Pulmonary tuberculosis during the developmental period contributes to COPD and pre-COPD in mid-to-late adulthood: a retrospective cohort study

Abstract

Background:

The effect of non-smoking factors on Chronic Obstructive Pulmonary Disease (COPD) is gaining attention. Long-term contributions of pulmonary tuberculosis (PTB) during the developmental period to COPD development in later adulthood remain poorly understood.

Objectives:

To investigate the impact of PTB during the developmental period of COPD and pre-COPD.

Design:

Retrospective cohort study

Methods:

This retrospective cohort study analyzed 496,534 participants using the UK Biobank cohort. PTB during the developmental period was defined as a diagnosis at age ⩽25 years. Outcomes were COPD and pre-COPD stages, including preserved ratio impaired spirometry (PRISm) and impaired spirometry (below Lower Limit of Normal (LLN) thresholds). Associations were evaluated using the multivariable logistic regression, adjusting for age, sex, body mass index, smoking, asthma, bronchiectasia, and air pollution.

Results:

Among 10,178 patients (62 (9) years old) diagnosed with COPD, PTB during the developmental period was statistically associated with increased COPD risk across four models, showing a fully adjusted odds ratio (aOR) of 1.82 (95% CI: 1.45–2.26), and remained significant regardless of sex or smoking status. Critically, PTB was also independently associated with both PRISm and airflow obstruction (aOR = 1.52, 95% CI: 1.35–1.71) and impaired spirometry below LLN (aOR = 1.32, 95% CI: 1.14–1.52). Sensitivity analyses further reinforced the robustness of these findings.

Conclusion:

PTB during the developmental period is an independent risk factor for COPD and pre-COPD status in mid-to-late adulthood, necessitating long-term respiratory monitoring in PTB during the developmental period survivors and the implementation of targeted public health strategies.

Keywords

chronic obstructive pulmonary disease early life impaired spirometry lower limit of normal preserved ratio pulmonary tuberculosis

Introduction

Chronic obstructive pulmonary disease (COPD) is a lung disease characterized by persistent airflow limitation and has become the third leading cause of death worldwide, imposing a significant burden on the socioeconomic and healthcare systems.¹ Besides its direct effect on people’s health, COPD has been proven to increase the risk of other diseases, including cardiovascular diseases, lung cancer and pneumonia.^2–4 Traditionally, cigarette smoking and air pollution have been recognized as the dominant risk factors for COPD.⁵ However, growing evidence highlights the role of non-smoking-related factors in the occurrence and development of COPD, such as occupational exposures, lower respiratory tract infections (LRTIs) and low socioeconomic status.^6–8

LRTIs can have profound effects on the respiratory system, leading to structural and functional impairments. Pulmonary tuberculosis (PTB) is a common respiratory infectious disease caused by Mycobacterium tuberculosis and still remains a major global health concern.⁹ Though most PTB patients can be cured or controlled successfully with the advances of antibiotics, several studies show the abnormal decrease of lung function in patients with a known history of PTB.^10–12

The developmental period is critical for lung development, during which premature delivery, pulmonary infections or injuries may have lasting consequences.¹³ Decreased peak spirometry in early adulthood is linked to COPD occurrence in older adult.¹⁴ LRTIs during the developmental period increase the risk of premature mortality in adulthood due to respiratory diseases, accounting for nearly one-fifth of these deaths.¹⁵ Nevertheless, the impact of LRTIs during the developmental period on COPD incidence, and whether differences exist across pathogen types, has not been established. Moreover, many studies have explored the association between a history of PTB and COPD,^16–18 none of them have specifically examined the impact of PTB history during the developmental period on the development of COPD.

Pre-COPD has been proposed as a status to identify individuals with symptoms and imaging abnormalities but normal forced expiratory volume in one second (FEV1) / forced vital capacity (FVC) (FEV1/FVC ⩾70%) at the present time who have an increased risk of developing COPD in the future.¹⁹ Within this framework, preserved ratio impaired spirometry (PRISm), defined as FEV1/FVC⩾70% and FEV1 percent predicted (FEV1% predicted) <80%, is considered as one of the important Pre-COPD phenotypes²⁰ Evidence indicates that PRISm is not a benign pulmonary functional abnormality but is closely associated with increasing comorbidity risks and adverse clinical outcomes.^21–26 Currently, there are no large-scale studies exploring the relationship between PTB during the developmental period and pre-COPD in mid-late adulthood.^27,28

To fill these gaps, we investigated the association of the history of pulmonary infection during the developmental period and COPD or pre-COPD, using the data from the UK Biobank. We innovatively demonstrated the relationship between them and explored the potential heterogeneity of this association across different subgroups and several sensitivity analyses.

Methods

Study design

This retrospective cohort study was conducted using data from the UK Biobank (UKB). The UKB database is a large-scale prospective cohort collecting data on health status, lifestyle, environmental exposure and genetic information from over 500,000 British residents aged between 40 and 69 years. It recruited participants between 2006 and 2010 to collect baseline data and has performed a long-term follow-up to date.²⁹ Figure 1 shows that initially included 502,167 participants from the UK Biobank. We performed a complete-case analysis; thus, participants with missing values for key covariates, including age, sex, body mass index (BMI), and smoking status, were excluded (n = 5633), leaving 496,534 individuals for further analysis.

Figure 1.

Flowchart of the study.

For the analysis of the association between COPD and PTB during the developmental period (the left column in Figure 1), we considered incident COPD events up to the first follow-up cut-off date of August 1, 2010, to ensure the integrality of the covariate data. Participants who had died before this cut-off date (n = 1900) were excluded, resulting in a final analytic sample of 494,634 participants, including 10,178 individuals with COPD and 484,456 without COPD. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (Supplemental File).³⁰

For the analysis of the association between pre-COPD and PTB during the developmental period (the right column in Figure 1), participants without complete lung function data were excluded (n = 144,890), resulting in an analytic sample of 351,644 participants.

Variable definition

The diagnosis of diseases in the UKB database is based on the integration of multiple information sources recording International Classification of Diseases (ICD-10) codes, including hospital admissions, death registries, primary care records, and self-reported medical conditions at baseline. Data field IDs of UKB used to define research variables and several diseases were reported in sTable 1, and the detailed descriptions could be retrieved on the official website of UKB.

