Analysis of the Mediating Role of Inflammatory Markers in the Association between COPD and Frailty

Analysis of the Mediating Role of Inflammatory Markers in the Association between COPD and Frailty | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Analysis of the Mediating Role of Inflammatory Markers in the Association between COPD and Frailty Yuanbo Wu, Jingyi Wang, Ziyu Dai, Mingxuan Xie, Yun Peng, Qiong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9245206/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Objective : Patients with chronic obstructive pulmonary disease (COPD) are at a high risk of frailty, and chronic inflammation is considered as an important mechanism mediating their association. This study aimed to systematically verify the correlation between COPD and frailty, and to further explore the statistically mediating role and specific effect ratio of the routine blood count-derived inflammatory markers. Methods: Based on public database and real hospital clinical data, the study aimed to explore the correlation intensity among COPD, inflammatory markers, and frailty used hierarchical linear regression. Then, we performed mediation analysis to quantify the proportion of the effect mediated by inflammatory markers; Finally, we verified the robustness of the results through sensitivity analysis and subgroup analysis. Results: COPD patients were significantly higher FI-Lab compared to non-COPD patients ( NHANES database: β=0.016, 95% confidence interval (CI): 0.009–0.022, P<0.001; hospital data: β=0.020, 95% CI: 0.015–0.024, P<0.001). inflammatory markers partially mediated the association. Specifically, SIRI explained 11.6 and 29% of the total effect in NHANES database and hospital database, respectively. The robustness of the mediation effect was confirmed by multiple sensitivity and subgroup analyses. Conclusion: COPD was independently associated with frailty, and this association was partially mediated by inflammatory markers such as SIRI. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Health sciences/Risk factors inflammatory indicators COPD Frailty FI-Lab mediation effect analysis Figures Figure 1 Figure 2 Introduction According to the official data from National Burean of Statistic, China’s population aged 60 and above is 323 million, accounting for 23% of the total in 2025. It is expected that by 2030, the proportion of China’s population aged 60 and above will rise to 28%, and the population aged 65 and above will exceed 20%, entering a super aging society[ 1 ]. In context of the continuous aging population, It has become a critical public health challenge to maintain the physical function and delay disability progression among middle-aged and elderly people. Frailty, a common aged-related clinical syndrome, is characterized by diminished physiological reserves and reduce stress response capacity[ 2 ]. Due to the closely association with multiple adverse outcomes, such as falls, disability, hospitalization and mortality[ 3 ], It represents a critical transition from healthy aging to disability Therefore, It’s critical to identifying risk factors for frailty and explore its underlying mechanisms. Those together are fundamental to advancing healthy aging and reducing the burden on healthcare and social care systems[ 4 ] The risk factors for frailty are complex and diverse, and chronic diseases plays an important role[ 5 ]. Among them, chronic obstructive pulmonary disease (COPD) has received extensive attention in the field of frailty research, owing to its high population prevalence, significant disability association, and heavy disease burden[ 6 ]. Evidence has confirmed a robust association between COPD and frailty. Cross-sectional studies have shown that COPD populations have a significantly higher risk than Non-COPD population, and this association remains stable across age and gender stratification[ 7 , 8 ]. Prospective cohort studies further conform that COPD is an independent risk factor for frailty[ 9 ]. More importantly, when COPD and frailty coexist, they may have synergistic amplification effects, which will result in accelerated decline in activities of daily living, significantly hastened loss of independence, and elevated the risk of adverse outcomes[ 10 , 11 ]. Therefore, it is of great significance to explore the potential mechanisms between COPD and frailty for disease management and screening of high risk population in clinical practice. Previous observational studies and cohort studies have confirmed that COPD patients have higher levels of inflammation compared with non-COPD controls. At the same time, higher circulating levels of inflammatory biomarkers are positively correlated with severity of frailty, such as C relative protein (CRP), IL-6 and so on[ 12 , 13 ]. Based on the close relationship between COPD and frailty and important role of inflammation in frailty, some researchers believe that chronic inflammation may play an important role in the mechanistic connecting COPD and weakness[ 14 ]. COPD is characterized as a chronic inflammatory disease. Persistent exposure to harmful particles, such as tobacco smoke can cause recurrent inflammation in the lung parenchyma and peripheral airway. This process activates various immune cells, including neutrophils and lymphocytes, releasing a large of pro-inflammatory cytokines such as Interleukin-6(IL-6) and Tumor Necrosis Factor-α[ 15 , 16 ] Consequently, a systemic inflammatory response is activated. Meanwhile, inflammatory factors and inflammatory microenvironment can directly act on skeletal muscle, affecting energy metabolism and protein synthesis and decomposition of skeletal muscle mitochondrial[ 17 ], directly or indirectly participating in the occurrence and development of weakness. Therefore, inflammation is considered as a potential pathophysiological mechanism connecting COPD and frailty. However, the multidimensional evidence from observational research and cohort studies mainly focused on the dependent correlation between COPD, inflammatory indices and frailty, while the integrated research evidence of analyzing the complex relationship among them and testing whether inflammation plays an intermediary role in the correlation between COPD and frailty is insufficient. In addition, there are amounts of researches focus on inflammatory indices, which are costly, required strict laboratory standardization, such as CRP, IL-6 and TNF-α. Therefore, they are difficult to apply in large-scale screening. In recent years, inflammatory markers derived from blood routine, such as systemic inflammatory response index (SIRI), which integrates neutrophils, lymphocytes and other immune inflammatory cells, have gradually emerged as an effective tool for assessing inflammation levels, and have gained increasing attention due to their low cost and high accessibility, positioning them as promising alternatives for population-based studies[ 18 , 19 ]. Therefore, this study used the National Health Nutrition Examination Survey (NHANES) in the United States and the real clinical data of the top three hospitals in China respectively, and adopted a cross-sectional research design to preliminarily explore the independent correlation among COPD, inflammatory indicators and frailty, and further systematize the mediating effect of inflammatory indicators and the proportion of specific mediating effects in the association between COPD and frailty used mediation analysis. This finding aims to provide evidence for mechanistic insights into the association between COPD and frailty and strategies for screening and intervention. Methods Study population The study employed a two-stage cross-sectional design, with exploratory analysis first conducted in the community-based NHANES population, followed by validation in clinical data from a large tertiary hospital in China. The NHANES is a nationally representative program designed to evaluate to health and nutritional status of U.S. population[ 20 ]. It collects a series of health data, including interview, physical examination, and other relevant metrics. All collections procedures were approved by the Ethics Review Board of the National Center for Health Statistics and written informed consent was obtained from participants[ 21 ]. For our study, we included participant from the 2005–2018 cycles and excluded individuals who under 40 years old, had a recent infection (within 30 days, e.g., influenza, pneumonia, or otitis media), were pregnant or lactating, or had missing data necessary for computing FI-Lab and inflammatory markers. A total of 7912 participants were ultimately included in the analysis. Further details regarding the data can be accessed at https://www.cdc.gov/nchs/nhanes . The followed stage utilized real-word clinical data from a China’s hospital. The hospital data included aged 40 or older who were hospitalized from January 2020 to June 2025 in Xiangya Hospital of Central South University. We excluded individuals with any of following conditions: 1) Patients admitted to the Intensive Care Unit (ICU), Infectious Diseases Department, or Oncology Radiotherapy and Chemotherapy Department.2) severe concurrent comorbidities (including major inflections, significant cardiopulmonary or systemic diseases,3) long-term bedridden status, malignancies, or cachexia), 4) severe cognitive impairment or psychiatric disorders, 5) pregnancy or lactation, 6) acute exacerbation of COPD or COPD with concurrent acute upper respiratory infection; 7) recent infectious disease (e.g., influenza, COVID-19), 8) incomplete clinical records. The final analytical sample comprised 8817 individuals. The study protocol received approval from the Medical Ethics Review Committee of Xiangya Hospital Central South University (2026020333). Owing to the retrospective nature of the study, which involved no intervention, no direct contact with participants, and posed no more than minimal risk; the secondary use of de-identified data that did not alter clinical outcomes or involve sensitive information; and the anonymization of all medical records prior to analysis for academic purposes, the committee granted a waiver of informed consent. The study was conducted in accordance with the Declaration of Helsinki. The detail inclusion and exclusion process is shown in Fig. 1 . Data collection The definition of COPD In this study, the definition of COPD in NHANES data follows the previous research, mainly based on the literature information in the medical condition data set, that is, extracting participants' answers to the following three questions: "Did the doctor tell you that you have chronic obstructive pulmonary disease", "Did the doctor tell you that you have chronic bronchitis" and "Did the doctor tell you that you have emphysema"; Participants who answer "Yes" to any of the above three questions are classified as having COPD. In hospital data, the diagnostic criteria of COPD are based on the diagnostic criteria of the Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD) in 2020, that is, hospitalized patients included in the study have persistent respiratory symptoms, such as cough, expectoration and dyspnea. And excluding other lung diseases, after inhaling bronchodilator, the ratio of Forced Expiratory Volume in one second (FEV1) to Forced Vital Capacity (FVC) in the first second was less than 0.7. General data collection According to previous studies, this study comprehensively considered many variables that affect frailty. Among them, the NHANES data primarily collects self-reported sociodemographic variables, including age, gender, race, marital status, smoking status, and alcohol consumption. Physical measurements such as BMI, height, and weight are extracted from physical examinations, while peripheral blood cell counts and other data are obtained from the mobile examination center. Comorbidities include chronic conditions such as diabetes, hypertension, diabetes, coronary heart disease, hyperlipidemia, and heart failure. Hospital clinical data was extracted from hospital records, including demographic and general characteristic (age, gender, ethnicity, marital status, occupation and BMI), behavioral habits (smoking and drinking), documented comorbidities (diabetes, hypertension, coronary heart disease, and tumors and so on), as well as laboratory results obtained within the first 48 hours. The definition of frailty Laboratory Frailty index is a continuous index based on defect accumulation theory, which aims to evaluate the frailty of individuals through quantifying the abnormal proportion of routine laboratory examination results[ 22 ]. FI-Lab operationalizes the concept of deficit accumulation, grounded in the established observation that laboratory abnormalities increase with age. Each item is dichotomously recoded: “0” for a result within the normal range and “1” otherwise. An individual’s FI-Lab score is derived from the proportion of deficits present, calculated as the sum of these binary scores divided by the total number of variables assessed, yielding a continuous score from 0 to 1 where higher value denote greater frailty severity. In this study, A 32-intems version were used to the public database[ 23 ], and 44-variables FI-Lab were used to the hospital, covering hematological, biochemical, and related parameters[ 24 ], and The index was computed for only for individuals with available data for at least 80% of the required variables in their respective index. Inflammatory marker Based on routine blood test data, we calculated several composite inflammatory markers, including Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Monocyte-to-Lymphocyte Ratio (MLR), Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and Pan-Immune Inflammation Value (PIV)[ 25 , 26 ]. These were calculated using the following formulas: NLR = Absolute neutrophil count / Absolute lymphocyte count (both in ×10⁹ L⁻¹); PLR = Absolute platelet count / Absolute lymphocyte count (both in ×10⁹ L⁻¹) MLR = Absolute monocyte count / Absolute lymphocyte count (both in ×10⁹ L⁻¹) SII = (Absolute platelet count × Absolute neutrophil count) / Absolute lymphocyte count (all in ×10⁹ L⁻¹) SIRI = (Absolute neutrophil count × Absolute monocyte count) / Absolute lymphocyte count (all in ×10⁹ L⁻¹) PIV = (Absolute neutrophil count × Absolute monocyte count × Absolute platelet count) / Absolute lymphocyte count (resulting unit: ×10²⁷ L⁻³) Statistical analysis A two-stage analytical approach was adopted. In the first stage, an exploratory analysis was conducted using the NHANES data to investigate the potential mediating effect of inflammatory markers in the association between COPD and frailty. The second stage involved a validation analysis in the real data to assess the generalizability of the observed mediation effect. we preprocess the two data respectively. For NHANES dataset, we say that the special codes of the questionnaire or laboratory test items (such as "7", "9", "77" and "99" which mean "refused to answer", "unknown" or "missing") are uniformly identified as missing values. After that, the missing variables in the two data sets are treated in the same way, that is, the variables with missing rate > 10% are considered as poor data quality and deleted; For variables with missing rate ≤ 10%, we assume that the missing type is random missing, use MICE package for chain multiple interpolation, use predicted mean matching to interpolate continuous variables, and use logistic regression to interpolate classified variables. For the NHANES data, continuous variables were assessed for normality. Normally distributed continuous variables were present as mean ± standard error, and compared using Student-t test. While non-normally distributed data are reported as median (IQR) and compared using Wilcoxon rank-sum test. Categorical variables are presented as n (%) and compared using the Chi-square or Fisher’s exact test. Subsequently, we performed univariate, multivariate analyses and hierarchical linear regression analysis to examine the independent association between risk factors for frailty and to examine the independent association between COPD and frailty. Building this, linear regression was used to assess the relationship among COPD, inflammatory indices and FI-Lab. All inflammation markers were standardized suing Z-scores prior to modeling to ensure comparability. We constructed four progressively adjusted model: model1 was unadjusted, model 2 adjusted for demographics; model 3 further adjusted for lifestyle factors, and model 4 further adjusted clinal comorbidities. Results are reported as β coefficients with 95% CI. The potential role of inflammatory markers was formally tested using R mediation package, and Bootstrap method repeated 5000 times to calculate the 95% CI for mediation effect, aiming to verify the potential mediating role of inflammatory markers in the association between COPD and frailty. To further evaluate the robustness of the findings, we conducted several sensitivity analyses. Firstly, the mediation analysis was replicated using the lavaan package with the same Bootstrap approach. Secondly, to address potential bias, we reconstructed the FI-lab though excluding the overlapping items between FI-Lab and inflammatory indices, and repeat the mediation. Thirdly, all inflammation marker were 10% winsorization to reduce the influence of extreme values. Finally, subgroup analyses were conducted to examine potential heterogeneity in the mediating effect. To validate our findings, we implemented the pre-defined modeling strategy. Based on the final covariates identified in the NHANES data, we examined the association between COPD, inflammatory markers, and FI-Lab, followed by formal mediation testing, using the final set of covariates derived from NHANES analysis. Due to missing data in some variables across the two datasets from different sources, we harmonized and standardized the covariates to ensure comparability in cross-data analyses. The core analytical procedure mirrored that used for the hospital data. Additionally, we performed extensive sensitivity analyses in the two cohort to evaluate the robustness of the mediation effect to different levels of covariate control. This involved running the mediation model under four progressively adjusted conditions: a minimal set (age and gender), a basic set (adding other demographics and lifestyle factors), a clinical set (further incorporating comorbidities), and a full set (including all available covariates). All analyses were conducted using R (version 4.5.0). Statistical significance was defined as a two-sided P-value of less than 0.05. This design and reporting of study followed the STROBE statement guidelines[ 27 ]. Results Baseline Characteristics Description Based on baseline characteristics comparison between NHANES dataset and hospital dataset. The NHANES data including 7912 participants, of whom 621 were in the COPD group (Table 1 ), the hospital dataset included 8813 eligible patients, of whom 1429 were in the COPD group (Supplement Table 1 ). Compared to their non-COPD counterparts, COPD patients are significantly older (NHANES data: 63 vs. 59 years, hospital data: 68 vs. 63 years; P < 0.001) and more likely to have a history of smoking (NHANES data: 73.3% vs. 48.5%; hospital data: 54.5% vs. 26.3%, P < 0.001) and alcohol history (NHANES data: 69.7% vs. 30.3%; hospital data: 31.4% vs. 18.4%). Furthermore, both datasets showed that COPD patients have higher proportions of comorbidities, including coronary heart disease, asthma, tuberculosis, and tumor (all P < 0.05). In addition, inflammatory makers such as NLR, MLR, as well as FI-lab score, were significantly higher in the COPD group. However, the difference in PLR was borderline significance in NHANES (P = 0.048) and was not statistically significant in the hospital dataset (p = 0.097). Meanwhile, there are some differences in some indicators existed between the two datasets, where the hospital-based COPD patients were predominantly Han Chinese males, while those in NHANES were primarily non-Hispanic Whites and lower socioeconomic status. In conclusion, the COPD group was characterized by older age, poorer lifestyle habits, higher levels of inflammation and FI-Lab, and A heavier burden of specific comorbidities. Table 1 Comparison of baseline characteristics between COPD and non-COPD patients in NHANES data Variable category total (n = 7912) Non-COPD (n = 7291) COPD (n = 621) P value Age (years) 59 (49,69) 59 (48,69) 63 (52,73) < 0.001 Height (cm) 167 (159.8,174.7) 167.1 (159.8,174.7) 166.5 (160,174.9) 0.73 Weight (kg) 79.3 (67.8,92.4) 79.1 (67.8,92.1) 81.7 (67.7,97.7) 0.013 BMI (kg/m²) 28.26 (24.88,32.26) 28.2 (24.88,32.08) 28.98 (24.76,34.61) 0.004 Neutrophil count 4 (3.1,5) 3.9 (3.1,5) 4.4 (3.4,5.6) < 0.001 Lymphocyte count 2 (1.6,2.5) 2 (1.6,2.4) 1.9 (1.5,2.5) 0.782 Monocyte count 0.5 (0.4,0.6) 0.5 (0.4,0.6) 0.6 (0.4,0.7) < 0.001 NLR 2 (1.5,2.65) 2 (1.48,2.62) 2.17 (1.63,2.94) < 0.001 PLR 125.33 (98.63,160) 125 (98.54,159.49) 129.09 (100,167.6) 0.048 MLR 0.26 (0.21,0.33) 0.26 (0.2,0.33) 0.27 (0.21,0.38) < 0.001 SII 494 (350.41,693.71) 489 (348,686.47) 557.89 (391.96,807.71) < 0.001 SIRI 1.03 (0.71,1.5) 1.02 (0.7,1.48) 1.17 (0.8,1.84) < 0.001 PIV 254.42 (167.26,390.62) 250 (165.69,383.32) 311.63 (194.38,492.2) < 0.001 Gender Male 4003 (50.6%) 3712 (50.9%) 291 (46.9%) 0.058 Race Mexican American 1303 (16.5%) 1263 (17.3%) 40 (6.4%) Other Hispanic 636 (8%) 603 (8.3%) 33 (5.3%) Non-Hispanic Black 4241 (53.6%) 3803 (52.2%) 438 (70.5%) Non-Hispanic Black 1474 (18.6%) 1386 (19%) 88 (14.2%) other 258 (3.3%) 236 (3.2%) 22 (3.5%) < 0.001 Marital status Married 5102 (64.5%) 4758 (65.3%) 344 (55.4%) < 0.001 Smoking status No 3992 (50.5%) 3537 (48.5%) 455 (73.3%) < 0.001 Drinking status Yes 5518 (69.7%) 5085 (69.7%) 433 (69.7%) < 0.001 Coronary heart disease No 7496 (94.7%) 6949 (95.3%) 547 (88.1%) < 0.001 Hypertension Yes 3980 (50.3%) 3608 (49.5%) 372 (59.9%) < 0.001 diabetes No 6445 (81.5%) 5975 (82%) 470 (75.7%) < 0.001 Chronic kidney disease No 6760 (85.4%) 6267 (86%) 493 (79.4%) < 0.001 Osteoporosis No 7287 (92.1%) 6767 (92.8%) 520 (83.7%) < 0.001 Connective tissue disease No 5039 (63.7%) 4788 (65.7%) 251 (40.4%) < 0.001 Asthma No 7025 (88.8%) 6660 (91.3%) 365 (58.8%) < 0.001 Hyperlipidemia No 5181 (65.5%) 4759 (65.3%) 422 (68%) 0.192 Heart failure No 7641 (96.6%) 7091 (97.3%) 550 (88.6%) < 0.001 Cancer No 6910 (87.3%) 6406 (87.9%) 504 (81.2%) < 0.001 Note: 1. Continuous variables: Normal distribution is expressed as mean±standard deviation, and group comparison is performed by T-test; non-normal distribution is expressed as median (interquartile range), and group comparison is performed by Wilcoxon rank-sum test. 2. Categorical variables are expressed as frequency (percentage, %), and group comparison is performed by chi-square test or Fisher exact test. 3. P value: <0.001 is presented as < 0.001, otherwise specific value is displayed. The correlation analysis among COPD, inflammatory markers and FI-Lab We first performed univariate and multivariate linear regression to identify independent risk factors and assess the association between COPD and the FI-Lab (Table 2 ). Univariate analysis revealed a significant positive association between COPD and FI-Lab (β = 0.032, 95% CI: 0.024,0.039, P < 0.001). Subsequently, variables with a P-value < 0.1 in the univariate analysis and those considered priori important confounders for osteoporosis was included in the multivariate stepwise regression analysis. The results showed that the association between COPD and FI-Lab remained significant (β = 0.016, 95% CI: 0.009,0.022, P < 0.001). Additionally, hypertension, diabetes, kidney disease, hyperlipidemia, and smoking status were also independently associated with frailty (all P < 0.05). Table 2 Univariate and multivariate analysis of association factors with frailty variable Univariate analysis Multivariate analysis β 95% CI P value β 95% CI P value Continuous variables Age (years) 0.002 (0.002,0.002) < 0.001 0.001 (0.001,0.001) < 0.001 BMI (kg/m²) 0.002 (0.002,0.003) < 0.001 0.002 (0.000,0.005) 0.025 Categorical variables Gender -0.012 (-0.016, -0.007) < 0.001 0.022 (-0.027,0.017) < 0.001 Race -0.015 (-0.025, -0.004) 0.007 -0.011 (-0.020, -0.002) 0.016 Marital status -0.004 (-0.012,0.004) 0.229 -0.004 (-0.010,0.002) 0.231 Smoking status 0.011 (0.007,0.015) < 0.001 0.009 (0.005,0.012) < 0.001 Drinking status -0.021 (-0.026, -0.017) < 0.001 -0.011 (-0.015, -0.007) < 0.001 Coronary heart disease 0.064 (0.055,0.072) < 0.001 0.015 (0.007,0.023) < 0.001 Hypertension 0.062 (0.058,0.066) < 0.001 0.032 (0.028,0.035) < 0.001 diabetes 0.091 (0.086,0.096) < 0.001 0.060 (0.056,0.065) < 0.001 Chronic kidney disease 0.085 (0.080,0.090) < 0.001 0.050 (0.045,0.055) < 0.001 Osteoporosis 0.005 (-0.003,0.012) 0.203 -0.008 (-0.015, -0.002) 0.014 Connective tissue disease 0.023 (0.019,0.027) < 0.001 -0.001 (-0.005,0.003) 0.630 Asthma 0.012 (0.006,0.019) < 0.001 0.001 (-0.005,0.006) 0.794 Hyperlipidemia 0.024 (0.020,0.028) < 0.001 0.024 (0.020,0.027) < 0.001 COPD 0.032 (0.024,0.039) < 0.001 0.016 (0.009,0.022) < 0.001 Heart failure 0.012 (0.004,0.019) 0.002 0.005 (-0.001,0.011) 0.096 Cancer 0.016 (0.010,0.022) 0.003 0.000 (-0.005,0.006) 0.856 Note: 1. β is presented as coefficient, 95% confidence interval in parenthesis. 2. Variables with P < 0.1 in the univariate analysis entered multivariate stepwise regression analysis. 3. '--'indicates that they did not enter multivariate analysis or the results were not available. To further assess the robustness of the association, we constructed a four-level regression analysis model based on significant variables identified by multivariate analysis. Resulted are presented in Table 3 . In the unadjusted model, a significant positive association was observed between COPD and frailty in both dataset (NHANES data: β = 0.032, 95% CI: 0.024,0.039; Hospital data: β = 0.027, 95% CI: 0.022,0.032, P < 0.001; P < 0.001). After sequentially adjusting for demographic factors (model 2), lifestyle factors (model3), and clinical comorbidities (model 4), the magnitude of the association gradually attenuated but remained statistically significant (NHANES data: β = 0.016, 95% CI: 0.010,0.023; Hospital data: β = 0.020, 95% CI: 0.015,0.024, P < 0.001). Table 3 Correlation analysis between COPD and FI-Lab Data sources model β (95% CI) P value sample NHANES database Model 1 (crude model) 0.032 (95% CI: 0.024,0.039) < 0.001*** 7912 Model 2 (+ demography) 0.028 (95% CI: 0.021,0.035) < 0.001*** 7912 Model 3 (+ lifestyle) 0.023 (95% CI: 0.016,0.030) < 0.001*** 7912 Model 4 (+ clinical comorbidities) 0.016 (95% CI: 0.009,0.022) < 0.001*** 7912 Hospital database Model 1 (crude model) 0.027 (95% CI: 0.022,0.032) < 0.001*** 8813 Model 2 (+ demography) 0.023 (95% CI: 0.018,0.027) < 0.001*** 8813 Model 3 (+ lifestyle) 0.020 (95% CI: 0.015,0.024) < 0.001*** 8813 Model 4 (+ clinical comorbidities) 0.020 (95% CI: 0.015,0.024) < 0.001*** 8813 Note: 1. β is presented as coefficient with 95% confidence interval in parenthesis.2. Model 1 is crude model containing only COPD variables. Model 2 adjusted demographic factors (age, gender, race and marital status). Model 3 further adjusted lifestyle (BMI, smoking status, drinking status) based on model2. Model 4 further adjusted clinical variables (hypertension, chronic kidney disease, osteoporosis, hyperlipidemia, coronary heart disease) based on model On the basis of clarifying the association between COPD and FI-Lab, we further analyzed