Comorbidity of chronic obstructive pulmonary disease and pulmonary tuberculosis: a bibliometric analysis (2011–2025) with narrative review

Introduction

Chronic obstructive pulmonary disease (COPD) and pulmonary tuberculosis (PTB) are two prevalent and severe respiratory diseases with significant global health implications. COPD is a progressive lung disease that has become the third leading cause of death worldwide (1). PTB, caused by infection with Mycobacterium tuberculosis, is characterized by symptoms including chronic cough, fever and weight loss. Recent World Health Organization (WHO) Global Tuberculosis (TB) Reports show that TB remains among the leading infectious causes of death worldwide, with high burdens concentrated in low- and middle-income countries (LMICs). However, in the community, many individuals with PTB do not show obvious symptoms such as coughing, and approximately a quarter of the patients may not exhibit any TB-related symptoms, which greatly increases the risk of transmission of Mycobacterium tuberculosis (2,3).

Although these two diseases exhibit significant differences in etiologies and clinical manifestations, their epidemiological characteristics and risk factors considerably overlap, especially in LMICs. Common risk factors such as smoking, environmental pollution and socioeconomic status contribute to the frequent co-occurrence of the two diseases (4,5). The comorbidity of COPD and PTB not only increases the complexity of clinical management but also potentially affects patient prognosis (6). These concurrent burdens and shared risk factors underscore the public-health urgency of COPD-PTB comorbidity, particularly in high-TB settings.

A comprehensive understanding of the epidemiological associations, clinical interactions, diagnostic criteria, pulmonary function manifestations, biomarker potential and fundamental research findings in existing clinical studies on COPD and PTB will help to better identify and evaluate patients with concurrent COPD and PTB. This will facilitate the development of personalized treatment plans for such individuals. In recent years, the rapid development of novel imaging techniques has been widely used in the diagnosis and management of respiratory diseases. However, a systematic evaluation of these imaging technologies in the context of COPD and PTB remains lacking.

Bibliometrics analysis is a systematic research methodology that quantitatively assesses scientific literature, thereby revealing research priorities, development trends, evolution trajectories, and academic influence within a specific field. This approach not only helps researchers and clinicians grasp the latest developments within their fields to better guide clinical decision-making and research direction, but also facilitates the rapid advancement of research. Simultaneously, bibliometrics also provides support for interdisciplinary collaboration and knowledge sharing, and offers necessary data for public health departments to formulate effective policies and optimize resource allocation. In recent years, the application of bibliometrics methods in the field of respiratory medicine has gradually increased. The Web of Science Core Collection (WoSCC) database has become an important tool for conducting bibliometric analysis, especially in medical research, where it is often used to assess the research status and development trends in specific disease areas. However, there have been no systematic analysis of the literature on the topic of both COPD and PTB.

Therefore, this study aims to achieve the following objectives: (I) conduct a bibliometric analysis of published studies that simultaneously focus on COPD and PTB, and utilize visualization tools such as VOSviewer and CiteSpace to explore the research trends and development dynamics in the field; (II) compile and summarize the epidemiological characteristics, the interplay of clinical manifestations, existing comorbidity diagnostic criteria, pulmonary function performance, biomarkers, and basic research findings of COPD and PTB, while exploring the applications of emerging imaging technologies in this context. This will provide an important reference for better recognition, identification and management of patients with comorbidity of COPD and PTB. We present this article in accordance with the BIBLIO reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2648/rc).

Methods

Inclusion and exclusion criteria

To ensure the rigor, relevance, and reproducibility of this bibliometric and narrative synthesis, studies were included based on the following criteria: (I) publications explicitly addressing both COPD and PTB or post-TB lung disease (PTLD) within their research scope; (II) articles and review articles published in English; (III) literature indexed in the WoSCC database; (IV) publication dates ranging from January 1, 2011 to June 19, 2025. The following types of literature were excluded: (I) meeting abstract; (II) proceeding paper; (III) letter; (IV) editorial material; (V) note; (VI) correction; (VII) early access; (VIII) book chapters; (IX) correction, addition; (X) abstract of published item; (XI) book review; (XII) data paper; (XIII) news item; (XIV) retracted publication; (XV) discussion; (XVI) reprint; (XVII) biographical-item; (XVIII) retraction; (XIX) item about an individual.