For example, tuberculosis infection in this study was defined as the presence of any of the following ICD-10 codes: (1) A15: Respiratory tuberculosis, bacteriologically or histologically confirmed; (2) A16: Respiratory tuberculosis, not confirmed bacteriologically or histologically; (3) A19: Miliary tuberculosis (sTable 1). Then, we obtained the diagnosis date for each ICD-10 code and chose the minimum of these dates as the tuberculosis diagnosis date. Age at diagnosis of tuberculosis was calculated as the time interval between the participant’s date of birth and the date of diagnosis of tuberculosis. As for pulmonary infections during the developmental period, we tried to set 12, 18, and 25 as the cut-off diagnostic ages, respectively. Considering the lung function normally increases from birth to a peak at age 18–22 years in females and age 21–25 years in males,³¹ and to include more patients with PTB and improve statistical efficiency, we eventually defined the PTB during the developmental period as a PTB occurring before 25 years old. It is worth noting that several date outliers were re-annotated as “NULL,” including: 1900-01-01, 1901-01-01, 1902-02-02, 1903-03-03, 1909-09-09, and 2037-07-07.

Outcome definition

COPD, with the field ID of 42016 in UKB, was defined using a validated algorithm developed by the UKB Outcomes Adjudication Group, which integrates self-reported medical conditions with linked hospital inpatient records, procedure codes and death registry data coded using the ICD-10 codes. This algorithm has previously been validated and is widely applied in UKB research.³² Participants without COPD were classified as controls.

Pre-COPD was defined based on the spirometry data of UKB in the baseline assessment stage, including FEV1 and FVC (field IDs 20150 and 20151, respectively). We calculated FEV1% predicted using pctpred_GLI() function in R package Rspiro. Participants were categorized into normal control (FEV1/FVC ⩾ 0.7 and FEV1% predicted ⩾ 0.8), PRISm (FEV1/FVC ⩾ 0.7 and FEV1% predicted < 0.8), and airway obstruction (FEV1/FVC < 0.7) based on spirometric criteria.^20,33 Individuals with PRISm are often considered within the spectrum of pre-COPD.

Lower Limit of Normal (LLN) is the statistically derived threshold (typically the 5th percentile) below which spirometric values are considered abnormal, contributing to distinguish true respiratory impairment from normal variability.³⁴ As for the sensitivity analysis of pre-COPD, LLN-based impairment was assessed, where lung function was considered impaired if any of FEV1, FVC, or FEV1/FVC was below the corresponding LLN value; participants with all parameters at or above LLN were classified as normal controls.³⁵ We the calculated LLN for FEV1, FVC, and FEV1/FVC using LLN_GLIgl() function in R package Rspiro.

Covariates

Age, sex, BMI, smoking status, other chronic airway diseases and air pollution were well-accepted influence factors of COPD and regarded as covariates in our study.^20,36,37 BMI (kg/m²) was classified into three groups: BMI < 25, 25 ⩽ BMI ⩽ 30 and BMI > 30.²⁰ Asthma history was defined based on algorithmically-defined outcome field ID 42014, 42015, and bronchiectasis history on field ID 131498, 131499. According to the study of Huang Y et al., air pollution was defined as meeting two or more of the following criteria³⁸: (1) high PM2.5 exposure (⩾10 ug/m³); (2) high PM10 exposure (⩾20 ug/m³); (3) high NO₂ exposure (⩾40 ug/m³); and (4) high NO_x (⩾median 42.38 ug/m³). Data field IDs of UKB used to define research variables and several diseases were reported in sTable 1, and the detailed descriptions could be retrieved on the official website of UKB.

Statistics analysis

Baseline characteristics of the study population were described as median with interquartile range (IQR) for continuous variables and count with percentage for categorical variables. We used the Kruskal–Wallis test to estimate the p value of continuous variables due to non-normal distribution or non-homoscedasticity, and used the chi-square test for categorical variables.

Multivariate logistic regression models were constructed to estimate the association between PTB during the developmental period and COPD or impaired spirometry (PRISm). We fitted a series of logistic regression models by adding covariates to investigate the odds ratios (ORs) and 95% confidence intervals (CIs) of PTB during the developmental period infection and outcomes: (1) unadjusted model; (2) model 1: adjusted by age and sex; (3) model 2: adjusted by model 1+BMI classification and smoking status; (4) model 3: adjusted by model 2+ asthma history and bronchiectasis history; and (5) model 4: adjusted by model 3+ air pollution. Stratified analyses were also performed to investigate the interaction effects of PTB during the developmental period on different outcomes in different subgroups of sex, age, and smoking status.

To assess the clinical relevance, we also calculated the Population Attributable Fraction (PAF) for the primary outcome using the OR in model 4.

Sensitivity analysis

We explored the robustness of results by performing the following sensitivity analyses: (1) choosing a different cut-off timepoint of COPD incidence (December 31, 2022) (sTables 6 and 7); (2) analyzing chronological relationship of pulmonary infections and COPD diagnosis (sTable 8); (3) using low LLN to define pre-COPD (sTables 11–15); (4) performing stratified analyses adjusted by model 2 and model 4, respectively (sTables 5, 10, 15).

All statistical analyses were performed using R version 4.2.2, with a two-sided significance level set at 0.05. Binary logistic regression models were fitted by the function of glm() and ordered logistic regression models by polr(). Non-COPD participants were set as the reference for the analysis of COPD outcome, and Normal control was set as the reference in both three classifications and two classifications for the analysis of impaired spirometry. GraphPad Prism 10.4.0 (GraphPad Software Inc., San Diego, CA, USA) was used to produce forest plots.

Results

PTB during the developmental period, but not other pneumonia, was associated with COPD

A total of 10,178 participants (2.1% of the entire cohort) were diagnosed with COPD on the cut-off timepoint of August 1, 2010. Baseline characteristics and covariates of the participants were reported in Table 1. Compared with the non-COPD participants (n = 484,456), the proportions of senile people, male, abnormal BMI, smokers, exposure to air pollution and other chronic airway diseases were statistically elevated in the COPD group.

Table 1.

Baseline characteristics and pulmonary infection histories by the diagnosis of COPD.