the relationship between COPD and inflammatory markers. As shown in Supplementary Tables 2 and 3, in both databases, COPD exhibited significant positive correlations with all inflammatory markers analyzed, except for PLR and MLR. This significant relationship persisted following stepwise adjustment for demographic, lifestyle and clinical comorbidity variables, COPD demonstrated the stronger association with SII (β = 0.166, 95% CI: 0.083,0.249, P < 0.001) and SIRI (β = 0.171, 95% CI: 0.091,0.250, P < 0.001). In the hospital dataset, PIV had a β of 0.293 (95% CI:0.234,0.351, P < 0.001) followed by SIRI (β = 0.289, 95CI: 0.231,0.347, P < 0.001). After that, we further analyzed the correlation between standardized inflammatory markers and FI-Lab. The results are presented in Supplementary Tables 4 and 5, all inflammatory analyzed were significantly and positively correlated with FI-Lab in both datasets except PLR. In the crude models (Model 1), all associations were highly significant except PLR. After adjusted demographic, lifestyle and clinical comorbidities, SIRI and SII demonstrated relatively stronger correlations with FI-lab in the NHANES dataset (β = 0.011 and 0.010, respectively; both P < 0.001). it is worth noting that PLR also had a significant statistical effect in Model 4 (β = 0.004, 95% CI: 0.002,0.005, P < 0.001). in the hospital database, the β value of MLR was 0.027 (95%CI:0.025,0.028, P < 0.001) followed by SIRI (β = 0.020, 95CI: 0.019,0.022, P < 0.001). The mediating role of inflammatory markers in the association between COPD and Frailty Based on above results, with the exception of PLR, all other inflammatory markers demonstrated associations with both COPD and FI-Lab. Accordingly, we performed a mediation analysis utilizing the mediation package. The results are shown in Table 4 , In the NHANES database, the mediating effects of all inflammatory markers expect MLR and PLR were statistically significant (P < 0.001). Among these, SIRI, NLR, and SII shows the most substantial mediating effects, with mediation proportions of 11.6%, 9.8%, and 9.8%, respectively. PIV also accounted for a portion of the observed effect. In the hospital dataset, the pattern of mediating effects was largely consistent with that of the NHANES data; however, unlike in NHANES, all inflammatory markers exhibited statistically significant effects. Notably, MLR demonstrated the highest mediation proportion at 33.5%, followed by SIRI at 29%, with PIV, SII, NLR, and PLR also contributing to the mediating effects. Table 4 Results of mediation analysis between hospital data and NHANES data. Inflammatory marker NHANES dataset Hospital dataset ACME ADE TE PM ACME ADE TE PM MLR 0.0003 0.0154*** 0.0158*** 2.2% 0.0066*** 0.0132*** 0.0198*** 33.5% NLR 0.0016*** 0.0142*** 0.0158*** 9.8% 0.0042*** 0.0156*** 0.0198*** 21.4% PIV 0.0014*** 0.0143* 0.0158*** 9.1% 0.0054*** 0.0144*** 0.0198*** 27.4% PLR 0.0003 0.0154*** 0.0158*** 2.1% 0.0032*** 0.0166*** 0.0198*** 16.4% SII 0.0015*** 0.0142*** 0.0158*** 9.8% 0.0045*** 0.0153*** 0.0198*** 22.8% SIRI 0.0018*** 0.0139*** 0.0158*** 11.6% 0.0057*** 0.0141*** 0.0198*** 29% Note: 1. ACEM = Average Causal Mediation Effect (indirect effect); ADE = Average Direct Effect; TE = Total Effect (sum of direct and indirect effects); PM = Proportion Mediation (ACME / TE); Bootstrap confidence intervals are based on 5000 resamples. 2. Effect values already contain significance markers (***P < 0.001, **P < 0.01, *P < 0.05), with specific P-values in the parentheses. 3. Adjusted for covariates: age, gender, race, marital status, BMI, smoking status, drinking status, hypertension, chronic kidney disease, osteoporosis, hyperlipidemia, coronary heart disease. Sensitivity Analysis and Subgroup Analysis To assess the robustness of the mediation analysis results, we performed a series of sensitivity analyses. we conducted mediation analyses using both the mediation package and the Lavaan package (Supplementary Table 6). Although the effect sizes differed in magnitude due to the different scales of inflammatory markers (original values vs. standardized values), the significance of the effects and the mediation proportions of all inflammatory markers were relatively consistent. To address potential concerns about overlap between FI-Lab and inflammatory markers, we reconstructed the FI-Lab score after removing all overlapping components and re-ran the analyses. As presented in Supplementary Tables 7–8, the indirect effect estimates and statistical significance for each inflammatory marker were highly consistent with the primary analysis (all P < 0.01), with only minimal changes in mediation proportions (NHANES data: ±1%; hospital data: ±5%). Additionally, after applying winsorization at the 10% extremes to the inflammatory markers, both effect estimates and mediation proportions showed no substantial deviation from those obtained with the original values (Supplementary Tables 9–10). Finally, we conducted mediation analyses with varying degrees of covariate adjustment, which revealed that while the mediation proportions for individual inflammatory markers exhibited some fluctuation, the mediating effects remained statistically significant across all models. Furthermore, we conducted subgroup analyses to evaluate potential differences in mediating effects among different population strata. As illustrated in Fig. 2 , the mediating effects of multiple inflammatory markers tend to be higher in overweight or overweight or obesity, aged ≥ 65 years old, male, and smoking status, especially composite markers such as SII, MLR, SIRI. In contrast, there were no statistically significant differences among the subgroups of marital status. Discussion Based on a large-scale database from the United States and real-world clinical data from a provincial tertiary hospital in China, this study systematically analyzed the independent associations among COPD, inflammatory markers, and frailty within cross-cultural and different care contexts, we further explored the statistical mediating role of inflammatory responses, represented by these markers in the COPD-frailty connection. Our findings consistently indicate a significant association between COPD and FI-Lab, with composite inflammatory markers such as NLR and SIRI a partially mediating this relationship. Notably, this finding was corroborated across two independent datasets, and its robustness was affirmed through comprehensive sensitivity analyses, providing novel insights into the pathophysiological mechanisms connecting COPD and frailty. This investigation provides objective evidence substantiating the association between COPD and frailty, derived from both NHANES and hospital-based real-world data. Despite rigorous adjustment for demographic characteristics, lifestyle factors, and clinical comorbidities, individuals with COPD exhibited significantly higher FI-Lab scores than their non-COPD counterparts (NHANES: β = 0.016, 95% CI: 0.009–0.022; hospital data: β = 0.020, 95% CI: 0.015–0.024; both P < 0.001). This study found that COPD is significantly associated with a higher burden of frailty, with inflammation-related pathophysiological changes partially explaining this association. These results is similar to previous studies, existing evidence indicates that COPD patients face approximately double the risk of frailty compared to those without COPD[ 28 ], with frailty risk demonstrating a positive correlation with both acute exacerbation frequency and declining lung function[ 29 , 30 ]. Further supporting this relationship, a 9.5-year prospective study by He and colleagues revealed that participants with COPD not only presented with higher baseline frailty index scores (β = 0.839) but also demonstrated accelerated frailty progression over time (β = 0.172)[ 9 ]. Therefore, COPD may be associated with progression of frailty through accelerated deficit accumulation. Consistent with previous research which was conducted by Xu et al., our study confirmed that individuals with COPD show higher inflammatory levels[ 31 ], which are positively associated with frailty[ 32 ]. However, existing investigations have limited to the independent relationships among COPD, inflammatory status, and frailty in isolation, leaving a notable gap in understanding whether inflammation mediates the COPD-frailty pathway. Our study used a cross-sectional design to preliminarily explore the intermediary path to a certain. The findings reveal that inflammatory indicators play a partial intermediary role in the correlation between COPD and frailty, while the indirect effect in NHANES data is 2.0%-11.6%, and hospital data is 16.4–33.5%. the results of this study suggest that inflammation may be one of the possible mechanisms of increasing the risk of frailty in COPD patients. This intermediary path has been reasonably confirmed by biology. In COPD patients, the activation of chronic hypoxia and oxidative stress triggers key inflammatory signaling pathways, such as NF-kB, and initiating the downstream inflammatory cascades[ 33 ], thus establishing a systemic low-grade inflammatory state. Inflammatory factors may affect the synthesis and degradation of protein in muscle tissue through ubiquitin-protease system[ 33 ]. At same times, the inflammatory microenvironment can also induce muscle satellite cells to enter the aging state, weaken the regeneration ability of muscle tissue[ 34 ], and accelerate the progress of frailty. Additionally, chronic inflammation promotes bone loss by enhancing osteoclast activity while suppressing osteoblast function[ 35 ]. Beyond these mechanisms, chronic inflammation also contributes to frailty through multiple pathways including mitochondrial dysfunction and oxidative stress[ 36 ]. Therefore, the statistically observed mediating effect in this study may reflect this pathophysiological process. Within this complex mechanism, existing research mainly focused on CRP, IL-6 and other single verification. while inflammatory indices based on routine blood tests integrate multiple inflammatory cells, including neutrophils and lymphocytes, which not only effectively reflect the degree of inflammatory response, but also can prompt the functional status and potential imbalances of immune system[ 37 ], thus potentially being more conductive to capturing the complexity and diversity of diseases. In addition, frailty is essentially a clinical syndrome characterized by a gradual decline in multi-system physiological reserve capacity,, with complex etiology and mechanisms [ 38 ]. Although there are numerous studies, there is currently no unified assessment instrument[ 39 ]. The FI-Lab used in this study is mainly based on objective laboratory indicators. According to the defect accumulation theory, the effectiveness of FI-Lab depends on the number of defective projects and the coverage of the system, not the specific project content[ 40 ], and methodological evidence proves that FI-lab is stable in the selection of included variables[ 41 ], and the evaluation validity among different construction methods is consistent [ 42 ]. Therefore, although there are differences in the specific items of FI-Lab in the two databases, consistent statistical mediation effects are observed in the two independent data, which further confirms the comparability of the two FI-LABs and the reliability of the research results. However, a methodological consideration warrants attention: the FI-Lab integrates routine blood parameters including neutrophil and lymphocyte counts, and so on., which partially overlap with the inflammatory markers presented as mediators. To rigorously eliminate this bias completely, we constructed a modified FI-Lab that completely eliminated all overlapping components in our sensitivity analyses. The results revealed that approximately 36% of the statistically mediating effect could be attributed to indicator overlap, while the remaining 64% represented genuine independent mediation, which is also successfully replicated in community populations. Moreover, we conducted analyses adjusting for various combinations of covariates present in both the hospital dataset and NHANES, and the results demonstrated relative stability in mediation proportions which proved that the effect was not sensitive to specific covariates. Notably, we observed discrepant mediation proportions between the two datasets (for example, SIRI: 11.6% in NHANES vs. 29% in the hospital data), a finding likely attributable to population characteristics. The hospital cohort, comprising patients with greater disease severity and consequently elevated inflammatory levels, may exhibit amplified mediation effects. Conversely, NHANES, representing a community-dwelling population encompassing numerous mild cases, may demonstrate attenuated mediation effects due to dilution by other contributing factors including nutritional status and social determinants[ 43 , 44 ]. This heterogeneity across settings paradoxically underscores both the context-dependent nature of inflammatory mediation and the robustness of the underlying biological relationship. In this study, it was found that the composite inflammatory index based on blood routine played a partial mediating role in the association between COPD and frailty, and the results were consistent between NHANES community population and clinical patients in China’s hospitals. Secondly, this study uses FI-Lab to evaluate frailty, avoiding the subjective bias brought by traditional questionnaires and physical fitness tests. Despite the inherent limitations of cross-sectional designs in establishing causality, these findings suggest that systematic inflammation management could potentially serve as an intervention strategy to mitigate frailty progression among COPD