Abstract
Background
Lung function trajectories in chronic obstructive pulmonary disease (COPD) vary among individuals. However, evidence on the clinical characteristics and prognosis of mild-to-moderate COPD developing from different lung function trajectories is limited. We aim to explore whether differences exist in the clinical features and prognosis of mild-to-moderate COPD developed through different lung function trajectories.
Methods
Individuals who participated in the Early COPD study (2019–2020) and who had available spirometry data from the China Epidemiological Survey of COPD (2012–2013) were included and categorized into three groups: (I) previously normal forced expiratory volume in one second (FEV1) trajectory group: participants had developed mild-to-moderate COPD in 2019–2020, with normal lung function in 2012–2013; (II) previously low FEV1 trajectory group: participants had developed mild-to-moderate COPD in 2019–2020, with subnormal lung function in 2012–2013; (III) non-COPD group: participants had not developed COPD in 2019–2020, with normal lung function in 2012–2013. Baseline assessments (2019–2020) included spirometry, impulse oscillometry (IOS), and chest computed tomography (CT). Lung function decline and exacerbation risk were analyzed over a 2-year follow-up (2021–2022).
Results
Overall, 96 participants were included in the previously normal FEV1 trajectory group, 32 in the previously low FEV1 trajectory group, and 118 in the non-COPD group. Compared with the previously normal FEV1 trajectory group, the previously low FEV1 trajectory group had poorer lung function, more severe dyspnea, and greater small-airway dysfunction and emphysema. During the 2-year follow-up, the previously normal FEV1 trajectory group had a faster annual rate of forced vital capacity (FVC) decline, and the previously low FEV1 trajectory group had a higher moderate-to-severe exacerbation risk than the non-COPD group.
Conclusions
There were significant differences in the clinical characteristics and longitudinal prognosis of mild-to-moderate COPD progressing from different pulmonary function trajectories, reflecting the need for individualized management.
Keywords: Lung function decline, mild-to-moderate, chronic obstructive pulmonary disease (COPD), spirometry, acute exacerbation
Highlight box.
Key findings
• Among participants with mild-to-moderate chronic obstructive pulmonary disease (COPD), those who start from a low lung function had more severe dyspnea, lower lung function, more severe small-airway dysfunction (SAD) and emphysema, and a significantly increased risk of moderate-to-severe exacerbations during the 2-year follow-up than those who start from a normal lung function.
What is known and what is new?
• COPD is a heterogeneous disease, and not all cases start their journey from normal lung function. Previous studies have demonstrated that distinct lung function trajectories leading to COPD are associated with different clinical features and can predict prognosis. However, evidence regarding the applicability of this trajectory-based heterogeneity to mild-to-moderate COPD remains limited.
• This study clarifies that participants with mild-to-moderate COPD exhibited distinct clinical, physiological, and prognostic features across different lung function trajectories.
What is the implication, and what should change now?
• Lung function trajectories should serve as a post-diagnostic stratification tool for early risk stratification and personalized management, thereby targeting the delay of disease progression and leading to a higher quality of life for patients with mild-to-moderate COPD.
Introduction
Chronic obstructive pulmonary disease (COPD) imposes a high disease burden globally (1). In China, the overall prevalence of COPD among adults aged >40 years rose from 8.2% in 2007 to 13.6% in 2018 (2,3). Additionally, based on 2017 data, the disease ranked fourth in years of life lost and third in disability-adjusted life years nationwide (4). It is noteworthy that 92.7% of patients with COPD have Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 1 (mild) or 2 (moderate) disease (2,3). Most of these patients receive limited treatment and inadequate clinical attention owing to their mild symptoms (5,6). However, evidence suggests that these patients can develop small-airway dysfunction (SAD), lung hyperinflation, air trapping, and extrapulmonary changes, and may have a higher mortality rate than the healthy population (7-10). Furthermore, they exhibit a faster decline in forced expiratory volume in one second (FEV1), and exacerbations have a greater impact on disease worsening compared with other GOLD grades (11-13). A more detailed study of this subgroup could provide a scientific basis for optimizing treatment timing, modifying the disease course, and improving prognosis.
Previous studies have demonstrated substantial heterogeneity among patients with mild-to-moderate COPD, particularly in terms of symptom severity, lung function decline, and acute exacerbation (AE) frequency (14-19). This heterogeneity may stem from distinct lung function progression trajectories (20-24). A recent study using data from three cohorts suggested that approximately half of patients with COPD exhibited a rapid decline in FEV1 from a normal level in early adulthood, while the other half started from a low initial FEV1 value and declined at a normal rate (23). These distinct trajectories were associated with different clinical characteristics and long-term prognoses, as shown in analyses of the Copenhagen City Heart Study (24). This evidence suggests that trajectory-based stratification may provide a robust foundation for formulating targeted monitoring and intervention strategies across different trajectory populations. It may assist clinicians in rapidly identifying high-risk patients to initiate timely intervention and monitoring, which could potentially slow disease progression (6). Meanwhile, it may help minimize unnecessary medications and diagnostic tests in low-risk subgroups, thereby contributing to alleviating the healthcare burden.
Nevertheless, few studies have specifically evaluated lung function trajectories in mild-to-moderate COPD, and findings have not been validated in Chinese community-based populations. Furthermore, the clinical characterization of mild-to-moderate COPD individuals progressing from different lung function trajectories remains incomplete, especially in terms of the structural lung changes and physiological impairments.
In the present study, we identified participants with mild-to-moderate COPD from the 2019–2020 Early COPD (ECOPD) study and determined their lung function trajectories based on the spirometry data recorded in the China Epidemiological Survey of COPD (2012–2013). We also investigated the clinical features, spirometry results, imaging changes, and longitudinal respiratory health prognoses to fill the current knowledge gaps. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2744/rc).
Methods
Study design and population
This study used data from the ECOPD cohort (ChiCTR1900024643), an ongoing prospective, observational study conducted across three community screening centers in Guangdong Province, China (25): (I) community screening site of The First Affiliated Hospital of Guangzhou Medical University; (II) community screening site of Lianping County People’s Hospital, Guangdong Province; (III) community screening site of Wengyuan County People’s Hospital, Guangdong Province. Community participants were screened using a COPD screening programmed and underwent a series of tests and investigations, including COPD epidemiological questionnaire interviews, prebronchodilator and postbronchodilator spirometry, impulse oscillometry (IOS), chest computed tomography (CT), and other relevant assessments. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The screening process, lung function testing, questionnaire collection, and imaging examinations at all centers were strictly performed in accordance with the unified design scheme of the ECOPD study. All testers received standardized training to ensure the homogeneity and comparability of data across different centers.
