predominantly male and unmarried. Regarding educational attainment, although statistical differences in distribution were observed between groups, individuals with a high school education or higher constituted the majority in both groups. Participants in the COPD group were more likely to reside in rural areas and have a history of smoking. Significant differences were observed in BMI distribution between the groups, with a notably higher proportion of underweight individuals in the COPD group compared to the non-COPD group. The COPD group also exhibited a higher proportion of disability and depression. Furthermore, the CCR values in the COPD group were significantly lower than those in the non-COPD group. Table 1 Characteristics of Participants by Number of COPD Characteristics a COPD(N = 9668) P value YES(n = 1835) NO(n = 7833) Age,y 63.37(9.59) 59.88(9.84) = 0.094 Gender Male 958(52.2) 3276(41.8) <0.001 Female 877(47.8) 4557(58.2) Marital status Married 1528(83.3) 6931(88.5) <0.001 Unmarried 307(16.7) 902(11.5) Educational level completed Primary school or below 346(18.9) 1297(16.6) = 0.003 Middle school 53(2.9) 331(4.2) High school or above 1436(78.2) 6205(79.2) Area of residence Rural 1450(79.0) 5822(74.3) <0.001 Urban 385(21.0) 2011(25.7) Current smoking status Yes 635(34.6) 1945(24.8) <0.001 No 1200(65.4) 5888(75.2) Current drinking status Yes 603(32.9) 2618(33.4) = 0.646 No 1232(67.1) 5215(66.6) BMI(kg/m2) Normal 1023(55.7) 4567(58.3) <0.001 Underweight 192(10.5) 355(4.5) Overweight 507(27.5) 2439(31.1) Obese 113(6.3) 472(6.1) Disability Yes 403(22.0) 1130(14.4) <0.001 No 1432(78.0) 6703(85.6) Health condition Q1 253(13.8) 2314(29.5) <0.001 Q2 972(53.0) 4426(56.5) Q3 610(33.2) 1093(14.0) Depression Yes 811(44.2) 2151(27.5) <0.001 No 1024(55.8) 5682(72.5) CCR 0.95(0.26) 0.97(0.27) = 0.005 Abbreviations: COPD, chronic obstructive pulmonary disease; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared);CCR,The serum creatinine/cystatin C ratio. a Continuous data are reported as the mean (SD), and categorical data are reported as the number and percentage of participants. BMI:Normal:18.5kg/m 2 ≤ BMI < 25.0kg/m 2 ;Underweight:<18.5 kg/m 2 ;Overweight:25.0 kg/m 2 ≤ BMI < 30.0 kg/m 2 ;Obese:BMI ≥ 30.0 kg/m 2 . Health condition: Q1:excellent or very good or good;Q2:fair;Q3:poor or very poor. To investigate the independent association of various factors with COPD risk among middle-aged and older adults, multivariate logistic regression analysis was conducted (Table 2 ). Underweight participants had a significantly increased risk of COPD compared to those with a normal BMI (OR = 1.97, 95% CI: 1.61–2.39, P < 0.001). Current smokers were at a significantly higher risk of COPD than non-smokers (OR = 1.29, 95% CI: 1.13–1.49, P < 0.001). The risk of COPD was significantly higher among individuals who rated their health as good, very good, or excellent (OR = 1.98, 95% CI: 1.70–2.29, P < 0.001), and even more so for those who rated their health as poor or very poor (OR = 4.78, 95% CI: 4.03–5.67, P < 0.001). The risk of COPD was significantly lower in women and married individuals. Table 2 Logistic regression analysis of the prevalence of chronic obstructive pulmonary disease in middle-aged and older adults Variable OR (95%CI) P BMI(ref=Normal) Underweight 1.97(1.61,2.39) <0.001 Overweight 1.01(0.90,1.14) = 0.85 Obese 1.20(0.96,1.50) = 0.12 Gender(ref = male) Female 0.60(0.52,0.69) <0.001 Marital status (ref = unmarried) Married 0.69(0.59,0.80) <0.001 Educational level(ref = Primary school or below) Middle school 0.67(0.49,0.93) = 0.02 High school or above 0.97(0.85,1.12) = 0.68 Area of residence(ref = Urban) Rural 1.10(0.96,1.25) = 0.16 Current smoking status(ref = NO) YES 1.29(1.13,1.49) <0.001 Current drinking status(ref = NO) YES 0.89(0.79,1.01) = 0.08 Health condition(ref = excellent or very good or good) fair 1.98(1.70,2.29) <0.001 poor or very poor 4.78(4.03,5.67) <0.001 Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared). BMI:Normal:18.5kg/m 2 ≤ BMI < 25.0kg/m 2 ;Underweight:<18.5 kg/m 2 ;Overweight:25.0 kg/m 2 ≤ BMI < 30.0 kg/m 2 ;Obese:BMI ≥ 30.0 kg/m 2 . To assess the relationship between CCR and COPD risk, logistic regression analyses were performed (Table 3 ). Without adjusting for potential confounders, participants in the low-level CCR group exhibited a significantly higher risk of COPD than those in the high-level CCR group (OR = 1.12, 95% CI: 1.02–1.24, P = 0.025). After adjusting for gender, smoking status, alcohol consumption, BMI, marital status, education level, area of residence, and health status, the risk of COPD remained significantly higher in the low-level CCR group compared to the high-level CCR group (OR = 1.15, 95% CI: 1.03–1.29, P = 0.014). These results suggest that lower CCR levels are an independent risk factor for COPD in middle-aged and older adults, even after controlling for sociodemographic characteristics and health behavior factors. Table 3 The correlation between CCR and COPD in middle-aged and older adults COPD OR (95% CI) by No. of CCR P value 0 1 Model 1 a 1 [Reference] b 1.12(1.02–1.24) = 0.025 Model 2 c 1 [Reference] 1.24(1.11–1.39) <0.001 Model 3 d 1 [Reference] 1.15(1.03–1.29) = 0.014 Abbreviations: COPD, chronic obstructive pulmonary disease; CCR,The serum creatinine/cystatin C ratio. a Model 1 was the crude model. b Reference: CCR ≥ 0.928, high-level CCR, defined as 0. CCR < 0.928, low-level CCR, defined as 1. c Model2 was adjusted for gender, smoking and drinking status. d Model3 was adjusted for gender,BMI,smoking,drinking,Marital status,education,area of residence and Health condition. To investigate the relationship between disability and COPD risk in middle-aged and older adults, logistic regression analysis was conducted (Table 4 ). In the unadjusted model, individuals with disability exhibited a significantly higher risk of COPD compared to those without disability (OR = 1.67, 95% CI: 1.47–1.90, P < 0.001). After adjusting for gender, smoking status, alcohol consumption, BMI, marital status, education level, area of residence, and health status, the risk of COPD in the disability group remained significant but slightly diminished (OR = 1.19, 95% CI: 1.04–1.37, P = 0.013). This indicates that disability is an independent risk factor for COPD in middle-aged and older adults, even when controlling for sociodemographic and health behavior factors. Table 4 The correlation between Disability and COPD in middle-aged and older adults COPD OR (95% CI) by No. Of Disability P value 0 1 Model 1 a 1 [Reference] b 1.67(1.47–1.90) <0.001 Model 2 c 1 [Reference] 1.72(1.51–1.96) <0.001 Model 3 d 1 [Reference] 1.19(1.04–1.37) = 0.013 Abbreviations: COPD, chronic obstructive pulmonary disease. a Model 1 was the crude model. b Reference: there was no Disability. c Model2 was adjusted for gender, smoking and drinking status. d Model3 was adjusted for gender, BMI, smoking,drinking,Marital status,education,area of residence and Health condition. The results of the interaction analysis between low-level CCR and disability on COPD prevalence are presented in Table 5 . In the unadjusted model (Low-level CCR * Disability 1 ), the combination of low-level CCR and disability significantly increased the risk of COPD (OR = 1.781, 95% CI: 1.571–2.088, P < 0.001). After adjusting for potential confounders, including gender, age, BMI, smoking, alcohol consumption, marital status, and education level (Low-level CCR * Disability 2 ), the interaction remained significant, although the effect size was reduced (adjusted OR = 1.60, 95% CI: 1.34–1.91, P < 0.001). These results suggest that the coexistence of low-level CCR and disability is strongly associated with an increased risk of COPD. Table 5 