Association between inflammatory index and mortality in advanced lung cancer patients with chronic bronchitis: A cross-sectional study

Association between inflammatory index and mortality in advanced lung cancer patients with chronic bronchitis: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between inflammatory index and mortality in advanced lung cancer patients with chronic bronchitis: A cross-sectional study Xiangwu Zhou, Huanyuan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7581931/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background Chronic bronchitis (CB) threatens public health, and its prognosis is related to inflammation and nutritional status. The advanced lung cancer inflammation index (ALI) integrates these two factors, but its association with CB prognosis remains unknown. Methods We collected data from NHANES (1999 - 2018) and used the National Death Index to calculate mortality until December 31, 2019. Kaplan - Meier analysis explored the ALI - mortality relationship in CB patients. Weighted univariate and multivariate Cox models, adjusting for relevant factors, further examined this relationship. Restricted cubic spline analysis estimated the non - linear association, and subgroup and sensitivity analyses ensured result reliability. Results Involving 2103 CB patients, elevated ALI was associated with reduced death risk. An inverse J - shaped non - linear relationship was found between ALI and all - cause mortality, with a turning point of 96.33 (p < 0.0001). Below 96.33, increased ALI meant lower death risk; above it, increased ALI led to higher risk. Findings were consistent across subgroups and stable in sensitivity analysis. Conclusion This study reveals a novel link between elevated ALI and reduced death risk in CB patients. Maintaining ALI within an optimal range is crucial for long - term survival. ALI's dynamic changes can help clinicians set personalized criteria for better long - term outcomes. advanced lung cancer inflammation index chronic bronchitis mortality NHANES Figures Figure 1 Figure 2 Figure 3 Introduction Chronic bronchitis (CB) is defined as a chronic productive cough lasting for at least three months per year over two consecutive years ( 1 , 2 ). It is a form of chronic obstructive pulmonary disease (COPD). For decades, CB has remained a major public health concern and a leading cause of death worldwide ( 3 ). The rising prevalence of respiratory diseases is attributed to factors such as air pollution, lifestyle habits, comorbidities, health disparities, occupational hazards, and genetics, contributing to steadily increasing morbidity and mortality. According to recent data, COPD cases are projected to rise by 23.3% by 2050 (from 480 million in 2020 to 592 million in 2050) ( 4 ). The pathogenesis of CB involves multiple pathophysiological processes, primarily including airway inflammation, mucus hypersecretion, oxidative stress, protease-antiprotease imbalance, and airway remodeling. Airway inflammation plays a crucial role in CB progression, where activated inflammatory cells release pro-inflammatory cytokines, leading to epithelial damage, impaired ciliary function, and reduced clearance capacity. These cytokines exacerbate inflammation and promote airway remodeling( 5 – 7 ). Florey et al. found that CB patients exhibit goblet cell hyperplasia, excessive mucus secretion, and impaired mucociliary clearance, resulting in small airway obstruction( 8 ). Neutrophils are pivotal in CB pathogenesis( 6 , 9 ), as bacterial products increase neutrophil activity, degrading elastin around alveoli and small airways, thereby driving airway remodeling. The severity of airflow limitation correlates with the intensity of airway inflammation, with severe obstruction linked to increased neutrophils, macrophages, NK and lymphocytes cells in bronchial mucosa ( 7 )Drost et al. found that oxidative stress in COPD airways escalates with disease progression ( 10 ). A known genetic factor, α1-antitrypsin deficiency, accounts for 1–2% of COPD cases ( 11 ). Previous genome-wide association studies (GWAS) have identified genomic regions significantly associated with COPD susceptibility, including loci near the HHIPgene on chromosome 4, the FAM13Agene region, and the CHRNAand IREB2gene clusters on chromosome 15( 12 – 14 ). Polosukhin et al. reported impaired mucosal immunity in CB, with reduced secretory IgA (SIgA) and increased CD4 + and CD8 + lymphocyte infiltration, perpetuating chronic inflammation and progressive airway remodeling( 15 ). In recent years, the neutrophil-to-lymphocyte ratio (NLR), derived from blood neutrophil (B-Neu) and lymphocyte (B-Lym) counts, has emerged as a potential biomarker for COPD( 16 ). Vaguliene et al. reported elevated NLR in stable COPD patients compared to healthy controls( 17 ). A meta-analysis suggested that NLR predicts COPD mortality, with higher ratios associated with increased death risk( 18 ). Furthermore, chronic inflammation can elevate inflammatory cytokines, leading to weight loss and hypoalbuminemia ( 19 , 20 ). Prior studies indicate that nutritional markers, including albumin and body mass index (BMI), are closely linked to COPD prognosis ( 21 , 22 ). Thus, inflammation may directly and indirectly affect COPD outcomes by influencing albumin and BMI. The Advanced Lung Cancer Inflammation Index (ALI) is a prognostic metric integrating nutritional status and systemic inflammation( 23 ). It offers a more comprehensive assessment than NLR or albumin alone. Originally developed to predict outcomes in lung cancer patients, ALI has also shown promise in diabetes ( 24 ) and heart failure( 25 ) prognostication. However, no studies have explored the association between ALI and CB patient outcomes. To address this gap, we utilized data from the National Health and Nutrition Examination Survey (NHANES, 1999–2018) involving 2,103 individuals aged ≥ 20 years to investigate the relationship between ALI and mortality risk in CB patients. Our ultimate goal is to inform therapeutic and management strategies for CB patients. Materials and Methods Study Population The National Health and Nutrition Examination Survey (NHANES) is a major public health survey conducted by the National Center for Health Statistics (NCHS), a division of the Centers for Disease Control and Prevention (CDC). It aims to assess the health and nutritional status of the U.S. population and provides critical statistical data to support public health policies, medical research, and disease prevention. The survey employs a stratified, multistage probability sampling design. The study was reviewed and approved by the NCHS Ethics Review Board, and all participants provided informed consent. All procedures in this study adhered to relevant regulations and standards. NHANES data are publicly accessible on the NHANES website at: https://www.cdc.gov/nchs/nhanes.htm. For this study, we analyzed NHANES data from 1999 to 2018, encompassing a total of 101,316 participants. Our focus was on adults aged ≥20 years diagnosed with chronic bronchitis (CB) who had complete Advanced Lung Cancer Inflammation Index (ALI) data and available mortality information. Initially, we collected data from 101,316 participants. Subsequently, 38,611 were excluded due to missing information on albumin, BMI, neutrophils, or lymphocytes. An additional 14,859 participants without CB were excluded. Furthermore, 19,664 subjects were omitted due to a lack of follow-up time and survival status data, and 26,079 were excluded due to missing covariate information. Ultimately, 2,103 eligible individuals were included in the study. The detailed participant selection process, including inclusion and exclusion criteria, is illustrated in Figure 1. Measurement of ALI The ALI score is calculated by integrating three factors: albumin level (Alb), body mass index (BMI), and neutrophil-to-lymphocyte ratio (NLR). Specifically, the score is determined by multiplying the albumin level (in grams per deciliter) by BMI (calculated as weight in kilograms divided by height in meters squared) and then dividing by the NLR (derived as the ratio of neutrophil count to lymphocyte count) (ALI = Alb × BMI / NLR) (26). CB patients were stratified into four groups based on ALI levels: Q1, Q2, Q3, Q4. Assessment of CB In this study, CB (Chronic Bronchitis) was defined based on a prior diagnosis by a healthcare provider. Participants were asked whether they had ever been diagnosed with chronic bronchitis by a medical professional. Mortality Assessment In this study, mortality was determined as the primary outcome, with data derived from the NHANES-linked Public-Use Linked Mortality File, up to December 31, 2019. Individuals coded as MORTSTAT=0 were presumed to be alive through the end of 2019. The observation period began at the date of NHANES enrollment and ended either at the date of death or December 31, 2019, for those still alive. Definition of Covariates To minimize the influence of confounding bias, we carefully selected relevant factors based on clinical relevance and previous research. Baseline data of the participants were collected through questionnaires and physical examinations, including demographic information such as age (20 - 40 years, 40 - 60 years, or > 60 years), sex (male or female), education level (above high school, high school graduate, or below high school), race/ethnicity (Mexican American, Non - Hispanic Black, Non - Hispanic White, Other Hispanic, or Other Race—Including Multi - Racial), and body mass index (BMI) (< 25.0, 25.0 - 29.9, or ≥ 29.9 kg/m²). In addition, we assessed the socioeconomic status by calculating the poverty - income ratio (PIR), which was categorized into three different groups: 3.5. The participants' smoking habits were classified into three categories: every day, some days, and not at all. Moreover, the data of blood cell counts and serum albumin levels required for calculating the Advanced Lung Cancer Inflammation Index (ALI) were retrieved from the database. Statistical Analysis The statistical analysis of this study was conducted using R software (Version 4.5.1). Statistical significance was defined as a p - value less than 0.05. The analysis strictly adhered to the recommendations of the National Health and Nutrition Examination Survey (NHANES), taking into account sample weights, clustering, and stratified analysis. Continuous variables with a normal distribution were presented as the mean and standard deviation (SD), while those with a non - normal distribution were presented as the median and interquartile range. Categorical variables were expressed as numbers (percentages). The chi - square test and one - way analysis of variance were employed to detect significant differences in categorical and continuous variables between groups, respectively. The Kaplan - Meier analysis was used to explore the relationship between the Advanced Lung Cancer Inflammation Index (ALI) and mortality in patients with chronic bronchitis (CB). The log - rank test was used to compare the survival rates of four groups of participants classified by quartiles of ALI levels. Weighted univariate and multivariate Cox proportional - hazards models were utilized to further examine the association between ALI and mortality in adult CB patients. Multiple factors that might affect the results were adjusted in the analysis. The hazard ratio (HR) and its 95% confidence interval (CI) were calculated. Three models were developed: a crude model without adjustment; Model 1 was adjusted for age, sex, race, smoking status, education level, Family.PIR, and body mass index (BMI); Model 2 was adjusted for the factors in Model 1 plus albumin; Model 3 was adjusted for the factors in Model 2 plus lymphocyte count to provide a comprehensive analysis. Subsequently, restricted cubic spline analysis was used to examine the non - linear relationship between ALI and mortality in CB patients. Finally, to ensure the reliability of our study, sensitivity analysis was performed through stratified and interaction analyses to explore the potential interactions between ALI levels and stratified variables. Results Baseline Characteristics Data from 20 consecutive years (1999 - 2018) of the National Health and Nutrition Examination Survey (NHANES) were analyzed. The baseline characteristics of the participants, grouped by quartiles of the Advanced Lung Cancer Inflammation Index (ALI), are presented in Table 1. In the study population, 42.6% were older than 60 years, and females accounted for the majority (58.6%). The predominant race was non - Hispanic white (63.0%). The inter - quartile range of ALI was [4.63, 2186.00]. When comparing the Q2, Q3, and Q4 groups with the Q1 group, significant differences were observed. Participants in these groups tended to be younger, have a higher proportion of females, a higher proportion of Non - Hispanic Black individuals, higher body mass index (BMI) values, higher educational levels, and lower Family Poverty Income Ratio (Family PIR). Laboratory tests showed that they tended to have higher levels of albumin, lymphocytes, and ALI, while having lower levels of white blood cells, neutrophils, and neutrophil - lymphocyte ratio (NLR). However, smoking status remained relatively consistent across all groups, as shown in Table 1. Kaplan-Meier analysis The Kaplan-Meier analysis was used to preliminarily distinguish the association between ALI and the mortality rate in patients with CB. A total of 598 deaths occurred. The results of the Kaplan-Meier survival analysis indicated that among patients with CB, individuals in the highest quartile of ALI had the lowest risk of death (log-rank test p < 0.0001; Figure 2). ALI and mortality Table 2 presents the results of the Cox regression analysis, which investigated the association between ALI and mortality