Impact of bronchodilator responsiveness on lung age: implications for clinical practice from a cross-sectional study

Impact of bronchodilator responsiveness on lung age: implications for clinical practice from 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 Impact of bronchodilator responsiveness on lung age: implications for clinical practice from a cross-sectional study Feifei Huang, Yun Li, Zhufeng Wang, Lina Liang, Shuting Li, Yi Gao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6943087/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to evaluate the impact of bronchodilator responsiveness (BDR) on lung age (LA). Methods This cross-sectional study analyzed patients undergoing spirometry and bronchodilator responsiveness (BDR) tests at the First Affiliated Hospital of Guangzhou Medical University between January 2023 and December 2024. The LA value was calculated using a reference equation derived from a healthy Chinese non-smoking population. Changes in LA and lung function parameters before and after BDR were compared. Linear regression equation was conducted to evaluate the relationship between The difference between LA and age (DeltaLA) and lung function parameters. A random forest model was employed to identify predictors of LA improvement. Receiver operating characteristic (ROC) analysis was performed to evaluate the predictive performance of pre-BDR parameters for post-BDR forced expiratory volume in one second to forced vital capacity (FEV 1 /FVC) ratio < 0.7. Results A total of 9,316 subjects were included in the study, with 7,452 assigned to the training set and 1,864 to the validation set. Following BDR, significant improvements were observed in LA, FEV 1 , FVC, forced expiratory flow at 50% of FVC (FEF 50% ), and forced expiratory flow at 75% of FVC (FEF 75% ) (all P < 0.001). In the random forest model, the top predictors of LA improvement after BDR were pre-bronchodilator LA (pre-LA), age, and pre-bronchodilator FEF 50% (pre-FEF 50% ). Internal validation results showed that the prediction model had an accuracy of 92.6%. DeltaLA was moderately to strongly negatively correlated with lung function parameters (r = -0.5 ~ -0.8, all P < 0.001). In the validation set, receiver operating characteristic (ROC) curve analysis showed that the ability of the difference between the upper limit of normal (ULN) for LA and LA (ULNLA) to predict FEV 1 /FVC ratio < 0.7 after BDR was comparable to that of FEV 1 (AUC: 0.84 vs. 0.85). Conclusion LA significantly improves after BDR test. Clinicians and technicians should consider both clinical context and resource availability whether to report post-bronchodilator LA value. Lung age bronchodilator responsiveness spirometry Figures Figure 1 Figure 2 Background Pulmonary function tests (PFTs) represent a key diagnostic and monitoring tool in the management of chronic respiratory diseases, playing a critical role in disease diagnosis, severity grading, treatment assessment, and prognosis evaluation[ 1 , 2 ]. Recognizing their importance in the early detection and longitudinal monitoring of airway diseases, several national policies in China have promoted the widespread adoption of PFTs in primary healthcare settings [ 3 – 6 ] However, the technical and conceptual complexity of PFTs pose challenges for both primary care physicians in interpretation and and patients in understanding their clinical relevance, thereby limiting their broader use at the primary care level[ 7 ]. The concept of lung age (LA) was introduced by Morris and Temple in 1985[ 8 ]. It is calculated using pulmonary function parameters via established reference equations. An LA that exceeds a patient’s chronological age may indicate accelerated pulmonary decline. For instance, a 45-year-old man with a LA of 65 may have pulmonary function comparable to that of an older individual. Multiple studies have shown that providing patients with LA feedback significantly improves their awareness of respiratory health and supports smoking cessation efforts [ 9 – 13 ]. Moreover, LA has been investigated as a predictor of postoperative complications and a marker for evaluating treatment responses [ 14 – 17 ]. Bronchodilator responsiveness (BDR) test is an approach for assessing airway reversibility. It involves comparing pre- and post-bronchodilator spirometry to determine the reversibility of airflow obstruction. Traditionally, LA has been estimated based solely on pre-bronchodilator spirometric values, without accounting for potential reversibility of airway after bronchodilators. As a result, it remains unclear whether bronchodilators significantly alters LA and how these changes correlate with pulmonary function parameters. A pre-bronchodilator LA (pre-LA) value may overestimate disease severity in patients with reversible obstruction. Understanding the impact of BDR on LA estimation is crucial for clinical application. In this context, our study investigates real-world patients undergoing both baseline and post-bronchodilator spirometry to evaluate changes in LA and to provide insights into the appropriate timing and context for LA estimation in clinical practice. Methods Study Design, settings and participants This cross-sectional observational study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (2020–124). Informed consent was waived due to the retrospective nature of the study. It included patients who underwent spirometry and BDR tests at the hospital between January 2023 and December 2024. Indications for testing were primarily based on clinical judgment. Most participants were diagnosed with asthma, chronic obstructive pulmonary disease (COPD), lung tumors, or bronchiectasis. Some participants presented symptoms such as cough, sputum production, chest tightness, dyspnea, or wheezing but had not received definitive diagnoses. Inclusion and exclusion criteria To minimize confounding due to ethnic variability, only individuals of Chinese ethnicity were included. To ensure applicability to standard pulmonary function reference values for the Chinese population, only cases with confirmed gender, aged 18 to 80 years, and with heights ranging from 95 to 190 cm were selected [ 18 , 19 ]. Cases with contraindications to PFTs and BDR were excluded [ 20 , 21 ]. Measurements and definitions Spirometry and BDR test procedures adhered to guidelines set by the European Respiratory Society (ERS) and the American Thoracic Society (ATS) [ 20 – 22 ]. The technicians were full-time staff from the PFT center of the First Affiliated Hospital of Guangzhou Medical University. For subjects who underwent multiple spirometry or BDR tests, only the initial report was considered, and only data graded as A-level quality were used for subsequent analyses. Variables collected included sex, age, height, weight, baseline spirometric data, and post-BDR spirometric measurements. The best-measured values from spirometry and BDR test were used for LA estimation and data analysis. Reference equations established by Jian et al. in 2017 and Wang et al. in 2024 were utilized to estimate normal predicted values for spirometric parameters, serving as the gold standard for interpreting these metrics [ 18 , 19 ]. Airflow obstruction was defined as a post-BDR forced expiratory volume in one second to forced vital capacity (₁/FVC) ratio < 0.7 [ 23 , 24 ].LA was estimated using a reference equation developed by Liang et al. (2022), based on a nonlinear regression (spline method) derived from a healthy Chinese non-smoker population [ 25 ]. The data for this equation were derived from a multicenter, large-sample spirometry study [ 19 , 26 ]. The specific formulas are: Male LA prediction: LA(years) = 2.25 + 0.49 × Height + ns(₁) + 3.47 × forced expiratory flow at 50% of forced vital capacity (FEF 50% ) − 8.92 × forced expiratory flow at 75% of forced vital capacity (FEF 75% ). Female LA prediction: LA(years) = 28.49 + 0.36 × Height + ns(FEV 1 ) + 4.45 × FEF 50% − 12.52 × FEF 75% , Where ns(FEV 1 ) represents spline coefficients obtained from lookup tables. The difference between LA and age (deltaLA) was calculated as: deltaLA (years) = LA - age. The upper limit of normal (ULN) for deltaLA (deltaLAULN) was calculated as: deltaLAULN (years) = 12.243–0.323 × age + 1.645 × 7.037 [ 25 ]. The difference between ULN for LA and LA (ULNLA) was calculated as: ULNLA(years) = ULN-LA. Participants were stratified into three groups based on their deltaLA relative to deltaLAULN: Normal LA group (deltaLA ≤ 0), Normal increased LA group (deltaLA > 0 and ≤ deltaLAULN), and Abnormally increased LA group (deltaLA > deltaLAULN). Statistical Analysis The sample was randomly divided into a training set (80%) and a validation set (20%). The primary statistical analyses were conducted on the training set, while the validation set was used for internal validation. Linear regression equation was used to evaluate the relationship between DeltaLA and lung function parameters. A random forest algorithm was used to identify predictors of post-LA improvement. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the predictive ability of pre-BDR parameters for post-BDR FEV 1 /FVC ratio < 0.7, with area under the curve (AUC) calculated to assess diagnostic performance. Normally distributed continuous variables were described using means (standard deviations) and compared across groups using analysis of variance. Non-normally distributed continuous variables were expressed as medians (interquartile ranges) and compared using the Wilcoxon rank-sum test. Categorical variables were described using frequencies (percentages) and compared using chi-square test or Fisher’s exact test. A p-value of less than 0.05 was considered significant for all tests. Missing data for important variables (< 5% missing) were imputed using multiple imputation methods. Data analysis and figure generation were performed using R software (version 4.4.1). Results Demographic, spirometric and lung age data A total of 9,316 participants were included in the study, with 7,452 assigned to the training set and 1,864 to the validation set. Both groups had similar age distributions, ranging from 18 to 80 years, and comparable ranges of height and weight. No significant differences were observed between the two groups in terms of FEV 1 , FVC, FEF 50% , FEF 75% , FEV 1 /FVC ratio, LA and deltaLA (all P > 0.05) ( Table 1 ) . Table 1 Baseline demographic characteristics, spirometric and lung age data in the training and validation sets. Variables Train set(n = 7452) Validation set(n = 1864) P value Sex Male 5196 (69.7%) 1299 (69.7%) 0.997 Female 2256 (30.3%) 565 (30.3%) Age (years) Continuous value 58.45 (12.83) 58.97 (12.91) 0.121 18–29 253 (3.4%) 70 (3.8%) 0.166 30–39 529 (7.1%) 110 (5.9%) 40–49 750 (10.1%) 181 (9.7%) 50–59 1872 (25.1%) 443 (23.8%) 60–69 2624 (35.2%) 669 (35.9%) 70–80 1424 (19.1%) 391 (21.0%) Height (m) 1.63 (0.08) 1.63 (0.08) 0.754 Weight (kg) 61.78 (10.18) 62.52 (11.00) 0.006 Body mass index( kg/m 2 ) Continuous value 23.09 (2.88) 23.33 (3.11) 0.002 Underweight (< 18.5 kg/m 2 ) 464 (6.2%) 117 (6.3%) < 0.001 Normal (18.5–23.9 kg/m 2 ) 4700 (63.1%) 1036 (55.6%) Overweight (24.0–27.9 kg/m 2 ) 1878 (25.2%) 595 (31.9%) Obesity (≥ 28 kg/m 2 ) 410 (5.5%) 116 (6.2%) Spirometric data Pre-FEV 1 (L) 1.75 (0.77) 1.75 (0.78) 0.962 Pre-FVC (L) 2.86 (0.85) 2.86 (0.91) 0.83 Pre-FEF 50% (L/s) 1.47 (1.23) 1.45 (1.20) 0.613 Pre-FEF 75% (L/s) 0.43 (0.42) 0.42 (0.39) 0.083 Pre-FEV 1 /FVC ratio 60.64 (17.08) 60.70 (16.56) 0.896 Post-FEV 1 (L) 1.88 (0.78) 1.89 (0.79) 0.91 Post-FVC (L) 2.98 (0.85) 2.98 (0.90) 0.698 Post-FEF 50% (L/s) 1.78 (1.39) 1.77 (1.38) 0.685 Post-FEF 75% (L/s) 0.57 (0.51) 0.54 (0.48) 0.112 Post-FEV 1 /FVC ratio 64.00 (17.47) 64.02 (16.99) 0.976 Lung age data (years) Pre LA 88.69 (26.13) 88.83 (25.59) 0.839 Pre deltaLA 30.24 (23.35) 29.86 (23.42) 0.531 Post LA 83.92 (26.26) 84.06 (25.77) 0.845 Post deltaLA 25.47 (22.99) 25.09 (23.09) 0.52 Note: Continuous data are described as mean (standard deviation), while categorical variables are described as frequency (percentage). Abbreviations: FEV 1 , forced expiratory volume in one second; FVC, forced vital capacity; FEF 50% , forced expiratory flow at 50% of forced vital capacity; FEF 75% , forced expiratory flow at 75% of forced vital capacity; FEV 1 /FVC ratio, forced expiratory volume in 1 second to forced vital capacity ratio; LA, lung age; delta LA, the difference between lung age and age; Pre, pre-bronchodilator; Post, Post-bronchodilator. Comparison of lung age and spirometric before and after bronchodilator responsiveness Following BDR, significant improvements were observed in LA (88.69 ± 26.13 vs. 83.92 ± 26.26 years), FEV 1 (1.75 ± 0.77 vs. 1.88 ± 0.78 L), FVC (2.86 ± 0.85 vs. 2.98 ± 0.85 L), FEF 50% (1.47 ± 1.23 vs. 1.78 ± 1.39 L/s), and FEF 75% (0.43 ± 0.42 vs. 0.57 ± 0.51 L/s), with all P-values were < 0.001 ( see Additional file S1 ). Among the 760 participants with normal LA before BDR, 96.8% remained within the normal range after bronchodilators. Of the 523 patients with normal increased LA at baseline, 62.91% remained in the same category, 34.03% improved to the normal LA, and 3.06% worsened to abnormally increased LA after bronchodilators. Among the 6169 patients with abnormally increased LA at baseline, 94.0% remained in the same category, while 6.0% improved to either normal or normal increased LA categories (all p < 0.001) ( Table 2 and Additional file S2 ). The weighted kappa coefficient for LA classification agreement before and after BDR was 0.857, indicating a high level of consistency (all p < 0.001)( see Additional file S3 ). Table 2 Change in lung age categories before and after bronchodilator responsiveness. Pre LA Total Normal Normal increased Abnormally increased Post LA Normal 736 (96.8%) 178 (34.03%) 116 (1.88%) 1030 Normal increased 12 (1.60%) 329 (62.91) 254 (4.12%) 595 Abnormally increased 12 (1.60%) 16 (3.06%) 5799 (94.00%) 5827 Total 760 523 6169 7452 Note: This table presents the cross-classification of lung age categories before and after bronchodilator responsiveness. Lung age was classified into three groups: normal, normal-increased, and abnormally-increased based on the relationship of deltaLA and deltaLAULN. Pre-LA refers to pre-bronchodilation lung age; post-LA refers to post-bronchodilation lung age. Abbreviations: Pre-LA, lung age before bronchodilator responsiveness; Post LA, lung age after bronchodilator responsiveness. Subgroup with improved lung Age after bronchodilator responsiveness Among the 7,452 patients in the training set, 548 individuals (7.35%) demonstrated improvement in LA after BDR, of whom 231 (42.2%) were male ( see Additional file S4 ). Random forest analysis identified pre-LA, age, and pre-bronchodilator FEF 50% (pre-FEF 50% ) as the top three predictors of LA improvement after bronchodilator. The out-of-bag (OOB) error rate was 6.92%, and internal validation demonstrated a model accuracy of 92.6% ( see Additional file S5 ). Correlation and predictive performance DeltaLA showed significant negative correlations with spirometric parameters, including FEV 1 (r = − 0.8), FEF 50% (r = − 0.7), FEF 75% (r = − 0.6), and FVC (r = − 0.5) (all P < 0.001) ( Fig. 1 ) . In the training set, the cutoff value for FEV 1 /FVC%pred in predicting post-BDR FEV 1 /FVC ratio < 0.7 was identified as 78.15. The corresponding cutoff values for ULNLA and FEV 1 %Pred were − 19.60 and 64.08, respectively ( Fig. 2 a and Additional file S6) . Internal validation confirmed that FEV 1 /FVC%Pred remained the most accurate predictor (AUC: 0.98) ( Fig. 2 b ) , while ULNLA and FEV 1 %Pred demonstrated comparable predictive performance (AUC: 0.84 vs. 0.85). Discussion LA has been increasingly recognized in primary care that facilitates understanding and supports behavior change [ 27 ]. Previous studies have shown that bronchodilators can partially alleviate lung hyperinflation and exerts a greater effect on FVC than FEV 1 [ 28 ]. Using pre-BDR spirometry for disease classification may overestimate the prevalence of COPD[ 29 ]. In our study, both spirometric parameters and LA showed significant improvements after bronchodilator. Notably, 34% of patients with normal increased LA at baseline reverted to normal LA after the BDR test. These findings align with previous reports in specific COPD subgroups[ 30 , 31 ]. Additionally, post-BDR spirometry has been reported to more accurately predict COPD phenotypes and clinical outcomes [ 32 ]. Therefore, it is important to consider the impact of BDR on LA estimation. LA estimation incorporates both large and small airway parameters. Small airway dysfunction is increasingly acknowledged as a hallmark of early airway disease, often preceding detectable changes in FEV 1 or the FEV 1 /FVC ratio [ 33 , 34 ]. Small airway parameters are also closely associated with disease progression and responsiveness to therapy [ 35 ]. In this work, LA demonstrated the strongest correlation with FEV 1 , FEF 50% and FEF 75% , suggesting that LA may serve as a sensitive marker for early respiratory impairment. Furthermore, we found that pre-LA