patients[ 45 ]. Nevertheless, several limitations warrant careful consideration. First, while mediation analyses indicate that inflammatory markers may partially explain the COPD-frailty association, the cross-sectional nature precludes causal inference. secondly, although we harmonized covariates across the two heterogeneous datasets to maximize comparability and adjusted for confounders in sensitivity analyses, residual differences may introduce confounding bias or affect direct comparability of effect estimates. For example, the proportion of female COPD patients in NHANES data is higher than that of male patients, which is similar to the data published by CDC in the United States[ 46 ], which may be related to the lag effect of smoking behavior patterns in the United States [ 47 ], and women's susceptibility to tobacco[ 48 ]. However, the consistent analytical approach applied to both datasets and the replication of findings across two distinct populations partially mitigate these concerns. Future prospective cohort studies are essential to establish whether baseline inflammatory markers predict incident frailty in COPD patients, thereby establishing causality, and randomized controlled trials are needed to evaluate the efficacy of anti-inflammatory interventions for frailty prevention. In conclusion, through dual validation leveraging hospital-based data and public databases, this study demonstrates a robust association between COPD and FI-Lab, with blood routine-derived inflammatory markers partially mediating this relationship. These findings underscore the clinical utility of interpreting inflammatory ratios derived from routine complete blood counts for assessing inflammatory burden and frailty risk. Future investigations should prioritize interventional designs to determine the practical effectiveness of inflammation-targeted comprehensive strategies in attenuating frailty progression among the COPD population. Declarations Ethics approval and consent to participate: This study used data from two sources: hospital data in this study was approved by the Medical Ethics Review Committee of Xiangya Hospital Central South University, while the NHANES data were approved by the National Center for Health Statistics (NCHS) Ethics Review Board in the United States. The study also adhered to the Declaration of Helsinki. Clinical trial number: Not applicable. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: All authors declare that there are no conflicts of interest. Funding Declaration: This study was supported by the Geriatric Diseases Clinical Research Fund of the National Clinical Research Center for Geriatric Diseases (Grant No. 2021LNJJ02), Central South University Xiangya Hospital. Authors' contributions: Yuanbo Wu contributed to experimental design, data collection, data analysis, and manuscript writing; Jingyi Wang and Ziyu Dai contributed to experimental design, data analysis and organization; Mingxuan Xie contributed to experimental design, data analysis and data analysis; Yun Peng contributed to data collection and data analysis, Qiong Chen contributed to experimental design, data analysis, data analysis, manuscript writing and manuscript review。 Acknowledgements: We sincerely thank all participants for sharing their experiences. References 王萍萍：2025年全国人口总量为140489万人 人口高质量发展持续推进. 国家统计局. 2026. https://www.stats.gov.cn/sj/sjjd/202601/t20260119_1962338.html. Accessed 25 Feb 2026. Maddocks M, Kon SSC, Canavan JL, Jones SE, Nolan CM, Labey A, et al. Physical frailty and pulmonary rehabilitation in COPD: A prospective cohort study. Thorax. 2016;71:988–95. https://doi.org/10.1136/thoraxjnl-2016-208460. An TJ, Lim J, Lee H, Ji S, Jung H-W, Baek JY, et al. 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13:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9245206/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9245206/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108837132,"identity":"cbd18346-b4a0-4d75-adc8-9efd586478a4","added_by":"auto","created_at":"2026-05-09 00:06:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":276394,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of exclusion and inclusion criteria\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9245206/v1/c643f031c8073bb0d9e0d91e.png"},{"id":108837131,"identity":"5ae5386e-69b3-47ee-81fc-3ffb7ab44a2f","added_by":"auto","created_at":"2026-05-09 00:06:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177518,"visible":true,"origin":"","legend":"\u003cp\u003eMediating effect of stratification according to different subgroups of variables\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9245206/v1/1b505273146fc8800689d576.png"},{"id":108837133,"identity":"801e05f4-01ad-4178-a1e7-93bc60751620","added_by":"auto","created_at":"2026-05-09 00:06:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":878474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9245206/v1/1c9b44ae-133c-448a-b94a-b89e0eb1ef01.pdf"},{"id":108837130,"identity":"9757951f-39d3-4652-8829-b408b78839b8","added_by":"auto","created_at":"2026-05-09 00:06:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":47094,"visible":true,"origin":"","legend":"","description":"","filename":"AnalysisoftheMediatingRoleofInflammatoryMarkersintheAssociationbetweenCOPDandFrailtyAdditionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9245206/v1/eb587a035b175c76cd03fdf0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the Mediating Role of Inflammatory Markers in the Association between COPD and Frailty","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the official data from National Burean of Statistic, China\u0026rsquo;s population aged 60 and above is 323\u0026nbsp;million, accounting for 23% of the total in 2025. It is expected that by 2030, the proportion of China\u0026rsquo;s population aged 60 and above will rise to 28%, and the population aged 65 and above will exceed 20%, entering a super aging society[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In context of the continuous aging population, It has become a critical public health challenge to maintain the physical function and delay disability progression among middle-aged and elderly people. Frailty, a common aged-related clinical syndrome, is characterized by diminished physiological reserves and reduce stress response capacity[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to the closely association with multiple adverse outcomes, such as falls, disability, hospitalization and mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], It represents a critical transition from healthy aging to disability Therefore, It\u0026rsquo;s critical to identifying risk factors for frailty and explore its underlying mechanisms. Those together are fundamental to advancing healthy aging and reducing the burden on healthcare and social care systems[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe risk factors for frailty are complex and diverse, and chronic diseases plays an important role[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among them, chronic obstructive pulmonary disease (COPD) has received extensive attention in the field of frailty research, owing to its high population prevalence, significant disability association, and heavy disease burden[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Evidence has confirmed a robust association between COPD and frailty. Cross-sectional studies have shown that COPD populations have a significantly higher risk than Non-COPD population, and this association remains stable across age and gender stratification[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Prospective cohort studies further conform that COPD is an independent risk factor for frailty[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. More importantly, when COPD and frailty coexist, they may have synergistic amplification effects, which will result in accelerated decline in activities of daily living, significantly hastened loss of independence, and elevated the risk of adverse outcomes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, it is of great significance to explore the potential mechanisms between COPD and frailty for disease management and screening of high risk population in clinical practice.\u003c/p\u003e \u003cp\u003ePrevious observational studies and cohort studies have confirmed that COPD patients have higher levels of inflammation compared with non-COPD controls. At the same time, higher circulating levels of inflammatory biomarkers are positively correlated with severity of frailty, such as C relative protein (CRP), IL-6 and so on[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Based on the close relationship between COPD and frailty and important role of inflammation in frailty, some researchers believe that chronic inflammation may play an important role in the mechanistic connecting COPD and weakness[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. COPD is characterized as a chronic inflammatory disease. Persistent exposure to harmful particles, such as tobacco smoke can cause recurrent inflammation in the lung parenchyma and peripheral airway. This process activates various immune cells, including neutrophils and lymphocytes, releasing a large of pro-inflammatory cytokines such as Interleukin-6(IL-6) and Tumor Necrosis Factor-α[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Consequently, a systemic inflammatory response is activated. Meanwhile, inflammatory factors and inflammatory microenvironment can directly act on skeletal muscle, affecting energy metabolism and protein synthesis and decomposition of skeletal muscle mitochondrial[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], directly or indirectly participating in the occurrence and development of weakness. Therefore, inflammation is considered as a potential pathophysiological mechanism connecting COPD and frailty. However, the multidimensional evidence from observational research and cohort studies mainly focused on the dependent correlation between COPD, inflammatory indices and frailty, while the integrated research evidence of analyzing the complex relationship among them and testing whether inflammation plays an intermediary role in the correlation between COPD and frailty is insufficient. In addition, there are amounts of researches focus on inflammatory indices, which are costly, required strict laboratory standardization, such as CRP, IL-6 and TNF-α. Therefore, they are difficult to apply in large-scale screening. In recent years, inflammatory markers derived from blood routine, such as systemic inflammatory response index (SIRI), which integrates neutrophils, lymphocytes and other immune inflammatory cells, have gradually emerged as an effective tool for assessing inflammation levels, and have gained increasing attention due to their low cost and high accessibility, positioning them as promising alternatives for population-based studies[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study used the National Health Nutrition Examination Survey (NHANES) in the United States and the real clinical data of the top three hospitals in China respectively, and adopted a cross-sectional research design to preliminarily explore the independent correlation among COPD, inflammatory indicators and frailty, and further systematize the mediating effect of inflammatory indicators and the proportion of specific mediating effects in the association between COPD and frailty used mediation analysis. This finding aims to provide evidence for mechanistic insights into the association between COPD and frailty and strategies for screening and intervention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eThe study employed a two-stage cross-sectional design, with exploratory analysis first conducted in the community-based NHANES population, followed by validation in clinical data from a large tertiary hospital in China.\u003c/p\u003e \u003cp\u003eThe NHANES is a nationally representative program designed to evaluate to health and nutritional status of U.S. population[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It collects a series of health data, including interview, physical examination, and other relevant metrics. All collections procedures were approved by the Ethics Review Board of the National Center for Health Statistics and written informed consent was obtained from participants[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For our study, we included participant from the 2005\u0026ndash;2018 cycles and excluded individuals who under 40 years old, had a recent infection (within 30 days, e.g., influenza, pneumonia, or otitis media), were pregnant or lactating, or had missing data necessary for computing FI-Lab and inflammatory markers. A total of 7912 participants were ultimately included in the analysis. Further details regarding the data can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e The followed stage utilized real-word clinical data from a China\u0026rsquo;s hospital. The hospital data included aged 40 or older who were hospitalized from January 2020 to June 2025 in Xiangya Hospital of Central South University. We excluded individuals with any of following conditions: 1) Patients admitted to the Intensive Care Unit (ICU), Infectious Diseases Department, or Oncology Radiotherapy and Chemotherapy Department.2) severe concurrent comorbidities (including major inflections, significant cardiopulmonary or systemic diseases,3) long-term bedridden status, malignancies, or cachexia), 4) severe cognitive impairment or psychiatric disorders, 5) pregnancy or lactation, 6) acute exacerbation of COPD or COPD with concurrent acute upper respiratory infection; 7) recent infectious disease (e.g., influenza, COVID-19), 8) incomplete clinical records. The final analytical sample comprised 8817 individuals. The study protocol received approval from the Medical Ethics Review Committee of Xiangya Hospital Central South University (2026020333). Owing to the retrospective nature of the study, which involved no intervention, no direct contact with participants, and posed no more than minimal risk; the secondary use of de-identified data that did not alter clinical outcomes or involve sensitive information; and the anonymization of all medical records prior to analysis for academic purposes, the committee granted a waiver of informed consent. The study was conducted in accordance with the Declaration of Helsinki. The detail inclusion and exclusion process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eThe definition of COPD\u003c/p\u003e \u003cp\u003eIn this study, the definition of COPD in NHANES data follows the previous research, mainly based on the literature information in the medical condition data set, that is, extracting participants' answers to the following three questions: \"Did the doctor tell you that you have chronic obstructive pulmonary disease\", \"Did the doctor tell you that you have chronic bronchitis\" and \"Did the doctor tell you that you have emphysema\"; Participants who answer \"Yes\" to any of the above three questions are classified as having COPD.