in adult patients with CB. Both the crude and multivariable - adjusted models showed a significant negative correlation between higher ALI levels in patients with CB and reduced mortality. Compared with the Q1 group, the hazard ratios (HRs) (95% confidence intervals [CIs]) of the Q2, Q3, and Q4 groups in the fully adjusted model (Model 3) were 0.75 (0.60, 0.95), 0.67 (0.52, 0.86), and 0.65 (0.48, 0.87), respectively (p < 0.001). Therefore, monitoring the dynamic changes of ALI is crucial for predicting the prognosis of patients with CB. Nonlinear relationship After considering all covariates (Model 3), restricted cubic spline (RCS) analysis was used to demonstrate the association between ALI and mortality in adult patients with CB. The overall pattern indicated a nonlinear correlation between ALI and death, with a turning point at 96.33 (p for nonlinearity < 0.0001) (Figure 3). For ALI values below 96.33, an increase in ALI was associated with a decreased risk of death. Conversely, when ALI exceeded 96.33, an increase in ALI led to an increased risk of death. For ALI values below 57.94, patients with CB had a higher risk of death. In contrast, when ALI exceeded 57.94, patients with CB had a lower risk of death. Subgroup and sensitivity analysis The results presented in Table 3 and Figure S1 indicate a significant association between ALI and mortality in different subgroups. Specifically, this relationship shows a statistically significant trend (p < 0.05) in individuals aged 60 and above, males, non - Hispanic Whites, those with less than high school education, those with more than high school education, non - smokers, and people with Family.PIR < 1.3 or Family.PIR 1.3 - 3.5. There is a significant interaction between ALI and various subgroup variables in terms of the mortality of patients with CB (p < 0.05) (Table 3, Figure S1). Specifically, this relationship shows a statistically significant trend in age, sex, and smoking. Discussion This study is the first to investigate the relationship between the level of ALI and the mortality of patients with CB. The Cox model adjusted for various factors indicates that an increase in ALI is associated with a reduced risk of death in patients with CB. The results of the RCS analysis show that the lowest risk of death occurs when the ALI level is 96.33. A non - linear inverse J - shaped curve relationship between ALI and mortality was observed among CB participants. With the turning point as the boundary, on the left side of the turning point, an increase in ALI leads to a decrease in the risk of death. Conversely, on the right side of the turning point, an increase in ALI leads to an increase in the risk of death. The study results are consistent across sociodemographic and relevant subgroups and remain stable in the sensitivity analysis. These results suggest that ALI is an effective tool for assessing the risk of death in patients with CB. Moreover, the turning point represents the ALI value with the lowest risk of death, which helps to accurately identify the risk of death in patients with CB and is beneficial for formulating personalized treatment plans. Chronic inflammation refers to an inflammatory response that lasts for a long time and is a pathological feature of diseases such as cancer, diabetes, atherosclerosis, asthma, chronic bronchitis, autoimmune diseases, neurodegenerative diseases, and inflammatory bowel disease( 27 ). All major classes of endogenous bioactive lipids, including eicosanoids, specialized pro - resolving mediators, lysoglycerophospholipids, and endocannabinoids, have been confirmed to be closely related to chronic inflammation. Chronic inflammation is characterized by excessive inflammatory signaling or dysregulation of pro - resolving/anti - inflammatory pathways. In the lungs, chronic inflammation of the airways is an important factor in the development and progression of CB( 28 ). Neutrophils, macrophages, T lymphocytes, and inflammatory mediators play crucial roles in the occurrence and progression of CB. Multiple studies have found that chemokine CXC motif ligands 2 (CXCL2), leukotriene B4 (LTB4), and formyl - met - leu - phe (fMLP) and (IL) − 8 can induce neutrophil migration( 29 – 35 ), and neutrophils destroy the alveolar elastic matrix by secreting proteases and small cationic peptides( 36 , 37 ). Exposure to cigarette smoke promotes the release of enzymes and peptides by neutrophils, which cleave collagen into fragments, thereby activating inflammatory cells and further promoting chronic inflammation( 38 ). Macrophages play a crucial role in CB. Macrophages can enhance the release of tumor necrosis factor (TNF) - alpha, IL − 8, other CXC chemokines, monocyte chemotactic peptide (MCP) − 1, and LTB4, thus promoting the spread of inflammation ( 39 , 40 ). Multiple studies have shown that T lymphocytes play an important role in the development of COPD. Th1 - type cytokines are involved in the persistence of the autoimmune response with interferon - γ as the main cytokine, leading to an excessive pro - inflammatory response and subsequent tissue damage( 41 , 42 ). Cytotoxic (CD8) cells are a cell subset capable of killing infected or damaged cells, while T - helper (CD4) cells release cytokines after activation and coordinate the activities of other inflammatory and related cells. Previous studies have found that dozens of cytokines and chemokines are associated with chronic obstructive pulmonary disease( 43 – 45 ). In addition, aging is crucial in the development of chronic obstructive pulmonary disease. Aging affects lung structure, inflammatory cells, fibroblasts, and progenitor cells, leading to insufficient repair and regeneration ( 46 – 49 ). Chronic inflammation can cause patients to have a decreased appetite and weight loss, resulting in insufficient nutritional intake and a decrease in albumin levels( 50 , 51 ). Zhou et al. showed that there is a close relationship between the nutritional status of patients with CB and their prognosis( 21 ). ALI is an index that combines serum albumin, body mass index, and the inflammatory marker NLR to evaluate an individual's inflammatory response and nutritional status. By assessing inflammation and nutritional status, it can more accurately predict disease outcomes. However, the relationship between ALI and the risk of death in patients with CB has not been determined. Our study shows that there is a significant correlation between an increase in ALI and a decrease in mortality in patients with CB. These results are consistent in multiple subgroup analyses and sensitivity analyses, highlighting ALI as a reliable prognostic indicator for patients with CB with high robustness. Initially, a non - linear, inverse J - shaped relationship between ALI and mortality was observed in patients with CB. The results of the Kaplan - Meier survival analysis indicate that individuals in the lowest quartile of ALI have the highest risk of death. This relationship can be explained from the following perspectives. According to the formula ALI = BMI * ALB/NLR, a lower ALI value represents a stronger inflammatory state and worse nutritional status, and a poorer prognosis. NLR represents the body's inflammatory burden. The higher the NLR level, the higher the intensity of inflammation. In patients with CB, in order to fight against pathogens, the bone marrow accelerates the release of neutrophils, resulting in a significant increase in the number of neutrophils, while the lymphocytes change relatively little. Therefore, NLR increases. Generally, the more obvious the increase in NLR, the greater the possibility of bacterial infection, the more severe the condition may be, and the higher the risk of death. Paliogiannis et al. found that the blood neutrophil - to - lymphocyte ratio can predict stable COPD, its exacerbations, and prognosis. The higher the NLR, the worse the prognosis( 52 ). Secondly, albumin is commonly used as a marker to assess nutritional status. In patients with CB, the chronic inflammatory state can increase the catabolism of albumin and inhibit the liver's synthesis of albumin. Low albumin can weaken the body's immune defense ability and aggravate the patient's condition. Therefore, a higher albumin level helps to continuously reduce the risk of death in patients with CB. Multiple studies have shown that there is a link between lower albumin levels and an increased incidence of complications in patients with CB( 53 , 54 )。 In addition, BMI is another commonly used indicator for assessing nutritional status. People with a low BMI often have problems with insufficient nutritional intake or malabsorption. The body lacks sufficient energy and nutrients to maintain normal physiological functions, leading to a decrease in immunity, making them more susceptible to various pathogens, causing infectious diseases, and increasing the risk of death. A high BMI can trigger a chronic low - grade inflammatory state in the body. Adipose tissue secretes a variety of inflammatory factors, which circulate throughout the body, leading to an inflammatory response in the airway mucosa, making the airway more susceptible to irritation and damage, and promoting the progression of CB ( 55 ). In addition, excessive adipose tissue can exert mechanical pressure on the thorax and diaphragm, causing ventilation dysfunction, affecting respiratory function, and increasing the chance of respiratory tract infection( 56 ) .In view of these findings, we believe that BMI plays an important role in the inverse J - shaped relationship between ALI and mortality in patients with CB. Limitation Our study has several limitations.First, as a cross-sectional study, it is susceptible to confounding factors, making causal inference challenging. Second, the diagnosis of chronic bronchitis (CB) relied on participant self-reports, which may introduce measurement bias. Furthermore, we did not account for pharmacological interventions (e.g., antibiotic use) that could influence airway lumen index (ALI) measurements. Although rigorous covariate adjustment was implemented, residual confounding cannot be entirely excluded. Notwithstanding these limitations,this study establishes a significant inverse association between elevated ALI and mortality risk in CB patients, providing a foundation for future longitudinal investigations. Conclusions Our study revealed that elevated ALI levels in CB patients were associated with a reduced risk of mortality. Notably, the relationship between ALI and mortality exhibited a J-shaped inverse curve, with the optimal ALI level corresponding to the lowest mortality risk being 96.33. This threshold can serve as a target for interventions aimed at reducing mortality risk in CB. Declarations Acknowledgments We express our gratitude to all participants of the National Health and Nutrition Examination Survey (NHANES) and the NHANES staff. Funding Statement The authors declare that the research and/or publication of this article has received the support of the corresponding author. The corresponding author will pay the publication fees. Data availability statement The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. Ethics statement Studies involving human participants were reviewed and approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). Detailed information on the approval of the NCHS Research Ethics Review Board can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/irba98.htm ). Written informed consent was obtained from all patients/participants prior to their participation in the study. These studies were conducted in accordance with local legislation and institutional requirements. Participants provided written informed consent to participate in this study. Author contributions Xiangwu Zhou: Conceptualization, Formal Analysis, Methodology, Supervision, Validation, Visualization, Writing – original draft. Huanyuan Wang: Conceptualization, Methodology, Project administration, Software, Supervision, Validation, Funding acquisition, Writing – review & editing. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Clinical trial number Not applicable References Wang Y, Yu Y, Zhang X, Zhang H, Zhang Y, Wang S, et al. Combined association of urinary volatile organic compounds with chronic bronchitis and emphysema among adults in NHANES 2011-2014: The mediating role of inflammation. Chemosphere. 2024 Aug;361:141485. Terminology, Definitions, and Classification of Chronic Pulmonary Emphysema and Related Conditions. Thorax. 1959 Dec;14(4):286–99. Holtjer JCS, Bloemsma LD, Beijers RJHCG, Cornelissen MEB, Hilvering B, Houweling L, et al. Identifying risk factors for COPD and adult-onset asthma: an umbrella review. Eur Respir Rev. 2023 Jun 30;32(168):230009. Boers E, Allen A, Barrett M, Benjafield AV, Rice MB, Wedzicha JA, et al. Forecasting the Global Economic and Health Burden of COPD From 2025 Through 2050. Chest. 2025 Apr 18;S0012-3692(25)00435-0. Aghasafari P, George U, Pidaparti R. A review of inflammatory mechanism in airway diseases. Inflamm Res. 2019 Jan;68(1):59–74. Keatings VM, Collins PD, Scott DM, Barnes PJ. Differences in interleukin-8 and tumor necrosis factor-alpha in induced sputum from patients with chronic obstructive pulmonary disease or asthma. Am J Respir Crit Care Med. 1996 Feb;153(2):530–4. Di Stefano A, Capelli A, Lusuardi M, Balbo P, Vecchio C, Maestrelli P, et al. Severity of airflow limitation is associated with severity of airway inflammation in smokers. Am J Respir Crit Care Med. 1998 Oct;158(4):1277–85. Florey H, Carleton HM, Wells AQ. Mucus Secretion in the Trachea. Br J Exp Pathol. 1932 Jun;13(3):269–84. Hogg JC, Chu F, Utokaparch S, Woods R, Elliott WM, Buzatu L, et al. The nature of small-airway obstruction in chronic obstructive pulmonary disease. N Engl J Med. 2004 Jun 24;350(26):2645–53. Drost EM, Skwarski KM, Sauleda J, Soler N, Roca J, Agusti A, et al. Oxidative stress and airway inflammation in severe exacerbations of COPD. Thorax. 