predicted post-BDR ratio < 0.7 with performance comparable to pre-FEV 1 , which indicates its potential application as a screening tool in primary care settings where BDR test is not routinely available. Other research has demonstrated that LA may help predict postoperative pulmonary complications and survival in patients undergoing lung cancer surgery[ 7 ]. DeltaLA has been associated with postoperative outcomes in esophageal cancer patients[ 15 ]. This study had some notable strengths. To the best of our knowledge, this is first study to systematically assess the impact of BDR on LA estimation. We employed a large and demographically diverse patient population, implemented rigorous quality control for spirometric measurements, and applied LA reference equations derived from Chinese cohorts. However, our study also has some limitations. Firstly, due to its cross-sectional design, it cannot evaluate longitudinal changes in LA over time. Secondly, Data on several potentially relevant variables—such as environmental exposure, prenatal tobacco smoke, smoking behavior, history of prematurity, childhood pulmonary infections, and other clinical comorbidities—were not available. We could not conclude whether these factors influenced results. Based on our findings, we propose the following preliminary recommendations. In resource-limited settings or for early screening purposes, reporting pre-LA may serve as a practical and informative tool for patient education, particularly among high-risk populations such as smokers. For individuals with a baseline classification of normal increased LA, BDR test is advisable to determine the reversibility of airway and reduce the likelihood of false-positive. In patients with diagnosed with chronic airway disease, post-LA may provide a more stable and clinically relevant metric for disease monitoring and treatment response evaluation. Conclusions BDR test has a significant impact on LA, particularly among individuals classified with normal increased LA. Clinicians and technicians should carefully consider the clinical context and available healthcare resources when determining whether to report post-bronchodilator LA values. Abbreviations BDR: bronchodilator responsiveness LA: lung age deltaLA: difference between LA and age ULN: upper limit of normal deltaLAULN: upper limit of normal for deltaLA ULNLA: difference between upper limit of normal for LA and LA FEV 1 : forced expiratory volume in one second FVC: forced vital capacity FEF 50% : forced expiratory flow at 50% of forced vital capacity FEF 75% : forced expiratory flow at 75% of forced vital capacity FEV 1 /FVC ratio: forced expiratory volume in 1 second to forced vital capacity ratio Pre: Pre-bronchodilator Post: Post-bronchodilator ROC: receiver operating characteristic AUC: area under the curve COPD: chronic obstructive pulmonary disease Declarations Ethics approval and consent to participate This was a retrospective study and was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of First Affiliated Hospital of Guangzhou Medical University (2020–124). Informed consent was waived due to the retrospective nature of the study Competing interests The authors declare that there are no competing interests in relation to this study. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to institutional data management regulations but are available from the corresponding author on reasonable request. Funding This study was funded by Special Grant for the Development of Medical High-Ground Initiatives (No.32082018020), and the R&D Program of Guangzhou National Laboratory (Grant No.SRPG22-018), and the Science and Technology Program of Guangzhou, China (202007040003), and the Medical Scientific Research Foundation of Guangdong Province, China (NO.C2021073), and Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0506300). The study funders had no role in the conceptualization, design, data collection, analysis, the decision to publish, or preparation of the manuscript. Authors' contributions JPZ, YG and FFH contributed to the conception and design of the work. FFH, YL and ZFW contributed to literature research, the data acquisition and image delineation. LNL and STL contributed to the data analysis. FFH, YL and ZFW contributed to the draft of the manuscript. All authors contributed substantially to the interpretation of the data results and the revision of the manuscript. All the authors have read and approved this study before submission. Acknowledgements Not applicable. References Chinese Medical Association Publishing House, Chinese Society of General Practice, Pulmonary Function Group of Chinese Thoracic Society, Editorial Board of Chinese Journal of General Practitioners of Chinese Medical Association, Expert Group of Guidelines for Primary Care of Respiratory System Disease. 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Bronchodilator responsiveness in patients with COPD [J]. Eur Respir J 2008;31:742–50. Albert P, Agusti A, Edwards L, Tal-Singer R, Yates J, Bakke P, et al. Bronchodilator responsiveness as a phenotypic characteristic of established chronic obstructive pulmonary disease [J]. Thorax 2012;67:701–8. Fortis S, Eberlein M, Georgopoulos D, Comellas AP. Predictive value of prebronchodilator and postbronchodilator spirometry for COPD features and outcomes [J]. BMJ Open Respir Res 2017;4:e000213. Stockley JA, Cooper BG, Stockley RA, Sapey E. Small airways disease: time for a revisit? [J]. Int J Chron Obstruct Pulmon Dis 2017;12:2343–53. Bao W, Tian X, Hao H, Jin Y, Xie X, Yin D, et al. Is small airway dysfunction an abnormal phenomenon for patients with normal forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced vital capacity? [J]. Ann Allergy Asthma Immunol 2022;128:68-77.e1. van der Wiel E, ten Hacken NHT, Postma DS, van den Berge M. Small-airways dysfunction associates with respiratory symptoms and clinical features of asthma: a systematic review [J]. J Allergy Clin Immunol 2013;131:646–57. Additional Declarations No competing interests reported. Supplementary Files Additionalfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6943087","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482943034,"identity":"e25dddbc-ce02-4ad7-a88c-83fc59ef81fe","order_by":0,"name":"Feifei Huang","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feifei","middleName":"","lastName":"Huang","suffix":""},{"id":482943035,"identity":"916c2b81-5aa5-408b-9695-205f6351334a","order_by":1,"name":"Yun Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Li","suffix":""},{"id":482943036,"identity":"93c9ebdf-2b38-4574-8d69-d6d7e7e31e9d","order_by":2,"name":"Zhufeng Wang","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhufeng","middleName":"","lastName":"Wang","suffix":""},{"id":482943037,"identity":"941a8eb1-06fd-4114-bba2-a511f582804d","order_by":3,"name":"Lina Liang","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Liang","suffix":""},{"id":482943038,"identity":"2d2d6594-8b5e-484d-b8d1-dd98ca3af5bc","order_by":4,"name":"Shuting Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuting","middleName":"","lastName":"Li","suffix":""},{"id":482943039,"identity":"b72744c4-394b-483c-980f-aede16aedc16","order_by":5,"name":"Yi Gao","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Gao","suffix":""},{"id":482943040,"identity":"8e8949c2-a663-4cf7-88e7-cf717efd3983","order_by":6,"name":"Jinping Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACxgYGNgiLvbHx4QfStPAcbjaWINIiqBaJ9DYBHmLUM89uf/bg445auw03H7YxSDDYyek2EHLYnAPphjPPHE/ecDux7UEBQ7Kx2QFCWmYkHJPmbTuWbHA7sd1AguFA4jbCWhLbpP+CtNw82CbBQ5yWZDZpxrYaO4MbjMRqmXOMTbK37UCC5JlEYCAbEOEXQ2CISfxsq7PnO3784cMPFXZyhLXMAFOHExeAVRoQUA4C8pAIr7OXbyBC9SgYBaNgFIxMAADim0l7ks9saAAAAABJRU5ErkJggg==","orcid":"","institution":"the First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jinping","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-06-21 06:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6943087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6943087/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86669542,"identity":"e80a70d1-9871-4277-9390-13ce41361deb","added_by":"auto","created_at":"2025-07-14 11:24:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":839589,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between DeltaLA and FEV\u003csub\u003e1\u003c/sub\u003e, FVC, FEF\u003csub\u003e50%\u003c/sub\u003e, FEF\u003csub\u003e75% \u003c/sub\u003ebefore and after bronchodilator responsiveness. Pre-DeltaLA, difference between Lung age and age before bronchodilator responsiveness; Pre-FEV\u003csub\u003e1\u003c/sub\u003e, forced expiratory volume in one second before bronchodilator responsiveness; Pre-FVC, forced vital capacity before bronchodilator responsiveness; Pre-FEF\u003csub\u003e50\u003c/sub\u003e, forced expiratory flow at 50% of forced vital capacity before bronchodilator responsiveness; Pre-FEF\u003csub\u003e75\u003c/sub\u003e, forced expiratory flow at 75% of forced vital capacity before bronchodilator responsiveness; Post-DeltaLA, difference between Lung age and age after bronchodilator responsiveness; Post-FEV\u003csub\u003e1\u003c/sub\u003e, forced expiratory volume in one second after bronchodilator responsiveness; Post-FVC, forced vital capacity after bronchodilator responsiveness; Post-FEF\u003csub\u003e50\u003c/sub\u003e, forced expiratory flow at 50% of forced vital capacity after bronchodilator responsiveness; Post-FEF\u003csub\u003e75\u003c/sub\u003e, forced expiratory flow at 75% of forced vital capacity after bronchodilator responsiveness.