Characteristics	Non COPD (n = 484,456)	COPD (n = 10,178)	p Value
Baseline demographics and covariates
Age (years old)	58 (13)	62 (9)	<0.001
⩾65 years old	90,524 (18.7%)	3477 (34.2%)	<0.001
Sex
Male	219,624 (45.3%)	5285 (51.9%)	<0.001
Female			<0.001
BMI (kg/m²)	26.7 (5.7)	27.8 (7.1)	<0.001
Classification
<25	160,833 (33.2%)	2826 (27.8%)	<0.001
25–29.9	206,316 (42.6%)	3829 (37.6%)
>30	117,307 (24.2%)	3523 (34.6%)
Smoking status
Never smoker	268,823 (55.5%)	2421 (23.8%)	<0.001
Ex-smoker	166,304 (34.3%)	5047 (49.6%)
Current smoker	49,329 (10.2%)	2710 (26.7%)
Spirometry
FEV1/FVC (%)	76.5 (7.3)	68.7 (15.3)	<0.001
FEV1% (%)	98.7 (21.1)	78.6 (32.3)	<0.001
Air pollution	33601 (7.6%)	923 (10.0%)	<0.001
High PM2.5	208872 (47.0%)	5070 (55.1%)	<0.001
High PM10	25719 (5.8%)	572 (6.2%)	<0.001
High NO2	21323 (4.5%)	650 (6.5%)	<0.001
High NOx	238083 (49.9%)	5752 (57.6%)	<0.001
Asthma	55306 (11.4%)	4510 (44.3%)	<0.001
Bronchiectasis	1346 (0.28%)	508 (0.50%)	<0.001
Previous history of pulmonary infection
Pulmonary tuberculosis	2814 (0.58%)	168 (1.65%)	<0.001
Early infection
⩽12 years old	1356 (0.28%)	64 (0.63%)	<0.001
⩽18 years old	1822 (0.38%)	89 (0.87%)	<0.001
⩽25 years old	2207 (0.46%)	108 (1.06%)	<0.001
Bacteria pneumonia	957 (0.20%)	110 (1.08%)	<0.001
Early infection
⩽12 years old	91 (0.019%)	4 (0.039%)	0.139
⩽18 years old	106 (0.022%)	7 (0.069%)	0.002
⩽25 years old	132 (0.027%)	9 (0.088%)	<0.001
Viral pneumonia	94 (0.019%)	14 (0.138%)	<0.001
Early infection
⩽12 years old	18 (0.0037%)	0 (0)	ns
⩽18 years old	21 (0.0043%)	0 (0)	ns
⩽25 years old	24 (0.0050%)	0 (0)	ns
Diphtheria	145 (0.03%)	7 (0.07%)	0.039
Pertussis	3313 (0.68%)	47 (0.46%)	0.007
Other pneumonia	11576 (2.39%)	1187 (11.67%)	<0.001
Early infection
⩽12 years old	3595 (0.74%)	86 (0.84%)	ns
⩽18 years old	4090 (0.84%)	96 (0.94%)	ns
⩽25 years old	4532 (0.94%)	106 (1.04%)	ns
All early infection
⩽12 years old	5043 (1.04%)	153 (1.50%)	<0.001
⩽18 years old	6017 (1.24%)	190 (1.87%)	<0.001
⩽25 years old	6869 (1.42%)	221 (2.17%)	<0.001

Continuous variables were described as median (interquartile range) and categorical variables were described as count (percentage).

BMI, body mass index; COPD, chronic obstructive pulmonary disease; FEV1%, FEV1 percent predicted; FEV1/FVC, forced expiratory volume in the first second of expiration/forced vital capacity; ns, non-significant; PM, particulate matter.

Moreover, Table 1 showed that previous history of pulmonary infections, including bacteria (1.08% vs 0.20%, p < 0.001), virus (0.138% vs 0.019%, p < 0.001), PTB (1.65% vs 0.58%, p < 0.001) and other or unknown pathogens (11.67% vs 2.39%, p < 0.001), was significantly associated with the diagnosis of COPD in comparison with non-COPD participants. However, for pulmonary infection (⩽12, 18 or 25 years old), only PTB during the developmental period was still statistically related to the increased occurrence of COPD: (1) 0.63% vs 0.28% for PTB ⩽12 years old, (2) 0.87% vs 0.38% for PTB ⩽18 years old, (3) 1.06% vs 0.46% for PTB ⩽25 years old.

Several early-life factors were demonstrated to be associated with the diagnosis of COPD (sTable 2). In addition, “Maternal smoking around birth” and “Light body size at age 10” were also relevant to early history of PTB or pneumonia (sTables 3 and 4) suggesting that early history of PTB might be a critical mediator between the early-life factors and the development of COPD in late adulthood.

These findings suggest that PTB in early life may confer a specific and clinically relevant long-term risk for the development of COPD.

PTB during the developmental period was robustly associated with COPD by several multivariate, subgroup, and sensitivity analyses

Table 2 shows the OR and 95% CI for PTB during the developmental period (⩽25 years old) for the diagnosis of COPD. Consistently, all fitted models proved that PTB during the developmental period was associated with an increased risk of developing COPD: (1) OR 2.34, 95% CI: 1.93–2.85 in the unadjusted model; (2) OR 1.98, 95% CI: 1.62–2.40 in model 1 (adjusted by age + sex); (3) OR 1.99, 95% CI: 1.63–2.42 in model 2 (adjusted by model 1 + BMI classification + smoking status); (4) OR 1.73, 95% CI: 1.39–2.21 in model 3 (adjusted by model 2 + asthma + bronchiectasis); (5) OR 1.82, 95% CI: 1.45–2.26 in model 4 (adjusted by model 3 + air pollution).

Table 2.

Associations between pulmonary tuberculosis during the developmental period and the risk of incident COPD.

Model	Variables	OR	95%CI	p Value
Unadjusted	PTB during developmental period	2.34	1.93–2.85	<0.001
Model 1	PTB during developmental period	1.98	1.62–2.40	<0.001
	Age ⩾ 65	2.22	2.13–2.32	<0.001
	Male	1.27	1.22–1.32	<0.001
Model 2	PTB during developmental period	1.99	1.63–2.42	<0.001
	Age ⩾ 65	2.28	2.18–2.37	<0.001
	Male	1.05	1.01–1.10	0.001
	BMI classification	1.29	1.25 - 1.32	<0.001
	Smoking status	2.52	2.45–2.59	<0.001
Model 3	PTB during developmental period	1.73	1.39–2.12	<0.001
	Age ⩾ 65	2.34	2.24–2.44	<0.001
	Male	1.16	1.11–1.21	<0.001
	BMI classification	1.23	1.19–1.26	<0.001
	Smoking status	2.66	2.59–2.74	<0.001
	Asthma	6.21	5.96–6.47	<0.001
	Bronchiectasis	14.36	12.75–16.15	<0.001
Model 4	PTB during developmental period	1.82	1.45–2.26	<0.001
	Age ⩾ 65	2.35	2.24–2.46	<0.001
	Male	1.18	1.13–1.23	<0.001
	BMI classification	1.23	1.2–1.27	<0.001
	Smoking status	2.59	2.52–2.67	<0.001
	Asthma	6.24	5.98–6.52	<0.001
	Bronchiectasis	15.00	13.24–16.96	<0.001
	Air pollution	1.22	1.14–1.31	<0.001

BMI, body mass index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; OR, odds ratio; PTB, pulmonary tuberculosis.