Interaction between Low-level CCR and Disability on COPD prevalence Variable OR S Z P 95%CI Low-level CCR* Disability 1 1.78 0.08 7.09 < 0.001 1.57 2.09 Low-level CCR* Disability 2 1.60 0.09 5.13 < 0.001 1.34 1.91 Abbreviations: CCR,The serum creatinine/cystatin C ratio; low-level CCR < 0.928. COPD, chronic obstructive pulmonary disease. Low-level CCR* Disability 1 was the crude model. Low-level CCR* Disability 2 was adjusted for variables of gender, age,BMI, smoking,drinking,Marital status and education. S is the standard error, Z is the regression coefficient divided by its standard error, P is the test level, CI is the confidence interval, and CI is the 95% CI. Discussion This study utilized a large population sample (n = 9,668) to systematically investigate the multidimensional risk factors for COPD in middle-aged and older adults. Independent risk factors identified include low body weight, smoking, self-rated poor health, low-level CCR, and disability, while being female and married serve as protective factors against COPD. Notably, this study uncovered a significant synergistic effect between low-level CCR and disability in increasing the risk of COPD, offering a new perspective on the multifactorial pathogenesis of the disease. The elevated risk (OR = 1.97) associated with underweight participants may be linked to compromised ventilatory function (e.g., diaphragmatic atrophy) and weakened immune defenses [ 34 , 35 ]. Smoking (OR = 1.29), a well-established risk factor, was reaffirmed as a significant trigger in this population. [ 36 ] Self-rated poor health (OR = 4.78), reflecting both the burden of disease and depletion of physiological reserves, serves as a critical early warning indicator for COPD screening [ 37 ]. Estrogen may exert lung-protective effects, including anti-inflammatory, antioxidant, and alveolar repair properties [ 38 ]. Women generally have higher estrogen levels before menopause compared to men [ 39 ], which may partially account for their lower COPD risk. Additionally, women may display distinct patterns of lung inflammation in response to tobacco exposure, and gender-related differences in lung development and aging processes also warrant further exploration [ 40 ]. Marriage offers important social support and emotional bonding, with spouses playing a key role in promoting health, such as encouraging smoking cessation, ensuring regular medical check-ups, reminding medication adherence, and fostering healthier lifestyles (e.g., balanced diet and increased physical activity) [41] . These factors collectively help mitigate exposure to COPD risk factors, particularly smoking, and improve overall respiratory health[ 42 ]. Low-level CCR was significantly associated with COPD risk (OR = 1.12), with the association strengthening after adjusting for confounders like smoking (OR = 1.15). The reduction in CCR, a sensitive marker of muscle mass and nutritional status, points to an imbalance between skeletal muscle degradation and protein metabolism [ 43 ]. This aligns with the high prevalence of "extrapulmonary manifestations," such as sarcopenia, in patients with COPD[ 43 ]. Potential mechanisms include accelerated muscle protein breakdown driven by systemic inflammation (e.g., elevated TNF-α, IL-6), mitochondrial dysfunction due to hypoxia, and disuse atrophy from reduced physical activity [ 24 , 44 ]. The findings suggest that low-level CCR is not only a biomarker for COPD but also an independent contributor to its pathogenesis. The risk of COPD was significantly higher in patients with disabilities (OR = 1.19), and this association remained strong even after adjusting for sociodemographic factors, lifestyle, and health status. Disability, which reflects a decline in daily functional abilities, may directly contribute to limited ventilation, dyspnea, and increased energy expenditure in patients with COPD[ 45 ]. Notably, disability often precedes the onset of overt respiratory symptoms[ 46 ], suggesting its potential as an early warning marker for identifying high-risk populations. The interaction model between low-level CCR and disability highlighted how impaired muscle metabolism and decreased function together form a "vicious cycle." Reduced muscle reserve (low-level CCR) leads to physical dysfunction, which in turn limits activity levels, further exacerbating muscle atrophy and contributing to a cycle of impaired respiratory compensatory function [ 1 , 34 , 47 , 48 ]. This finding provides empirical evidence for the "systemic inflammatory-metabolic-dysfunctional" pathological model of COPD. This study innovatively integrated CCR and disability within a unified analytical framework. Unlike prior studies that focused on individual dimensions (either CCR or disability), the interaction model confirmed that the synergy between muscle metabolism and physical function constitutes a critical risk factor for COPD [ 1 ]. Strengths and Limitations The combined assessment of CCR and disability enhances the identification of high-risk groups, particularly in primary care settings. A dual-track intervention approach that targets both muscle metabolism (e.g., protein supplementation, resistance training) and functional maintenance (e.g., pulmonary rehabilitation, lifestyle modification) may prove more effective. However, several limitations should be acknowledged: 1) The cross-sectional design prevents the establishment of causal relationships and should be further validated in prospective cohort studies. 2) The CCR cut-point (0.928) was derived from this sample, and its generalizability needs to be tested in external populations. 3) The study did not include a stratified analysis based on pulmonary function severity, which could help explore the gradient of association between various factors and disease stages in future research. Conclusions In conclusion, this study identified low-level CCR and disability as independent risk factors for COPD in middle-aged and older adults in China and demonstrated their synergistic effect in increasing COPD risk. A comprehensive prevention and control strategy that integrates nutritional support, muscle function maintenance, and functional ability interventions is recommended for the clinical management of patients with COPD. Declarations Acknowledgements We would like to acknowledge the China Health and Retirement Longitudinal Study team for providing data and the training of using the dataset. Authors’ contributions Concept and design: Cui,Cheng Acquisition, analysis, or interpretation of data:Cui,Cheng,Huang,Wang . Drafting of the manuscript: Cheng. Critical revision of the manuscript for important intellectual content: He,Li,Cui. Statistical analysis: Cheng,Cui. Administrative, technical, or material support: He,Li,Huang. Supervision: Wang,Cui. Ethics approval and consent to participate Our research was performed in accordance with the Declaration of Helsinki. The original CHARLS project was approved by the ethical review committee of Peking University (IRB00001052–11015), and all participants signed an informed consent form at the time of enrollment. Funding No Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and material This study used open-access data from the China Health and Retirement Longitudinal Study, which could be downloaded from http://charls.ccer.edu.cn/zh-CN. Author details 1 Department of Emergency medicine,Department of Critical Care Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No.136,Jingzhou Street,Xiangcheng District,Xiangyang 441021, Hubei, China 2 Department of Respiratory and Critical Care Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No.136,Jingzhou Street,Xiangcheng District,Xiangyang 441021, Hubei, China Corresponding author: Yunhua Cui ,E-mail： [email protected] First author: Chao Cheng References Agustí A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes de Oca M et al : Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary . Eur Respir J 2023, 61 (4). Murray CJL: Findings from the Global Burden of Disease Study 2021 . Lancet 2024, 403 (10440):2259-2262. 