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6943087/v1/0ec5a33695b1ed896b886348.jpg"},{"id":86669532,"identity":"ba1a1b83-d784-4a8e-8136-394c7fb781b4","added_by":"auto","created_at":"2025-07-14 11:24:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":387467,"visible":true,"origin":"","legend":"\u003cp\u003eThe ability of %predicted of spirometric parameters before bronchodilator to predict groups with and without FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio \u0026lt; 0.7 after bronchodilation. Pre-FEV\u003csub\u003e1\u003c/sub\u003e/FVC%pred, forced expiratory volume in one second to forced vital capacity ratio percent predicted before bronchodilator responsiveness; Pre-FEF\u003csub\u003e50%\u003c/sub\u003epred, forced expiratory flow at 50% of forced vital capacity percent predicted before bronchodilator responsiveness; Pre-FEF\u003csub\u003e75%\u003c/sub\u003epred, forced expiratory flow at 75% of FVC percent predicted before bronchodilator responsiveness; Pre-ULNLA, the difference between the upper limit of the normal for Lung age and lung age before bronchodilator responsiveness; Pre-FEV\u003csub\u003e1\u003c/sub\u003e%pred, forced expiratory volume in one second percent predicted before bronchodilator responsiveness; Pre-FVC%pred, forced vital capacity percent predicted before bronchodilator responsiveness. (a) In the training set; (b) In the validation set\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6943087/v1/2b663ff3bd7ea32a0c47fda5.jpg"},{"id":95654009,"identity":"d93749db-9c29-47de-9f32-681f5175abfe","added_by":"auto","created_at":"2025-11-11 16:08:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2206464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6943087/v1/2d7354ae-45a8-40ef-a59d-8589dbd6124f.pdf"},{"id":86669546,"identity":"2b9c3f5e-3e4e-4a0c-908d-49c4fca8a42a","added_by":"auto","created_at":"2025-07-14 11:24:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25956,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6943087/v1/e64e95bf13ebcbbb91b75d59.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of bronchodilator responsiveness on lung age: implications for clinical practice from a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003ePulmonary function tests (PFTs) represent a key diagnostic and monitoring tool in the management of chronic respiratory diseases, playing a critical role in disease diagnosis, severity grading, treatment assessment, and prognosis evaluation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recognizing their importance in the early detection and longitudinal monitoring of airway diseases, several national policies in China have promoted the widespread adoption of PFTs in primary healthcare settings [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] However, the technical and conceptual complexity of PFTs pose challenges for both primary care physicians in interpretation and and patients in understanding their clinical relevance, thereby limiting their broader use at the primary care level[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The concept of lung age (LA) was introduced by Morris and Temple in 1985[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is calculated using pulmonary function parameters via established reference equations. An LA that exceeds a patient’s chronological age may indicate accelerated pulmonary decline. For instance, a 45-year-old man with a LA of 65 may have pulmonary function comparable to that of an older individual. Multiple studies have shown that providing patients with LA feedback significantly improves their awareness of respiratory health and supports smoking cessation efforts [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, LA has been investigated as a predictor of postoperative complications and a marker for evaluating treatment responses [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBronchodilator responsiveness (BDR) test is an approach for assessing airway reversibility. It involves comparing pre- and post-bronchodilator spirometry to determine the reversibility of airflow obstruction. Traditionally, LA has been estimated based solely on pre-bronchodilator spirometric values, without accounting for potential reversibility of airway after bronchodilators. As a result, it remains unclear whether bronchodilators significantly alters LA and how these changes correlate with pulmonary function parameters. A pre-bronchodilator LA (pre-LA) value may overestimate disease severity in patients with reversible obstruction. Understanding the impact of BDR on LA estimation is crucial for clinical application. In this context, our study investigates real-world patients undergoing both baseline and post-bronchodilator spirometry to evaluate changes in LA and to provide insights into the appropriate timing and context for LA estimation in clinical practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design, settings and participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This cross-sectional observational study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (2020–124). Informed consent was waived due to the retrospective nature of the study. It included patients who underwent spirometry and BDR tests at the hospital between January 2023 and December 2024. Indications for testing were primarily based on clinical judgment. Most participants were diagnosed with asthma, chronic obstructive pulmonary disease (COPD), lung tumors, or bronchiectasis. Some participants presented symptoms such as cough, sputum production, chest tightness, dyspnea, or wheezing but had not received definitive diagnoses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion and exclusion criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo minimize confounding due to ethnic variability, only individuals of Chinese ethnicity were included. To ensure applicability to standard pulmonary function reference values for the Chinese population, only cases with confirmed gender, aged 18 to 80 years, and with heights ranging from 95 to 190 cm were selected [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Cases with contraindications to PFTs and BDR were excluded [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurements and definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpirometry and BDR test procedures adhered to guidelines set by the European Respiratory Society (ERS) and the American Thoracic Society (ATS) [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The technicians were full-time staff from the PFT center of the First Affiliated Hospital of Guangzhou Medical University. For subjects who underwent multiple spirometry or BDR tests, only the initial report was considered, and only data graded as A-level quality were used for subsequent analyses. Variables collected included sex, age, height, weight, baseline spirometric data, and post-BDR spirometric measurements. The best-measured values from spirometry and BDR test were used for LA estimation and data analysis. Reference equations established by Jian et al. in 2017 and Wang et al. in 2024 were utilized to estimate normal predicted values for spirometric parameters, serving as the gold standard for interpreting these metrics [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAirflow obstruction was defined as a post-BDR forced expiratory volume in one second to forced vital capacity (₁/FVC) ratio \u0026lt; 0.7 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].LA was estimated using a reference equation developed by Liang et al. (2022), based on a nonlinear regression (spline method) derived from a healthy Chinese non-smoker population [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The data for this equation were derived from a multicenter, large-sample spirometry study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The specific formulas are:\u003c/p\u003e\u003cp\u003eMale LA prediction: LA(years) = 2.25 + 0.49 × Height + ns(₁) + 3.47 × forced expiratory flow at 50% of forced vital capacity (FEF\u003csub\u003e50%\u003c/sub\u003e) − 8.92 × forced expiratory flow at 75% of forced vital capacity (FEF\u003csub\u003e75%\u003c/sub\u003e). Female LA prediction: LA(years) = 28.49 + 0.36 × Height + ns(FEV\u003csub\u003e1\u003c/sub\u003e) + 4.45 × FEF\u003csub\u003e50%\u003c/sub\u003e − 12.52 × FEF\u003csub\u003e75%\u003c/sub\u003e, Where ns(FEV\u003csub\u003e1\u003c/sub\u003e) represents spline coefficients obtained from lookup tables.