The bold values were used to highlight the primary variable of interest in our study.

In addition, subgroup analyses demonstrated that PTB during the developmental period was a consistent risk factor of COPD in different sex and smoking status groups, with no significant P interaction: (1) OR 2.05, 95% CI: 1.54–2.67 in the male group; (2) OR 1.92, 95% CI: 1.42–2.53 in the female group; (3) OR 2.26, 95% CI: 2.26 1.51–3.23 in the non-smoker group; (4) OR 1.68, 95% CI: 1.24–2.22 in the ex-smoker group; (5) OR 2.37, 95% CI: 1.58–3.45 in the current smoker group. (Table 3 and sTable 5) Nevertheless, no significance was observed in the young group (⩽50 years old) (p = 0.724, p interaction = 0.518), perhaps due to the limited duration of disease development or insufficient sample size of young participants.

Table 3.

Stratified analyses of the relationship between pulmonary tuberculosis during developmental period and COPD.

p Values were adjusted by age (categorized as < 65 and ⩾65 years), sex, smoking status and BMI classification, in addition to the stratification factor.

CI, confidence interval; COPD, chronic obstructive pulmonary disease; OR, odds ratio; PTB, pulmonary tuberculosis.

The PAF was calculated using OR in model 4, yielding a value of 0.0038 [0.0021–0.0057]. This suggests that, if PTB during the developmental period were eliminated, it could theoretically reduce the number of COPD patients by 0.38%.

To enroll more patients with COPD into analysis, we performed the sensitivity analysis by extending the cut-off timepoint to December, 2022, which lacked the demographic characteristics at that time and thus was not appropriate for the multivariate analysis. sTables 6 and 7 showed that a total of 29,002 participants (5.8% of the entire population) were diagnosed with COPD on December 31, 2022, and PTB during the developmental period was also statistically related to the increased occurrence of COPD (0.85% vs 0.44%, p < 0.001, for PTB⩽25 years old). Furthermore, sTable 8 reported the chronological relationship between the diagnosis of pulmonary infections and the diagnosis of COPD. We found that a majority of PTB occurred before COPD, but half of the other pneumonia cases were diagnosed after the COPD, suggesting the underlying causal relationship between PTB during the developmental period and COPD.

The consistency of this association across all these multiple analytical approaches supports the robustness of PTB during the developmental period as a clinically meaningful risk factor for COPD.

PTB during the developmental period was associated with PRISm and airflow obstruction

Baseline characteristics, covariates, previous history of pulmonary infections and early-life factors between normal control (n = 272,048), PRISm (n = 24,036) and airflow obstruction (n = 55,560) were reported in Table 4 and sTable 9. Likewise, PTB during the developmental period, but not other pneumonia, was a risk factor of PRISm and airflow obstruction (0.4% vs 0.6% vs 0.7%, p < 0.001, for PTB ⩽25 years old). The stepwise multivariate analysis demonstrated that PTB during the developmental period was related to PRISm and airflow obstruction in all fitted models: (1) OR 1.73, 95% CI: 1.56-1.92 in the unadjusted model; (2) OR 1.56, 95% CI: 1.41–1.74 in model 1; (3) OR 1.58, 95% CI: 1.41–1.75 in model 2; (4) OR 1.51, 95% CI: 1.38–1.68 in model 3; (5)OR 1.52, 95% CI: 1.35–1.71 in model 4 (Table 5).

Table 4.

Baseline characteristics and pulmonary infection histories by pre-COPD.

Characteristics	Normal control (n = 272,048)	PRISm (n = 24,036)	Airflow obstruction (n = 55,560)	p Value
Baseline demographics and covariates
Age (years old)	57 (13)	58 (13)	61 (11)	<0.001
⩾65 years old	44,452 (16.3%)	4641 (19.3%)	15,638 (28.1%)	<0.001
Sex
Male	121,519 (44.7%)	10,010 (41.6%)	31,007 (55.8%)	<0.001
Female	150,529 (55.3%)	14,026 (58.4%)	24,553 (44.2%)	<0.001
BMI (kg/m²)	26.7 (5.5)	28.5 (7.0)	26.1 (5.4)	<0.001
Classification
<25	89,444 (32.9%)	5357 (22.3%)	21,659 (39.0%)	<0.001
25–29.9	119,277 (43.8%)	9273 (38.6%)	23,138 (41.6%)
>30	63,327 (23.3%)	9406 (39.1%)	10,763 (19.4%)
Smoking status
Never smoker	153,631 (56.5%)	12,254 (51.0%)	22,687 (40.8%)	<0.001
Ex-smoker	96,226 (35.4%)	8744 (36.4%)	22,219 (40.0%)
Current smoker	22,191 (8.2%)	3038 (12.6%)	10,654 (19.2%)
Spirometry
FEV1/FVC (%)	77.7 (5.8)	75.1 (5.6)	66.2 (6.3)	<0.001
FEV1% (%)	102.1 (17.9)	74.3 (8.8)	82.8 (24.1)	<0.001
Air pollution	16,730 (6.7%)	1715 (7.7%)	3829 (7.5%)	<0.001
High PM2.5	110,912 (44.7%)	10,614 (47.8%)	24,606 (48.2%)	<0.001
High PM10	13,801 (5.5%)	1336 (6.0%)	2791 (5.5%)	0.008
High NO2	10221 (3.8%)	1056 (4.7%)	2550 (4.7%)	<0.001
High NOx	132,052 (49.2%)	12,370 (52.3%)	28,904 (52.8%)	<0.001
Asthma	25,117 (9.2%)	3696 (15.4%)	13,832 (24.9%)	<0.001
Bronchiectasis	440 (0.16%)	127 (0.53%)	403 (0.73%)	<0.001
Previous history of pulmonary infection
Pulmonary tuberculosis	1208 (0.44%)	170 (0.71%)	494 (0.89%)	<0.001
Early infection
⩽12 years old	663 (0.2%)	94 (0.4%)	208 (0.4%)	<0.001
⩽18 years old	864 (0.3%)	116 (0.5%)	304 (0.5%)	<0.001
⩽25 years old	1021 (0.4%)	141 (0.6%)	381 (0.7%)	<0.001
Bacteria pneumonia	444 (0.16%)	56 (0.23%)	182 (0.33%)	<0.001
Early infection
⩽12 years old	52 (0.019%)	7 (0.029%)	15 (0.027%)	ns
⩽18 years old	60 (0.022%)	7 (0.029%)	20 (0.036%)	ns
⩽25 years old	74 (0.027%)	7 (0.029%)	26 (0.047%)	0.054
Viral pneumonia	49 (0.018%)	10 (0.042%)	17 (0.031%)	<0.001
Early infection
⩽12 years old	11 (0.004%)	2 (0.008%)	3 (0.005%)	ns
⩽18 years old	11 (0.004%)	2 (0.008%)	4 (0.007%)	ns
⩽25 years old	13 (0.005%)	3 (0.012%)	4 (0.007%)	ns
Diphtheria	74 (0.027%)	4 (0.016%)	27 (0.048%)	0.013
Pertussis	2030 (0.75%)	136 (0.57%)	377 (0.68%)	0.002
Other pneumonia	5681 (2.1%)	833 (3.5%)	2042 (3.7%)	<0.001
Early infection
⩽12 years old	2013 (0.7%)	199 (0.8%)	406 (0.7%)	ns
⩽18 years old	2275 (0.8%)	217 (0.9%)	467 (0.8%)	ns
⩽25 years old	2519 (0.9%)	235 (1.0%)	509 (0.9%)	ns
All early infection
⩽12 years old	4584 (1.7%)	431 (1.8%)	984 (1.8%)	0.201
⩽18 years old	5074 (1.9%)	471 (2.0%)	1146 (2.1%)	0.006
⩽25 years old	5502 (2.0%)	515 (2.1%)	1271 (2.3%)	<0.001