The study was approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (No. 2018-53) and written informed consent was obtained from all participants. The present study is based on the data of participants enrolled in the ECOPD cohort from July 2019 to August 2020. Those with reliable postbronchodilator spirometry data from the China Epidemiological Survey of COPD in 2012–2013 were included. The 2019–2020 data were used for the baseline analysis, and 2-year prospective follow-up data up to August 2022 were analyzed for the prognostic analysis.
The main inclusion criteria were as follows: (I) age 40–80 years; (II) written informed consent; (III) completion of the standard respiratory epidemiological questionnaire; and (IV) completion of postbronchodilator spirometry. The main exclusion criteria were as follows: (I) presence of respiratory infection or exacerbation within 4 weeks; (II) myocardial infarction or malignant arrhythmia within 3 months or heart disease hospitalization within 1 month; (III) chest, abdominal, or eye surgery within 3 months; (IV) receiving treatment for malignant tumors; (V) receiving anti-tuberculosis treatment or with active tuberculosis; (VI) a history of mental disorders, hallucinations, or use of antipsychotics; (VII) a history of cognitive disorders or paraplegia; and (VIII) pregnancy or lactation.
Questionnaires
The COPD epidemiological questionnaire was modified based on a COPD epidemiological survey previously conducted in China (2). The questionnaire evaluated smoking status, smoking index, passive smoking, biomass exposure, history of occupational exposure to dust/gases/fumes, family history of respiratory diseases, some comorbidities and chronic respiratory symptoms associated with COPD, and history of respiratory medications and COPD diagnosis (25,26). The COPD epidemiological questionnaire was administered by well-trained and qualified personnel. The degree of chronic respiratory symptoms was assessed at all visits using the modified Medical Research Council (mMRC) dyspnea scale and COPD assessment test (CAT) (27).
Lung function
The operational approaches and quality control criteria for lung function testing, which were in accordance with the recommendations of the American Thoracic Society and the European Respiratory Society published in 2005, were implemented by specifically trained personnel (28,29). At least three acceptable measurements and two reproducible tests [i.e., the maximum and sub-maximum values of forced vital capacity (FVC) and FEV1 within 150 mL or 5%] were obtained for each participant. The reference values for FEV1 from the European Coal and Steel Community in 1993 were adjusted according to the conversion factors for the Chinese population (0.95 for men and 0.93 for women) to maintain consistency with previous studies in the Chinese population (30,31). Airflow reversibility was defined as an increase in FEV1 of ≥12% and an absolute increase of ≥200 mL following the administration of a bronchodilator inhaler (32). A reduction in any two of maximal mid-expiratory flow (MMEF), forced expiratory flow at 50% (FEF50), and forced expiratory flow at 75% (FEF75) to a level <65% of the predicted value was considered indicative of spirometry-defined SAD (33,34).
The operational approaches and quality control criteria of the IOS adhered to the European Respiratory Society guidelines published in 2003 (30). The measurements were performed using a pulse oscillation measurement system (Masterscreen IOS, Hochberg, Germany). Respiratory resistance at 5 Hz (R5), 20 Hz (R20), and the difference obtained by subtracting R20 from R5 (R5−R20) were recorded to evaluate the degree of airway resistance (35). Additionally, other indicators, such as reactance at 5 Hz (X5), resonant frequency (Fres), and reactance area (Ax), were considered for a more comprehensive evaluation (36). R5−R20 of >0.07 kPa/L/s was defined as the presence of IOS-defined SAD (37,38).
Definition of different lung function progression trajectories
According to the 2025 GOLD Report, mild-to-moderate COPD is defined as post-bronchodilator FEV1 ≥50% predicted and post-bronchodilator FEV1/FVC <0.70 (39). We stratified participants into three mutually exclusive clinical trajectories based on two objective criteria: (I) postbronchodilator FEV1% predicted levels [≥80% (normal FEV1) or <80% (low FEV1)] from the 2012–2013 China National COPD Epidemiological Survey; and (II) the presence or absence of COPD at the baseline of the 2019–2020 ECOPD study (Figure 1). This stratification strategy has been widely validated in multiple classic COPD lung function trajectory studies (23):
Figure 1.
Flow diagram of study population. COPD, chronic obstructive pulmonary disease; ECOPD, early COPD; FEV1, forced expiratory volume in one second; FVC, forced vital capacity.
Previously normal FEV1 trajectory: FEV1 ≥80% predicted in 2012–2013 and mild-to-moderate COPD in 2019–2020;
Previously low FEV1 trajectory: FEV1 <80% predicted in 2012–2013 and mild-to-moderate COPD in 2019–2020;
Non-COPD: FEV1 ≥80% predicted in 2012–2013 and no COPD in 2019–2020.
Imaging
High-resolution inspiratory and expiratory CT scans were performed by radiologists using a multidetector-row CT scanner [128-slice Siemens Definition AS Plus and 128-slice United-imaging uCT 760] (25). Emphysema was quantified by measuring the emphysema index of each patient. Low-attenuation areas lower than −950 Hounsfield units (HU) on inspiratory CT were considered indicative of emphysema (LAA−950), and low-attenuation areas lower than −856 HU on expiratory CT were considered indicative of air trapping (LAA−856). The severity of airway remodeling was assessed using the square root of the wall area within a 10-mm lumen perimeter on inspiratory CT (inspiratory Pi10) (40). CT images were quantified using Chest Imaging Platform (www.chestimagingplatform.org) on the semi-automated 3D Slicer 4.11 AVIEW software (www.slicer.org) (41). Parametric response mapping (PRM) of the CT images was performed using medical artificial intelligence software (Coreline Soft, www.corelinesoft.com) (42). PRM was classified as PRMfSAD if the value was ≥−950 HU at inspiration and <−856 HU at expiration, or as PRMEmph if the value was <−950 HU at inspiration and <−856 HU at expiration (43).