\u003c/p\u003e\u003cp\u003eThe difference between LA and age (deltaLA) was calculated as: deltaLA (years) = LA - age. The upper limit of normal (ULN) for deltaLA (deltaLAULN) was calculated as: deltaLAULN (years) = 12.243–0.323 × age + 1.645 × 7.037 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The difference between ULN for LA and LA (ULNLA) was calculated as: ULNLA(years) = ULN-LA. Participants were stratified into three groups based on their deltaLA relative to deltaLAULN: Normal LA group (deltaLA ≤ 0), Normal increased LA group (deltaLA \u0026gt; 0 and ≤ deltaLAULN), and Abnormally increased LA group (deltaLA \u0026gt; deltaLAULN).\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe sample was randomly divided into a training set (80%) and a validation set (20%). The primary statistical analyses were conducted on the training set, while the validation set was used for internal validation. Linear regression equation was used to evaluate the relationship between DeltaLA and lung function parameters. A random forest algorithm was used to identify predictors of post-LA improvement. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the predictive ability of pre-BDR parameters for post-BDR FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio \u0026lt; 0.7, with area under the curve (AUC) calculated to assess diagnostic performance. Normally distributed continuous variables were described using means (standard deviations) and compared across groups using analysis of variance. Non-normally distributed continuous variables were expressed as medians (interquartile ranges) and compared using the Wilcoxon rank-sum test. Categorical variables were described using frequencies (percentages) and compared using chi-square test or Fisher’s exact test. A p-value of less than 0.05 was considered significant for all tests. Missing data for important variables (\u0026lt; 5% missing) were imputed using multiple imputation methods. Data analysis and figure generation were performed using R software (version 4.4.1).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDemographic, spirometric and lung age data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 9,316 participants were included in the study, with 7,452 assigned to the training set and 1,864 to the validation set. Both groups had similar age distributions, ranging from 18 to 80 years, and comparable ranges of height and weight. No significant differences were observed between the two groups in terms of FEV\u003csub\u003e1\u003c/sub\u003e, FVC, FEF\u003csub\u003e50%\u003c/sub\u003e, FEF\u003csub\u003e75%\u003c/sub\u003e, FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio, LA and deltaLA (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline demographic characteristics, spirometric and lung age data in the training and validation sets.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrain set(n\u0026thinsp;=\u0026thinsp;7452)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValidation set(n\u0026thinsp;=\u0026thinsp;1864)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5196 (69.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1299 (69.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2256 (30.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e565 (30.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinuous value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.45 (12.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.97 (12.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e253 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e529 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e750 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1872 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e443 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2624 (35.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e669 (35.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1424 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e391 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.63 (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.63 (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.78 (10.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.52 (11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody mass index(\u003c/b\u003ekg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinuous value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.09 (2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.33 (3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e464 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (18.5\u0026ndash;23.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4700 (63.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1036 (55.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight (24.0\u0026ndash;27.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1878 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e595 (31.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity (\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e410 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpirometric data\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-FEV\u003csub\u003e1\u003c/sub\u003e (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.75 (0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.75 (0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-FVC (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.86 (0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.86 (0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-FEF\u003csub\u003e50%\u003c/sub\u003e (L/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.47 (1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.45 (1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-FEF\u003csub\u003e75%\u003c/sub\u003e (L/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43 (0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.42 (0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.64 (17.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.70 (16.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-FEV\u003csub\u003e1\u003c/sub\u003e (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.88 (0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.89 (0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-FVC (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.98 (0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.98 (0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-FEF\u003csub\u003e50%\u003c/sub\u003e (L/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.78 (1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.77 (1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-FEF\u003csub\u003e75%\u003c/sub\u003e (L/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.57 (0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.54 (0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.00 (17.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.02 (16.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLung age data\u003c/b\u003e (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre LA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88.69 (26.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.83 (25.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre deltaLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.24 (23.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.86 (23.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost LA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.92 (26.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.06 (25.