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Beaudart C, McCloskey E, Bruyère O, Cesari M, Rolland Y, Rizzoli R, Araujo de Carvalho I, Amuthavalli Thiyagarajan J, Bautmans I, Bertière MC et al : Sarcopenia in daily practice: assessment and management . BMC Geriatr 2016, 16 (1):170. Barnes PJ, Celli BR: Systemic manifestations and comorbidities of COPD . Eur Respir J 2009, 33 (5):1165-1185. Agustí A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes de Oca M et al : Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary . Am J Respir Crit Care Med 2023, 207 (7):819-837. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G: Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS) . Int J Epidemiol 2014, 43 (1):61-68. Song P, Zha M, Xia W, Zeng C, Zhu Y: Asthma-chronic obstructive pulmonary disease overlap in China: prevalence, associated factors and comorbidities in middle-aged and older adults . Curr Med Res Opin 2020, 36 (4):667-675. Altman DG, Royston P: The cost of dichotomising continuous variables . Bmj 2006, 332 (7549):1080. Streiner DL: Breaking up is hard to do: the heartbreak of dichotomizing continuous data . Can J Psychiatry 2002, 47 (3):262-266. Tabara Y, Kohara K, Okada Y, Ohyagi Y, Igase M: Creatinine-to-cystatin C ratio as a marker of skeletal muscle mass in older adults: J-SHIPP study . Clin Nutr 2020, 39 (6):1857-1862. Lawton MP, Brody EM: Assessment of older people: self-maintaining and instrumental activities of daily living . Gerontologist 1969, 9 (3):179-186. Westmore MR, Chakraborty P, Thomas LA, Jenkins L, Ohri F, Baiden P: BMI moderates the association between adverse childhood experiences and COPD . J Psychosom Res 2022, 160 :110990. Cheng ST, Chan AC: The Center for Epidemiologic Studies Depression Scale in older Chinese: thresholds for long and short forms . Int J Geriatr Psychiatry 2005, 20 (5):465-470. Schols AM, Broekhuizen R, Weling-Scheepers CA, Wouters EF: Body composition and mortality in chronic obstructive pulmonary disease . Am J Clin Nutr 2005, 82 (1):53-59. Ferreira IM, Brooks D, White J, Goldstein R: Nutritional supplementation for stable chronic obstructive pulmonary disease . Cochrane Database Syst Rev 2012, 12 (12):Cd000998. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019 . Lancet 2020, 396 (10258):1223-1249. Eckel SP, Louis TA, Chaves PH, Fried LP, Margolis AH: Modification of the association between ambient air pollution and lung function by frailty status among older adults in the Cardiovascular Health Study . Am J Epidemiol 2012, 176 (3):214-223. Ambhore NS, Katragadda R, Raju Kalidhindi RS, Thompson MA, Pabelick CM, Prakash YS, Sathish V: Estrogen receptor beta signaling inhibits PDGF induced human airway smooth muscle proliferation . Mol Cell Endocrinol 2018, 476 :37-47. Barrett-Connor E, Goodman-Gruen D, Patay B: Endogenous sex hormones and cognitive function in older men . J Clin Endocrinol Metab 1999, 84 (10):3681-3685. Gortner L, Shen J, Tutdibi E: Sexual dimorphism of neonatal lung development . Klin Padiatr 2013, 225 (2):64-69. Homish GG, Leonard KE: Spousal influence on smoking behaviors in a US community sample of newly married couples . Soc Sci Med 2005, 61 (12):2557-2567. Holt-Lunstad J, Smith TB, Layton JB: Social relationships and mortality risk: a meta-analytic review . PLoS Med 2010, 7 (7):e1000316. Soeters PB, Reijven PL, van Bokhorst-de van der Schueren MA, Schols JM, Halfens RJ, Meijers JM, van Gemert WG: A rational approach to nutritional assessment . Clin Nutr 2008, 27 (5):706-716. Slebos DJ, van der Toorn M, Bakker SJ, Kauffman HF: Mitochondrial dysfunction in COPD patients with low body mass index . Eur Respir J 2007, 30 (3):600; author reply 600-601. Thomas MJ, Simpson J, Riley R, Grant E: The impact of home-based physiotherapy interventions on breathlessness during activities of daily living in severe COPD: a systematic review . Physiotherapy 2010, 96 (2):108-119. Vestbo J, Edwards LD, Scanlon PD, Yates JC, Agusti A, Bakke P, Calverley PM, Celli B, Coxson HO, Crim C et al : Changes in forced expiratory volume in 1 second over time in COPD . N Engl J Med 2011, 365 (13):1184-1192. Spruit MA, Singh SJ, Garvey C, ZuWallack R, Nici L, Rochester C, Hill K, Holland AE, Lareau SC, Man WD et al : An official American Thoracic Society/European Respiratory Society statement: key concepts and advances in pulmonary rehabilitation . Am J Respir Crit Care Med 2013, 188 (8):e13-64. Pitta F, Troosters T, Spruit MA, Probst VS, Decramer M, Gosselink R: Characteristics of physical activities in daily life in chronic obstructive pulmonary disease . Am J Respir Crit Care Med 2005, 171 (9):972-977. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 16 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 04 Sep, 2025 First submitted to journal 04 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7413461","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532929307,"identity":"bd06717f-e38c-4222-aefb-37667d9aa97b","order_by":0,"name":"Chao Cheng","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Cheng","suffix":""},{"id":532929308,"identity":"cc86777d-8477-4720-b1a8-3f6e6f27e44c","order_by":1,"name":"Xiaoyu Wang","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Wang","suffix":""},{"id":532929309,"identity":"d6e91bb2-20a5-475b-9acd-22d0574d2e94","order_by":2,"name":"Min Huang","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Huang","suffix":""},{"id":532929310,"identity":"073590bc-4de5-471b-8b74-81ab5095a134","order_by":3,"name":"Jiafu He","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Jiafu","middleName":"","lastName":"He","suffix":""},{"id":532929311,"identity":"579ac68a-0da3-4668-8bf1-cf04f3853656","order_by":4,"name":"Yan Li","email":"","orcid":"","institution":"Xiangyang Central 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19:34:54","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132022,"visible":true,"origin":"","legend":"","description":"","filename":"bbd493da23334d0d9264c3a6157e907e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7413461/v1/e37e7577e5a2aab93cfc506e.xml"},{"id":94139668,"identity":"115c8945-66b4-4358-9ac9-f192175b082a","added_by":"auto","created_at":"2025-10-22 19:34:55","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140002,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7413461/v1/3a813f490b820cd860159a3e.html"},{"id":94139662,"identity":"4e1dcc56-45a4-422e-bb41-cd1a9a44d640","added_by":"auto","created_at":"2025-10-22 19:34:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":188192,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7413461/v1/820c80d60d12b234eba46029.png"},{"id":94141634,"identity":"5d40a485-d83e-4f67-b053-0dc06648d3d6","added_by":"auto","created_at":"2025-10-22 20:06:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3055428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7413461/v1/2a691379-0270-4bdb-a038-3a66786ae84d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The interaction between CCR and disability on the risk of chronic obstructive pulmonary disease: evidence from the China Health and Retirement Longitudinal Study Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition marked by chronic respiratory symptoms such as dyspnea, cough, and sputum production, alongside persistent and progressively worsening airflow limitation. This limitation arises due to airway abnormalities (bronchitis, bronchiolitis) and/or alveolar damage (emphysema) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence and mortality rates of COPD remain substantial globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the World Health Organization (WHO), COPD is the third leading cause of death worldwide, affecting over 380\u0026nbsp;million individuals [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Traditionally, the risk factors for COPD have centered on environmental influences like smoking, air pollution, and exposure to occupational dust [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In recent years, research has increasingly highlighted that systemic pathophysiological changes (e.g., sarcopenia) and reduced functional capacity (e.g., disability) further contribute to the heightened risk of COPD[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe serum creatinine to cystatin C ratio (CCR) has recently emerged as a potential marker for muscle mass. Cystatin C (CysC) is a stable, non-glycated, low-molecular-weight protein that is less influenced by factors such as age, gender, and muscle mass. Blood creatinine (Cr), primarily produced through a slow, non-enzymatic cyclization of creatine in the liver, is then transported to muscles, where 98% of creatine is stored, and subsequently released into the bloodstream to be excreted by the kidneys [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As Cr levels are closely tied to muscle content, the CCR ratio has been proposed as a tool for assessing muscle mass. Kashani et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] reported lower CCR levels in patients with sarcopenia, suggesting its potential utility in evaluating sarcopenia[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Sarcopenia, a syndrome characterized by progressive loss of skeletal muscle mass, strength, and function, has been closely linked to various chronic conditions, including cardiovascular disease and diabetes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Skeletal muscle dysfunction is particularly pronounced in patients with COPD, potentially due to mechanisms such as chronic hypoxia, systemic inflammation, oxidative stress, and reduced physical activity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The prevalence of sarcopenia among patients with COPD ranges from 8.4\u0026ndash;52.1%[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and is strongly associated with worsened lung function, reduced exercise capacity, and an increased risk of hospitalization.\u003c/p\u003e\u003cp\u003eDisability, a critical challenge in healthy aging, is notably prevalent in patients with COPD[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Beyond physical immobility, it encompasses multidimensional functional deficits, including reduced respiratory muscle function, energy metabolism imbalances, and psychosocial disorders [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Epidemiological data consistently indicate a significantly higher incidence of disability in patients with COPD compared to healthy populations [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile the individual associations between CCR, disability, and COPD have been investigated in prior studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the combined effects of their interactions on COPD prevalence remain insufficiently explored. This interaction may arise from a confluence of biological mechanisms, such as inflammatory cytokine-driven muscle-lung tissue interactions[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and behavioral factors, like systemic metabolic dysregulation due to diminished physical activity[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the precise pathways are yet to be fully defined. Thus, understanding the synergistic impact of disability and CCR on the onset and progression of COPD is essential for refining the multidimensional etiological model of the disease, and may also lay the groundwork for developing intervention strategies focused on muscle function and physical mobility.\u003c/p\u003e\u003cp\u003eThis study aims to elucidate the interaction between disability and CCR in relation to COPD prevalence through both epidemiological analysis and mechanism exploration, with particular emphasis on their combined effects in the pathophysiological process. The findings are anticipated to offer novel insights for the early prevention, risk stratification, and comprehensive management of COPD.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and populations\u003c/h2\u003e\u003cp\u003eData from the CHARLS project were utilized in this analysis. In 2011, a baseline survey was conducted across 150 county-level and 450 village-level units in 28 provinces, collecting information on the socio-economic and health status of middle-aged and elderly individuals. Follow-up surveys have been conducted biennially since then [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The current analysis used data from the follow-up surveys carried out between July 1 and September 30, 2015, which included sociodemographic characteristics and health behavior factors. A total of 13,273 individuals participated in the 2015 survey, from which 1,904 participants lacking COPD diagnostic information were excluded. Additionally, 1,701 individuals with missing data on other variables (such as ability to perform daily living activities, CCR, gender, age, residence, alcohol consumption, and smoking) were further excluded. The final analysis included 9,668 participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The CHARLS survey was approved by the Ethics Review Board of Peking University (approval number: IRB0000105211015), and all participants provided written informed consent [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinition of chronic obstructive pulmonary disease\u003c/h3\u003e\n\u003cp\u003eCOPD diagnosis was assessed using the CHARLS baseline questionnaire, which included the question, \"Have you ever been diagnosed with COPD by a doctor?\"[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Participants who answered \"yes\" were classified as having COPD, and those who answered \"no\" were considered non-COPD.\u003c/p\u003e\n\u003ch3\u003eDefinition of CCR\u003c/h3\u003e\n\u003cp\u003eCCR, defined as the ratio of Cr to CysC, was treated as a dichotomous variable. The median value of 0.928 was used as the cutoff point, with values\u0026thinsp;\u0026ge;\u0026thinsp;0.928 classified as high-level CCR (coded as 0) and values\u0026thinsp;\u0026lt;\u0026thinsp;0.928 classified as low-level CCR (coded as 1)[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDefinition of disability\u003c/h3\u003e\n\u003cp\u003eImpaired activities of daily living (IADL) are a primary cause of disability in the elderly. The IADL scale [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] was used to assess disability among participants. The scale evaluates the ability to live independently across eight domains: going out for activities, shopping, food preparation, housework, laundry, medication management, telephone use, and financial management. Each item is rated as \"no difficulty,\" \"difficulty, can complete independently,\" \"difficulty, requires help,\" or \"unable to complete.\" If any item indicates \"difficulty requiring help\" or \"inability to complete,\" the participant is classified as having IADL dysfunction, indicative of disability.