Data collection and retrieval

All data used in this bibliometric analysis were retrieved from the WoSCC database. The search strategy included the following terms in the Topic field (TS): “TB-COPD”, “Tuberculosis-associated obstructive pulmonary disease”, “TB-related COPD”, “TB with obstruction”, and “post-TB chronic obstructive lung disease”. Additionally, studies were included if they mentioned both “Pulmonary Disease, Chronic Obstructive”, “Chronic Obstructive Pulmonary Disease”, “COPD”, as well as “Tuberculosis, Pulmonary”, “Pulmonary Tuberculosis”, “Tuberculosis” in their topics. Preliminary results indicated that the number of research papers published in this field before 2011 did not exceed 30 each year. Previous studies have shown that post-TB patients experience severe pulmonary dysfunction, including obstructive, restrictive, and mixed ventilatory dysfunction (7). In the literature on PTLD, over two-thirds of the papers had been published in the past 15 years (8). Therefore, we set the search time range from 2011 to 2025. The final retrieval date was June 19, 2025. Information collected included publications, authors, countries, institutions, journals, keywords and citation counts. The retrieved literature data were output in the format of Full Record and Cited References and saved as plain text files for further analysis.

Data analysis

We utilized the comprehensive literature statistics tool of the WoSCC database to summarize and present the data of publication trends through bar charts. Meanwhile, we conducted a comprehensive analysis of the retrieved literature in terms of countries, institutions, authors, and keywords using VOSviewer (version 1.6.20), and a network view was created to map the core research entities and their collaborative relationships. The key parameters were set as follows: a minimum of 5 relevant publications per author, a minimum of 5 relevant publications in the source journal, and a minimum co-occurrence frequency of each keyword of 5. In the visualization graphs of VOSviewer, each node is presented as a circle with the corresponding label. The thickness and length of the links between the nodes indicate the strength of relationships between them.

In addition, we also used CiteSpace software to conduct an in-depth exploration of dynamic development and trends in the research field. The link retaining factor (LRF) was set to 3.0 and the lookback years (LBY) was set to 5, which constrained the maximum citation span. The research time range was from 2011 to 2025, with each time period containing two years of data. The keywords in the literature from 2011 to 2025 were further examined by citation burst analysis method to reveal the important hotspots and emerging trends in the fields of COPD and PTB.

Results

From 2011 to 2025, a total of 1,004 publications focusing on both COPD and PTB were retrieved identified from WoSCC database. The number of annual publications relatively remained stable from 2011 to 2016, ranging from 30 to 40 articles per year. From 2017 onward, the annual publication volume has significantly increased and exceeded 100 papers for the first time in 2021. Since then, the annual publication volume remained above 100 papers for four consecutive years, peaking at 114 articles in 2024. Overall, in the past 15 years, research in this field has generally shown an upward trend, reflecting growing attention from the academic community to the issues of COPD and PTB (Figure 1).

Figure 1 Annual number of publications on COPD-TB comorbidity from 2011 to 2025. Annual cumulative publications are represented by blue bars (y-axis, left), while annual publications are shown by the orange line (y-axis, right). COPD, chronic obstructive pulmonary disease; TB, tuberculosis.

The retrieved publications originated from 104 countries or regions. Among them, the number of papers from China was the highest, totaling 242, followed by the United States and the United Kingdom, with 202 and 139 respectively. In terms of citation counts, the United States had the highest total citations, reaching 7,977, followed by China (n=6,600) and the United Kingdom (n=5,703) (Table 1). The collaborative visualization analysis conducted through VOSviewer software showed that national or regional cooperation network (as shown in Figure 2) revealed international cooperation within this field. This network served China, the United Kingdom, and the United States as major nodes, and had established collaborative links with other countries and regions. The larger the node, the more papers were published in recent years, and it can be clearly seen that China has the largest number of publications.