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost deltaLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.47 (22.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.09 (23.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Continuous data are described as mean (standard deviation), while categorical variables are described as frequency (percentage).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: FEV\u003csub\u003e1\u003c/sub\u003e, forced expiratory volume in one second; FVC, forced vital capacity; FEF\u003csub\u003e50%\u003c/sub\u003e, forced expiratory flow at 50% of forced vital capacity; FEF\u003csub\u003e75%\u003c/sub\u003e, forced expiratory flow at 75% of forced vital capacity; FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio, forced expiratory volume in 1 second to forced vital capacity ratio; LA, lung age; delta LA, the difference between lung age and age; Pre, pre-bronchodilator; Post, Post-bronchodilator.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of lung age and spirometric before and after bronchodilator responsiveness\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing BDR, significant improvements were observed in LA (88.69\u0026thinsp;\u0026plusmn;\u0026thinsp;26.13 vs. 83.92\u0026thinsp;\u0026plusmn;\u0026thinsp;26.26 years), FEV\u003csub\u003e1\u003c/sub\u003e (1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77 vs. 1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78 L), FVC (2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 vs. 2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 L), FEF\u003csub\u003e50%\u003c/sub\u003e (1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23 vs. 1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39 L/s), and FEF\u003csub\u003e75%\u003c/sub\u003e (0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 vs. 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51 L/s), with all P-values were \u0026lt;\u0026thinsp;0.001 (\u003cb\u003esee Additional file S1\u003c/b\u003e). Among the 760 participants with normal LA before BDR, 96.8% remained within the normal range after bronchodilators. Of the 523 patients with normal increased LA at baseline, 62.91% remained in the same category, 34.03% improved to the normal LA, and 3.06% worsened to abnormally increased LA after bronchodilators. Among the 6169 patients with abnormally increased LA at baseline, 94.0% remained in the same category, while 6.0% improved to either normal or normal increased LA categories (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) ( Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eAdditional file S2\u003c/b\u003e). The weighted kappa coefficient for LA classification agreement before and after BDR was 0.857, indicating a high level of consistency (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)( \u003cb\u003esee Additional file S3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChange in lung age categories before and after bronchodilator responsiveness.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003ePre LA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNormal increased\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAbnormally increased\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePost LA\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e736 (96.8%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e178 (34.03%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e116 (1.88%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1030\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNormal increased\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (1.60%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e329 (62.91)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e254 (4.12%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e595\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAbnormally increased\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (1.60%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (3.06%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5799 (94.00%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5827\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: This table presents the cross-classification of lung age categories before and after bronchodilator responsiveness. Lung age was classified into three groups: normal, normal-increased, and abnormally-increased based on the relationship of deltaLA and deltaLAULN. Pre-LA refers to pre-bronchodilation lung age; post-LA refers to post-bronchodilation lung age.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: Pre-LA, lung age before bronchodilator responsiveness; Post LA, lung age after bronchodilator responsiveness.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup with improved lung Age after bronchodilator responsiveness\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the 7,452 patients in the training set, 548 individuals (7.35%) demonstrated improvement in LA after BDR, of whom 231 (42.2%) were male (\u003cb\u003esee Additional file S4\u003c/b\u003e). Random forest analysis identified pre-LA, age, and pre-bronchodilator FEF\u003csub\u003e50%\u003c/sub\u003e(pre-FEF\u003csub\u003e50%\u003c/sub\u003e) as the top three predictors of LA improvement after bronchodilator. The out-of-bag (OOB) error rate was 6.92%, and internal validation demonstrated a model accuracy of 92.6% (\u003cb\u003esee Additional file S5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation and predictive performance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDeltaLA showed significant negative correlations with spirometric parameters, including FEV\u003csub\u003e1\u003c/sub\u003e (r = \u0026minus;\u0026thinsp;0.8), FEF\u003csub\u003e50%\u003c/sub\u003e (r = \u0026minus;\u0026thinsp;0.7), FEF\u003csub\u003e75%\u003c/sub\u003e (r = \u0026minus;\u0026thinsp;0.6), and FVC (r = \u0026minus;\u0026thinsp;0.5) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In the training set, the cutoff value for FEV\u003csub\u003e1\u003c/sub\u003e/FVC%pred in predicting post-BDR FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.7 was identified as 78.15. The corresponding cutoff values for ULNLA and FEV\u003csub\u003e1\u003c/sub\u003e%Pred were \u0026minus;\u0026thinsp;19.60 and 64.08, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u003cb\u003eand Additional file S6)\u003c/b\u003e. Internal validation confirmed that FEV\u003csub\u003e1\u003c/sub\u003e/FVC%Pred remained the most accurate predictor (AUC: 0.98) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e, while ULNLA and FEV\u003csub\u003e1\u003c/sub\u003e%Pred demonstrated comparable predictive performance (AUC: 0.84 vs. 0.85).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLA has been increasingly recognized in primary care that facilitates understanding and supports behavior change [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies have shown that bronchodilators can partially alleviate lung hyperinflation and exerts a greater effect on FVC than FEV\u003csub\u003e1\u003c/sub\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Using pre-BDR spirometry for disease classification may overestimate the prevalence of COPD[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our study, both spirometric parameters and LA showed significant improvements after bronchodilator. Notably, 34% of patients with normal increased LA at baseline reverted to normal LA after the BDR test. These findings align with previous reports in specific COPD subgroups[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, post-BDR spirometry has been reported to more accurately predict COPD phenotypes and clinical outcomes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, it is important to consider the impact of BDR on LA estimation.