Respiratory health outcomes
We analyzed the annual decline in lung function and the AE risk at the 2-year follow-up in 2021–2022. The annual decline in FEV1 and FVC was calculated as the slope between the baseline examination in 2019–2020 and the 2-year follow-up examination in 2021–2022.
Acute COPD exacerbation was defined as episodes of at least two new or worsening respiratory symptoms (e.g., cough, sputum production, purulent sputum, wheezing, or dyspnea) lasting for ≥48 hours, after excluding other causes, such as cardiac dysfunction, pulmonary embolism, pneumothorax, pleural effusion, or arrhythmia, and the severity of exacerbations was categorized as mild, moderate, or severe, consistent with the GOLD 2025 Report and previous authoritative studies (5,39,44).
Statistical analysis
Continuous variables are described as the mean ± standard deviation and categorical variables as n (%). For baseline demographic and clinical characteristics, the between-group difference was identified by one-way analysis of variance and the χ2 test or Fisher’s exact test, depending on the expected cell frequencies. Median imputation with adjacent points was performed for missing continuous variables. Categorical variables with missing values were retained as a separate “missing” category to avoid bias from imputation.
To improve intergroup comparability, all core lung function indicators (FEV1, FVC, FEV1/FVC, MMEF, FEF50, FEF75) were standardized using z-scores. Z-scores were calculated as (measured value − predicted value)/standard deviation of the predicted value, following the Global Lung Function Initiative (GLI) 2012 equations (45). Predicted values were derived from the GLI 2012 South East Asian reference standards to align with the ethnic characteristics of our study population (46).
To reduce the influence of confounding factors and reach more accurate estimates, multivariable linear regression analysis with adjustment for confounding factors was used to compare the chronic respiratory symptom scores, z-score-standardized lung function parameters, IOS indices, and CT continuous variables between the groups. Categorical variables were also compared between the groups using multivariable logistic regression analysis, also with adjustment for confounding factors.
The confounding factors in this study were selected based on known variables strongly associated with COPD prognosis in previous research, combined with differences in demographic and clinical features between groups at baseline. The final adjusted variables included age, sex, body mass index (BMI), smoking status, and smoking index (47,48). For longitudinal prognostic analysis of respiratory health outcomes, multivariable linear regression analysis was used to assess the annual rate of lung function decline during the 2-year follow-up period. The risk of AEs was analyzed using negative binomial regression models. As this study was an exploratory analysis, no adjustment for multiple comparisons was performed (49). All analyses were performed using IBM SPSS 27.0 software, and a two-sided P value of <0.05 was considered statistically significant.
Results
Participant screening process
Figure 1 presents the participant selection process for this study. Overall, 1,439 participants were included in the ECOPD study [2019–2020]. After excluding those without postbronchodilator spirometry data from the China Epidemiological Survey of COPD [2012–2013] (n=1,151) and 42 participants who either developed severe COPD (n=17) or who did not have COPD in 2019–2020 but who had a predicted FEV1 of <80% in 2012–2013 (n=25), a total of 246 participants were included in the present analysis and were classified into three clinical groups: previously normal FEV1 trajectory group (n=96), previously low FEV1 trajectory group (n=32), and non-COPD group (n=118).
Demographic and clinical features at baseline
Table 1 displays the demographic and clinical characteristics of the participants at baseline. Compared with the other two groups, the non-COPD group was younger, had a lower BMI, had a higher proportion of never smokers, and had lower proportions of participants with an occupational history, previous medication for respiratory diseases, or AEs during the preceding year.
Table 1. Demographics and clinical characteristics of participants in 2019–2020.
| Characteristics | Non-COPD group (n=118) | Mild-to-moderate COPD at baseline examination | |
|---|---|---|---|
| Previously normal FEV1 trajectory group (n=96) | Previously low FEV1 trajectory group (n=32) | ||
| Age (years) | 56.7±8.5 [31–75] | 65.0±6.6 [47–83]‡ | 65.8±8.3 [50–82]‡ |
| Male sex | 64 (54.2) | 85 (88.5)‡ | 28 (87.5)‡ |
| Body mass index (kg/m2) | 23.5±2.9 | 22.9±3.5 | 22.8±3.4 |
| Smoking status | |||
| Never smoked | 68 (57.6) | 17 (17.7)‡ | 8 (25.0)‡ |
| Former smoked | 22 (18.6) | 20 (20.8) | 13 (40.6)‡ |
| Current smoked | 28 (23.7) | 59 (61.5)‡ | 11 (34.4)† |
| Smoking index (pack-years) | 17.1±30.1 | 30.7±29.3‡ | 34.1±36.1‡ |
| Biomass exposure | 50 (42.4) | 45 (46.9) | 15 (46.9) |
| Occupational history of dusts/gases/fumes | 18 (15.3) | 29 (30.2)‡ | 13 (40.6)‡ |
| Family history of respiratory diseases | 10 (8.6) | 12 (12.5) | 13 (40.6)‡† |
| Previous medication for respiratory diseases | 9 (7.6) | 50 (52.1)‡ | 19 (59.4)‡ |
| Previous diagnosis of asthma | 0 | 0 | 1 (3.1) |
| Acute exacerbations during the preceding year | 7 (5.9) | 10 (10.4) | 7 (21.9)‡ |
| Comorbidity | |||
| Hypertension | 19 (16.1) | 17 (17.7) | 5 (15.6) |
| Diabetes | 2 (1.7) | 4 (4.2) | 0 |
| Coronary heart disease | 3 (2.5) | 4 (4.2) | 3 (9.4) |
| Stroke | 3 (2.5) | 5 (5.2) | 1 (3.1) |
| Chronic respiratory symptoms | |||
| Chronic cough | 26 (22.0) | 36 (37.5)‡ | 10 (31.3) |
| Chronic phlegm | 28 (23.7) | 42 (43.8)‡ | 13 (40.6) |
| Dyspnea | 22 (18.6) | 26 (27.1) | 16 (50.0)‡ |
| Wheeze | 0 | 0 | 1 (3.1) |
Data are presented as mean ± standard deviation [range] or n (%). ‡, significantly different from non-COPD group (P<0.05); †, significantly different from previously normal FEV1 trajectory group (P<0.05). COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in one second.