\u003c/p\u003e\n\u003ch3\u003eRelevant covariates\u003c/h3\u003e\n\u003cp\u003eSociodemographic characteristics (age, gender, current residence, marital status, education level) and health behavior factors (body mass index [BMI], smoking, alcohol consumption, health status, and depression) were collected through face-to-face interviews. Sociodemographic characteristics were defined as follows: gender was categorized as male or female; current residence as urban or rural; marital status as married (if the participant is currently married, regardless of whether they live with their spouse) or unmarried (if divorced, widowed, or never married); education level was classified into \"elementary school or below,\" \"junior high school graduate,\" or \"high school or above.\" For health behavior factors, BMI was calculated as weight (kg) divided by the square of height (m). Participants with a BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e were classified as underweight, those with a BMI between 18.5 kg/m\u003csup\u003e2\u003c/sup\u003e and 24.9 kg/m\u003csup\u003e2\u003c/sup\u003e were categorized as normal weight, those with a BMI between 25.0 kg/m\u003csup\u003e2\u003c/sup\u003e and 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e as overweight, and those with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e as obese, with normal weight serving as the reference category[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Smoking status was categorized as non-smokers (including those who had quit) and current smokers. Alcohol consumption was categorized as non-drinkers (including those abstaining) and current drinkers. Health status was assessed by the question, \"How do you feel about your own health?\" and responses were divided into three groups: \"very good\" or \"good\" as group 1, \"average\" as group 2, and \"bad\" or \"very bad\" as group 3. Depression was assessed using the 10-item Depression Self-Rating Scale (CESD-10), with participants scoring\u0026thinsp;\u0026ge;\u0026thinsp;10 points considered to have depressive symptoms, and those with \u0026lt;\u0026thinsp;10 points categorized as having no depressive symptoms [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eTo compare characteristics between groups with and without COPD, continuous variables were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (xˉ \u0026plusmn; s) for normally distributed data, and the independent samples t-test was used for comparisons. Categorical variables were presented as proportions, and the chi-square test was applied to assess differences between groups. A logistic regression model was employed to evaluate factors associated with the prevalence of COPD, reporting odds ratios (OR) with 95% confidence intervals. The interaction between CCR and disability in relation to COPD prevalence was also analyzed. Statistical analyses were performed using IBM SPSS Statistics 26, with a two-tailed P value of \u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 9,668 participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 1,835 (19.0%) were diagnosed with COPD. The COPD group was predominantly male and unmarried. Regarding educational attainment, although statistical differences in distribution were observed between groups, individuals with a high school education or higher constituted the majority in both groups. Participants in the COPD group were more likely to reside in rural areas and have a history of smoking. Significant differences were observed in BMI distribution between the groups, with a notably higher proportion of underweight individuals in the COPD group compared to the non-COPD group. The COPD group also exhibited a higher proportion of disability and depression. Furthermore, the CCR values in the COPD group were significantly lower than those in the non-COPD group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of Participants by Number of COPD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPD(N\u0026thinsp;=\u0026thinsp;9668)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES(n\u0026thinsp;=\u0026thinsp;1835)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO(n\u0026thinsp;=\u0026thinsp;7833)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge,y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.37(9.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.88(9.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e=\u0026thinsp;0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e958(52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3276(41.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e877(47.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4557(58.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1528(83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6931(88.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e307(16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e902(11.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level completed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e346(18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1297(16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e=\u0026thinsp;0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53(2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e331(4.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1436(78.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6205(79.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1450(79.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5822(74.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385(21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2011(25.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e635(34.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1945(24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1200(65.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5888(75.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent drinking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e603(32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2618(33.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e=\u0026thinsp;0.646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1232(67.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5215(66.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1023(55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4567(58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e192(10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e355(4.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e507(27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2439(31.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113(6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e472(6.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e403(22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1130(14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1432(78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6703(85.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth condition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e253(13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2314(29.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e972(53.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4426(56.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e610(33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1093(14.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e811(44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2151(27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1024(55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5682(72.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95(0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97(0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e=\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: COPD, chronic obstructive pulmonary disease; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared);CCR,The serum creatinine/cystatin C ratio.