\u003c/p\u003e \u003cp\u003eIn hospital data, the diagnostic criteria of COPD are based on the diagnostic criteria of the Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD) in 2020, that is, hospitalized patients included in the study have persistent respiratory symptoms, such as cough, expectoration and dyspnea. And excluding other lung diseases, after inhaling bronchodilator, the ratio of Forced Expiratory Volume in one second (FEV1) to Forced Vital Capacity (FVC) in the first second was less than 0.7.\u003c/p\u003e \u003cp\u003eGeneral data collection\u003c/p\u003e \u003cp\u003eAccording to previous studies, this study comprehensively considered many variables that affect frailty. Among them, the NHANES data primarily collects self-reported sociodemographic variables, including age, gender, race, marital status, smoking status, and alcohol consumption. Physical measurements such as BMI, height, and weight are extracted from physical examinations, while peripheral blood cell counts and other data are obtained from the mobile examination center. Comorbidities include chronic conditions such as diabetes, hypertension, diabetes, coronary heart disease, hyperlipidemia, and heart failure. Hospital clinical data was extracted from hospital records, including demographic and general characteristic (age, gender, ethnicity, marital status, occupation and BMI), behavioral habits (smoking and drinking), documented comorbidities (diabetes, hypertension, coronary heart disease, and tumors and so on), as well as laboratory results obtained within the first 48 hours.\u003c/p\u003e \u003cp\u003eThe definition of frailty\u003c/p\u003e \u003cp\u003eLaboratory Frailty index is a continuous index based on defect accumulation theory, which aims to evaluate the frailty of individuals through quantifying the abnormal proportion of routine laboratory examination results[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. FI-Lab operationalizes the concept of deficit accumulation, grounded in the established observation that laboratory abnormalities increase with age. Each item is dichotomously recoded: \u0026ldquo;0\u0026rdquo; for a result within the normal range and \u0026ldquo;1\u0026rdquo; otherwise. An individual\u0026rsquo;s FI-Lab score is derived from the proportion of deficits present, calculated as the sum of these binary scores divided by the total number of variables assessed, yielding a continuous score from 0 to 1 where higher value denote greater frailty severity. In this study, A 32-intems version were used to the public database[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and 44-variables FI-Lab were used to the hospital, covering hematological, biochemical, and related parameters[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and The index was computed for only for individuals with available data for at least 80% of the required variables in their respective index.\u003c/p\u003e \u003cp\u003eInflammatory marker\u003c/p\u003e \u003cp\u003eBased on routine blood test data, we calculated several composite inflammatory markers, including Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Monocyte-to-Lymphocyte Ratio (MLR), Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and Pan-Immune Inflammation Value (PIV)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These were calculated using the following formulas:\u003c/p\u003e \u003cp\u003eNLR\u0026thinsp;=\u0026thinsp;Absolute neutrophil count / Absolute lymphocyte count (both in \u0026times;10⁹ L⁻\u0026sup1;);\u003c/p\u003e \u003cp\u003ePLR\u0026thinsp;=\u0026thinsp;Absolute platelet count / Absolute lymphocyte count (both in \u0026times;10⁹ L⁻\u0026sup1;)\u003c/p\u003e \u003cp\u003eMLR\u0026thinsp;=\u0026thinsp;Absolute monocyte count / Absolute lymphocyte count (both in \u0026times;10⁹ L⁻\u0026sup1;)\u003c/p\u003e \u003cp\u003eSII = (Absolute platelet count \u0026times; Absolute neutrophil count) / Absolute lymphocyte count (all in \u0026times;10⁹ L⁻\u0026sup1;)\u003c/p\u003e \u003cp\u003eSIRI = (Absolute neutrophil count \u0026times; Absolute monocyte count) / Absolute lymphocyte count (all in \u0026times;10⁹ L⁻\u0026sup1;)\u003c/p\u003e \u003cp\u003ePIV = (Absolute neutrophil count \u0026times; Absolute monocyte count \u0026times; Absolute platelet count) / Absolute lymphocyte count (resulting unit: \u0026times;10\u0026sup2;⁷ L⁻\u0026sup3;)\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA two-stage analytical approach was adopted. In the first stage, an exploratory analysis was conducted using the NHANES data to investigate the potential mediating effect of inflammatory markers in the association between COPD and frailty. The second stage involved a validation analysis in the real data to assess the generalizability of the observed mediation effect.\u003c/p\u003e \u003cp\u003ewe preprocess the two data respectively. For NHANES dataset, we say that the special codes of the questionnaire or laboratory test items (such as \"7\", \"9\", \"77\" and \"99\" which mean \"refused to answer\", \"unknown\" or \"missing\") are uniformly identified as missing values. After that, the missing variables in the two data sets are treated in the same way, that is, the variables with missing rate\u0026thinsp;\u0026gt;\u0026thinsp;10% are considered as poor data quality and deleted; For variables with missing rate\u0026thinsp;\u0026le;\u0026thinsp;10%, we assume that the missing type is random missing, use MICE package for chain multiple interpolation, use predicted mean matching to interpolate continuous variables, and use logistic regression to interpolate classified variables.\u003c/p\u003e \u003cp\u003eFor the NHANES data, continuous variables were assessed for normality. Normally distributed continuous variables were present as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error, and compared using Student-t test. While non-normally distributed data are reported as median (IQR) and compared using Wilcoxon rank-sum test. Categorical variables are presented as n (%) and compared using the Chi-square or Fisher\u0026rsquo;s exact test. Subsequently, we performed univariate, multivariate analyses and hierarchical linear regression analysis to examine the independent association between risk factors for frailty and to examine the independent association between COPD and frailty. Building this, linear regression was used to assess the relationship among COPD, inflammatory indices and FI-Lab. All inflammation markers were standardized suing Z-scores prior to modeling to ensure comparability. We constructed four progressively adjusted model: model1 was unadjusted, model 2 adjusted for demographics; model 3 further adjusted for lifestyle factors, and model 4 further adjusted clinal comorbidities. Results are reported as β coefficients with 95% CI. The potential role of inflammatory markers was formally tested using R mediation package, and Bootstrap method repeated 5000 times to calculate the 95% CI for mediation effect, aiming to verify the potential mediating role of inflammatory markers in the association between COPD and frailty.\u003c/p\u003e \u003cp\u003eTo further evaluate the robustness of the findings, we conducted several sensitivity analyses. Firstly, the mediation analysis was replicated using the lavaan package with the same Bootstrap approach. Secondly, to address potential bias, we reconstructed the FI-lab though excluding the overlapping items between FI-Lab and inflammatory indices, and repeat the mediation. Thirdly, all inflammation marker were 10% winsorization to reduce the influence of extreme values. Finally, subgroup analyses were conducted to examine potential heterogeneity in the mediating effect.\u003c/p\u003e \u003cp\u003eTo validate our findings, we implemented the pre-defined modeling strategy. Based on the final covariates identified in the NHANES data, we examined the association between COPD, inflammatory markers, and FI-Lab, followed by formal mediation testing, using the final set of covariates derived from NHANES analysis. Due to missing data in some variables across the two datasets from different sources, we harmonized and standardized the covariates to ensure comparability in cross-data analyses. The core analytical procedure mirrored that used for the hospital data. Additionally, we performed extensive sensitivity analyses in the two cohort to evaluate the robustness of the mediation effect to different levels of covariate control. This involved running the mediation model under four progressively adjusted conditions: a minimal set (age and gender), a basic set (adding other demographics and lifestyle factors), a clinical set (further incorporating comorbidities), and a full set (including all available covariates).\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R (version 4.5.0). Statistical significance was defined as a two-sided P-value of less than 0.05. This design and reporting of study followed the STROBE statement guidelines[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics Description\u003c/p\u003e\n\u003cp\u003eBased on baseline characteristics comparison between NHANES dataset and hospital dataset. The NHANES data including 7912 participants, of whom 621 were in the COPD group (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the hospital dataset included 8813 eligible patients, of whom 1429 were in the COPD group (Supplement Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared to their non-COPD counterparts, COPD patients are significantly older (NHANES data: 63 vs. 59 years, hospital data: 68 vs. 63 years; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and more likely to have a history of smoking (NHANES data: 73.3% vs. 48.5%; hospital data: 54.5% vs. 26.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and alcohol history (NHANES data: 69.7% vs. 30.3%; hospital data: 31.4% vs. 18.4%). Furthermore, both datasets showed that COPD patients have higher proportions of comorbidities, including coronary heart disease, asthma, tuberculosis, and tumor (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, inflammatory makers such as NLR, MLR, as well as FI-lab score, were significantly higher in the COPD group. However, the difference in PLR was borderline significance in NHANES (P\u0026thinsp;=\u0026thinsp;0.048) and was not statistically significant in the hospital dataset (p\u0026thinsp;=\u0026thinsp;0.097). Meanwhile, there are some differences in some indicators existed between the two datasets, where the hospital-based COPD patients were predominantly Han Chinese males, while those in NHANES were primarily non-Hispanic Whites and lower socioeconomic status.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the COPD group was characterized by older age, poorer lifestyle habits, higher levels of inflammation and FI-Lab, and A heavier burden of specific comorbidities.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of baseline characteristics between COPD and non-COPD patients in NHANES data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ecategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7912)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNon-COPD\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7291)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;621)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e59 (49,69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e59 (48,69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e63 (52,73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e167 (159.8,174.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e167.1 (159.8,174.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e166.5 (160,174.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e79.3 (67.8,92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e79.1 (67.8,92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e81.7 (67.7,97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28.26 (24.88,32.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e28.2 (24.88,32.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e28.98 (24.76,34.