Between the two groups of participants with mild-to-moderate COPD, the previously normal FEV1 trajectory group had a higher proportion of current smokers (61.5% vs. 34.4%) and a lower proportion of family history of respiratory diseases (12.5% vs. 40.6%) than the previously low FEV1 trajectory group. There were no significant differences in age, sex, BMI, smoking index, biomass exposure, occupational history, previous medication for respiratory diseases, previous diagnosis of asthma, AEs during the preceding year and comorbidities (hypertension, diabetes mellitus, and coronary heart disease).
We analyzed the continuous and categorical variables related to the mMRC dyspnea scale and CAT score (Tables 2,3). Among the non-COPD group, the previously normal FEV1 trajectory group, and the previously low FEV1 trajectory group, the mMRC dyspnea score (0.23±0.53, 0.33±0.61, and 0.75±0.84, respectively) and the proportion of mMRC score ≥2 (3.4%, 5.2%, and 25.0%, respectively) were significantly higher in the previously low FEV1 trajectory group than in the other two groups. The CAT score (4.7±4.9, 5.2±4.9, and 5.4±4.6, respectively) and the proportion of CAT score of ≥10 (16.1%, 17.7%, and 21.9%, respectively) exhibited a similar trend, but there were no statistically significant differences in the pairwise comparisons between the groups.
Table 2. Between-group comparison outcome for baseline chronic respiratory symptoms, spirometry, impulse oscillometry, and computed tomography continuous variable in participants in 2019–2020.
| Variable | Group 1: non-COPD group | Mild-to-moderate COPD at baseline examination | Group 2 vs. Group 1 | Group 3 vs. Group 1 | Group 2 vs. Group 3 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group 2: previously normal FEV1 trajectory group | Group 3: previously low FEV1 trajectory group | Adjusted mean difference (95% CI)† | Adjusted P value† | Adjusted mean difference (95% CI)† | Adjusted P value† | Adjusted mean difference (95% CI) † | Adjusted P value† |
|||||
| Chronic respiratory symptoms | n=118 | n=96 | n=32 | |||||||||
| mMRC dyspnea score | 0.23±0.53 | 0.33±0.61 | 0.75±0.84 | −0.02 (−0.21 to 0.17) | 0.83 | 0.38 (0.12 to 0.64) | 0.004* | −0.40 (−0.65 to −0.16) | 0.001* | |||
| CAT score | 4.7±4.9 | 5.2±4.9 | 5.4±4.6 | −0.6 (−2.2 to 0.9) | 0.43 | −0.4 (−2.4 to 1.7) | 0.71 | −0.2 (−2.2 to 1.7) | 0.81 | |||
| Postbronchodilator spirometry | n=118 | n=96 | n=32 | |||||||||
| FEV1 (L) | 2.52±0.55 | 2.22±0.46 | 1.67±0.46 | −0.29 (−0.40 to −0.18) | <0.001* | −0.81 (−0.96 to −0.66) | <0.001* | 0.52 (0.38 to 0.67) | <0.001* | |||
| FEV1 (% of predicted value) | 101.8±12.8 | 86.9±13.1 | 65.2±9.6 | −13.0 (−16.9 to −9.1) | <0.001* | −34.9 (−40.2 to −29.7) | <0.001* | 21.9 (16.9 to 26.9) | <0.001* | |||
| FVC (L) | 3.19±0.72 | 3.60±0.69 | 2.97±0.68 | 0.27 (0.11 to 0.43) | 0.001* | −0.31 (−0.53 to −0.10) | 0.004* | 0.58 (0.38 to 0.79) | <0.001* | |||
| FVC (% of predicted value) | 105.1±14.0 | 111.3±15.1 | 92.0±12.3 | 6.7 (2.3 to 11.2) | 0.003* | −12.3 (−18.2 to −6.3) | <0.001* | 19.0 (13.3 to 24.7) | <0.001* | |||
| FEV1/FVC (%) | 79.5±5.9 | 61.9±6.4 | 56.5±8.1 | −14.1 (−16.0 to −12.2) | <0.001* | −19.8 (−22.3 to −17.2) | <0.001* | 5.6 (3.2 to 8.0) | <0.001* | |||
| MMEF (L/s) | 2.31±0.89 | 0.98±0.38 | 0.66±0.30 | −1.04 (−1.22 to −0.85) | <0.001* | −1.37 (−1.62 to −1.12) | <0.001* | 0.34 (0.10 to 0.58) | 0.006* | |||
| MMEF (% of predicted value) | 71.5±23.8 | 31.7±11.1 | 21.2±7.4 | −33.5 (−39.2 to −27.8) | <0.001* | −45.1 (−52.7 to −37.6) | <0.001* | 11.6 (4.4 to 18.8) | 0.002* | |||
| FEF50 (L/s) | 3.08±1.11 | 1.38±0.53 | 0.90±0.40 | −1.44 (−1.68 to −1.20) | <0.001* | −1.94 (−2.26 to −1.61) | <0.001* | 0.50 (0.19 to 0.80) | 0.002* | |||
| FEF50 (% of predicted value) | 79.4±25.0 | 36.4±12.9 | 23.5±8.7 | −37.6 (−43.6 to −37.6) | <0.001* | −51.3 (−59.2 to −43.3) | <0.001* | 13.7 (6.1 to 21.2) | <0.001* | |||
| FEF75 (L/s) | 0.81±0.42 | 0.31±0.14 | 0.21±0.11 | −0.34 (−0.43 to −0.25) | <0.001* | −0.46 (−0.58 to −0.34) | <0.001* | 0.12 (0.01 to 0.23) | 0.03* | |||