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ea Continuous data are reported as the mean (SD), and categorical data are reported as the number and percentage of participants.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI:Normal:18.5kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0kg/m\u003csup\u003e2\u003c/sup\u003e;Underweight:\u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e ;Overweight:25.0 kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e;Obese:BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eHealth condition: Q1:excellent or very good or good;Q2:fair;Q3:poor or very poor.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo investigate the independent association of various factors with COPD risk among middle-aged and older adults, multivariate logistic regression analysis was conducted (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Underweight participants had a significantly increased risk of COPD compared to those with a normal BMI (OR\u0026thinsp;=\u0026thinsp;1.97, 95% CI: 1.61\u0026ndash;2.39, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Current smokers were at a significantly higher risk of COPD than non-smokers (OR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.13\u0026ndash;1.49, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The risk of COPD was significantly higher among individuals who rated their health as good, very good, or excellent (OR\u0026thinsp;=\u0026thinsp;1.98, 95% CI: 1.70\u0026ndash;2.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and even more so for those who rated their health as poor or very poor (OR\u0026thinsp;=\u0026thinsp;4.78, 95% CI: 4.03\u0026ndash;5.67, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The risk of COPD was significantly lower in women and married individuals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression analysis of the prevalence of chronic obstructive pulmonary disease in middle-aged and older adults\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(ref=Normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.97(1.61,2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01(0.90,1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.20(0.96,1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(ref\u0026thinsp;=\u0026thinsp;male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.60(0.52,0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status (ref\u0026thinsp;=\u0026thinsp;unmarried)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69(0.59,0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level(ref\u0026thinsp;=\u0026thinsp;Primary school or below)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67(0.49,0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97(0.85,1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea of residence(ref\u0026thinsp;=\u0026thinsp;Urban)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10(0.96,1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking status(ref\u0026thinsp;=\u0026thinsp;NO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.29(1.13,1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent drinking status(ref\u0026thinsp;=\u0026thinsp;NO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89(0.79,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth condition(ref\u0026thinsp;=\u0026thinsp;excellent or very good or good)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.98(1.70,2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epoor or very poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.78(4.03,5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eBMI, body mass index (calculated as weight in kilograms divided by height in meters squared).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eBMI:Normal:18.5kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0kg/m\u003csup\u003e2\u003c/sup\u003e;Underweight:\u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e ;Overweight:25.0 kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e;Obese:BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo assess the relationship between CCR and COPD risk, logistic regression analyses were performed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Without adjusting for potential confounders, participants in the low-level CCR group exhibited a significantly higher risk of COPD than those in the high-level CCR group (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.02\u0026ndash;1.24, P\u0026thinsp;=\u0026thinsp;0.025). After adjusting for gender, smoking status, alcohol consumption, BMI, marital status, education level, area of residence, and health status, the risk of COPD remained significantly higher in the low-level CCR group compared to the high-level CCR group (OR\u0026thinsp;=\u0026thinsp;1.15, 95% CI: 1.03\u0026ndash;1.29, P\u0026thinsp;=\u0026thinsp;0.014). These results suggest that lower CCR levels are an independent risk factor for COPD in middle-aged and older adults, even after controlling for sociodemographic characteristics and health behavior factors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe correlation between CCR and COPD in middle-aged and older adults\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eOR (95% CI) by No. of CCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 [Reference]\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.12(1.02\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e=\u0026thinsp;0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.24(1.11\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.15(1.03\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e=\u0026thinsp;0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: COPD, chronic obstructive pulmonary disease; CCR,The serum creatinine/cystatin C ratio.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 was the crude model.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003e Reference: CCR\u0026thinsp;\u0026ge;\u0026thinsp;0.928, high-level CCR, defined as 0.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eCCR\u0026thinsp;\u0026lt;\u0026thinsp;0.928, low-level CCR, defined as 1.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ec\u003c/sup\u003eModel2 was adjusted for gender, smoking and drinking status.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ed\u003c/sup\u003eModel3 was adjusted for gender,BMI,smoking,drinking,Marital status,education,area of residence and Health condition.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo investigate the relationship between disability and COPD risk in middle-aged and older adults, logistic regression analysis was conducted (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the unadjusted model, individuals with disability exhibited a significantly higher risk of COPD compared to those without disability (OR\u0026thinsp;=\u0026thinsp;1.67, 95% CI: 1.47\u0026ndash;1.90, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for gender, smoking status, alcohol consumption, BMI, marital status, education level, area of residence, and health status, the risk of COPD in the disability group remained significant but slightly diminished (OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI: 1.04\u0026ndash;1.37, P\u0026thinsp;=\u0026thinsp;0.013). This indicates that disability is an independent risk factor for COPD in middle-aged and older adults, even when controlling for sociodemographic and health behavior factors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe correlation between Disability and COPD in middle-aged and older adults\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eOR (95% CI) by No. Of Disability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 [Reference]\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.67(1.47\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.72(1.51\u0026ndash;1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.19(1.04\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e=\u0026thinsp;0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: COPD, chronic obstructive pulmonary disease.