\u003c/p\u003e\u003cp\u003eLA estimation incorporates both large and small airway parameters. Small airway dysfunction is increasingly acknowledged as a hallmark of early airway disease, often preceding detectable changes in FEV\u003csub\u003e1\u003c/sub\u003e or the FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Small airway parameters are also closely associated with disease progression and responsiveness to therapy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this work, LA demonstrated the strongest correlation with FEV\u003csub\u003e1\u003c/sub\u003e, FEF\u003csub\u003e50%\u003c/sub\u003e and FEF\u003csub\u003e75%\u003c/sub\u003e, suggesting that LA may serve as a sensitive marker for early respiratory impairment. Furthermore, we found that pre-LA predicted post-BDR ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.7 with performance comparable to pre-FEV\u003csub\u003e1\u003c/sub\u003e, which indicates its potential application as a screening tool in primary care settings where BDR test is not routinely available. Other research has demonstrated that LA may help predict postoperative pulmonary complications and survival in patients undergoing lung cancer surgery[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. DeltaLA has been associated with postoperative outcomes in esophageal cancer patients[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study had some notable strengths. To the best of our knowledge, this is first study to systematically assess the impact of BDR on LA estimation. We employed a large and demographically diverse patient population, implemented rigorous quality control for spirometric measurements, and applied LA reference equations derived from Chinese cohorts. However, our study also has some limitations. Firstly, due to its cross-sectional design, it cannot evaluate longitudinal changes in LA over time. Secondly, Data on several potentially relevant variables\u0026mdash;such as environmental exposure, prenatal tobacco smoke, smoking behavior, history of prematurity, childhood pulmonary infections, and other clinical comorbidities\u0026mdash;were not available. We could not conclude whether these factors influenced results.\u003c/p\u003e\u003cp\u003eBased on our findings, we propose the following preliminary recommendations. In resource-limited settings or for early screening purposes, reporting pre-LA may serve as a practical and informative tool for patient education, particularly among high-risk populations such as smokers. For individuals with a baseline classification of normal increased LA, BDR test is advisable to determine the reversibility of airway and reduce the likelihood of false-positive. In patients with diagnosed with chronic airway disease, post-LA may provide a more stable and clinically relevant metric for disease monitoring and treatment response evaluation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBDR test has a significant impact on LA, particularly among individuals classified with normal increased LA. Clinicians and technicians should carefully consider the clinical context and available healthcare resources when determining whether to report post-bronchodilator LA values.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBDR: bronchodilator responsiveness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLA: lung age\u003c/p\u003e\n\u003cp\u003edeltaLA: difference between LA and age\u003c/p\u003e\n\u003cp\u003eULN: upper limit of normal\u003c/p\u003e\n\u003cp\u003edeltaLAULN: upper limit of normal for deltaLA\u003c/p\u003e\n\u003cp\u003eULNLA: difference between upper limit of normal for LA and LA\u003c/p\u003e\n\u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e: forced expiratory volume in one second\u003c/p\u003e\n\u003cp\u003eFVC: forced vital capacity\u003c/p\u003e\n\u003cp\u003eFEF\u003csub\u003e50%\u003c/sub\u003e: forced expiratory flow at 50% of forced vital capacity\u003c/p\u003e\n\u003cp\u003eFEF\u003csub\u003e75%\u003c/sub\u003e: forced expiratory flow at 75% of forced vital capacity\u003c/p\u003e\n\u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio: forced expiratory volume in 1 second to forced vital capacity ratio Pre: Pre-bronchodilator\u003c/p\u003e\n\u003cp\u003ePost: Post-bronchodilator\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u003c/p\u003e\n\u003cp\u003eCOPD: chronic obstructive pulmonary disease\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective study and was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of First Affiliated Hospital of Guangzhou Medical University (2020–124). Informed consent was waived due to the retrospective nature of the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests in relation to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to institutional data management regulations but are available from the corresponding author on reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Special Grant for the Development of Medical High-Ground Initiatives (No.32082018020), and the R\u0026amp;D Program of Guangzhou National Laboratory (Grant No.SRPG22-018), and the Science and Technology Program of Guangzhou, China (202007040003), and the Medical Scientific Research Foundation of Guangdong Province, China (NO.C2021073), and Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0506300). The study funders had no role in the conceptualization, design, data collection, analysis, the decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJPZ, YG and FFH contributed to the conception and design of the work. FFH, YL and ZFW contributed to literature research, the data acquisition and image delineation. LNL and STL contributed to the data analysis. FFH, YL and ZFW contributed to the draft of the manuscript. All authors contributed substantially to the interpretation of the data results and the revision of the manuscript. All the authors have read and approved this study before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChinese Medical Association Publishing House, Chinese Society of General Practice, Pulmonary Function Group of Chinese Thoracic Society, Editorial Board of Chinese Journal of General Practitioners of Chinese Medical Association, Expert Group of Guidelines for Primary Care of Respiratory System Disease. Chinese guideline for routine pulmonary function tests in primary care (2024) [J]. Chinese Journal of General Practitioners 2025;24:121\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eDivision of Pulmonary Function, Chinese Society of Tuberculosis and Respiratory Diseases, Chinese Medical Association. Guidelines for Pulmonary Function Testing (Part I): Overview and General Requirements [J]. 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Accessed April 25, 2025.\u003c/li\u003e\n\u003cli\u003eNational Health Commission of the People\u0026rsquo;s Republic of China, Ministry of Finance of the People\u0026rsquo;s Republic of China, National Administration of Traditional Chinese Medicine, National Disease Control and Prevention Administration. Notice on Implementing Basic Public Health Services in 2024[EB/OL] 2024. http://www.nhc.gov.cn/jws/s7874/202409/33132b328d804634858b54b4681db5a0.shtml. Accessed April 25, 2025.\u003c/li\u003e\n\u003cli\u003eHaruki T, Nakamura H, Taniguchi Y, Miwa K, Adachi Y, Fujioka S. \u0026ldquo;Lung age\u0026rdquo; predicts post-operative complications and survival in lung cancer patients [J]. Respirology 2010;15:495\u0026ndash;500.\u003c/li\u003e\n\u003cli\u003eMorris JF, Temple W. Spirometric \u0026ldquo;lung age\u0026rdquo; estimation for motivating smoking cessation [J]. Prev Med 1985;14:655\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eKhaldi S, Derbel K, Ghannouchi I, Guezguez F, Sayhi A, Benzarti W, et al. Short-term effects of announcing spirometric lung-age on smokers\u0026rsquo; attitudes: results from a Tunisian real-life pilot study [J]. Expert Rev Respir Med 2024;18:655\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eParkes G, Greenhalgh T, Griffin M, Dent R. Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial [J]. BMJ 2008;336:598\u0026ndash;600.\u003c/li\u003e\n\u003cli\u003eGjorgjievski D, Stavrikj K, Jordan R, Adab P, Stanoevski G, Stamenova A, et al. Randomised controlled trial testing effectiveness of feedback about lung age or exhaled CO combined with very brief advice for smoking cessation compared to very brief advice alone in North Macedonia: findings from the Breathe Well group [J]. BMC Public Health 2023;23:1887.\u003c/li\u003e\n\u003cli\u003eDerbel K, Maatoug C, Mchita A, Mabrouk KH, Saad HB. Self-Reported Smoking Status 10-Months After a Single Session Intervention Including an Education Conference About Smoking Harms and Announcement of Spirometric Lung-Age [J]. Clin Med Insights Circ Respir Pulm Med 2021;15:11795484211047041.\u003c/li\u003e\n\u003cli\u003eBen Fredj M, Garrach B, Bennasrallah C, Migaou A, Abroug H, Dhouib W, et al. Spirometry as a motivator for smoking cessation among patients attending the smoking cessation clinic of Monastir [J]. BMC Public Health 2022;22:1164.