Continuous variables were described as median (interquartile range) and categorical variables were described as count (percentage).

BMI, body mass index; FEV1%, FEV1 percent predicted; FEV1/FVC, forced expiratory volume in the first second of expiration/forced vital capacity; ns, non-significant; PM, particulate matter; PRISm, preserved ratio impaired spirometry.

The bold values were used to highlight the primary variable of interest in our study.

Table 5.

Associations between pulmonary tuberculosis during developmental period and pre-COPD.

Models	Variables	OR	95%CI	p Value
Unadjusted	PTB during developmental period	1.73	1.56–1.92	<0.001
Model 1	PTB during developmental period	1.56	1.41–1.74	<0.001
	Age ⩾ 65	1.78	1.74–1.81	<0.001
	Male	1.34	1.32–1.36	<0.001
Model 2	PTB during developmental period	1.58	1.41–1.75	<0.001
	Age ⩾ 65	1.78	1.75–1.81	<0.001
	Male	1.28	1.26–1.30	<0.001
	BMI classification	0.93	0.92–0.94	<0.001
	Smoking status	1.59	1.57–1.61	<0.001
Model 3	PTB during developmental period	1.51	1.35–1.68	<0.001
	Age ⩾ 65	1.83	1.79–1.86	<0.001
	Male	1.34	1.32–1.36	<0.001
	BMI classification	0.91	0.90–0.92	<0.001
	Smoking status	1.62	1.60–1.64	<0.001
	Asthma	3.08	3.02–3.15	<0.001
	Bronchiectasis	3.14	2.77–3.56	<0.001
Model 4	PTB during developmental period	1.52	1.35–1.71	<0.001
	Age ⩾ 65	1.82	1.79–1.86	<0.001
	Male	1.35	1.32–1.37	<0.001
	BMI classification	0.91	0.90–0.92	<0.001
	Smoking status	1.60	1.58–1.62	<0.001
	Asthma	3.07	3.01–3.14	<0.001
	Bronchiectasis	3.03	2.66–3.46	<0.001
	Air pollution	1.08	1.05–1.12	<0.001

BMI, body mass index; CI, confidence interval; OR, odds ratio; PTB, pulmonary tuberculosis.

The bold values were used to highlight the primary variable of interest in our study.

As for subgroup analyses, PTB during the developmental period was a consistent risk factor of PRISm and airflow obstruction in different age, sex and smoking status groups, with no significant value of P interaction (Table 6 and sTable 10) Different from COPD, PTB during the developmental period was statistically associated with PRISm and airflow obstruction in the young group (⩽50 years old) (OR = 1.86, 95% CI: 1.18–2.83, p interaction = 0.142), suggesting that PTB during the developmental period was associated with impaired lung function but not meet the criteria of COPD diagnosis.

Table 6.

Stratified analyses of the relationship between pulmonary tuberculosis during developmental period and pre-COPD.

p Values were adjusted by age (categorized as <65 and ⩾65 years), sex, smoking status and BMI classification, in addition to the stratification factor.

CI, confidence interval; OR, odds ratio; PTB, pulmonary tuberculosis.

The PAF was calculated with OR in model 4, yielding a value of 0.0023[0.0015–0.0031]. This suggests that, if PTB during the developmental period were eliminated, it could theoretically reduce the number of pre-COPD patients by 0.23%.

These results indicate that individuals with PTB during the developmental period are more likely to exhibit high-risk pre-COPD phenotypes, including PRISm and airflow obstruction, before overt COPD develops.

PTB during the developmental period was also associated with below LLN

Baseline characteristics, covariates, previous history of pulmonary infections and early-life factors between normal control (n = 302,000) and below LLN (n = 49,644) were reported in sTables 11 and 12. Similarly, PTB during the developmental period was a risk factor of below LLN (0.41% vs 0.61%, p < 0.001, for PTB ⩽ 25 years old). sTable 13 presented the ORs estimated by the multivariate logistic regression method for the associations between PTB during the developmental period and below LLN. All fitted models revealed that PTB during the developmental period was connected with an increased risk of below LLN: (1) OR 1.50, 95% CI: 1.32–1.69 in the unadjusted model; (2) OR 1.43, 95% CI: 1.26–1.62 in model 1; (3) OR 1.43, 95% CI: 1.26–1.62 in model 2; (4) OR 1.35, 95% CI: 1.18–1.54 in model 3; (5) OR 1.32, 95% CI: 1.14–1.52 in model 4.