| FEF75 (% of predicted value) | 57.1±225.5 | 25.2±11.3 | 17.2±6.5 | −27.1 (−33.2 to −21.1) | <0.001* | −37.2 (−45.2 to −29.2) | <0.001* | 10.1 (2.4 to 17.7) | 0.01* | |||
| Decline of FEV1 (mL/year) | 23.7±31.5 | 55.9±36.8 | 30.8±29.5 | 27.0 (16.3 to 37.7) | <0.001* | 5.1 (−9.1 to 19.3) | 0.48 | 21.9 (8.4 to 35.5) | 0.002* | |||
| Decline of FVC (mL/year) | 22.3±47.2 | 24.9±57.3 | 24.2±52.9 | −8.7 (−25.9 to 8.4) | 0.31 | −7.0 (−29.4 to 15.4) | 0.53 | −1.7 (−23.2 to 19.7) | 0.87 | |||
| z-FEV1 | 0.23±0.81 | −0.66±0.82 | −1.92±0.52 | −0.92 (−1.15 to −0.70) | <0.001* | −2.21 (−2.54 to −1.88) | <0.001* | 1.29 (0.95 to 1.62) | <0.001* | |||
| z-FVC | 0.45±0.86 | 1.01±0.96 | −0.22±0.81 | 0.33 (0.06 to 0.59) | 0.01* | −1.05 (−1.43 to −0.67) | <0.001* | 1.38 (0.99 to 1.77) | <0.001* | |||
| z-FEV1/FVC | −0.37±0.92 | −2.53±0.74 | −3.11±0.79 | −1.82 (−2.07 to −1.56) | <0.001* | −2.39 (−2.76 to −2.02) | <0.001* | 0.57 (0.19 to 0.95) | 0.003* | |||
| z-MMEF | −0.30±1.04 | −2.04±0.67 | −2.65±0.42 | −1.35 (−1.57 to −1.12) | <0.001* | −2.00 (−2.32 to −1.67) | <0.001* | 0.64 (0.31 to 0.98) | <0.001* | |||
| z-FEF75 | −0.34±1.02 | −1.91±0.94 | −2.54±0.81 | −1.24 (−1.50 to −0.98) | <0.001* | −1.85 (−2.23 to −1.47) | <0.001* | 0.61 (0.22 to 1.00) | 0.002* | |||
| Impulse oscillometry | n=110 | n=96 | n=32 | |||||||||
| R5 (kPa/L/s) | 0.32±0.09 | 0.33±0.10 | 0.43±0.15 | 0.04 (0.01 to 0.07) | 0.008* | 0.14 (0.10 to 0.18) | <0.001* | −0.10 (−0.13 to −0.06) | <0.001* | |||
| R20 (kPa/L/s) | 0.28±0.007 | 0.27±0.07 | 0.29±0.07 | 0.02 (0.00 to 0.04) | 0.042* | 0.05 (0.02 to 0.07) | 0.001* | −0.02 (−0.05 to 0.00) | 0.06 | |||
| R5-R20 (kPa/L/s) | 0.04±0.04 | 0.06±0.06 | 0.13±0.10 | 0.02 (0.00 to 0.04) | 0.02* | 0.09 (0.07 to 0.12) | <0.001* | −0.07 (−0.10 to −0.05) | <0.001* | |||
| X5 (kPa/L/s) | −0.10±0.03 | −0.11±0.05 | −0.16±0.08 | −0.02 (−0.03 to −0.00) | 0.03* | −0.07 (−0.09 to −0.05) | <0.001* | 0.05 (0.03 to 0.07) | <0.001* | |||
| Ax (kPa/L) | 0.35±0.30 | 0.63±0.74 | 1.39±1.24 | 0.29 (0.07 to 0.51) | 0.01* | 1.02 (0.73 to 1.31) | <0.001* | −0.73 (−1.01 to −0.45) | <0.001* | |||
| Fres (Hz) | 12.6±3.7 | 15.4±5.4 | 19.7±6.8 | 2.3 (0.8 to 3.9) | 0.004* | 6.4 (4.3 to 8.5) | <0.001* | −4.1 (−6.1 o −2.1) | <0.001* | |||
| Inspiratory Pi 10 (mm) | 3.6±0.7 | 3.9±0.8 | 4.8±2.0 | 0.4 (0.1 to 0.7) | 0.01* | 1.2 (0.8 to 1.6) | <0.001* | −0.8 (−1.2 to −0.4) | <0.001* | |||
| Computed tomography | n=110 | n=96 | n=32 | |||||||||
| Inspiratory LAA−950 | 0.60±0.79 | 2.77±4.26 | 3.75±4.96 | 1.12 (0.12 to 2.11) | 0.02* | 2.05 (0.72 to 3.37) | 0.003* | −0.93 (−2.20 to 0.33) | 0.14 | |||
| Expiratory LAA−856 | 7.6±10.9 | 23.6±17.9 | 32.2±20.4 | 9.9 (5.5 to 14.3) | <0.001* | 16.8 (11.0 to 22.7) | <0.001* | −6.9 (−12.5 to −1.3) | 0.01* | |||
| PRMemph | 0.21±0.49 | 2.32±4.13 | 3.48±5.05 | 1.13 (0.15 to 2.10) | 0.02* | 2.23 (0.93 to 3.53) | <0.001* | −1.10 (−2.34 to 0.14) | 0.08 | |||
| PRMfSAD | 5.4±8.9 | 17.7±15.3 | 24.3±16.9 | 7.5 (3.8 to 11.2) | <0.001* | 12.6 (7.7 to 17.5) | <0.001* | −5.1 (−9.8 to −0.4) | 0.03* | |||
Data are presented as mean ± standard deviation; †, all were adjusted for age, sex, body mass index, smoking status, and smoking index; *, P<0.05. Ax, reactance area; CAT, chronic obstructive pulmonary disease assessment test; CI, confidence interval; COPD, chronic obstructive pulmonary disease; emph, emphysema; FEF50, forced expiratory flow 50%; FEF75, forced expiratory flow 75%; FEV1, forced expiratory volume in one second; Fres, resonant frequency in Hz; fSAD, functional small airways disease; FVC, forced vital capacity; LAA−856, the percentage of the low-attenuation area below −856 Hounsfield units on full-expiration computed tomography; LAA−950, the percentage of the low-attenuation area below −950 Hounsfield units on full-inspiration computed tomography; MMEF, maximal mid-expiratory flow; mMRC, modified Medical Research Council; Pi 10, square root of wall area of a 10-mm lumen perimeter; PRM, parametric response mapping; R20, resistances at 20 Hz; R5, resistances at 5 Hz; R5-R20, resistances at 5 and 20 Hz; X5, reactance at 5 Hz; z-FEV1, FEV1 z-score; z-FEV1/FVC, FEV1/FVC z-score; z-FVC, FVC z-score.