\u003c/li\u003e\n\u003cli\u003eCarr WW, McDonald M, Meizlik P. Effect of intravenously administered reslizumab on spirometric lung age in patients with moderate-to-severe eosinophilic asthma [J]. Allergy Asthma Proc 2019;40:240\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eZn H, K W, Z C, K P, M K. Difference between \u0026ldquo;Lung Age\u0026rdquo; and Real Age as a Novel Predictor of Postoperative Complications, Long-Term Survival for Patients with Esophageal Cancer after Minimally Invasive Esophagectomy [J]. Frontiers in Surgery 2022;9.\u003c/li\u003e\n\u003cli\u003eOkamura A, Watanabe M, Mine S, Nishida K, Kurogochi T, Imamura Y. Spirometric Lung Age Predicts Postoperative Pneumonia After Esophagectomy [J]. World J Surg 2016;40:2412\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eOgawa F, Satoh Y, Iyoda A, Amano H, Kumagai Y, Majima M. Clinical impact of lung age on postoperative readmission in non-small cell lung cancer [J]. J Surg Res 2015;193:442\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eWang Z, Gao Y, Jian W, Li Y, Wang Z, Lin J, et al. Establishment and application of reference equations for FEF50 and FEF75 in the Chinese population [J]. J Thorac Dis 2024;16:379\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eJian W, Gao Y, Hao C, Wang N, Ai T, Liu C, et al. Reference values for spirometry in Chinese aged 4-80 years [J]. J Thorac Dis 2017;9:4538\u0026ndash;49.\u003c/li\u003e\n\u003cli\u003eGraham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, et al. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement [J]. Am J Respir Crit Care Med 2019;200:e70\u0026ndash;88.\u003c/li\u003e\n\u003cli\u003eStanojevic S, Kaminsky DA, Miller MR, Thompson B, Aliverti A, Barjaktarevic I, et al. ERS/ATS technical standard on interpretive strategies for routine lung function tests [J]. Eur Respir J 2022;60:2101499.\u003c/li\u003e\n\u003cli\u003ePellegrino R, Viegi G, Brusasco V, Crapo RO, Burgos F, Casaburi R, et al. Interpretative strategies for lung function tests [J]. Eur Respir J 2005;26:948\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eQaseem A, Wilt TJ, Weinberger SE, Hanania NA, Criner G, van der Molen T, et al. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society [J]. Ann Intern Med 2011;155:179\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eGlobal Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis management and prevention of chronic obstructive pulmonary disease (2025 report) [EB/OL]. https://goldcopd.org/2025-gold-report/ .Accessed April 7, 2025.\u003c/li\u003e\n\u003cli\u003eLiang X, Xie Y, Gao Y, Zhou Y, Jian W, Jiang M, et al. Estimation of lung age via a spline method and its application in chronic respiratory diseases [J]. NPJ Prim Care Respir Med 2022;32:36.\u003c/li\u003e\n\u003cli\u003eLiang X-L, Gao Y, Guan W-J, Du J, Chen L, Han W, et al. Reference values of respiratory impedance with impulse oscillometry in healthy Chinese adults [J]. J Thorac Dis 2021;13:3680\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eParker DR, Eltinge S, Rafferty C, Eaton CB, Clarke JG, Goldman RE. Primary care providers\u0026rsquo; views on using lung age as an aid to smoking cessation counseling for patients with chronic obstructive pulmonary disease [J]. Lung 2015;193:321\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLi Y, Lin J, Wang Z, Wang Z, Tan L, Liu S, et al. Bronchodilator Responsiveness Defined by the 2005 and 2021 ERS/ATS Criteria in Patients with Asthma as Well as Chronic Obstructive Pulmonary Disease [J]. Int J Chron Obstruct Pulmon Dis 2022;17:2623\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eTilert T, Dillon C, Paulose-Ram R, Hnizdo E, Doney B. Estimating the U.S. prevalence of chronic obstructive pulmonary disease using pre- and post-bronchodilator spirometry: the National Health and Nutrition Examination Survey (NHANES) 2007-2010 [J]. Respir Res 2013;14:103.\u003c/li\u003e\n\u003cli\u003eTashkin DP, Celli B, Decramer M, Liu D, Burkhart D, Cassino C, et al. Bronchodilator responsiveness in patients with COPD [J]. Eur Respir J 2008;31:742\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eAlbert P, Agusti A, Edwards L, Tal-Singer R, Yates J, Bakke P, et al. Bronchodilator responsiveness as a phenotypic characteristic of established chronic obstructive pulmonary disease [J]. Thorax 2012;67:701\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eFortis S, Eberlein M, Georgopoulos D, Comellas AP. Predictive value of prebronchodilator and postbronchodilator spirometry for COPD features and outcomes [J]. BMJ Open Respir Res 2017;4:e000213.\u003c/li\u003e\n\u003cli\u003eStockley JA, Cooper BG, Stockley RA, Sapey E. Small airways disease: time for a revisit? [J]. Int J Chron Obstruct Pulmon Dis 2017;12:2343\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eBao W, Tian X, Hao H, Jin Y, Xie X, Yin D, et al. Is small airway dysfunction an abnormal phenomenon for patients with normal forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced vital capacity? [J]. Ann Allergy Asthma Immunol 2022;128:68-77.e1.\u003c/li\u003e\n\u003cli\u003evan der Wiel E, ten Hacken NHT, Postma DS, van den Berge M. Small-airways dysfunction associates with respiratory symptoms and clinical features of asthma: a systematic review [J]. J Allergy Clin Immunol 2013;131:646\u0026ndash;57.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung age, bronchodilator responsiveness, spirometry","lastPublishedDoi":"10.21203/rs.3.rs-6943087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6943087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study aimed to evaluate the impact of bronchodilator responsiveness (BDR) on lung age (LA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study analyzed patients undergoing spirometry and bronchodilator responsiveness (BDR) tests at the First Affiliated Hospital of Guangzhou Medical University between January 2023 and December 2024. The LA value was calculated using a reference equation derived from a healthy Chinese non-smoking population. Changes in LA and lung function parameters before and after BDR were compared. Linear regression equation was conducted to evaluate the relationship between The difference between LA and age (DeltaLA) and lung function parameters. A random forest model was employed to identify predictors of LA improvement. Receiver operating characteristic (ROC) analysis was performed to evaluate the predictive performance of pre-BDR parameters for post-BDR forced expiratory volume in one second to forced vital capacity (FEV\u003csub\u003e1\u003c/sub\u003e/FVC) ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.7.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 9,316 subjects were included in the study, with 7,452 assigned to the training set and 1,864 to the validation set. Following BDR, significant improvements were observed in LA, FEV\u003csub\u003e1\u003c/sub\u003e, FVC, forced expiratory flow at 50% of FVC (FEF\u003csub\u003e50%\u003c/sub\u003e), and forced expiratory flow at 75% of FVC (FEF\u003csub\u003e75%\u003c/sub\u003e) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the random forest model, the top predictors of LA improvement after BDR were pre-bronchodilator LA (pre-LA), age, and pre-bronchodilator FEF\u003csub\u003e50%\u003c/sub\u003e (pre-FEF\u003csub\u003e50%\u003c/sub\u003e). Internal validation results showed that the prediction model had an accuracy of 92.6%. DeltaLA was moderately to strongly negatively correlated with lung function parameters (r = -0.5 ~ -0.8, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the validation set, receiver operating characteristic (ROC) curve analysis showed that the ability of the difference between the upper limit of normal (ULN) for LA and LA (ULNLA) to predict FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.7 after BDR was comparable to that of FEV\u003csub\u003e1\u003c/sub\u003e (AUC: 0.84 vs. 0.85).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eLA significantly improves after BDR test. Clinicians and technicians should consider both clinical context and resource availability whether to report post-bronchodilator LA value.\u003c/p\u003e","manuscriptTitle":"Impact of bronchodilator responsiveness on lung age: implications for clinical practice from a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 11:23:49","doi":"10.21203/rs.3.rs-6943087/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"701f9f35-654c-41b6-aa79-11703711dc5c","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T10:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 11:23:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6943087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6943087","identity":"rs-6943087","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}