Results of subgroup analyses and interaction tests were shown in sTables 14 and 15. No significant interaction effect between PTB during the developmental period and age, sex or smoking status was observed, demonstrating that PTB during the developmental period had negative effects on spirometry in all the subgroups. In similar to the analysis of PRISm and airflow obstruction, PTB during the developmental period was significantly associated with below LLN in the young group (⩽50 years old) (OR = 2.01, 95% CI: 1.25–3.11, p interaction = 0.181), proving the robustness of our results. This suggests that PTB during the developmental period is associated with subclinical lung function impairment detectable by LLN criteria, even in the absence of a formal COPD diagnosis.

Discussion

Innovatively, we identified that PTB during the developmental period was significantly associated with the elevated risk of COPD occurrence after adjusting for common risk factors, but not other types of pulmonary infections. We also firstly demonstrated the relationship between PTB during the developmental period and impaired spirometry (Normal control – PRISm – Airflow limitation). Subgroup analyses showed no interaction effect between PTB during the developmental period and sex and smoking status, proving that PTB infection during the developmental period was an independent risk factor for developing COPD or impaired spirometry. The calculated PAF also provides a clinically tangible measure of the long-term burden of PTB during the developmental period, reinforcing that this history is not only statistically significant but also carries a substantial absolute impact on population health. Furthermore, our statistical results remain robust across various sensitivity analyses.

Consistently, several previous studies with a limited sample size reported that previous PTB infection was an essential contributor to the development of airway limitation and COPD in the short term.^10–12 Two Asian prospective cohort studies also showed that PTB survivors had increased risks of developing COPD, of which the adjusted hazard ratio (aHR) in the Korean cohort was similar to our study’s OR.^16,17 Another prospective study based on the UK Biobank database, which involved 216,130 participants, followed a period of 12.6 years and obtained 2788 incident cases of COPD, identified that previous PTB history at baseline was an important independent risk factor for COPD (aHR = 1.87, 95% CI = 1.26–2.77, p = 0.002).¹⁸ However, these studies did not explore whether PTB infection occurring in early life would have a lasting impact on the incidence of COPD. By focusing on the infection history of PTB during the developmental period (⩽25 years old), our study is the first to evaluate the long-term effect of PTB infection on the development of COPD, filling an important gap in the existing literature. Different from Zeng’s study,¹⁸ we selected the retrospective cohort design based on the UK Biobank database to involve more cases of COPD and improve the statistical power. In addition, we firstly analyzed the effects of pulmonary infection during the developmental period on pre-COPD (PRISm or below LLN) in the large-scale database.

Several mechanisms have been proposed to explain the association between lower respiratory tract infection during developmental period and COPD.³⁹ Recently, the lung function trajectories have been increasingly appreciated, highlighting the importance of exposures during the developmental period that might impair the trajectories.^40,41 The impairment in lung function during the developmental period may potentially set a developmental pathway for impaired lung health through adulthood.^41,42 A prospective study demonstrated that PTB adversely affected tidal volume, expiratory flow rates, and respiratory rate in children, and these impairments persisted even after adjustment for early-life lung function prior to the occurrence of PTB. Moreover, despite completion of appropriate treatment, PTB in childhood may still lead to long-term adverse effects on growth and lung health.²⁸ These findings suggest that PTB infection during the developmental period might alter lung developmental trajectories, ultimately predisposing individuals to the development of COPD in adulthood.

Studies have indicated that PTB can drive chronic pulmonary inflammation and tissue necrosis.^43–45 PTB infection can activate the Toll-like receptor 2 (TLR2) signaling path, inducing the lung epithelial cells to secrete C-X-C Motif Chemokine Ligand 5 (CXCL5), which is an important link in the inflammatory response and airway remodeling.⁴⁶ In particular, in the distal airways, activation of alveolar macrophages, dendritic cells, innate lymphoid cells, and γδ-T cells promotes the recruitment and activation of neutrophils, monocytes, and adaptive immune cells, leading to airway fibrosis and lung parenchyma damage.⁴⁷ Some studies also proposed that the treatment of PTB can lead to changes in inflammatory cytokines in the body, particularly the increase of interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α),⁴⁸ and interferon-γ (IFN-γ), and the inflammation might persist even after a microbiological cure,⁴⁹ thereby increasing the risk of developing COPD.⁴⁷ These findings indicate that chronic inflammation following TB infection can persist, leading to parenchymal lung damage and airway remodeling, which may ultimately contribute to the development of COPD.

Interestingly, mixed ventilatory impairment patterns are common in PTB patients.⁵⁰ A study investigating the long-term sequelae of tuberculosis reported that approximately 70% of patients continued to exhibit abnormal lung function despite successful completion of anti-tuberculosis treatment, with a heterogeneous range of physiological patterns, including both obstructive and restrictive ventilatory defects.⁵¹ This mixed physiological pattern may represent an intermediate or transitional phenotype along the continuum from post-tuberculous lung disease to established TB-associated COPD, particularly in individuals with ongoing inflammation or additional environmental exposures.

Though a history of PTB infection had an impact on COPD, our study found that pneumonia during the developmental period caused by bacterial, other or unspecified pathogens had a limited influence on the development of COPD and impaired spirometry in the whole population of the UK Biobank. Different from a retrospective study based on the COPDGene study, it reported that childhood pneumonia (<16 years old) increased the risk of COPD in adult smokers.⁵² Interestingly, pertussis appeared to be a protective factor in our study, but the underlying reason remained unclear.

In this study, our findings suggest the need for screening for chronic respiratory symptoms and long-term spirometry monitoring among adult individuals with a PTB history during the developmental period, particularly in those with additional risk factors such as smoking or environmental exposure. In addition, efforts should be made to actively promote BCG vaccination rates in regions with a high burden of COPD and tuberculosis. Implementing pulmonary rehabilitation for individuals suffering from post-PTB symptoms and exploring targeted treatments to reverse inflammatory and pathological changes are also necessary.