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNeutrophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4 (3.1,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.9 (3.1,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.4 (3.4,5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLymphocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2 (1.6,2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2 (1.6,2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.9 (1.5,2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMonocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.5 (0.4,0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.5 (0.4,0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.6 (0.4,0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2 (1.5,2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2 (1.48,2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.17 (1.63,2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e125.33 (98.63,160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e125 (98.54,159.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e129.09 (100,167.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.26 (0.21,0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.26 (0.2,0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.27 (0.21,0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e494 (350.41,693.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e489 (348,686.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e557.89 (391.96,807.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.03 (0.71,1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.02 (0.7,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.17 (0.8,1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e254.42 (167.26,390.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e250 (165.69,383.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e311.63 (194.38,492.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4003 (50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3712 (50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e291 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1303 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1263 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e40 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e636 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e603 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e33 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4241 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3803 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e438 (70.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1474 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1386 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e88 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e258 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e236 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e22 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5102 (64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4758 (65.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e344 (55.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3992 (50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3537 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e455 (73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDrinking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5518 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5085 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e433 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7496 (94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6949 (95.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e547 (88.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3980 (50.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3608 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e372 (59.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6445 (81.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5975 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e470 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6760 (85.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6267 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e493 (79.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7287 (92.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6767 (92.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e520 (83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eConnective tissue disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5039 (63.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4788 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e251 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7025 (88.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6660 (91.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e365 (58.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5181 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4759 (65.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e422 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7641 (96.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7091 (97.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e550 (88.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6910 (87.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6406 (87.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e504 (81.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote:\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. Continuous variables: Normal distribution is expressed as mean\u0026plusmn;standard deviation, and group comparison is performed by T-test; non-normal distribution is expressed as median (interquartile range), and group comparison is performed by Wilcoxon rank-sum test.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e2. Categorical variables are expressed as frequency (percentage, %), and group comparison is performed by chi-square test or Fisher exact test.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. P value: \u0026lt;0.001 is presented as \u0026lt;\u0026thinsp;0.001, otherwise specific value is displayed.\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation analysis among COPD, inflammatory markers and FI-Lab\u003c/p\u003e\n\u003cp\u003eWe first performed univariate and multivariate linear regression to identify independent risk factors and assess the association between COPD and the FI-Lab (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Univariate analysis revealed a significant positive association between COPD and FI-Lab (\u0026beta;\u0026thinsp;=\u0026thinsp;0.032, 95% CI: 0.024,0.039, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subsequently, variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in the univariate analysis and those considered priori important confounders for osteoporosis was included in the multivariate stepwise regression analysis. The results showed that the association between COPD and FI-Lab remained significant (\u0026beta;\u0026thinsp;=\u0026thinsp;0.016, 95% CI: 0.009,0.022, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, hypertension, diabetes, kidney disease, hyperlipidemia, and smoking status were also independently associated with frailty (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate analysis of association factors with frailty\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003evariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\n \u003cp\u003eContinuous variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.002,0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.001,0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.002,0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.000,0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\n \u003cp\u003eCategorical variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(-0.016, -0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.027,0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(-0.025, -0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.020, -0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(-0.012,0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.010,0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.007,0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.005,0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDrinking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(-0.026, -0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.015, -0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.055,0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.007,0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.058,0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.028,0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.086,0.096)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.056,0.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.080,0.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.045,0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(-0.003,0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.015, -0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eConnective tissue disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.019,0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.005,0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.006,0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.005,0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.020,0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.020,0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.024,0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(0.009,0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.004,0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.001,0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e(0.010,0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e(-0.005,0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote:\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. \u0026beta; is presented as coefficient, 95% confidence interval in parenthesis.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e2. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in the univariate analysis entered multivariate stepwise regression analysis.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. \u0026apos;--\u0026apos;indicates that they did not enter multivariate analysis or the results were not available.\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTo further assess the robustness of the association, we constructed a four-level regression analysis model based on significant variables identified by multivariate analysis. Resulted are presented in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the unadjusted model, a significant positive association was observed between COPD and frailty in both dataset (NHANES data: \u0026beta;\u0026thinsp;=\u0026thinsp;0.032, 95% CI: 0.024,0.039; Hospital data: \u0026beta;\u0026thinsp;=\u0026thinsp;0.027, 95% CI: 0.022,0.032, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After sequentially adjusting for demographic factors (model 2), lifestyle factors (model3), and clinical comorbidities (model 4), the magnitude of the association gradually attenuated but remained statistically significant (NHANES data: \u0026beta;\u0026thinsp;=\u0026thinsp;0.016, 95% CI: 0.010,0.023; Hospital data: \u0026beta;\u0026thinsp;=\u0026thinsp;0.020, 95% CI: 0.015,0.024, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation analysis between COPD and FI-Lab\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eData sources\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emodel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003esample\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eNHANES database\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModel 1 (crude model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.032 (95% CI: 0.024,0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModel 2 (+\u0026thinsp;demography)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.028 (95% CI: 0.021,0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModel 3 (+\u0026thinsp;lifestyle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.023 (95% CI: 0.016,0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModel 4 (+\u0026thinsp;clinical comorbidities)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.016 (95% CI: 0.009,0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eHospital database\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModel 1 (crude model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.027 (95% CI: 0.022,0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e8813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModel 2 (+\u0026thinsp;demography)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.023 (95% CI: 0.018,0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e8813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModel 3 (+\u0026thinsp;lifestyle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.020 (95% CI: 0.015,0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e8813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eModel 4 (+\u0026thinsp;clinical comorbidities)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.020 (95% CI: 0.015,0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e8813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote:\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e1. \u0026beta; is presented as coefficient with 95% confidence interval in parenthesis.2. Model 1 is crude model containing only COPD variables. Model 2 adjusted demographic factors (age, gender, race and marital status). Model 3 further adjusted lifestyle (BMI, smoking status, drinking status) based on model2. Model 4 further adjusted clinical variables (hypertension, chronic kidney disease, osteoporosis, hyperlipidemia, coronary heart disease) based on model\u003c/p\u003e\n\u003cp\u003eOn the basis of clarifying the association between COPD and FI-Lab, we