2005 Apr;60(4):293–300. Gooptu B, Ekeowa UI, Lomas DA. Mechanisms of emphysema in alpha1-antitrypsin deficiency: molecular and cellular insights. Eur Respir J. 2009 Aug;34(2):475–88. Pillai SG, Ge D, Zhu G, Kong X, Shianna KV, Need AC, et al. A genome-wide association study in chronic obstructive pulmonary disease (COPD): identification of two major susceptibility loci. PLoS Genet. 2009 Mar;5(3):e1000421. Hancock DB, Eijgelsheim M, Wilk JB, Gharib SA, Loehr LR, Marciante KD, et al. Meta-analyses of genome-wide association studies identify multiple novel loci associated with pulmonary function. Nat Genet. 2010 Jan;42(1):45–52. Wilk JB, Chen TH, Gottlieb DJ, Walter RE, Nagle MW, Brandler BJ, et al. A genome-wide association study of pulmonary function measures in the Framingham Heart Study. PLoS Genet. 2009 Mar;5(3):e1000429. Polosukhin VV, Cates JM, Lawson WE, Zaynagetdinov R, Milstone AP, Massion PP, et al. Bronchial Secretory Immunoglobulin A Deficiency Correlates With Airway Inflammation and Progression of Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2011 Aug 1;184(3):317–27. Paliogiannis P, Fois AG, Sotgia S, Mangoni AA, Zinellu E, Pirina P, et al. The neutrophil-to-lymphocyte ratio as a marker of chronic obstructive pulmonary disease and its exacerbations: A systematic review and meta-analysis. Eur J Clin Invest. 2018 Aug;48(8):e12984. Vaguliene N, Zemaitis M, Lavinskiene S, Miliauskas S, Sakalauskas R. Local and systemic neutrophilic inflammation in patients with lung cancer and chronic obstructive pulmonary disease. BMC Immunol. 2013 Aug 6;14:36. Ye Z, Ai X, Liao Z, You C, Cheng Y. The prognostic values of neutrophil to lymphocyte ratio for outcomes in chronic obstructive pulmonary disease. Medicine (Baltimore). 2019 Jul;98(28):e16371. Babu MS, Kaul S, Dadheech S, Rajeshwar K, Jyothy A, Munshi A. Serum albumin levels in ischemic stroke and its subtypes: correlation with clinical outcome. Nutrition. 2013 Jun;29(6):872–5. Lennie TA. Relationship of body energy status to inflammation-induced anorexia and weight loss. Physiol Behav. 1998 Jun 15;64(4):475–81. Zhou D, Liu C, Wang L, Li J, Zhao Y, Deng Z, et al. Prediction of clinical risk assessment and survival in chronic obstructive pulmonary disease with pulmonary hypertension. Clin Transl Med. 2024 Jun;14(6):e1702. Wada H, Ikeda A, Maruyama K, Yamagishi K, Barnes PJ, Tanigawa T, et al. Low BMI and weight loss aggravate COPD mortality in men, findings from a large prospective cohort: the JACC study. Sci Rep. 2021 Jan 15;11(1):1531. Jafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer. 2013 Mar 27;13:158. Chen Y, Guan M, Wang R, Wang X. Relationship between advanced lung cancer inflammation index and long-term all-cause, cardiovascular, and cancer mortality among type 2 diabetes mellitus patients: NHANES, 1999-2018. Front Endocrinol (Lausanne). 2023;14:1298345. Maeda D, Kanzaki Y, Sakane K, Ito T, Sohmiya K, Hoshiga M. Prognostic impact of a novel index of nutrition and inflammation for patients with acute decompensated heart failure. Heart Vessels. 2020 Sep;35(9):1201–8. Fu J, Zou Y, Luo L, Zhang J, Wang X, Zhang D. Associations of advanced lung cancer inflammation index with all-cause and respiratory disease mortality in adults with asthma: NHANES, 1999-2018. Sci Rep. 2024 Nov 29;14(1):29693. Leuti A, Fazio D, Fava M, Piccoli A, Oddi S, Maccarrone M. Bioactive lipids, inflammation and chronic diseases. Adv Drug Deliv Rev. 2020;159:133–69. Dong LL, Liu ZY, Chen KJ, Li ZY, Zhou JS, Shen HH, et al. The persistent inflammation in COPD: is autoimmunity the core mechanism? Eur Respir Rev. 2024 Jan 31;33(171):230137. Kobayashi SD, DeLeo FR. Role of neutrophils in innate immunity: a systems biology-level approach. Wiley Interdiscip Rev Syst Biol Med. 2009;1(3):309–33. Kobayashi SD, Voyich JM, Burlak C, DeLeo FR. Neutrophils in the innate immune response. Arch Immunol Ther Exp (Warsz). 2005;53(6):505–17. Barnett ML, Lamb KA, Costello KM, Pike MC. Characterization of interleukin-8 receptors in human neutrophil membranes: regulation by guanine nucleotides. Biochim Biophys Acta. 1993 Jun 30;1177(3):275–82. Reilly IA, Knapp HR, Fitzgerald GA. Leukotriene B4 synthesis and neutrophil chemotaxis in chronic granulocytic leukaemia. J Clin Pathol. 1988 Nov;41(11):1163–7. Mathis SP, Jala VR, Lee DM, Haribabu B. Nonredundant roles for leukotriene B4 receptors BLT1 and BLT2 in inflammatory arthritis. J Immunol. 2010 Sep 1;185(5):3049–56. Kreisle RA, Parker CW. Specific binding of leukotriene B4 to a receptor on human polymorphonuclear leukocytes. J Exp Med. 1983 Feb 1;157(2):628–41. Cavicchioni G, Fraulini A, Falzarano S, Spisani S. Oligomeric formylpeptide activity on human neutrophils. Eur J Med Chem. 2009 Dec;44(12):4926–30. Paone G, Conti V, Vestri A, Leone A, Puglisi G, Benassi F, et al. Analysis of sputum markers in the evaluation of lung inflammation and functional impairment in symptomatic smokers and COPD patients. Dis Markers. 2011;31(2):91–100. Frasca L, Lande R. Role of defensins and cathelicidin LL37 in auto-immune and auto-inflammatory diseases. Curr Pharm Biotechnol. 2012 Aug;13(10):1882–97. Overbeek SA, Braber S, Koelink PJ, Henricks PAJ, Mortaz E, LoTam Loi AT, et al. Cigarette smoke-induced collagen destruction; key to chronic neutrophilic airway inflammation? PLoS One. 2013;8(1):e55612. Greenlee KJ, Werb Z, Kheradmand F. Matrix metalloproteinases in lung: multiple, multifarious, and multifaceted. Physiol Rev. 2007 Jan;87(1):69–98. Barnes PJ, Shapiro SD, Pauwels RA. Chronic obstructive pulmonary disease: molecular and cellular mechanisms. Eur Respir J. 2003 Oct;22(4):672–88. Shirai T, Suda T, Inui N, Chida K. Correlation between peripheral blood T-cell profiles and clinical and inflammatory parameters in stable COPD. Allergol Int. 2010 Mar;59(1):75–82. Majori M, Corradi M, Caminati A, Cacciani G, Bertacco S, Pesci A. Predominant TH1 cytokine pattern in peripheral blood from subjects with chronic obstructive pulmonary disease. J Allergy Clin Immunol. 1999 Mar;103(3 Pt 1):458–62. Chung KF, Adcock IM. Multifaceted mechanisms in COPD: inflammation, immunity, and tissue repair and destruction. Eur Respir J. 2008 Jun;31(6):1334–56. Caramori G, Di Stefano A, Casolari P, Kirkham PA, Padovani A, Chung KF, et al. Chemokines and chemokine receptors blockers as new drugs for the treatment of chronic obstructive pulmonary disease. Curr Med Chem. 2013;20(35):4317–49. Barnes PJ. The cytokine network in chronic obstructive pulmonary disease. Am J Respir Cell Mol Biol. 2009 Dec;41(6):631–8. Savale L, Chaouat A, Bastuji-Garin S, Marcos E, Boyer L, Maitre B, et al. Shortened telomeres in circulating leukocytes of patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2009 Apr 1;179(7):566–71. Mui TSY, Man JM, McElhaney JE, Sandford AJ, Coxson HO, Birmingham CL, et al. Telomere length and chronic obstructive pulmonary disease: evidence of accelerated aging. J Am Geriatr Soc. 2009 Dec;57(12):2372–4. Walters MS, De BP, Salit J, Buro-Auriemma LJ, Wilson T, Rogalski AM, et al. Smoking accelerates aging of the small airway epithelium. Respir Res. 2014 Sep 24;15(1):94. Houben JMJ, Mercken EM, Ketelslegers HB, Bast A, Wouters EF, Hageman GJ, et al. Telomere shortening in chronic obstructive pulmonary disease. Respir Med. 2009 Feb;103(2):230–6. Aldhwayan MM, Al-Najim W, Ruban A, Glaysher MA, Johnson B, Chhina N, et al. Does Bypass of the Proximal Small Intestine Impact Food Intake, Preference, and Taste Function in Humans? An Experimental Medicine Study Using the Duodenal-Jejunal Bypass Liner. Nutrients. 2022 May 20;14(10):2141. Dou L, Shi M, Song J, Niu X, Niu J, Wei S, et al. The Prognostic Significance of C-Reactive Protein to Albumin Ratio in Newly Diagnosed Acute Myeloid Leukaemia Patients. Cancer Manag Res. 2022;14:303–16. Paliogiannis P, Fois AG, Sotgia S, Mangoni AA, Zinellu E, Pirina P, et al. Neutrophil to lymphocyte ratio and clinical outcomes in COPD: recent evidence and future perspectives. Eur Respir Rev. 2018 Feb 7;27(147):170113. Zhou D, Liu C, Wang L, Li J, Zhao Y, Deng Z, et al. Prediction of clinical risk assessment and survival in chronic obstructive pulmonary disease with pulmonary hypertension. Clin Transl Med. 2024 Jun;14(6):e1702. Wu DC, Zhang XY, Li AD, Wang T, Wang ZY, Song SY, et al. Neuroticism and asthma: Mendelian randomization analysis reveals causal link with mood swings and BMI mediation. J Asthma. 2025 Apr;62(4):674–83. Mancuso P. Obesity and lung inflammation. J Appl Physiol (1985). 2010 Mar;108(3):722–8. Grassi L, Kacmarek R, Berra L. Ventilatory Mechanics in the Patient with Obesity. Anesthesiology. 2020 May;132(5):1246–56. Tables Table 1. Cohort characteristics at baseline for study participants according to quartiles of ALI. Variable Total(N=2103) ALI P value Q1(N=526) Q2(N=526) Q3(N=525) Q4(N=526) [4.63,39.36] [39.36,57.94] [57.94,79.24] [79.24,2186.00] Sex, % 0.011 Female 1231 ( 58.6) 280 ( 53.2) 315 ( 59.9) 304 ( 57.9) 332 ( 63.1) Male 872 ( 41.4) 246 ( 46.8) 211 ( 40.1) 221 ( 42.1) 194 ( 36.9) Race, % <0.001 Mexican American 166 ( 7.9) 39 ( 7.4) 47 ( 8.9) 50 ( 9.5) 30 ( 5.7) Non-Hispanic Black 375 ( 17.8) 57 ( 10.8) 72 ( 13.7) 88 ( 16.8) 158 ( 30.0) Non-Hispanic White 1325 ( 63.0) 363 ( 69.0) 347 ( 66.0) 334 ( 63.6) 281 ( 53.4) Other Hispanic 113 ( 5.4) 28 ( 5.3) 40 ( 7.6) 20 ( 3.8) 25 ( 4.8) Other Race - Including Multi-Racial 124 ( 5.9) 39 ( 7.4) 20 ( 3.8) 33 ( 6.3) 32 ( 6.1) Education level, % 0.037 Less than high school 669 ( 31.8) 178 ( 33.8) 190 ( 36.1) 149 ( 28.4) 152 ( 28.9) Completed high school 527 ( 25.0) 139 ( 26.4) 116 ( 22.1) 142 ( 27.0) 130 ( 24.7) More than high school 907 ( 43.2) 209 ( 39.7) 220 ( 41.8) 234 ( 44.6) 244 ( 46.4) Smoking status, % 0.131 Every day 972 ( 46.2) 241 ( 45.8) 267 ( 50.8) 229 ( 43.6) 235 ( 44.7) Not at all 1029 ( 49.0) 255 ( 48.5) 236 ( 44.9) 266 ( 50.7) 272 ( 51.7) Some days 102 ( 4.8) 30 ( 5.7) 23 ( 4.4) 30 ( 5.7) 19 ( 3.6) Age, % 60 896 ( 42.6) 277 ( 52.7) 221 ( 42.0) 204 ( 38.9) 194 ( 36.9) Family PIR, % 0.18 3.5 413 ( 19.6) 103 ( 19.6) 91 ( 17.3) 116 ( 22.1) 103 ( 19.6) Body mass index, % <0.001 29.9 949 ( 45.1) 124 ( 23.6) 198 ( 37.6) 279 ( 53.1) 348 ( 66.2) Albumin, g/dL,(mean,SD) 4.14 (0.38) 4.04 (0.45) 4.18 (0.32) 4.17 (0.37) 4.17 (0.35) <0.001 White blood cell(1000 cell/mL),(mean,SD) 8.05 (3.06) 8.76 (2.47) 8.18 (2.29) 7.67 (2.05) 7.57 (4.58) <0.001 Lymphocyte count (1000 cell/mL),(mean,SD) 2.27 (2.03) 1.58 (0.51) 2.13 (0.66) 2.33 (0.64) 3.06 (3.78) <0.001 Neutrophil count (1000 cell/mL),(mean,SD) 4.89 (1.95) 6.29 (2.13) 5.16 (1.60) 4.47 (1.42) 3.64 (1.48) <0.001 NLR,(mean,SD) 2.53 (1.64) 4.31 (2.08) 2.48 (0.57) 1.95 (0.46) 1.32 (0.41) <0.001 ALI,(mean,SD) 66.60 (74.95) 27.13 (8.12) 48.54 (5.54) 67.60 (5.96) 123.27 (131.37) <0.001 ALI, advanced lung cancer inflammation index; NLR, neutrophil to Lymphocyte ratio; PIR, family poverty-to-income ratio; Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4. Table 2. ALI, advanced lung cancer inflammation index; CB, Chronic bronchitis; Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4; ref, reference; HR, hazard ratios; CI, confidence interval. Crude model Model 1 Model 2 Model 3 ALI HR, 95%CI P HR, 95%CI P HR, 95%CI P HR, 95%CI P Q1 ref ref ref Q2 0.65(0.52, 0.80) <0.001 0.67(0.54, 0.83) <0.001 0.73(0.59, 0.92) 0.007 0.75(0.60, 0.95) 0.016 Q3 0.54(0.43, 0.67) <0.001 0.59(0.46, 0.74) <0.001 0.64(0.51, 0.82) <0.001 0.67(0.52, 0.86) 0.002 Q4 0.52(0.42, 0.66) <0.001 0.54(0.42, 0.70) <0.001 0.61(0.47, 0.79) <0.001 0.65(0.48, 0.87) 0.003 p for trend <0.001 <0.001 <0.001 <0.001 Crude model was not adjusted for any covariates. Model 1 was adjusted for age , sex,race,smoking status,education level,Family.PIR,BMI. Model 2 was adjusted for Model 1 plus albumin. Model 3 was adjusted for Model 2 plus lymphocyte count. Table 3. Subgroup analysis of the association between quartiles of ALI and mortality in patients with CB from the NHANES 1999-2018 cohort. ALI, advanced lung cancer inflammation index; Count Percent,% Q1 Q2,HR (95% CI),p Q3,HR (95% CI),p Q4,HR (95% CI),p P for interaction Overall 2103 100 ref 0.65(0.52,0.80),<0.001 0.54(0.43,0.67),<0.001 0.52(0.42,0.66),60 896 42.6 ref 0.53(0.41,0.68),<0.001 0.51(0.39,0.66),<0.001 0.42(0.31,0.56),<0.001 sex 0.049 Female 1231 58.5 0.83(0.62,1.11),0.218 0.68(0.50,0.92),0.013 0.66(0.49,0.91),0.01 Male 872 41.5 0.49(0.36,0.68),<0.001 0.42(0.31,0.58),<0.001 0.42(0.30,0.60),<0.001 race 0.063 Non-Hispanic White 1325 63 ref 0.61(0.47,0.78),<0.001 0.47(0.36,0.61),<0.001 0.54(0.40,0.71),<0.001 Non-Hispanic Black 375 17.8 ref 0.61(0.35,1.06),0.082 0.45(0.26,0.79),0.005 0.28(0.16,0.49),<0.001 Mexican American 166 7.9 ref 0.78(0.33,1.84),0.575 1.14(0.52,2.51),0.751 0.84(0.29,2.43),0.752 Other Hispanic 113 5.4 ref 1.51(0.50,4.56),0.463 0.49(0.09,2.59),0.404 2.14(0.62,7.38),0.228 Other Race - Including Multi-Racial 124 5.9 ref 0.45(0.10,2.02),0.296 1.18(0.46,3.03),0.736 0.90(0.34,2.34),0.823 education 0.794 Less than high school 669 31.8 ref 0.70(0.51,0.97),0.032 0.60(0.42,0.86),0.006 0.50(0.34,0.72),<0.001 Completed high school 527 25.1 ref 0.65(0.41,1.04),0.074 0.59(0.38,0.92),0.02 0.68(0.43,1.08),0.106 More than high school 907 43.1 ref 0.56(0.40,0.80),0.001 0.47(0.33,0.67),<0.001 0.47(0.33,0.68),<0.001 smoking 0.007 Every day 972 46.2 ref 0.85(0.61,1.19),0.344 0.63(0.43,0.91),0.015 0.76(0.53,1.09),0.129 Some days 102 4.9 ref 2.05(0.74,5.69),0.169 1.31(0.49,3.52),0.592 0.49(0.10,2.38),0.379 Not at all 1029 48.9 ref 0.48(0.36,0.63),<0.001 0.42(0.32,0.56),<0.001 0.38(0.28,0.52),<0.001 Family.PIR 0.959 <1.3 878 41.7 ref 0.68(0.49,0.94),0.020 0.60(0.42,0.85),0.004 0.59(0.42,0.83),0.003 1.3-3.5 812 38.6 ref 0.59(0.42,0.81),0.001 0.51(0.36,0.72),<0.001 0.46(0.32,0.68),3.5 413 19.6 ref 0.66(0.39,1.10),0.113 0.47(0.28,0.79),0.004 