Our study had some limitations. First, the statistical power of the retrospective cohort study was relatively limited. However, the sufficient sample size and outcome cases and the chronological order of PTB during the developmental period and COPD diagnosis or baseline spirometry testing contribute to postulating the causal relationship. Second, it was hard to determine the association between viral pneumonia during the developmental period and COPD, due to the small number of cases. We inferred that it might be attributed to the unavailability of the polymerase chain reaction in the late 20th century. The ICD-10 code of influenza was not included in viral pneumonia in our study, since influenza did not necessarily cause pneumonia. Third, the definition of PTB was based on the ICD 10 of A15, A16, and A19, not involving latent PTB. Thus, the effects of latent PTB on COPD and impaired spirometry might be underestimated. Fourth, ICD-based definitions lack details regarding TB severity, radiologic extent, and treatment course, potentially resulting in residual confounding. Considering that our PTB definition included clinically diagnosed cases without bacteriological or histological confirmation (ICD-10 A16), some degree of disease misclassification is possible. In particular, a small proportion of individuals classified as PTB may have had alternative respiratory conditions with overlapping clinical or radiological features. Such non-differential misclassification of TB status would be expected to bias the observed associations toward the null. Furthermore, though the PTB during the developmental period threshold (⩽25 years) allowed sufficient sample size for analyses, it may have introduced exposure misclassification by grouping together tuberculosis occurring in childhood, adolescence, and early adulthood. These life stages differ in lung development and vulnerability. Therefore, the observed associations should be interpreted with caution.

Our findings are derived from a large, population-based cohort with standardized data collection and long-term health record linkage, supporting the robustness of the observed associations. However, participants in the UK BioBank are known to be healthier and socioeconomically more advantaged than the general population, which may limit the generalizability of the findings. Therefore, caution is warranted when extrapolating these results to populations with high tuberculosis burden, limited access to healthcare, or substantially different patterns of respiratory infections during the developmental period.

Conclusion

PTB during the developmental period is significantly associated with COPD and impaired spirometry, not related to different smoking status or gender. Thus, public health policies should focus on the lasting effects of PTB during the developmental period and strengthen early screening and management for high-risk populations.

Supplemental Material

sj-docx-1-tar-10.1177_17534666261445203 – Supplemental material for Pulmonary tuberculosis during the developmental period contributes to COPD and pre-COPD in mid-to-late adulthood: a retrospective cohort study

Supplemental material, sj-docx-1-tar-10.1177_17534666261445203 for Pulmonary tuberculosis during the developmental period contributes to COPD and pre-COPD in mid-to-late adulthood: a retrospective cohort study by Wei-ping Hu, Jian-cheng Zhu, Yue Ren, Yixing Wu, Li Liu, Xia Wang, Xiao Wang, Kehan Song, Jie Wu and Jing Zhang in Therapeutic Advances in Respiratory Disease

Supplemental Material

sj-xlsx-2-tar-10.1177_17534666261445203 – Supplemental material for Pulmonary tuberculosis during the developmental period contributes to COPD and pre-COPD in mid-to-late adulthood: a retrospective cohort study

Supplemental material, sj-xlsx-2-tar-10.1177_17534666261445203 for Pulmonary tuberculosis during the developmental period contributes to COPD and pre-COPD in mid-to-late adulthood: a retrospective cohort study by Wei-ping Hu, Jian-cheng Zhu, Yue Ren, Yixing Wu, Li Liu, Xia Wang, Xiao Wang, Kehan Song, Jie Wu and Jing Zhang in Therapeutic Advances in Respiratory Disease

Footnotes

Acknowledgements

The authors utilized data from the UK Biobank (Application No. 81888), and expressed their gratitude to all UK Biobank participants and researchers involved in the project.

Declarations

ORCID iD

Jian-cheng Zhu

Supplemental material

Supplemental material for this article is available online.

References

Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med 2020; 8: 585–596.

Sin

Tashkin

Zhang

, et al. Budesonide and the risk of pneumonia: a meta-analysis of individual patient data. Lancet (London, England) 2009; 374: 712–719.

Tammemagi

Schmidt

Martel

, et al. Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study. Lancet Oncol 2017; 18: 1523–1531.

Chen

Thomas

Sadatsafavi

, et al. Risk of cardiovascular comorbidity in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. Lancet Respir Med 2015; 3: 631–639.

Safiri

Carson-Chahhoud

Noori

, et al. Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990-2019: results from the Global Burden of Disease Study 2019. BMJ (Clin Res ed) 2022; 378: e069679.

Hagstad

Backman

Bjerg

, et al. Prevalence and risk factors of COPD among never-smokers in two areas of Sweden – Occupational exposure to gas, dust or fumes is an important risk factor. Respir Med 2015; 109: 1439–1445.

Sarkar

Srinivasa Madabhavi

, et al. Tuberculosis associated chronic obstructive pulmonary disease. Clin Respir J 2017; 11: 285–295.

Yin

Zhang

, et al. Prevalence of COPD and its association with socioeconomic status in China: findings from China Chronic Disease Risk Factor Surveillance 2007. BMC Public Health 2011; 11: 586.

Furin

Cox

Pai

Tuberculosis. Lancet 2019; 393: 1642–1656.

10.

Manji

Shayo

Mamuya

, et al. Lung functions among patients with pulmonary tuberculosis in Dar es Salaam – a cross-sectional study. BMC Pulmon Med 2016; 16: 58.

11.

Nihues Sde

Mancuzo

Sulmonetti

, et al. Chronic symptoms and pulmonary dysfunction in post-tuberculosis Brazilian patients. Brazil J Infect Dis 2015; 19: 492–497.

12.

de la Mora

Martínez-Oceguera

Laniado-Laborín

Chronic airway obstruction after successful treatment of tuberculosis and its impact on quality of life. Int J Tuberc Lung Dis 2015; 19: 808–810.

13.

Hartert

Kvysgaard

Thaver

, et al. Understanding the childhood origins of asthma and chronic obstructive pulmonary disease: Insights from birth cohorts and studies across the life-span. J Allergy Clin Immunol 2025; 155: 1703–1719.

14.

Lange

Celli

Agusti

, et al. Lung-function trajectories leading to chronic obstructive pulmonary disease. N Engl J Med 2015; 373: 111–122.

15.

Allinson

Chaturvedi

Wong

, et al. Early childhood lower respiratory tract infection and premature adult death from respiratory disease in Great Britain: a national birth cohort study. Lancet 2023; 401: 1183–1193.

16.

Wang

Impact of previous pulmonary tuberculosis on chronic obstructive pulmonary disease: baseline results from a prospective cohort study. Comb Chem High Throughput Screening 2023; 26: 93–102.

17.

Kim

Choi

Kim

, et al. Increased risk of incident chronic obstructive pulmonary disease and related hospitalizations in tuberculosis survivors: a population-based matched cohort study. J Korean Med Sci 2024; 39: e105.

18.

Zeng

Chen

Shao

, et al. Associations of prior pulmonary tuberculosis with the incident COPD: a prospective cohort study. Ther Adv Respir Dis 2024; 18: 17534666241239455.

19.