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 was the crude model.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003e Reference: there was no Disability.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ec\u003c/sup\u003eModel2 was adjusted for gender, smoking and drinking status.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ed\u003c/sup\u003eModel3 was adjusted for gender, BMI, smoking,drinking,Marital status,education,area of residence and Health condition.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of the interaction analysis between low-level CCR and disability on COPD prevalence are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In the unadjusted model (Low-level CCR * Disability\u003csup\u003e1\u003c/sup\u003e), the combination of low-level CCR and disability significantly increased the risk of COPD (OR\u0026thinsp;=\u0026thinsp;1.781, 95% CI: 1.571\u0026ndash;2.088, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for potential confounders, including gender, age, BMI, smoking, alcohol consumption, marital status, and education level (Low-level CCR * Disability\u003csup\u003e2\u003c/sup\u003e), the interaction remained significant, although the effect size was reduced (adjusted OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI: 1.34\u0026ndash;1.91, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results suggest that the coexistence of low-level CCR and disability is strongly associated with an increased risk of COPD.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInteraction between Low-level CCR and Disability on COPD prevalence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-level CCR* Disability\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.57 2.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-level CCR* Disability\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.34 1.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CCR,The serum creatinine/cystatin C ratio; low-level CCR\u0026thinsp;\u0026lt;\u0026thinsp;0.928.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eCOPD, chronic obstructive pulmonary disease.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eLow-level CCR* Disability\u003csup\u003e1\u003c/sup\u003e was the crude model.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eLow-level CCR* Disability\u003csup\u003e2\u003c/sup\u003ewas adjusted for variables of gender, age,BMI, smoking,drinking,Marital status and education.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eS is the standard error, Z is the regression coefficient divided by its standard error, P is the test level, CI is the confidence interval, and CI is the 95% CI.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilized a large population sample (n\u0026thinsp;=\u0026thinsp;9,668) to systematically investigate the multidimensional risk factors for COPD in middle-aged and older adults. Independent risk factors identified include low body weight, smoking, self-rated poor health, low-level CCR, and disability, while being female and married serve as protective factors against COPD. Notably, this study uncovered a significant synergistic effect between low-level CCR and disability in increasing the risk of COPD, offering a new perspective on the multifactorial pathogenesis of the disease.\u003c/p\u003e\u003cp\u003eThe elevated risk (OR\u0026thinsp;=\u0026thinsp;1.97) associated with underweight participants may be linked to compromised ventilatory function (e.g., diaphragmatic atrophy) and weakened immune defenses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Smoking (OR\u0026thinsp;=\u0026thinsp;1.29), a well-established risk factor, was reaffirmed as a significant trigger in this population. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Self-rated poor health (OR\u0026thinsp;=\u0026thinsp;4.78), reflecting both the burden of disease and depletion of physiological reserves, serves as a critical early warning indicator for COPD screening [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEstrogen may exert lung-protective effects, including anti-inflammatory, antioxidant, and alveolar repair properties [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Women generally have higher estrogen levels before menopause compared to men [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which may partially account for their lower COPD risk. Additionally, women may display distinct patterns of lung inflammation in response to tobacco exposure, and gender-related differences in lung development and aging processes also warrant further exploration [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMarriage offers important social support and emotional bonding, with spouses playing a key role in promoting health, such as encouraging smoking cessation, ensuring regular medical check-ups, reminding medication adherence, and fostering healthier lifestyles (e.g., balanced diet and increased physical activity)\u003csup\u003e[41]\u003c/sup\u003e. These factors collectively help mitigate exposure to COPD risk factors, particularly smoking, and improve overall respiratory health[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLow-level CCR was significantly associated with COPD risk (OR\u0026thinsp;=\u0026thinsp;1.12), with the association strengthening after adjusting for confounders like smoking (OR\u0026thinsp;=\u0026thinsp;1.15). The reduction in CCR, a sensitive marker of muscle mass and nutritional status, points to an imbalance between skeletal muscle degradation and protein metabolism [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This aligns with the high prevalence of \"extrapulmonary manifestations,\" such as sarcopenia, in patients with COPD[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Potential mechanisms include accelerated muscle protein breakdown driven by systemic inflammation (e.g., elevated TNF-α, IL-6), mitochondrial dysfunction due to hypoxia, and disuse atrophy from reduced physical activity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The findings suggest that low-level CCR is not only a biomarker for COPD but also an independent contributor to its pathogenesis.\u003c/p\u003e\u003cp\u003eThe risk of COPD was significantly higher in patients with disabilities (OR\u0026thinsp;=\u0026thinsp;1.19), and this association remained strong even after adjusting for sociodemographic factors, lifestyle, and health status. Disability, which reflects a decline in daily functional abilities, may directly contribute to limited ventilation, dyspnea, and increased energy expenditure in patients with COPD[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Notably, disability often precedes the onset of overt respiratory symptoms[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], suggesting its potential as an early warning marker for identifying high-risk populations.\u003c/p\u003e\u003cp\u003eThe interaction model between low-level CCR and disability highlighted how impaired muscle metabolism and decreased function together form a \"vicious cycle.\" Reduced muscle reserve (low-level CCR) leads to physical dysfunction, which in turn limits activity levels, further exacerbating muscle atrophy and contributing to a cycle of impaired respiratory compensatory function [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This finding provides empirical evidence for the \"systemic inflammatory-metabolic-dysfunctional\" pathological model of COPD.