0.51(0.30,0.86),0.012 CB, chronic bronchitis; PIR, family poverty-to-income ratio; Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterials.doc Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviews received at journal 21 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers invited by journal 07 Oct, 2025 Editor invited by journal 12 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 10 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7581931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531309617,"identity":"272a679e-344e-4c51-9cce-99bcc17fee9b","order_by":0,"name":"Xiangwu Zhou","email":"","orcid":"","institution":"The First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiangwu","middleName":"","lastName":"Zhou","suffix":""},{"id":531309618,"identity":"85ca1d01-b737-44e4-95d4-aac8b5228644","order_by":1,"name":"Huanyuan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYJACZgaGGjnG9uYDBz78IF7LMWPmnmOJB2f2EK+FObF9Ro7xYQ42IpQbHD97+HVhG1ti74ycD4cZeBjk+cUOENByJi/NemabjPHMnrcbDhdYMBjOnJ2AX4vZgRwzY942NtmN7bkbDs/gYUgwuE1Iy/k3IC3MjPsP5Dw4zMNGjJYbOcaPgVoUGztyGIjTYn/jjRkzz7ljxow9xwyAgSxB2C+S/TnGn3nKwFH5+MOHHzby/NIEtAABmwQjIjokCCoHAeYPDH+IUjgKRsEoGAUjFQAAorJLcMZeBrkAAAAASUVORK5CYII=","orcid":"","institution":"Jiangxi Provincial Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huanyuan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-09-10 10:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7581931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7581931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93940542,"identity":"ce4c5a7f-8cba-4c4e-8b67-1bc6f07a48b3","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14153728,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.doc","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/a4bb658f96fd6e5ab3d986e2.doc"},{"id":93942061,"identity":"03d08a19-0b4e-410b-8376-2f790a3f940c","added_by":"auto","created_at":"2025-10-20 13:45:36","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4352,"visible":true,"origin":"","legend":"","description":"","filename":"d233fb8ef8f84f61b8a0be7708d6b106.json","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/9d521b2a5a6bcb11d4c4630f.json"},{"id":93940547,"identity":"596ec4c6-69c0-4325-aa87-5b3806b117d7","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8492544,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.doc","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/abf9daa969e95cf9926a09f9.doc"},{"id":93942065,"identity":"aec036f9-d5e7-4b1b-9b81-0f7fb0daeb1d","added_by":"auto","created_at":"2025-10-20 13:45:36","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154143,"visible":true,"origin":"","legend":"","description":"","filename":"d233fb8ef8f84f61b8a0be7708d6b1061enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/1234dc9290f60f8c890abe1f.xml"},{"id":93940536,"identity":"a068de98-df46-47ac-ae81-57263541d5d7","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73114,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/9954e6bced09d9f31a226ea8.png"},{"id":93942064,"identity":"c9ef0c45-87dd-442c-98a3-91e67572419c","added_by":"auto","created_at":"2025-10-20 13:45:36","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110052,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/0f28632742547328a82d5e03.png"},{"id":93943159,"identity":"d0cbda90-f441-41ab-bc3d-1e9ac500c4ba","added_by":"auto","created_at":"2025-10-20 13:53:36","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29287,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/b32845318cc74e89a9632783.png"},{"id":93942062,"identity":"837b009a-0e87-4d00-8e6e-c4d9c114ea3d","added_by":"auto","created_at":"2025-10-20 13:45:36","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31367,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/7175603bb9a8cbe242dae68b.png"},{"id":93940538,"identity":"80d6d180-a18d-411a-b213-c15e854ec15d","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29404,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/f81e1adbb9738f495818ce3c.png"},{"id":93940545,"identity":"ab946af2-3133-428d-ad7d-92f966ac0a9c","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9444,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/57695a4f1aa9709acf5e2af6.png"},{"id":93940549,"identity":"0cca9050-9407-4b8c-99b0-cef34d16249b","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153842,"visible":true,"origin":"","legend":"","description":"","filename":"d233fb8ef8f84f61b8a0be7708d6b1061structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/0c9166baca1484f5ad216c7e.xml"},{"id":93940548,"identity":"1088206b-9587-4427-a5ee-959831710fcb","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160972,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/bc036e2bfed182e8996524f0.html"},{"id":93940534,"identity":"4f057c9f-b53f-46b2-80e6-2d1511759709","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73114,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study participants. BMI, body mass index; CB, Chronic bronchitis; NHANES, national health and nutrition examination survey.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/4d118b750f6c1e0c8c36649b.png"},{"id":93940541,"identity":"92b853b7-b46e-41b3-a3a2-3c0ed966a680","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110052,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves of ALI impact on long-term mortality in patients with Chronic bronchitis (CB) categorized by quartiles of advanced lung cancer inflammation index (ALI) levels. Q1, Quantile 1; Q2, Quantile 2; Q3, Quantile 3; Q4, Quartile 4.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/c7874eb90f70d1b64db2e2ab.png"},{"id":93940535,"identity":"d585408b-2db2-4b4e-907d-af6230dabc44","added_by":"auto","created_at":"2025-10-20 13:37:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29287,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analyses the relationship of advanced lung cancer inflammation index (ALI) levels and the risk of mortality in adults with CB(chronic bronchitis).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/06bce19fea8c539c8634c682.png"},{"id":93943907,"identity":"6cbcb3dd-7588-4048-ab34-41be010504a0","added_by":"auto","created_at":"2025-10-20 14:01:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1014182,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/fe61875c-f58a-4aaf-ac01-795d543e6589.pdf"},{"id":93942066,"identity":"4eeb472d-49e0-4874-9c3b-e44a36cbede4","added_by":"auto","created_at":"2025-10-20 13:45:36","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8492544,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.doc","url":"https://assets-eu.researchsquare.com/files/rs-7581931/v1/7529da81f198e83a7fe138e0.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between inflammatory index and mortality in advanced lung cancer patients with chronic bronchitis: A cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic bronchitis (CB) is defined as a chronic productive cough lasting for at least three months per year over two consecutive years (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It is a form of chronic obstructive pulmonary disease (COPD). For decades, CB has remained a major public health concern and a leading cause of death worldwide (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The rising prevalence of respiratory diseases is attributed to factors such as air pollution, lifestyle habits, comorbidities, health disparities, occupational hazards, and genetics, contributing to steadily increasing morbidity and mortality. According to recent data, COPD cases are projected to rise by 23.3% by 2050 (from 480\u0026nbsp;million in 2020 to 592\u0026nbsp;million in 2050) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe pathogenesis of CB involves multiple pathophysiological processes, primarily including airway inflammation, mucus hypersecretion, oxidative stress, protease-antiprotease imbalance, and airway remodeling. Airway inflammation plays a crucial role in CB progression, where activated inflammatory cells release pro-inflammatory cytokines, leading to epithelial damage, impaired ciliary function, and reduced clearance capacity. These cytokines exacerbate inflammation and promote airway remodeling(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Florey et al. found that CB patients exhibit goblet cell hyperplasia, excessive mucus secretion, and impaired mucociliary clearance, resulting in small airway obstruction(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Neutrophils are pivotal in CB pathogenesis(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), as bacterial products increase neutrophil activity, degrading elastin around alveoli and small airways, thereby driving airway remodeling. The severity of airflow limitation correlates with the intensity of airway inflammation, with severe obstruction linked to increased neutrophils, macrophages, NK and lymphocytes cells in bronchial mucosa (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)Drost et al. found that oxidative stress in COPD airways escalates with disease progression (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A known genetic factor, α1-antitrypsin deficiency, accounts for 1\u0026ndash;2% of COPD cases (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Previous genome-wide association studies (GWAS) have identified genomic regions significantly associated with COPD susceptibility, including loci near the HHIPgene on chromosome 4, the FAM13Agene region, and the CHRNAand IREB2gene clusters on chromosome 15(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Polosukhin et al. reported impaired mucosal immunity in CB, with reduced secretory IgA (SIgA) and increased CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;lymphocyte infiltration, perpetuating chronic inflammation and progressive airway remodeling(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, the neutrophil-to-lymphocyte ratio (NLR), derived from blood neutrophil (B-Neu) and lymphocyte (B-Lym) counts, has emerged as a potential biomarker for COPD(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Vaguliene et al. reported elevated NLR in stable COPD patients compared to healthy controls(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). A meta-analysis suggested that NLR predicts COPD mortality, with higher ratios associated with increased death risk(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Furthermore, chronic inflammation can elevate inflammatory cytokines, leading to weight loss and hypoalbuminemia (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Prior studies indicate that nutritional markers, including albumin and body mass index (BMI), are closely linked to COPD prognosis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Thus, inflammation may directly and indirectly affect COPD outcomes by influencing albumin and BMI.\u003c/p\u003e\u003cp\u003eThe Advanced Lung Cancer Inflammation Index (ALI) is a prognostic metric integrating nutritional status and systemic inflammation(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). It offers a more comprehensive assessment than NLR or albumin alone. Originally developed to predict outcomes in lung cancer patients, ALI has also shown promise in diabetes (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and heart failure(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) prognostication. However, no studies have explored the association between ALI and CB patient outcomes.\u003c/p\u003e\u003cp\u003eTo address this gap, we utilized data from the National Health and Nutrition Examination Survey (NHANES, 1999\u0026ndash;2018) involving 2,103 individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years to investigate the relationship between ALI and mortality risk in CB patients. Our ultimate goal is to inform therapeutic and management strategies for CB patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy Population The National Health and Nutrition Examination Survey (NHANES) is a major public health survey conducted by the National Center for Health Statistics (NCHS), a division of the Centers for Disease Control and Prevention (CDC). It aims to assess the health and nutritional status of the U.S. population and provides critical statistical data to support public health policies, medical research, and disease prevention. The survey employs a stratified, multistage probability sampling design. The study was reviewed and approved by the NCHS Ethics Review Board, and all participants provided informed consent. All procedures in this study adhered to relevant regulations and standards. NHANES data are publicly accessible on the NHANES website at: https://www.cdc.gov/nchs/nhanes.htm. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor this study, we analyzed NHANES data from 1999 to 2018, encompassing a total of 101,316 participants. Our focus was on adults aged \u0026ge;20 years diagnosed with chronic bronchitis (CB) who had complete Advanced Lung Cancer Inflammation Index (ALI) data and available mortality information. Initially, we collected data from 101,316 participants. Subsequently, 38,611 were excluded due to missing information on albumin, BMI, neutrophils, or lymphocytes. An additional 14,859 participants without CB were excluded. Furthermore, 19,664 subjects were omitted due to a lack of follow-up time and survival status data, and 26,079 were excluded due to missing covariate information. Ultimately, 2,103 eligible individuals were included in the study. The detailed participant selection process, including inclusion and exclusion criteria, is illustrated in Figure 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of ALI\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ALI score is calculated by integrating three factors: albumin level (Alb), body mass index (BMI), and neutrophil-to-lymphocyte ratio (NLR). Specifically, the score is determined by multiplying the albumin level (in grams per deciliter) by BMI (calculated as weight in kilograms divided by height in meters squared) and then dividing by the NLR (derived as the ratio of neutrophil count to lymphocyte count) (ALI = Alb \u0026times; BMI / NLR) (26). CB patients were stratified into four groups based on ALI levels: Q1, Q2, Q3, Q4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of CB\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, CB (Chronic Bronchitis) was defined based on a prior diagnosis by a healthcare provider. Participants were asked whether they had ever been diagnosed with chronic bronchitis by a medical professional.