Table 3. Between-group comparison outcome for baseline chronic respiratory symptoms, spirometry, impulse oscillometry, and computed tomography categorical variable in participants in 2019–2020.
| Variable | Group 1: non-COPD group | Mild-to-moderate COPD at baseline examination | Group 2 vs. Group 1 | Group 3 vs. Group 1 | Group 2 vs. Group 3 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group 2: previously normal FEV1 trajectory group | Group 3: previously low FEV1 trajectory group | Adjusted OR (95% CI)† | Adjusted P value† |
Adjusted OR (95% CI)† | Adjusted P value† |
Adjusted OR (95% CI)† | Adjusted P value† | |||||
| Chronic respiratory symptoms | n=118 | n=96 | n=32 | |||||||||
| mMRC dyspnea score ≥2 | 4 (3.4) | 5 (5.2) | 8 (25.0) | 1.31 (0.28–6.09) | 0.72 | 8.74 (1.96–38.96) | 0.004* | 0.15 (0.04–0.54) | 0.003* | |||
| CAT score ≥10 | 19 (16.1) | 17 (17.7) | 7 (21.9) | 0.69 (0.29–1.65) | 0.40 | 0.93 (0.31–2.82) | 0.89 | 0.74 (0.26–2.10) | 0.57 | |||
| Airflow reversibility | 3 (2.5) | 20 (20.8) | 12 (37.5) | 9.16 (2.31–36.27) | 0.002* | 21.92 (4.98–96.43) | <0.001* | 0.42 (0.17–1.05) | 0.06 | |||
| Impulse oscillometry | n=104 | n=91 | n=30 | |||||||||
| R5-R20 >0.07 kPa/L/s | 14 (13.5) | 26 (28.6) | 18 (60.0) | 3.74 (1.43–9.77) | 0.007* | 16.35 (5.12–52.23) | <0.001* | 0.23 (0.09–0.58) | 0.002* | |||
| Computed tomography | n=118 | n=96 | n=32 | |||||||||
| Inspiratory LAA−950 >5% | 1 (0.8) | 13 (13.5) | 8 (25.0) | 6.87 (0.79–59.69) | 0.08 | 14.55 (1.54–137.48) | 0.01* | 0.47 (0.15–1.52) | 0.20 | |||
| Expiratory LAA−856 >20% | 10 (8.5) | 45 (46.9) | 21 (65.6) | 5.63 (2.28–13.87) | <0.001* | 12.01 (3.94–36.61) | <0.001* | 0.47 (0.18–1.23) | 0.12 | |||
Data are presented as n (%). †, all were adjusted for age, sex, body mass index, smoking status, and smoking index; *, P<0.05. CAT, chronic obstructive pulmonary disease assessment test; CI, confidence interval; COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in one second; LAA−950, the percentage of the low-attenuation area below −950 Hounsfield units on full-inspiration computed tomography; LAA−856, the percentage of the low-attenuation area below −856 Hounsfield units on full-expiration computed tomography; mMRC, modified Medical Research Council; OR, odds ratio; R5-R20, resistances at 5 and 20 Hz.
Tables 2,3 present the analysis results of the continuous and categorical variables related to the spirometry, IOS, and CT results of the participants at baseline. The previously low FEV1 trajectory group demonstrated lower postbronchodilator FEV1, FEV1% predicted, FVC, FVC% predicted, FEV1/FVC, and SAD spirometry parameters (MMEF, FEF50, and FEF75) than both the non-COPD group and the previously normal FEV1 trajectory group (adjusted P<0.05 for all comparisons). The z-scores of FEV1, FVC, MMEF, FEF50, and FEF75 in the previously low lung function trajectory group were significantly lower than those in the previously normal lung function trajectory group and non-COPD group (adjusted P<0.05 for all comparisons), which further verifies the robustness of the core findings. Multivariable logistic regression revealed that the proportion of patients with positive airflow reversibility was higher in the previously low FEV1 trajectory group than in the other two groups; however, a significant difference was only identified when compared with the non-COPD group.
SAD-related IOS parameters (R5−R20, AX, |X5|, Fres, and inspiratory Pi 10) were significantly higher in the previously low FEV1 trajectory group than in the previously normal FEV1 trajectory group (adjusted P<0.001 for all comparisons). Additionally, the proportion of patients with R5−R20 of >0.07 kPa/L/s was higher in the previously low FEV1 trajectory group than in the previously normal FEV1 trajectory group [28.6% vs. 60.0%, adjusted odds ratio 0.23, 95% confidence interval (CI): 0.09–0.58; P=0.002]. In terms of the CT parameters of air trapping, the previously low FEV1 trajectory group had significantly more expiratory LAA−856 and PRMfSAD. Inspiratory LAA−950 and PRMemph also tended to be more frequent in the previously low FEV1 trajectory group, though the difference was not statistically significant. Similarly, the proportion of patients in whom expiratory LAA−950 were >5% and inspiratory LAA−856 were >20% was higher in the previously low FEV1 trajectory group, but the differences did not reach statistical significance.
Longitudinal respiratory health prognosis
Table 4 shows the annual decline in postbronchodilator lung function during the 2-year follow-up period. Compared with the non-COPD group, both the previously normal FEV1 trajectory group and the previously low FEV1 trajectory group had a faster annual decline in FEV1 and FVC, but a significant difference was only observed when comparing the annual decline in FVC between the previously normal FEV1 trajectory group and the non-COPD group (adjusted mean difference 90.3 mL/year, 95% CI: 32.3–136.3; P=0.002). Concurrently, the previously normal FEV1 trajectory group had a faster annual rate of decline in FVC and a slower annual rate of decline in FEV1 than the previously low FEV1 trajectory group. However, there was no significant difference between the two groups.