further analyzed the relationship between COPD and inflammatory markers. As shown in Supplementary Tables\u0026nbsp;2 and 3, in both databases, COPD exhibited significant positive correlations with all inflammatory markers analyzed, except for PLR and MLR. This significant relationship persisted following stepwise adjustment for demographic, lifestyle and clinical comorbidity variables, COPD demonstrated the stronger association with SII (\u0026beta;\u0026thinsp;=\u0026thinsp;0.166, 95% CI: 0.083,0.249, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SIRI (\u0026beta;\u0026thinsp;=\u0026thinsp;0.171, 95% CI: 0.091,0.250, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the hospital dataset, PIV had a \u0026beta; of 0.293 (95% CI:0.234,0.351, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) followed by SIRI (\u0026beta;\u0026thinsp;=\u0026thinsp;0.289, 95CI: 0.231,0.347, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eAfter that, we further analyzed the correlation between standardized inflammatory markers and FI-Lab. The results are presented in Supplementary Tables\u0026nbsp;4 and 5, all inflammatory analyzed were significantly and positively correlated with FI-Lab in both datasets except PLR. In the crude models (Model 1), all associations were highly significant except PLR. After adjusted demographic, lifestyle and clinical comorbidities, SIRI and SII demonstrated relatively stronger correlations with FI-lab in the NHANES dataset (\u0026beta;\u0026thinsp;=\u0026thinsp;0.011 and 0.010, respectively; both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). it is worth noting that PLR also had a significant statistical effect in Model 4 (\u0026beta;\u0026thinsp;=\u0026thinsp;0.004, 95% CI: 0.002,0.005, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). in the hospital database, the \u0026beta; value of MLR was 0.027 (95%CI:0.025,0.028, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) followed by SIRI (\u0026beta;\u0026thinsp;=\u0026thinsp;0.020, 95CI: 0.019,0.022, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eThe mediating role of inflammatory markers in the association between COPD and Frailty\u003c/p\u003e\n\u003cp\u003eBased on above results, with the exception of PLR, all other inflammatory markers demonstrated associations with both COPD and FI-Lab. Accordingly, we performed a mediation analysis utilizing the mediation package. The results are shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, In the NHANES database, the mediating effects of all inflammatory markers expect MLR and PLR were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among these, SIRI, NLR, and SII shows the most substantial mediating effects, with mediation proportions of 11.6%, 9.8%, and 9.8%, respectively. PIV also accounted for a portion of the observed effect. In the hospital dataset, the pattern of mediating effects was largely consistent with that of the NHANES data; however, unlike in NHANES, all inflammatory markers exhibited statistically significant effects. Notably, MLR demonstrated the highest mediation proportion at 33.5%, followed by SIRI at 29%, with PIV, SII, NLR, and PLR also contributing to the mediating effects.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of mediation analysis between hospital data and NHANES data.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eInflammatory marker\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\n \u003cp\u003eNHANES dataset\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\n \u003cp\u003eHospital dataset\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eACME\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eADE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ePM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eACME\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eADE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003ePM\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0154***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0158***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0066***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0132***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0198***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e33.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.0016***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0142***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0158***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0042***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0156***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0198***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e21.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.0014***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0143*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0158***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0144***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0198***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e27.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0154***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0158***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0032***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0166***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0198***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.0015***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0142***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0158***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0045***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0153***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0198***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e22.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.0018***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.0139***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0158***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0057***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0141***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0198***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\n \u003cp\u003eNote:\u003c/p\u003e\n \u003cp\u003e1. ACEM\u0026thinsp;=\u0026thinsp;Average Causal Mediation Effect (indirect effect); ADE\u0026thinsp;=\u0026thinsp;Average Direct Effect; TE\u0026thinsp;=\u0026thinsp;Total Effect (sum of direct and indirect effects); PM\u0026thinsp;=\u0026thinsp;Proportion Mediation (ACME / TE); Bootstrap confidence intervals are based on 5000 resamples.\u003c/p\u003e\n \u003cp\u003e2. Effect values already contain significance markers (***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with specific P-values in the parentheses.\u003c/p\u003e\n \u003cp\u003e3. Adjusted for covariates: age, gender, race, marital status, BMI, smoking status, drinking status, hypertension, chronic kidney disease, osteoporosis, hyperlipidemia, coronary heart disease.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSensitivity Analysis and Subgroup Analysis\u003c/p\u003e\n\u003cp\u003eTo assess the robustness of the mediation analysis results, we performed a series of sensitivity analyses. we conducted mediation analyses using both the mediation package and the Lavaan package (Supplementary Table\u0026nbsp;6). Although the effect sizes differed in magnitude due to the different scales of inflammatory markers (original values vs. standardized values), the significance of the effects and the mediation proportions of all inflammatory markers were relatively consistent.\u003c/p\u003e\n\u003cp\u003eTo address potential concerns about overlap between FI-Lab and inflammatory markers, we reconstructed the FI-Lab score after removing all overlapping components and re-ran the analyses. As presented in Supplementary Tables\u0026nbsp;7\u0026ndash;8, the indirect effect estimates and statistical significance for each inflammatory marker were highly consistent with the primary analysis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with only minimal changes in mediation proportions (NHANES data: \u0026plusmn;1%; hospital data: \u0026plusmn;5%). Additionally, after applying winsorization at the 10% extremes to the inflammatory markers, both effect estimates and mediation proportions showed no substantial deviation from those obtained with the original values (Supplementary Tables\u0026nbsp;9\u0026ndash;10). Finally, we conducted mediation analyses with varying degrees of covariate adjustment, which revealed that while the mediation proportions for individual inflammatory markers exhibited some fluctuation, the mediating effects remained statistically significant across all models.\u003c/p\u003e\n\u003cp\u003eFurthermore, we conducted subgroup analyses to evaluate potential differences in mediating effects among different population strata. As illustrated in Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the mediating effects of multiple inflammatory markers tend to be higher in overweight or overweight or obesity, aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years old, male, and smoking status, especially composite markers such as SII, MLR, SIRI. In contrast, there were no statistically significant differences among the subgroups of marital status.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a large-scale database from the United States and real-world clinical data from a provincial tertiary hospital in China, this study systematically analyzed the independent associations among COPD, inflammatory markers, and frailty within cross-cultural and different care contexts, we further explored the statistical mediating role of inflammatory responses, represented by these markers in the COPD-frailty connection. Our findings consistently indicate a significant association between COPD and FI-Lab, with composite inflammatory markers such as NLR and SIRI a partially mediating this relationship. Notably, this finding was corroborated across two independent datasets, and its robustness was affirmed through comprehensive sensitivity analyses, providing novel insights into the pathophysiological mechanisms connecting COPD and frailty.\u003c/p\u003e \u003cp\u003eThis investigation provides objective evidence substantiating the association between COPD and frailty, derived from both NHANES and hospital-based real-world data. Despite rigorous adjustment for demographic characteristics, lifestyle factors, and clinical comorbidities, individuals with COPD exhibited significantly higher FI-Lab scores than their non-COPD counterparts (NHANES: β\u0026thinsp;=\u0026thinsp;0.016, 95% CI: 0.009\u0026ndash;0.022; hospital data: β\u0026thinsp;=\u0026thinsp;0.020, 95% CI: 0.015\u0026ndash;0.024; both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This study found that COPD is significantly associated with a higher burden of frailty, with inflammation-related pathophysiological changes partially explaining this association. These results is similar to previous studies, existing evidence indicates that COPD patients face approximately double the risk of frailty compared to those without COPD[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], with frailty risk demonstrating a positive correlation with both acute exacerbation frequency and declining lung function[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Further supporting this relationship, a 9.5-year prospective study by He and colleagues revealed that participants with COPD not only presented with higher baseline frailty index scores (β\u0026thinsp;=\u0026thinsp;0.839) but also demonstrated accelerated frailty progression over time (β\u0026thinsp;=\u0026thinsp;0.172)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, COPD may be associated with progression of frailty through accelerated deficit accumulation.\u003c/p\u003e \u003cp\u003eConsistent with previous research which was conducted by Xu et al., our study confirmed that individuals with COPD show higher inflammatory levels[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which are positively associated with frailty[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, existing investigations have limited to the independent relationships among COPD, inflammatory status, and frailty in isolation, leaving a notable gap in understanding whether inflammation mediates the COPD-frailty pathway.\u003c/p\u003e \u003cp\u003eOur study used a cross-sectional design to preliminarily explore the intermediary path to a certain. The findings reveal that inflammatory indicators play a partial intermediary role in the correlation between COPD and frailty, while the indirect effect in NHANES data is 2.0%-11.6%, and hospital data is 16.4\u0026ndash;33.5%. the results of this study suggest that inflammation may be one of the possible mechanisms of increasing the risk of frailty in COPD patients. This intermediary path has been reasonably confirmed by biology. In COPD patients, the activation of chronic hypoxia and oxidative stress triggers key inflammatory signaling pathways, such as NF-kB, and initiating the downstream inflammatory cascades[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], thus establishing a systemic low-grade inflammatory state. Inflammatory factors may affect the synthesis and degradation of protein in muscle tissue through ubiquitin-protease system[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. At same times, the inflammatory microenvironment can also induce muscle satellite cells to enter the aging state, weaken the regeneration ability of muscle tissue[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and accelerate the progress of frailty. Additionally, chronic inflammation promotes bone loss by enhancing osteoclast activity while suppressing osteoblast function[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond these mechanisms, chronic inflammation also contributes to frailty through multiple pathways including mitochondrial dysfunction and oxidative stress[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, the statistically observed mediating effect in this study may reflect this pathophysiological process.