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality Assessment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, mortality was determined as the primary outcome, with data derived from the NHANES-linked Public-Use Linked Mortality File, up to December 31, 2019. Individuals coded as MORTSTAT=0 were presumed to be alive through the end of 2019. The observation period began at the date of NHANES enrollment and ended either at the date of death or December 31, 2019, for those still alive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of Covariates\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo minimize the influence of confounding bias, we carefully selected relevant factors based on clinical relevance and previous research. Baseline data of the participants were collected through questionnaires and physical examinations, including demographic information such as age (20 - 40 years, 40 - 60 years, or \u0026gt; 60 years), sex (male or female), education level (above high school, high school graduate, or below high school), race/ethnicity (Mexican American, Non - Hispanic Black, Non - Hispanic White, Other Hispanic, or Other Race\u0026mdash;Including Multi - Racial), and body mass index (BMI) (\u0026lt; 25.0, 25.0 - 29.9, or \u0026ge; 29.9 kg/m\u0026sup2;).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, we assessed the socioeconomic status by calculating the poverty - income ratio (PIR), which was categorized into three different groups: \u0026lt; 1.3, 1.3 - 3.5, and \u0026gt; 3.5. The participants\u0026apos; smoking habits were classified into three categories: every day, some days, and not at all. Moreover, the data of blood cell counts and serum albumin levels required for calculating the Advanced Lung Cancer Inflammation Index (ALI) were retrieved from the database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analysis of this study was conducted using R software (Version 4.5.1). Statistical significance was defined as a p - value less than 0.05. The analysis strictly adhered to the recommendations of the National Health and Nutrition Examination Survey (NHANES), taking into account sample weights, clustering, and stratified analysis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContinuous variables with a normal distribution were presented as the mean and standard deviation (SD), while those with a non - normal distribution were presented as the median and interquartile range. Categorical variables were expressed as numbers (percentages). The chi - square test and one - way analysis of variance were employed to detect significant differences in categorical and continuous variables between groups, respectively. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Kaplan - Meier analysis was used to explore the relationship between the Advanced Lung Cancer Inflammation Index (ALI) and mortality in patients with chronic bronchitis (CB). The log - rank test was used to compare the survival rates of four groups of participants classified by quartiles of ALI levels. Weighted univariate and multivariate Cox proportional - hazards models were utilized to further examine the association between ALI and mortality in adult CB patients. Multiple factors that might affect the results were adjusted in the analysis. The hazard ratio (HR) and its 95% confidence interval (CI) were calculated. Three models were developed: a crude model without adjustment; Model 1 was adjusted for age, sex, race, smoking status, education level, Family.PIR, and body mass index (BMI); Model 2 was adjusted for the factors in Model 1 plus albumin; Model 3 was adjusted for the factors in Model 2 plus lymphocyte count to provide a comprehensive analysis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequently, restricted cubic spline analysis was used to examine the non - linear relationship between ALI and mortality in CB patients. Finally, to ensure the reliability of our study, sensitivity analysis was performed through stratified and interaction analyses to explore the potential interactions between ALI levels and stratified variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from 20 consecutive years (1999 - 2018) of the National Health and Nutrition Examination Survey (NHANES) were analyzed. The baseline characteristics of the participants, grouped by quartiles of the Advanced Lung Cancer Inflammation Index (ALI), are presented in Table 1. In the study population, 42.6% were older than 60 years, and females accounted for the majority (58.6%). The predominant race was non - Hispanic white (63.0%). The inter - quartile range of ALI was [4.63, 2186.00].\u003c/p\u003e\n\u003cp\u003eWhen comparing the Q2, Q3, and Q4 groups with the Q1 group, significant differences were observed. Participants in these groups tended to be younger, have a higher proportion of females, a higher proportion of Non - Hispanic Black individuals, higher body mass index (BMI) values, higher educational levels, and lower Family Poverty Income Ratio (Family PIR). Laboratory tests showed that they tended to have higher levels of albumin, lymphocytes, and ALI, while having lower levels of white blood cells, neutrophils, and neutrophil - lymphocyte ratio (NLR). However, smoking status remained relatively consistent across all groups, as shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKaplan-Meier analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier analysis was used to preliminarily distinguish the association between ALI and the mortality rate in patients with CB. A total of 598 deaths occurred. The results of the Kaplan-Meier survival analysis indicated that among patients with CB, individuals in the highest quartile of ALI had the lowest risk of death (log-rank test p \u0026lt; 0.0001; Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eALI and mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results of the Cox regression analysis, which investigated the association between ALI and mortality in adult patients with CB. Both the crude and multivariable - adjusted models showed a significant negative correlation between higher ALI levels in patients with CB and reduced mortality. Compared with the Q1 group, the hazard ratios (HRs) (95% confidence intervals [CIs]) of the Q2, Q3, and Q4 groups in the fully adjusted model (Model 3) were 0.75 (0.60, 0.95), 0.67 (0.52, 0.86), and 0.65 (0.48, 0.87), respectively (p \u0026lt; 0.001). Therefore, monitoring the dynamic changes of ALI is crucial for predicting the prognosis of patients with CB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNonlinear relationship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter considering all covariates (Model 3), restricted cubic spline (RCS) analysis was used to demonstrate the association between ALI and mortality in adult patients with CB. The overall pattern indicated a nonlinear correlation between ALI and death, with a turning point at 96.33 (p for nonlinearity \u0026lt; 0.0001) (Figure 3). For ALI values below 96.33, an increase in ALI was associated with a decreased risk of death. Conversely, when ALI exceeded 96.33, an increase in ALI led to an increased risk of death. For ALI values below 57.94, patients with CB had a higher risk of death. In contrast, when ALI exceeded 57.94, patients with CB had a lower risk of death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup and sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results presented in Table 3 and Figure S1 indicate a significant association between ALI and mortality in different subgroups. Specifically, this relationship shows a statistically significant trend (p \u0026lt; 0.05) in individuals aged 60 and above, males, non - Hispanic Whites, those with less than high school education, those with more than high school education, non - smokers, and people with Family.PIR \u0026lt; 1.3 or Family.PIR 1.3 - 3.5. There is a significant interaction between ALI and various subgroup variables in terms of the mortality of patients with CB (p \u0026lt; 0.05) (Table 3, Figure S1). Specifically, this relationship shows a statistically significant trend in age, sex, and smoking.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to investigate the relationship between the level of ALI and the mortality of patients with CB. The Cox model adjusted for various factors indicates that an increase in ALI is associated with a reduced risk of death in patients with CB. The results of the RCS analysis show that the lowest risk of death occurs when the ALI level is 96.33. A non - linear inverse J - shaped curve relationship between ALI and mortality was observed among CB participants. With the turning point as the boundary, on the left side of the turning point, an increase in ALI leads to a decrease in the risk of death. Conversely, on the right side of the turning point, an increase in ALI leads to an increase in the risk of death. The study results are consistent across sociodemographic and relevant subgroups and remain stable in the sensitivity analysis. These results suggest that ALI is an effective tool for assessing the risk of death in patients with CB. Moreover, the turning point represents the ALI value with the lowest risk of death, which helps to accurately identify the risk of death in patients with CB and is beneficial for formulating personalized treatment plans.\u003c/p\u003e\u003cp\u003eChronic inflammation refers to an inflammatory response that lasts for a long time and is a pathological feature of diseases such as cancer, diabetes, atherosclerosis, asthma, chronic bronchitis, autoimmune diseases, neurodegenerative diseases, and inflammatory bowel disease(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). All major classes of endogenous bioactive lipids, including eicosanoids, specialized pro - resolving mediators, lysoglycerophospholipids, and endocannabinoids, have been confirmed to be closely related to chronic inflammation. Chronic inflammation is characterized by excessive inflammatory signaling or dysregulation of pro - resolving/anti - inflammatory pathways. In the lungs, chronic inflammation of the airways is an important factor in the development and progression of CB(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Neutrophils, macrophages, T lymphocytes, and inflammatory mediators play crucial roles in the occurrence and progression of CB. Multiple studies have found that chemokine CXC motif ligands 2 (CXCL2), leukotriene B4 (LTB4), and formyl - met - leu - phe (fMLP) and (IL) \u0026minus;\u0026thinsp;8 can induce neutrophil migration(\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), and neutrophils destroy the alveolar elastic matrix by secreting proteases and small cationic peptides(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Exposure to cigarette smoke promotes the release of enzymes and peptides by neutrophils, which cleave collagen into fragments, thereby activating inflammatory cells and further promoting chronic inflammation(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Macrophages play a crucial role in CB. Macrophages can enhance the release of tumor necrosis factor (TNF) - alpha, IL \u0026minus;\u0026thinsp;8, other CXC chemokines, monocyte chemotactic peptide (MCP) \u0026minus;\u0026thinsp;1, and LTB4, thus promoting the spread of inflammation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Multiple studies have shown that T lymphocytes play an important role in the development of COPD. Th1 - type cytokines are involved in the persistence of the autoimmune response with interferon - γ as the main cytokine, leading to an excessive pro - inflammatory response and subsequent tissue damage(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Cytotoxic (CD8) cells are a cell subset capable of killing infected or damaged cells, while T - helper (CD4) cells release cytokines after activation and coordinate the activities of other inflammatory and related cells. Previous studies have found that dozens of cytokines and chemokines are associated with chronic obstructive pulmonary disease(\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). In addition, aging is crucial in the development of chronic obstructive pulmonary disease. Aging affects lung structure, inflammatory cells, fibroblasts, and progenitor cells, leading to insufficient repair and regeneration (\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChronic inflammation can cause patients to have a decreased appetite and weight loss, resulting in insufficient nutritional intake and a decrease in albumin levels(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Zhou et al. showed that there is a close relationship between the nutritional status of patients with CB and their prognosis(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). ALI is an index that combines serum albumin, body mass index, and the inflammatory marker NLR to evaluate an individual's inflammatory response and nutritional status. By assessing inflammation and nutritional status, it can more accurately predict disease outcomes. However, the relationship between ALI and the risk of death in patients with CB has not been determined. Our study shows that there is a significant correlation between an increase in ALI and a decrease in mortality in patients with CB. These results are consistent in multiple subgroup analyses and sensitivity analyses, highlighting ALI as a reliable prognostic indicator for patients with CB with high robustness.