Table 4. Annual decline in lung function of participants from 2019–2020 to 2021–2022.
| Variable | Non-COPD group (n=101) | Mild-to-moderate COPD at baseline examination | ||||||
|---|---|---|---|---|---|---|---|---|
| Previously normal FEV1 trajectory group (n=89) | Previously low FEV1 trajectory group (n=28) | |||||||
| Value | P value | Value | P value | Value | P value | |||
| Decline of postbronchodilator FEV1 (mL/year) | 19.1±9.1 | – | 24.6±12.7 | – | 48.9±15.5 | – | ||
| Mean difference (95% CI) | Ref. | – | 5.5 (−23.9 to 34.9) | 0.71 | 29.8 (−15.4 to 74.9) | 0.19 | ||
| −29.8 (−74.9 to 15.4) | 0.19 | −24.3 (−70.4 to 21.8) | 0.30 | Ref. | – | |||
| Adjusted mean difference† (95% CI) | Ref. | – | 6.9 (−29.0 to 42.7) | 0.70 | 34.5 (−14.1 to 83.1) | 0.16 | ||
| −34.5 (−83.1 to 14.1) | 0.16 | −27.6 (−75.2 to 19.9) | 0.25 | Ref. | – | |||
| Decline of postbronchodilator FVC (mL/year) | −6.5±15.6 | – | 77.9±22.6 | – | 56.1±32.0 | – | ||
| Mean difference (95% CI) | Ref. | – | 84.3 (32.3 to 136.3) | 0.002* | 62.6 (−17.4 to 142.5) | 0.12 | ||
| −62.6 (−142.5 to 17.4) | 0.12 | 21.8 (−59.9 to 103.4) | 0.60 | Ref. | – | |||
| Adjusted mean difference† (95% CI) | Ref. | – | 90.3 (27.3 to 153.3) | 0.005* | 70.4 (−15.0 to 155.8) | 0.10 | ||
| −70.4 (−155.8 to 15.0) | 0.10 | 19.9 (−63.5 to 103.3) | 0.63 | Ref. | – | |||
Data are presented as mean ± standard error unless otherwise specified. †, all were adjusted for age, sex, body mass index, smoking status, and smoking index; *, P<0.05. CI, confidence interval; COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in one second; FVC, forced vital capacity.
Figure 2 shows the rates of AEs over the 2-year follow-up. Around 98% of the participants (240/246) enrolled at baseline completed the 2-year follow-up for AEs events. Compared with the non-COPD group, both the previously normal FEV1 trajectory group and the previously low FEV1 trajectory group exhibited a numerically higher risk of total AEs and moderate-to-severe AEs. However, a statistically significant difference was only observed for moderate-to-severe AEs between the previously low FEV1 trajectory group and the non-COPD group [0.43±0.09 vs. 1.00±0.21 per patient-year, adjusted rate ratio (RR) 0.47, 95% CI: 0.25–0.89; P=0.02]. Notably, the risks of total AEs [0.87±0.16 vs. 1.20±0.26 per patient-year, adjusted RR 0.76, 95% CI: 0.43–1.33; P=0.33] and moderate-to-severe AEs (0.54±0.10 vs. 1.00±0.21 per patient-year, adjusted RR 0.56, 95% CI: 0.32–0.98; P=0.042) were numerically lower in the previously normal FEV1 trajectory group than in the previously low FEV1 trajectory group. However, only the risk of moderate-to-severe AE was significantly different.
Figure 2.
Risk of acute exacerbations in participants from 2019–2020 to 2021–2022. Data are mean ± standard error. Negative binomial regression models were adjusted for age, sex, body mass index, smoking status, and smoking index; *, P<0.05. CI, confidence interval; COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in one second; IRR, incidence rate ratio.
Discussion
This study is a follow-up clinical and prognostic analysis based on the ECOPD cohort, the research design and scientific basis of which have been published in the Journal of Thoracic Disease (25). The main finding of this study is that participants with mild-to-moderate COPD who progressed from low lung function had more severe dyspnea, poorer lung function, more severe SAD and emphysema, and a significantly increased risk of moderate-to-severe AEs during the 2-year follow-up than participants with mild-to-moderate COPD who progressed from normal lung function.
To our best knowledge, this study is the first to explore the clinical characteristics and longitudinal prognosis of individuals with mild-to-moderate COPD based on different longitudinal lung function trajectories in a Chinese community population. The findings supplement the relatively scarce research evidence in this subgroup within the population with mild-to-moderate COPD, especially regarding imaging assessments and IOS data related to respiratory physiology and structural lung changes. Our results emphasize that patients with mild-to-moderate COPD evolved from different lung function trajectories exhibit distinct clinical characteristics and longitudinal prognoses. This suggests that clinical practice should emphasize long-term longitudinal monitoring of pulmonary function, incorporating trajectory-based stratification as a secondary stratification strategy post-diagnosis into clinical decision-making. Subsequently, a more personalized diagnosis, treatment, and follow-up management should be implemented with the aim of delaying disease progression and improving patient outcomes at an early stage.
Since Fletcher et al.’s 1970s research, the age-related accelerated decline in FEV1 has been regarded as the mainstream view of COPD pathogenesis (50). This concept has spurred numerous subsequent studies and treatment trials aimed at slowing the rapid decline in FEV1 (51,52). However, a previous study found not all patients follow this typical pattern: approximately half of patients with COPD progress from normal lung function, while the other half develop COPD from a low initial level of lung function (23). This discovery has prompted the academic community to pay attention to the diversity in lung function trajectories and their association with the occurrence of COPD. Currently, there is substantial evidence indicating that COPD can develop through different FEV1 trajectories, and patients with different trajectories vary in their clinical characteristics and in multiple longitudinal prognostic outcome indicators, suggesting a potential association between lung function trajectories and the heterogeneity in COPD (20-24). The results of the present study in the population with mild-to-moderate COPD are partially consistent with previous cohort studies, but there are also key differences. For instance, data from three large independent cohorts indicated that the trajectories of normal lung function progressing to COPD and low lung function progressing to COPD each accounted for half of COPD cases, and the total number of continuous smokers accounted for 81% (268/331) of the included COPD participants (23). In contrast, our study showed that for participants aged 40–80 years with mild-to-moderate COPD, these two trajectories accounted for 75% and 25% of COPD cases, respectively. Meanwhile, the overall proportion of current smokers was 55% (70/128). In other words, despite the lower proportion of continuous smokers, the Chinese population exhibited a higher proportion of individuals on a trajectory of rapid lung function decline. We speculate that this difference may stem partly from racial specificity in pulmonary function trajectories, but more significantly from differences in disease stages considered in the studies. Previous studies on lung function trajectories have included patients across all COPD stages (GOLD 1–4), with the “low baseline lung function trajectory” predominantly (23,53). This leads to a “floor effect” (insufficient room for further FEV1 decline due to pre-existing severe impairment), resulting in biased trajectory distribution that underestimates the true rapid decline pattern in early-stage disease. In contrast, our study specifically focused on mild-to-moderate COPD (GOLD 1–2), where lung function retains substantial decline potential, allowing full manifestation of the cumulative effects of early smoking exposure and thus reflecting the natural course of ECOPD more accurately.