Han

Agusti

Celli

, et al. From GOLD 0 to Pre-COPD. Am J Respiratory Crit Care Med 2021; 203: 414–423.

20.

Higbee

Granell

Davey Smith

, et al. Prevalence, risk factors, and clinical implications of preserved ratio impaired spirometry: a UK Biobank cohort analysis. Lancet Respir Med 2022; 10: 149–157.

21.

Wan

Castaldi

Cho

, et al. Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene. Respir Res 2014; 15: 89.

22.

Heo

Kim

TH.

Health-related quality of life and related factors in persons with preserved ratio impaired spirometry: data from the Korea National Health and nutrition examination survey. Medicina (Kaunas, Lithuania) 2020; 57(1): 4.

23.

Marott

Ingebrigtsen

Çolak

, et al. Trajectory of preserved ratio impaired spirometry: natural history and long-term prognosis. Am J Respir Crit Care Med 2021; 204: 910–920.

24.

Washio

Sakata

Fukuyama

, et al. Risks of mortality and airflow limitation in Japanese individuals with preserved ratio impaired spirometry. Am J Respir Crit Care Med 2022; 206: 563–572.

25.

Wan

Fortis

Regan

, et al. Longitudinal phenotypes and mortality in preserved ratio impaired spirometry in the COPDGene study. Am J Respir Crit Care Med 2018; 198: 1397–1405.

26.

Wijnant

SRA

De Roos

Kavousi

, et al. Trajectory and mortality of preserved ratio impaired spirometry: the Rotterdam Study. Eur Respir J 2020; 55(1): 1901217.

27.

Nkereuwem

Agbla

Sallahdeen

, et al. Reduced lung function and health-related quality of life after treatment for pulmonary tuberculosis in Gambian children: a cross-sectional comparative study. Thorax 2023; 78: 281–287.

28.

Martinez

Gray

Botha

, et al. The long-term impact of early-life tuberculosis disease on child health: a prospective birth cohort study. Am J Respir Crit Care Med 2023; 207: 1080–1088.

29.

Sudlow

Gallacher

Allen

, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015; 12: e1001779.

30.

von Elm

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Annals Internal Med 2007; 147: 573–577.

31.

Garcia-Aymerich

de Las Heras

Carsin

, et al. General population-based lung function trajectories over the life course: an accelerated cohort study. Lancet Respir Med 2025; 13: 611–622.

32.

Eastwood

Mathur

Atkinson

, et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLoS One 2016; 11: e0162388.

33.

Echazarreta

Bennoor

KS.

Global strategy for the prevention, diagnosis and management of COPD: 2025 report, https://goldcopd.org/2025-gold-report/ (2025, accessed 13 March 2026).

34.

Culver

BH.

How should the lower limit of the normal range be defined?

Respir Care 2012; 57: 136–145; discussion 143-135.

35.

Bhatt

Bodduluri

Nakhmani

, et al. Unadjusted lower limit of normal for airflow obstruction. Am J Respir Crit Care Med 2024; 209: 1028–1030.

36.

Yang

Yao

, et al. Associations between long-term night shift work and incidence of chronic obstructive pulmonary disease: a prospective cohort study of 277,059 UK Biobank participants. BMC Med 2024; 22: 16.

37.

Doiron

de Hoogh

Probst-Hensch

, et al. Air pollution, lung function and COPD: results from the population-based UK Biobank study. Eur Respir J 2019; 54(1): 1802140.

38.

Huang

Zhu

, et al. Air pollution, genetic factors, and the risk of lung cancer: a prospective study in the UK biobank. Am J Respir Crit Care Med 2021; 204: 817–825.

39.

Auld

Barczak

Bishai

, et al. Pathogenesis of post-tuberculosis lung disease: defining knowledge gaps and research priorities at the second international post-tuberculosis symposium. Am J Respir Crit Care Med 2024; 210: 979–993.

40.

Bush

Impact of early life exposures on respiratory disease. Paediatr Respir Rev 2021; 40: 24–32.

41.

Pavord

Beasley

Agusti

, et al. After asthma: redefining airways diseases. Lancet (London, England) 2018; 391: 350–400.

42.

Guerra

Lombardi

Stern

, et al. Fetal origins of asthma: a longitudinal study from birth to age 36 years. Am J Respir Crit Care Med 2020; 202: 1646–1655.

43.

Chin

Rylance

Makumbirofa

, et al. Chronic lung disease in adult recurrent tuberculosis survivors in Zimbabwe: a cohort study. Int J Tuberc Lung Dis 2019; 23: 203–211.

44.

Singh

Allwood

Chiyaka

, et al. Immunologic and imaging signatures in post tuberculosis lung disease. Tuberculosis (Edinburgh, Scotland) 2022; 136: 102244.

45.

Choi

JY.

Pathophysiology, clinical manifestation, and treatment of tuberculosis-associated chronic obstructive pulmonary disease: a narrative review. Ewha Med J 2025; 48: e24.

46.

Nouailles

Dorhoi

Koch

, et al. CXCL5-secreting pulmonary epithelial cells drive destructive neutrophilic inflammation in tuberculosis. J Clin Investig 2014; 124: 1268–1282.

47.

Kayongo

Nyiro

Siddharthan

, et al. Mechanisms of lung damage in tuberculosis: implications for chronic obstructive pulmonary disease. Front Cell Infect Microbiol 2023; 13: 1146571.

48.

Perng

Huang

, et al. Association of reduced tumor necrosis factor alpha, gamma interferon, and interleukin-1beta (IL-1beta) but increased IL-10 expression with improved chest radiography in patients with pulmonary tuberculosis. Clin Vaccine Immunol CVI 2010; 17: 223–231.

49.

Malherbe

Shenai

Ronacher

, et al. Persisting positron emission tomography lesion activity and Mycobacterium tuberculosis mRNA after tuberculosis cure. Nature Med 2016; 22: 1094–1100.

50.

Allwood

Maasdorp

Kim

, et al. Transition from restrictive to obstructive lung function impairment during treatment and follow-up of active tuberculosis. Int J Chronic Obstruct Pulmon Dis 2020; 15: 1039–1047.

51.

Bansal

Arunachala

Kaleem Ullah

, et al. Unveiling silent consequences: impact of pulmonary tuberculosis on lung health and functional wellbeing after treatment. J Clin Med 2024; 13: 4115.

52.

Hayden

Hobbs

Cohen

, et al. Childhood pneumonia increases risk for chronic obstructive pulmonary disease: the COPDGene study. Respir Res 2015; 16: 115.

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