\u003c/p\u003e\u003cp\u003eInitially, a non - linear, inverse J - shaped relationship between ALI and mortality was observed in patients with CB. The results of the Kaplan - Meier survival analysis indicate that individuals in the lowest quartile of ALI have the highest risk of death. This relationship can be explained from the following perspectives. According to the formula ALI\u0026thinsp;=\u0026thinsp;BMI * ALB/NLR, a lower ALI value represents a stronger inflammatory state and worse nutritional status, and a poorer prognosis. NLR represents the body's inflammatory burden. The higher the NLR level, the higher the intensity of inflammation. In patients with CB, in order to fight against pathogens, the bone marrow accelerates the release of neutrophils, resulting in a significant increase in the number of neutrophils, while the lymphocytes change relatively little. Therefore, NLR increases. Generally, the more obvious the increase in NLR, the greater the possibility of bacterial infection, the more severe the condition may be, and the higher the risk of death. Paliogiannis et al. found that the blood neutrophil - to - lymphocyte ratio can predict stable COPD, its exacerbations, and prognosis. The higher the NLR, the worse the prognosis(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecondly, albumin is commonly used as a marker to assess nutritional status. In patients with CB, the chronic inflammatory state can increase the catabolism of albumin and inhibit the liver's synthesis of albumin. Low albumin can weaken the body's immune defense ability and aggravate the patient's condition. Therefore, a higher albumin level helps to continuously reduce the risk of death in patients with CB. Multiple studies have shown that there is a link between lower albumin levels and an increased incidence of complications in patients with CB(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)。\u003c/p\u003e\u003cp\u003eIn addition, BMI is another commonly used indicator for assessing nutritional status. People with a low BMI often have problems with insufficient nutritional intake or malabsorption. The body lacks sufficient energy and nutrients to maintain normal physiological functions, leading to a decrease in immunity, making them more susceptible to various pathogens, causing infectious diseases, and increasing the risk of death. A high BMI can trigger a chronic low - grade inflammatory state in the body. Adipose tissue secretes a variety of inflammatory factors, which circulate throughout the body, leading to an inflammatory response in the airway mucosa, making the airway more susceptible to irritation and damage, and promoting the progression of CB (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In addition, excessive adipose tissue can exert mechanical pressure on the thorax and diaphragm, causing ventilation dysfunction, affecting respiratory function, and increasing the chance of respiratory tract infection(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) .In view of these findings, we believe that BMI plays an important role in the inverse J - shaped relationship between ALI and mortality in patients with CB.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLimitation\u003c/h2\u003e\u003cp\u003eOur study has several limitations.First, as a cross-sectional study, it is susceptible to confounding factors, making causal inference challenging. Second, the diagnosis of chronic bronchitis (CB) relied on participant self-reports, which may introduce measurement bias. Furthermore, we did not account for pharmacological interventions (e.g., antibiotic use) that could influence airway lumen index (ALI) measurements. Although rigorous covariate adjustment was implemented, residual confounding cannot be entirely excluded. Notwithstanding these limitations,this study establishes a significant inverse association between elevated ALI and mortality risk in CB patients, providing a foundation for future longitudinal investigations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study revealed that elevated ALI levels in CB patients were associated with a reduced risk of mortality. Notably, the relationship between ALI and mortality exhibited a J-shaped inverse curve, with the optimal ALI level corresponding to the lowest mortality risk being 96.33. This threshold can serve as a target for interventions aimed at reducing mortality risk in CB.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to all participants of the National Health and Nutrition Examination Survey (NHANES) and the NHANES staff.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research and/or publication of this article has received the support of the corresponding author. The corresponding author will pay the publication fees.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies involving human participants were reviewed and approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). Detailed information on the approval of the NCHS Research Ethics Review Board can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/irba98.htm ). Written informed consent was obtained from all patients/participants prior to their participation in the study. These studies were conducted in accordance with local legislation and institutional requirements. Participants provided written informed consent to participate in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiangwu Zhou: Conceptualization, Formal Analysis, Methodology, Supervision, Validation, Visualization, Writing \u0026ndash; original draft. Huanyuan Wang: Conceptualization, Methodology, Project administration, Software, Supervision, Validation, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang Y, Yu Y, Zhang X, Zhang H, Zhang Y, Wang S, et al. Combined association of urinary volatile organic compounds with chronic bronchitis and emphysema among adults in NHANES 2011-2014: The mediating role of inflammation. Chemosphere. 2024 Aug;361:141485. \u003c/li\u003e\n\u003cli\u003eTerminology, Definitions, and Classification of Chronic Pulmonary Emphysema and Related Conditions. Thorax. 1959 Dec;14(4):286\u0026ndash;99. \u003c/li\u003e\n\u003cli\u003eHoltjer JCS, Bloemsma LD, Beijers RJHCG, Cornelissen MEB, Hilvering B, Houweling L, et al. Identifying risk factors for COPD and adult-onset asthma: an umbrella review. Eur Respir Rev. 2023 Jun 30;32(168):230009. \u003c/li\u003e\n\u003cli\u003eBoers E, Allen A, Barrett M, Benjafield AV, Rice MB, Wedzicha JA, et al. Forecasting the Global Economic and Health Burden of COPD From 2025 Through 2050. Chest. 2025 Apr 18;S0012-3692(25)00435-0. \u003c/li\u003e\n\u003cli\u003eAghasafari P, George U, Pidaparti R. A review of inflammatory mechanism in airway diseases. Inflamm Res. 2019 Jan;68(1):59\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eKeatings VM, Collins PD, Scott DM, Barnes PJ. Differences in interleukin-8 and tumor necrosis factor-alpha in induced sputum from patients with chronic obstructive pulmonary disease or asthma. Am J Respir Crit Care Med. 1996 Feb;153(2):530\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eDi Stefano A, Capelli A, Lusuardi M, Balbo P, Vecchio C, Maestrelli P, et al. Severity of airflow limitation is associated with severity of airway inflammation in smokers. Am J Respir Crit Care Med. 1998 Oct;158(4):1277\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eFlorey H, Carleton HM, Wells AQ. Mucus Secretion in the Trachea. Br J Exp Pathol. 1932 Jun;13(3):269\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eHogg JC, Chu F, Utokaparch S, Woods R, Elliott WM, Buzatu L, et al. The nature of small-airway obstruction in chronic obstructive pulmonary disease. N Engl J Med. 2004 Jun 24;350(26):2645\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eDrost EM, Skwarski KM, Sauleda J, Soler N, Roca J, Agusti A, et al. Oxidative stress and airway inflammation in severe exacerbations of COPD. Thorax. 2005 Apr;60(4):293\u0026ndash;300. \u003c/li\u003e\n\u003cli\u003eGooptu B, Ekeowa UI, Lomas DA. Mechanisms of emphysema in alpha1-antitrypsin deficiency: molecular and cellular insights. Eur Respir J. 2009 Aug;34(2):475\u0026ndash;88. \u003c/li\u003e\n\u003cli\u003ePillai SG, Ge D, Zhu G, Kong X, Shianna KV, Need AC, et al. A genome-wide association study in chronic obstructive pulmonary disease (COPD): identification of two major susceptibility loci. PLoS Genet. 2009 Mar;5(3):e1000421. \u003c/li\u003e\n\u003cli\u003eHancock DB, Eijgelsheim M, Wilk JB, Gharib SA, Loehr LR, Marciante KD, et al. Meta-analyses of genome-wide association studies identify multiple novel loci associated with pulmonary function. Nat Genet. 2010 Jan;42(1):45\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eWilk JB, Chen TH, Gottlieb DJ, Walter RE, Nagle MW, Brandler BJ, et al. A genome-wide association study of pulmonary function measures in the Framingham Heart Study. PLoS Genet. 2009 Mar;5(3):e1000429. \u003c/li\u003e\n\u003cli\u003ePolosukhin VV, Cates JM, Lawson WE, Zaynagetdinov R, Milstone AP, Massion PP, et al. Bronchial Secretory Immunoglobulin A Deficiency Correlates With Airway Inflammation and Progression of Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2011 Aug 1;184(3):317\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003ePaliogiannis P, Fois AG, Sotgia S, Mangoni AA, Zinellu E, Pirina P, et al. The neutrophil-to-lymphocyte ratio as a marker of chronic obstructive pulmonary disease and its exacerbations: A systematic review and meta-analysis. Eur J Clin Invest. 2018 Aug;48(8):e12984. \u003c/li\u003e\n\u003cli\u003eVaguliene N, Zemaitis M, Lavinskiene S, Miliauskas S, Sakalauskas R. Local and systemic neutrophilic inflammation in patients with lung cancer and chronic obstructive pulmonary disease. BMC Immunol. 2013 Aug 6;14:36. \u003c/li\u003e\n\u003cli\u003eYe Z, Ai X, Liao Z, You C, Cheng Y. The prognostic values of neutrophil to lymphocyte ratio for outcomes in chronic obstructive pulmonary disease. Medicine (Baltimore). 2019 Jul;98(28):e16371. \u003c/li\u003e\n\u003cli\u003eBabu MS, Kaul S, Dadheech S, Rajeshwar K, Jyothy A, Munshi A. Serum albumin levels in ischemic stroke and its subtypes: correlation with clinical outcome. Nutrition. 2013 Jun;29(6):872\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eLennie TA. Relationship of body energy status to inflammation-induced anorexia and weight loss. Physiol Behav. 1998 Jun 15;64(4):475\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eZhou D, Liu C, Wang L, Li J, Zhao Y, Deng Z, et al. Prediction of clinical risk assessment and survival in chronic obstructive pulmonary disease with pulmonary hypertension. Clin Transl Med. 2024 Jun;14(6):e1702. \u003c/li\u003e\n\u003cli\u003eWada H, Ikeda A, Maruyama K, Yamagishi K, Barnes PJ, Tanigawa T, et al. Low BMI and weight loss aggravate COPD mortality in men, findings from a large prospective cohort: the JACC study. Sci Rep. 2021 Jan 15;11(1):1531. \u003c/li\u003e\n\u003cli\u003eJafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer. 2013 Mar 27;13:158. \u003c/li\u003e\n\u003cli\u003eChen Y, Guan M, Wang R, Wang X. Relationship between advanced lung cancer inflammation index and long-term all-cause, cardiovascular, and cancer mortality among type 2 diabetes mellitus patients: NHANES, 1999-2018. Front Endocrinol (Lausanne). 2023;14:1298345. \u003c/li\u003e\n\u003cli\u003eMaeda D, Kanzaki Y, Sakane K, Ito T, Sohmiya K, Hoshiga M. Prognostic impact of a novel index of nutrition and inflammation for patients with acute decompensated heart failure. Heart Vessels. 2020 Sep;35(9):1201\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eFu J, Zou Y, Luo L, Zhang J, Wang X, Zhang D. Associations of advanced lung cancer inflammation index with all-cause and respiratory disease mortality in adults with asthma: NHANES, 1999-2018. Sci Rep. 2024 Nov 29;14(1):29693. \u003c/li\u003e\n\u003cli\u003eLeuti A, Fazio D, Fava M, Piccoli A, Oddi S, Maccarrone M. Bioactive lipids, inflammation and chronic diseases. Adv Drug Deliv Rev. 2020;159:133\u0026ndash;69. \u003c/li\u003e\n\u003cli\u003eDong LL, Liu ZY, Chen KJ, Li ZY, Zhou JS, Shen HH, et al. The persistent inflammation in COPD: is autoimmunity the core mechanism? Eur Respir Rev. 2024 Jan 31;33(171):230137. \u003c/li\u003e\n\u003cli\u003eKobayashi SD, DeLeo FR. Role of neutrophils in innate immunity: a systems biology-level approach. Wiley Interdiscip Rev Syst Biol Med. 2009;1(3):309\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eKobayashi SD, Voyich JM, Burlak C, DeLeo FR. Neutrophils in the innate immune response. Arch Immunol Ther Exp (Warsz). 2005;53(6):505\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eBarnett ML, Lamb KA, Costello KM, Pike MC. Characterization of interleukin-8 receptors in human neutrophil membranes: regulation by guanine nucleotides. Biochim Biophys Acta. 1993 Jun 30;1177(3):275\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003eReilly IA, Knapp HR, Fitzgerald GA. Leukotriene B4 synthesis and neutrophil chemotaxis in chronic granulocytic leukaemia. J Clin Pathol. 1988 Nov;41(11):1163\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eMathis SP, Jala VR, Lee DM, Haribabu B. Nonredundant roles for leukotriene B4 receptors BLT1 and BLT2 in inflammatory arthritis. J Immunol. 