\u003c/p\u003e\u003cp\u003eThis study innovatively integrated CCR and disability within a unified analytical framework. Unlike prior studies that focused on individual dimensions (either CCR or disability), the interaction model confirmed that the synergy between muscle metabolism and physical function constitutes a critical risk factor for COPD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThe combined assessment of CCR and disability enhances the identification of high-risk groups, particularly in primary care settings. A dual-track intervention approach that targets both muscle metabolism (e.g., protein supplementation, resistance training) and functional maintenance (e.g., pulmonary rehabilitation, lifestyle modification) may prove more effective.\u003c/p\u003e\u003cp\u003eHowever, several limitations should be acknowledged: 1) The cross-sectional design prevents the establishment of causal relationships and should be further validated in prospective cohort studies. 2) The CCR cut-point (0.928) was derived from this sample, and its generalizability needs to be tested in external populations. 3) The study did not include a stratified analysis based on pulmonary function severity, which could help explore the gradient of association between various factors and disease stages in future research.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study identified low-level CCR and disability as independent risk factors for COPD in middle-aged and older adults in China and demonstrated their synergistic effect in increasing COPD risk. A comprehensive prevention and control strategy that integrates nutritional support, muscle function maintenance, and functional ability interventions is recommended for the clinical management of patients with COPD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the China Health and Retirement Longitudinal Study team for providing data and the training of using the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: Cui,Cheng\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data:Cui,Cheng,Huang,Wang .\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Cheng.\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript for important intellectual content: He,Li,Cui.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Cheng,Cui.\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: He,Li,Huang.\u003c/p\u003e\n\u003cp\u003eSupervision: Wang,Cui.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research was performed in accordance with the Declaration of Helsinki. \u003c/p\u003e\n\u003cp\u003eThe original CHARLS project was approved by the ethical review committee \u003c/p\u003e\n\u003cp\u003eof Peking University (IRB00001052\u0026ndash;11015), and all participants signed an \u003c/p\u003e\n\u003cp\u003einformed consent form at the time of enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used open-access data from the China Health and Retirement Longitudinal Study, which could be downloaded from http://charls.ccer.edu.cn/zh-CN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1 \u003c/sup\u003eDepartment of Emergency medicine,Department of Critical Care Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No.136,Jingzhou Street,Xiangcheng District,Xiangyang 441021, Hubei, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Department of Respiratory and Critical Care Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No.136,Jingzhou Street,Xiangcheng District,Xiangyang 441021, Hubei, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author: \u003c/strong\u003eYunhua Cui ,E-mail：[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFirst author:\u003c/strong\u003eChao Cheng\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgust\u0026iacute; A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes de Oca M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary\u003c/strong\u003e. \u003cem\u003eEur Respir J \u003c/em\u003e2023, \u003cstrong\u003e61\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eMurray CJL: \u003cstrong\u003eFindings from the Global Burden of Disease Study 2021\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2024, 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\u003cstrong\u003eCharacteristics of physical activities in daily life in chronic obstructive pulmonary disease\u003c/strong\u003e. \u003cem\u003eAm J Respir Crit Care Med \u003c/em\u003e2005, \u003cstrong\u003e171\u003c/strong\u003e(9):972-977.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COPD, serum creatinine to cystatin C ratio, disability","lastPublishedDoi":"10.21203/rs.3.rs-7413461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7413461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAlthough previous studies have separately examined the association between the serum creatinine to cystatin C ratio (CCR) or disability and chronic obstructive pulmonary disease (COPD), the interaction between these factors and their combined impact on COPD prevalence remains insufficiently explored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eSelf-reported physician-diagnosed COPD, as measured by the validated CHARLS baseline questionnaire, served as the primary outcome. Logistic regression was utilized to assess the association between CCR, disability, and COPD, as well as to evaluate the interaction between these factors and COPD prevalence.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e\u003cp\u003eAmong the 9,668 participants, 1,835 (19.0%) had a COPD diagnosis. The risk of COPD was significantly higher in the low-level CCR group compared to the high-level CCR group (OR\u0026thinsp;=\u0026thinsp;1.15, 95% CI: 1.03\u0026ndash;1.29, P\u0026thinsp;=\u0026thinsp;0.014). Similarly, individuals with disability exhibited a significantly higher risk of COPD compared to non-disabled individuals (OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI: 1.04\u0026ndash;1.37, P\u0026thinsp;=\u0026thinsp;0.013). The co-presence of low-level CCR and disability substantially increased the risk of COPD (OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI: 1.34\u0026ndash;1.91, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study identified low-level CCR and disability as independent risk factors for COPD in middle-aged and older adults in China and demonstrated their synergistic effect in increasing COPD risk. A comprehensive prevention and control strategy that integrates nutritional support, muscle function maintenance, and functional ability interventions is recommended for the clinical management of patients with COPD.\u003c/p\u003e","manuscriptTitle":"The interaction between CCR and disability on the risk of chronic obstructive pulmonary disease: evidence from the China Health and Retirement Longitudinal Study Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:34:50","doi":"10.21203/rs.3.rs-7413461/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-09T10:54:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-16T10:11:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T11:20:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T03:49:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-09-05T03:46:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e4d1118-579f-49a5-96b9-867a84d69c41","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T19:34:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:34:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7413461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7413461","identity":"rs-7413461","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}