Prior studies have shown that patients with COPD who experience a ‘rapid decline trajectory’ have a higher long-term risk of AEs and respiratory-related mortality than those who experience a ’slow decline trajectory’ (24). Notably, our 2-year follow-up yielded an unexpected finding in mild-to-moderate COPD: the ‘previously normal FEV1 trajectory’ (corresponding to the ‘rapid decline trajectory’ in prior study) was associated with a lower risk of AEs than the ‘previously low FEV1 trajectory’ (corresponding to the ’slow decline trajectory’ in prior study). This is likely due to the former’s higher initial lung function and less structural lung damage. This advantage may buffer short-term symptom deterioration post-diagnosis and temporarily masked long-term progression risk. Such patients are easily overlooked in routine care, highlighting the need for vigilance towards ‘asymptomatic rapid progressors’.
Additionally, our results showed no significant differences in the prevalence of comorbidities among the three groups. This ensures that the observed differences in clinical outcomes (e.g., AE risk, lung function decline rate) are primarily attributed to variations in lung function trajectories rather than differences in comorbidity management. This reduces the confounding effect of comorbidities on the core conclusions of the study.
Numerous studies have confirmed the destructive effect of smoking on COPD development and progression (13,24,54). Our study results are consistent with previous findings. Less than 20% of the patients with mild-to-moderate COPD never smoked (25/128), which is approximately one-third of the number of patients that had never smoked in the healthy population (68/118). Moreover, among the patients with mild-to-moderate COPD, the proportion of continuous smokers in the previously normal FEV1 trajectory group was significantly higher than the proportion in the previously low FEV1 trajectory group. This corroborates the close association between smoking and the higher prevalence of mild-to-moderate COPD, as well as the faster rate of lung function decline, highlighting the importance of strengthening tobacco control to improve lung health.
A major gap in previous trajectory studies is the lack of integrated structural and functional assessments. Our study incorporates CT-derived PRM indices and IOS parameters, providing direct evidence that the previously low FEV1 trajectory group had more severe SAD and air trapping, which are key pathological features of ECOPD (37,40). Furthermore, the previously low FEV1 trajectory group had a significantly higher proportion of participants with a family history of respiratory diseases than the previously normal FEV1 trajectory group. Combined with the finding from OLIN cohorts that previously low FEV1 trajectory is associated with early-life exposures, our results suggest that early lung development abnormalities (rather than smoking-related damage alone) may be a key pathogenic factor in the progression of mild-to-moderate COPD from the previously low FEV1 trajectory (14).
There are several limitations in this study that should be considered. Firstly, the issue of insufficient sample size within the previously low FEV1 trajectory group is particularly pronounced, due to the limited number of subjects in the ECOPD cohort with reliable post-bronchodilator lung function in 2012–2013, coupled with the small proportion of patients originally assigned to this group. Secondly, the 2-year follow-up period was relatively short, and not long enough to fully assess long-term outcomes. However, previous studies suggest that at least two annual follow-up measurements over 18 months are sufficient for determining the long-term average annual decline in lung function (55). Thirdly, we did not adjust for medication use in the analysis. Although baseline respiratory medication use was balanced between the two trajectory groups, we cannot rule out the potential impact of medication use during follow-up on lung function decline, trajectories, and test results. However, multiple studies have confirmed that mild-to-moderate COPD patients in China have extremely low detection and standardized treatment rates (14,47,56). Therefore, the interference of medication use on the core conclusions of this study may be limited. Fourthly, the decision to divide mild-to-moderate COPD into two specific lung function trajectories may be considered overly simplistic given that there may be more complex lung function trajectories (57). Fifthly, there is a notable imbalance in gender distribution, with females accounting for only 11.5% and 12.5% in the previously normal and previously low FEV1 trajectory groups, respectively. Although gender was included as a key confounding factor in all multivariate regression analyses to mitigate potential bias, the generalisability of the findings to the female COPD population should still be interpreted with caution.
Conclusions
Patients with mild-to-moderate COPD exhibited distinct clinical, physiological, and prognostic features across different lung function trajectories. We advocate incorporating trajectory-based stratification as a post-diagnostic secondary stratification strategy integrated into clinical decision making to further guide follow-up and management, enabling more comprehensive and precise personalized care.
Supplementary
The article’s supplementary files as
Acknowledgments
Thank all participants and their families who volunteered for the ECOPD cohort study. We thank the medical staff at The First Affiliated Hospital of Guangzhou Medical University (Rongchang Chen, Qingsi Zeng, Yu Deng, Huai Chen, and Xiaoyan Huang) and Lianping County People’s Hospital (Xiangwen Luo) for their unwavering support, assistance, and collaboration throughout the study. We also thank Heshen Tian, Jianwu Xu, Bijia Lin, Shaodan Wei, Peiyu Huang, and Xiaopeng Ling from the State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, and The First Affiliated Hospital of Guangzhou Medical University, for their dedicated efforts in data collection and verification. We thank Emily Woodhouse, PhD from Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (No. 2018-53) and written informed consent was obtained from all participants.
Footnotes
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2744/rc
Funding: This research was funded by the Guangzhou National Laboratory Foundation (grant Nos. SRPG22-016 and SRPG22-018), the Clinical and Epidemiological Research Project of the State Key Laboratory of Respiratory Disease (grant No. SKLRD-L-202402), the Guangzhou Medical University Research Enhancement Plan (grant No. GMUCR2024-01012), and the National Natural Science Foundation of China (No. 82570065).
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2744/coif). The authors have no conflicts of interest to declare.
Data Sharing Statement
Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2744/dss
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