2010 Sep 1;185(5):3049\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eKreisle RA, Parker CW. Specific binding of leukotriene B4 to a receptor on human polymorphonuclear leukocytes. J Exp Med. 1983 Feb 1;157(2):628\u0026ndash;41. \u003c/li\u003e\n\u003cli\u003eCavicchioni G, Fraulini A, Falzarano S, Spisani S. Oligomeric formylpeptide activity on human neutrophils. Eur J Med Chem. 2009 Dec;44(12):4926\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003ePaone G, Conti V, Vestri A, Leone A, Puglisi G, Benassi F, et al. Analysis of sputum markers in the evaluation of lung inflammation and functional impairment in symptomatic smokers and COPD patients. Dis Markers. 2011;31(2):91\u0026ndash;100. \u003c/li\u003e\n\u003cli\u003eFrasca L, Lande R. Role of defensins and cathelicidin LL37 in auto-immune and auto-inflammatory diseases. Curr Pharm Biotechnol. 2012 Aug;13(10):1882\u0026ndash;97. \u003c/li\u003e\n\u003cli\u003eOverbeek SA, Braber S, Koelink PJ, Henricks PAJ, Mortaz E, LoTam Loi AT, et al. Cigarette smoke-induced collagen destruction; key to chronic neutrophilic airway inflammation? PLoS One. 2013;8(1):e55612. \u003c/li\u003e\n\u003cli\u003eGreenlee KJ, Werb Z, Kheradmand F. Matrix metalloproteinases in lung: multiple, multifarious, and multifaceted. Physiol Rev. 2007 Jan;87(1):69\u0026ndash;98. \u003c/li\u003e\n\u003cli\u003eBarnes PJ, Shapiro SD, Pauwels RA. Chronic obstructive pulmonary disease: molecular and cellular mechanisms. Eur Respir J. 2003 Oct;22(4):672\u0026ndash;88. \u003c/li\u003e\n\u003cli\u003eShirai T, Suda T, Inui N, Chida K. Correlation between peripheral blood T-cell profiles and clinical and inflammatory parameters in stable COPD. Allergol Int. 2010 Mar;59(1):75\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003eMajori M, Corradi M, Caminati A, Cacciani G, Bertacco S, Pesci A. Predominant TH1 cytokine pattern in peripheral blood from subjects with chronic obstructive pulmonary disease. J Allergy Clin Immunol. 1999 Mar;103(3 Pt 1):458\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eChung KF, Adcock IM. Multifaceted mechanisms in COPD: inflammation, immunity, and tissue repair and destruction. Eur Respir J. 2008 Jun;31(6):1334\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eCaramori G, Di Stefano A, Casolari P, Kirkham PA, Padovani A, Chung KF, et al. Chemokines and chemokine receptors blockers as new drugs for the treatment of chronic obstructive pulmonary disease. Curr Med Chem. 2013;20(35):4317\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eBarnes PJ. The cytokine network in chronic obstructive pulmonary disease. Am J Respir Cell Mol Biol. 2009 Dec;41(6):631\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eSavale L, Chaouat A, Bastuji-Garin S, Marcos E, Boyer L, Maitre B, et al. Shortened telomeres in circulating leukocytes of patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2009 Apr 1;179(7):566\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eMui TSY, Man JM, McElhaney JE, Sandford AJ, Coxson HO, Birmingham CL, et al. Telomere length and chronic obstructive pulmonary disease: evidence of accelerated aging. J Am Geriatr Soc. 2009 Dec;57(12):2372\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eWalters MS, De BP, Salit J, Buro-Auriemma LJ, Wilson T, Rogalski AM, et al. Smoking accelerates aging of the small airway epithelium. Respir Res. 2014 Sep 24;15(1):94. \u003c/li\u003e\n\u003cli\u003eHouben JMJ, Mercken EM, Ketelslegers HB, Bast A, Wouters EF, Hageman GJ, et al. Telomere shortening in chronic obstructive pulmonary disease. Respir Med. 2009 Feb;103(2):230\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eAldhwayan MM, Al-Najim W, Ruban A, Glaysher MA, Johnson B, Chhina N, et al. Does Bypass of the Proximal Small Intestine Impact Food Intake, Preference, and Taste Function in Humans? An Experimental Medicine Study Using the Duodenal-Jejunal Bypass Liner. Nutrients. 2022 May 20;14(10):2141. \u003c/li\u003e\n\u003cli\u003eDou L, Shi M, Song J, Niu X, Niu J, Wei S, et al. The Prognostic Significance of C-Reactive Protein to Albumin Ratio in Newly Diagnosed Acute Myeloid Leukaemia Patients. Cancer Manag Res. 2022;14:303\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003ePaliogiannis P, Fois AG, Sotgia S, Mangoni AA, Zinellu E, Pirina P, et al. Neutrophil to lymphocyte ratio and clinical outcomes in COPD: recent evidence and future perspectives. Eur Respir Rev. 2018 Feb 7;27(147):170113. \u003c/li\u003e\n\u003cli\u003eZhou D, Liu C, Wang L, Li J, Zhao Y, Deng Z, et al. Prediction of clinical risk assessment and survival in chronic obstructive pulmonary disease with pulmonary hypertension. Clin Transl Med. 2024 Jun;14(6):e1702. \u003c/li\u003e\n\u003cli\u003eWu DC, Zhang XY, Li AD, Wang T, Wang ZY, Song SY, et al. Neuroticism and asthma: Mendelian randomization analysis reveals causal link with mood swings and BMI mediation. J Asthma. 2025 Apr;62(4):674\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eMancuso P. Obesity and lung inflammation. J Appl Physiol (1985). 2010 Mar;108(3):722\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eGrassi L, Kacmarek R, Berra L. Ventilatory Mechanics in the Patient with Obesity. Anesthesiology. 2020 May;132(5):1246\u0026ndash;56. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Cohort characteristics at baseline for study participants according to quartiles of ALI.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 138px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 59px;\"\u003e\n \u003cp\u003eTotal(N=2103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 318px;\"\u003e\n \u003cp\u003eALI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 54px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ1(N=526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eQ2(N=526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eQ3(N=525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eQ4(N=526)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[4.63,39.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e[39.36,57.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e[57.94,79.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e[79.24,2186.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSex, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1231 ( 58.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;280 ( 53.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;315 ( 59.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;304 ( 57.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;332 ( 63.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;872 ( 41.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;246 ( 46.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;211 ( 40.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;221 ( 42.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;194 ( 36.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eRace, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;166 ( \u0026nbsp;7.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 39 ( \u0026nbsp;7.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 47 ( \u0026nbsp;8.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 50 ( \u0026nbsp;9.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 30 ( \u0026nbsp;5.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;375 ( 17.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 57 ( 10.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 72 ( 13.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 88 ( 16.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;158 ( 30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1325 ( 63.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;363 ( 69.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;347 ( 66.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;334 ( 63.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;281 ( 53.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;113 ( \u0026nbsp;5.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 28 ( \u0026nbsp;5.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 40 ( \u0026nbsp;7.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 20 ( \u0026nbsp;3.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 25 ( \u0026nbsp;4.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;124 ( \u0026nbsp;5.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 39 ( \u0026nbsp;7.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 20 ( \u0026nbsp;3.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 33 ( \u0026nbsp;6.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 32 ( \u0026nbsp;6.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eEducation \u0026nbsp;level, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;669 ( 31.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;178 ( 33.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;190 ( 36.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;149 ( 28.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;152 ( 28.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eCompleted high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;527 ( 25.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;139 ( 26.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;116 ( 22.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;142 ( 27.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;130 ( 24.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eMore than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;907 ( 43.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;209 ( 39.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;220 ( 41.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;234 ( 44.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;244 ( 46.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eSmoking status, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eEvery day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;972 ( 46.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;241 ( 45.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;267 ( 50.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;229 ( 43.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;235 ( 44.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1029 ( 49.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;255 ( 48.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;236 ( 44.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;266 ( 50.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;272 ( 51.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eSome days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;102 ( \u0026nbsp;4.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 30 ( \u0026nbsp;5.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 23 ( \u0026nbsp;4.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 30 ( \u0026nbsp;5.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 19 ( \u0026nbsp;3.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eAge, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e20-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;455 ( 21.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;104 ( 19.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;121 ( 23.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;112 ( 21.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;118 ( 22.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e40-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;752 ( 35.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;145 ( 27.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;184 ( 35.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;209 ( 39.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;214 ( 40.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;896 ( 42.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;277 ( 52.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;221 ( 42.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;204 ( 38.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;194 ( 36.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eFamily PIR, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026lt;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;878 ( 41.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;232 ( 44.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;223 ( 42.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;195 ( 37.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;228 ( 43.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.3-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;812 ( 38.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;191 ( 36.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;212 ( 40.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;214 ( 40.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;195 ( 37.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026gt;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;413 ( 19.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;103 ( 19.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 91 ( 17.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;116 ( 22.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;103 ( 19.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eBody mass index, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;584 ( 27.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;251 ( 47.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;163 ( 31.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;103 ( 19.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 67 ( 12.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e25-29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;570 ( 27.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;151 ( 28.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;165 ( 31.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;143 ( 27.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;111 ( 21.