\u003c/p\u003e \u003cp\u003eWithin this complex mechanism, existing research mainly focused on CRP, IL-6 and other single verification. while inflammatory indices based on routine blood tests integrate multiple inflammatory cells, including neutrophils and lymphocytes, which not only effectively reflect the degree of inflammatory response, but also can prompt the functional status and potential imbalances of immune system[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], thus potentially being more conductive to capturing the complexity and diversity of diseases. In addition, frailty is essentially a clinical syndrome characterized by a gradual decline in multi-system physiological reserve capacity,, with complex etiology and mechanisms [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Although there are numerous studies, there is currently no unified assessment instrument[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The FI-Lab used in this study is mainly based on objective laboratory indicators. According to the defect accumulation theory, the effectiveness of FI-Lab depends on the number of defective projects and the coverage of the system, not the specific project content[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and methodological evidence proves that FI-lab is stable in the selection of included variables[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and the evaluation validity among different construction methods is consistent [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, although there are differences in the specific items of FI-Lab in the two databases, consistent statistical mediation effects are observed in the two independent data, which further confirms the comparability of the two FI-LABs and the reliability of the research results. However, a methodological consideration warrants attention: the FI-Lab integrates routine blood parameters including neutrophil and lymphocyte counts, and so on., which partially overlap with the inflammatory markers presented as mediators. To rigorously eliminate this bias completely, we constructed a modified FI-Lab that completely eliminated all overlapping components in our sensitivity analyses. The results revealed that approximately 36% of the statistically mediating effect could be attributed to indicator overlap, while the remaining 64% represented genuine independent mediation, which is also successfully replicated in community populations. Moreover, we conducted analyses adjusting for various combinations of covariates present in both the hospital dataset and NHANES, and the results demonstrated relative stability in mediation proportions which proved that the effect was not sensitive to specific covariates.\u003c/p\u003e \u003cp\u003eNotably, we observed discrepant mediation proportions between the two datasets (for example, SIRI: 11.6% in NHANES vs. 29% in the hospital data), a finding likely attributable to population characteristics. The hospital cohort, comprising patients with greater disease severity and consequently elevated inflammatory levels, may exhibit amplified mediation effects. Conversely, NHANES, representing a community-dwelling population encompassing numerous mild cases, may demonstrate attenuated mediation effects due to dilution by other contributing factors including nutritional status and social determinants[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This heterogeneity across settings paradoxically underscores both the context-dependent nature of inflammatory mediation and the robustness of the underlying biological relationship.\u003c/p\u003e \u003cp\u003eIn this study, it was found that the composite inflammatory index based on blood routine played a partial mediating role in the association between COPD and frailty, and the results were consistent between NHANES community population and clinical patients in China\u0026rsquo;s hospitals. Secondly, this study uses FI-Lab to evaluate frailty, avoiding the subjective bias brought by traditional questionnaires and physical fitness tests. Despite the inherent limitations of cross-sectional designs in establishing causality, these findings suggest that systematic inflammation management could potentially serve as an intervention strategy to mitigate frailty progression among COPD patients[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Nevertheless, several limitations warrant careful consideration. First, while mediation analyses indicate that inflammatory markers may partially explain the COPD-frailty association, the cross-sectional nature precludes causal inference. secondly, although we harmonized covariates across the two heterogeneous datasets to maximize comparability and adjusted for confounders in sensitivity analyses, residual differences may introduce confounding bias or affect direct comparability of effect estimates. For example, the proportion of female COPD patients in NHANES data is higher than that of male patients, which is similar to the data published by CDC in the United States[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which may be related to the lag effect of smoking behavior patterns in the United States [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and women's susceptibility to tobacco[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, the consistent analytical approach applied to both datasets and the replication of findings across two distinct populations partially mitigate these concerns. Future prospective cohort studies are essential to establish whether baseline inflammatory markers predict incident frailty in COPD patients, thereby establishing causality, and randomized controlled trials are needed to evaluate the efficacy of anti-inflammatory interventions for frailty prevention.\u003c/p\u003e \u003cp\u003eIn conclusion, through dual validation leveraging hospital-based data and public databases, this study demonstrates a robust association between COPD and FI-Lab, with blood routine-derived inflammatory markers partially mediating this relationship. These findings underscore the clinical utility of interpreting inflammatory ratios derived from routine complete blood counts for assessing inflammatory burden and frailty risk. Future investigations should prioritize interventional designs to determine the practical effectiveness of inflammation-targeted comprehensive strategies in attenuating frailty progression among the COPD population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: This study used data from two sources: hospital data in this study was approved by the Medical Ethics Review Committee of Xiangya Hospital Central South University, while the NHANES data were approved by the National Center for Health Statistics (NCHS) Ethics Review Board in the United States. The study also adhered to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: All authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding Declaration:\u0026nbsp;This study was supported by the Geriatric Diseases Clinical Research Fund of the National Clinical Research Center for Geriatric Diseases (Grant No. 2021LNJJ02), Central South University Xiangya Hospital.\u003c/p\u003e\n\u003cp\u003eAuthors' contributions: Yuanbo Wu contributed to experimental design, data collection, data analysis, and manuscript writing; Jingyi Wang and Ziyu Dai contributed to experimental design, data analysis and organization; Mingxuan Xie contributed to experimental design, data analysis and data analysis; Yun Peng contributed to data collection and data analysis, Qiong Chen contributed to experimental design, data analysis, data analysis, manuscript writing and manuscript review。\u003c/p\u003e\n\u003cp\u003eAcknowledgements: We sincerely thank all participants for sharing their experiences.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e王萍萍：2025年全国人口总量为140489万人 人口高质量发展持续推进. 国家统计局. 2026. https://www.stats.gov.cn/sj/sjjd/202601/t20260119_1962338.html. 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Geroscience. 2025. https://doi.org/10.1007/s11357-025-01734-2.\u003c/li\u003e\n \u003cli\u003eI A-A, P S-L, N S-H, N F-G, V M. The relationship between nutrition and frailty: Effects of protein intake, nutritional supplementation, vitamin D and exercise on muscle metabolism in the elderly. A systematic review. Maturitas. 2016;93. https://doi.org/10.1016/j.maturitas.2016.04.009.\u003c/li\u003e\n \u003cli\u003eLi L, Li Y-L, Li C-L, Yan Y. Mechanism validation of recuperating lung decoction for COPD: Targeting the TLR4/PI3K/akt/mTOR signaling pathway to mitigate smoke- and LPS-induced pulmonary inflammation. J Ethnopharmacol. 2026;355 Pt B:120750. https://doi.org/10.1016/j.jep.2025.120750.\u003c/li\u003e\n \u003cli\u003eWeeks JD, Ph.D., Elgaddal N, M.S. Chronic Obstructive Pulmonary Disease in Adults Age 18 and Older: United States, 2023. 2025. https://doi.org/10.15620/cdc/174596.\u003c/li\u003e\n \u003cli\u003eOzga JE, Steinberg AW, Sargent JD, Tang Z, Stanton CA, Paulin LM. An examination of sex differences and cigarette smoking as predictors of COPD prevalence and incidence in older US adults. Nicotine Tob Res Off J Soc Res Nicotine Tob. 2025;27:1975\u0026ndash;84. https://doi.org/10.1093/ntr/ntaf162.\u003c/li\u003e\n \u003cli\u003e\u0026Ccedil;olak Y, Nordestgaard BG, Lange P, Afzal S. Sex differences in COPD in relation to smoking exposure: A population-based cohort study. Thorax. 2025;80:512\u0026ndash;9. https://doi.org/10.1136/thorax-2024-222682.\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"inflammatory indicators, COPD, Frailty, FI-Lab, mediation effect analysis","lastPublishedDoi":"10.21203/rs.3.rs-9245206/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9245206/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective\u003cstrong\u003e: \u003c/strong\u003ePatients with chronic obstructive pulmonary disease (COPD) are at a high risk of frailty, and chronic inflammation is considered as an important mechanism mediating their association. This study aimed to systematically verify the correlation between COPD and frailty, and to further explore the statistically mediating role and specific effect ratio of the routine blood count-derived inflammatory markers.\u003c/p\u003e\n\u003cp\u003eMethods: Based on public database and real hospital clinical data, the study aimed to explore the correlation intensity among COPD, inflammatory markers, and frailty used hierarchical linear regression. Then, we performed mediation analysis to quantify the proportion of the effect mediated by inflammatory markers; Finally, we verified the robustness of the results through sensitivity analysis and subgroup analysis.\u003c/p\u003e\n\u003cp\u003eResults: COPD patients were significantly higher FI-Lab compared to non-COPD patients ( NHANES database: β=0.016, 95% confidence interval (CI): 0.009–0.022, P\u0026lt;0.001; hospital data: β=0.020, 95% CI: 0.015–0.024, P\u0026lt;0.001). inflammatory markers partially mediated the association. Specifically, SIRI explained 11.6 and 29% of the total effect in NHANES database and hospital database, respectively. The robustness of the mediation effect was confirmed by multiple sensitivity and subgroup analyses.\u003c/p\u003e\n\u003cp\u003eConclusion: COPD was independently associated with frailty, and this association was partially mediated by inflammatory markers such as SIRI.\u003c/p\u003e","manuscriptTitle":"Analysis of the Mediating Role of Inflammatory Markers in the Association between COPD and Frailty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:06:13","doi":"10.21203/rs.3.rs-9245206/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T02:45:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T12:57:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226230711597519499040871386909304294308","date":"2026-04-29T11:09:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T13:03:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135800047788212786630372759713948994742","date":"2026-04-27T07:09:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151862321398235491350884897675887130410","date":"2026-04-24T13:56:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T09:24:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T09:21:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-10T08:20:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-04T03:35:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-04T03:29:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a350bc68-d51f-4709-8e9e-2ed2ad4430ad","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T02:45:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T12:57:48+00:00","index":79,"fulltext":""},{"type":"reviewerAgreed","content":"226230711597519499040871386909304294308","date":"2026-04-29T11:09:08+00:00","index":78,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":67450711,"name":"Health sciences/Biomarkers"},{"id":67450712,"name":"Health sciences/Diseases"},{"id":67450713,"name":"Biological sciences/Immunology"},{"id":67450714,"name":"Health sciences/Medical research"},{"id":67450715,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-09T00:06:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:06:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9245206","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9245206","identity":"rs-9245206","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}