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026gt;29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;949 ( 45.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;124 ( 23.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;198 ( 37.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;279 ( 53.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;348 ( 66.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eAlbumin, g/dL,(mean,SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4.14 (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4.04 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4.18 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4.17 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4.17 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eWhite blood cell(1000 cell/mL),(mean,SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 8.05 (3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; 8.76 (2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 8.18 (2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 7.67 (2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; 7.57 (4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eLymphocyte count (1000 cell/mL),(mean,SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2.27 (2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1.58 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2.13 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2.33 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3.06 (3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eNeutrophil count (1000 cell/mL),(mean,SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4.89 (1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; 6.29 (2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 5.16 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4.47 (1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3.64 (1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eNLR,(mean,SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2.53 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4.31 (2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2.48 (0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1.95 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1.32 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eALI,(mean,SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;66.60 (74.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;27.13 (8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;48.54 (5.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;67.60 (5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e123.27 (131.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eALI, advanced lung cancer inflammation index; NLR, neutrophil to Lymphocyte ratio; PIR, family poverty-to-income ratio; Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4.\u003c/p\u003e\n\u003cp\u003eTable 2. ALI, advanced lung cancer inflammation index; CB, Chronic bronchitis; Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4; ref, reference; HR, hazard ratios; CI, confidence interval.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eCrude model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eALI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eHR, 95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eHR, 95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eHR, 95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHR, 95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.65(0.52, 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.67(0.54, 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.73(0.59, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.75(0.60, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.54(0.43, 0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.59(0.46, 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.64(0.51, 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.67(0.52, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.52(0.42, 0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.54(0.42, 0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.61(0.47, 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.65(0.48, 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\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\u003eCrude model was not adjusted for any covariates.\u003c/p\u003e\n\u003cp\u003eModel 1 was adjusted for age , sex,race,smoking status,education level,Family.PIR,BMI.\u003c/p\u003e\n\u003cp\u003eModel 2 was adjusted for Model 1 plus albumin.\u003c/p\u003e\n\u003cp\u003eModel 3 was adjusted for Model 2 plus lymphocyte count.\u003c/p\u003e\n\u003cp\u003eTable 3. Subgroup analysis of the association between quartiles of ALI and mortality in patients with CB from the NHANES 1999-2018 cohort. \u0026nbsp;ALI, \u0026nbsp; advanced lung cancer inflammation index;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003ePercent,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eQ2,HR (95% CI),p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eQ3,HR (95% CI),p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eQ4,HR (95% CI),p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e2103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.65(0.52,0.80),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.54(0.43,0.67),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.52(0.42,0.66),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e20-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.95(0.95,3.99),0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.12(0.50,2.55),0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.81(0.85,3.89),0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e40-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e35.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.97(0.59,1.61),0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.76(0.46,1.27),0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.88(0.53,1.45),0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e42.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.53(0.41,0.68),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.51(0.39,0.66),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.42(0.31,0.56),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003esex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e1231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.83(0.62,1.11),0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.68(0.50,0.92),0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.66(0.49,0.91),0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.49(0.36,0.68),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.42(0.31,0.58),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.42(0.30,0.60),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003erace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.61(0.47,0.78),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.47(0.36,0.61),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.54(0.40,0.71),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.61(0.35,1.06),0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.45(0.26,0.79),0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.28(0.16,0.49),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.78(0.33,1.84),0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.14(0.52,2.51),0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.84(0.29,2.43),0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.51(0.50,4.56),0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.49(0.09,2.59),0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e2.14(0.62,7.38),0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.45(0.10,2.02),0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.18(0.46,3.03),0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.90(0.34,2.34),0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eeducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e31.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.70(0.51,0.97),0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.60(0.42,0.86),0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.50(0.34,0.72),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eCompleted high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.65(0.41,1.04),0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.59(0.38,0.92),0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.68(0.43,1.08),0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eMore than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e43.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.56(0.40,0.80),0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.47(0.33,0.67),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.47(0.33,0.68),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003esmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eEvery day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.85(0.61,1.19),0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.63(0.43,0.91),0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.76(0.53,1.09),0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSome days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.05(0.74,5.69),0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.31(0.49,3.52),0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.49(0.10,2.38),0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eNot at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.48(0.36,0.63),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.42(0.32,0.56),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.38(0.28,0.52),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eFamily.PIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.68(0.49,0.94),0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.60(0.42,0.85),0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.59(0.42,0.83),0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.3-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.59(0.42,0.81),0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.51(0.36,0.72),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.46(0.32,0.68),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026gt;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.66(0.39,1.10),0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.47(0.28,0.79),0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.51(0.30,0.86),0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCB, chronic bronchitis; PIR, family poverty-to-income ratio; Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"advanced lung cancer inflammation index, chronic bronchitis, mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7581931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7581931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChronic bronchitis (CB) threatens public health, and its prognosis is related to inflammation and nutritional status. The advanced lung cancer inflammation index (ALI) integrates these two factors, but its association with CB prognosis remains unknown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected data from NHANES (1999 - 2018) and used the National Death Index to calculate mortality until December 31, 2019. Kaplan - Meier analysis explored the ALI - mortality relationship in CB patients. Weighted univariate and multivariate Cox models, adjusting for relevant factors, further examined this relationship. Restricted cubic spline analysis estimated the non - linear association, and subgroup and sensitivity analyses ensured result reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvolving 2103 CB patients, elevated ALI was associated with reduced death risk. An inverse J - shaped non - linear relationship was found between ALI and all - cause mortality, with a turning point of 96.33 (p \u0026lt; 0.0001). Below 96.33, increased ALI meant lower death risk; above it, increased ALI led to higher risk. Findings were consistent across subgroups and stable in sensitivity analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study reveals a novel link between elevated ALI and reduced death risk in CB patients. Maintaining ALI within an optimal range is crucial for long - term survival. ALI's dynamic changes can help clinicians set personalized criteria for better long - term outcomes.\u003c/p\u003e","manuscriptTitle":"Association between inflammatory index and mortality in advanced lung cancer patients with chronic bronchitis: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 13:37:31","doi":"10.21203/rs.3.rs-7581931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T20:48:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T09:40:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337418749155854280482431310211658714971","date":"2026-02-03T10:06:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T15:58:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T14:29:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97875082022393354698449468898188853364","date":"2025-10-11T03:16:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264086978183321569849810952255404090611","date":"2025-10-07T11:37:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-07T11:04:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-12T07:58:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T02:31:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T02:31:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-09-10T10:30:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86e0d4cb-7ac6-429e-97e4-370ece211dfd","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T20:54:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 13:37:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7581931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7581931","identity":"rs-7581931","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}