Figure 2 International collaboration network among countries/regions in COPD and PTB research. COPD, chronic obstructive pulmonary disease; PTB, pulmonary tuberculosis.

A total of 457 journals published articles related to both COPD and PTB (Figure 3). Table 2 listed the top ten journals in terms of publications. Among them, the International Journal of Chronic Obstructive Pulmonary Disease was the top publishing journal with 44 articles and 753 citations, followed by PLoS One, with 40 papers and 1,309 citations, and the International Journal of Tuberculosis and Lung Disease had 39 articles and 850 citations. In addition, although the European Respiratory Journal published only 14 papers, its citation count reached 1,150 times.

Figure 3 Journal network of publications on COPD and TB from 2011 to 2025. COPD, chronic obstructive pulmonary disease; TB, tuberculosis.

Table 3 presented the top 10 articles with the highest citation frequency in the literature related to both COPD and PTB. Among them, the most frequently cited article was the Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010, published in the Lancet in 2012, with 7,869 citations at the time of data collection (9). This study systematically analyzed mortality rates for 235 causes of death worldwide in 1990 and 2010, revealing that TB caused approximately 1.2 million deaths in 2010, while COPD was the leading cause of death that year (9). Notably, the top four highly cited papers were from the Lancet’s Global Burden of Disease series, highlighting the foundational contributions of this large-scale collaborative project in elucidating global prevalence and burden of respiratory diseases such as COPD and PTB (9-12). These highly influential studies had laid a solid evidence-based foundation for subsequent exploration in this field.

A total of 5,646 authors published research papers with both COPD and PTB as keywords. Using VOSviewer software for author collaboration network analysis (threshold set: minimum publication count ≥5 papers), 55 high-output scholars were finally selected. The visualization results of the cooperation network were shown in Figure 4, which demonstrated extensive scientific collaboration relationships in this field. The analysis revealed a notable geographical concentration of high-output authors, with a predominant contribution from institutions in the Republic of Korea.

Figure 4 Author collaboration network of publications on COPD and PTB from 2011 to 2025. COPD, chronic obstructive pulmonary disease; PTB, pulmonary tuberculosis.

Keyword co-occurrence analysis can identify important hotspots in research fields related to COPD and PTB. In this study, VOSviewer software was used to extract and analyze the co-occurrence of keywords in included literature. A total of 2,055 keywords were identified, of which 96 appeared five times or more. As shown in Table 4, the most frequent terms were “Chronic Obstructive Pulmonary Disease”, “Tuberculosis”, and “Asthma”. To visually present distribution density and core focus areas of keywords, we had created a keyword density map (Figure 5). The density map showed that the most prominent high-density areas centered on “Chronic Obstructive Pulmonary Disease”, and “Tuberculosis”, indicating that they were research focus in the field. Surrounding this core, keywords such as “prevalence”, “incidence”, “risk factor”, “diagnosis”, “infection”, “management”, and “multimorbidity” formed closely linked clusters of hotspots, jointly depicting that current research core issues were concentrated on aspects such as disease burden assessment, risk factor exploration, and clinical diagnosis and management.

Figure 5 Keyword density map of publications on COPD and PTB from 2011 to 2025. COPD, chronic obstructive pulmonary disease; PTB, pulmonary tuberculosis.

Keyword burst analysis is an important tool for detecting cutting-edge research trends and predicting emerging trends. In order to deeply analyze research hotspots from January 2011 to June 2025, this study used CiteSpace software (selection criteria: γ =1.0, minimum duration =2) to detect keyword outbreaks and identified the top 25 keywords with the strongest burst intensity (Figure 6). The green line in the figure represents time intervals, while the red segments mark the start and end times as well as the duration of explosive growth of keywords. Notably, keywords such as “chronic obstructive”, “features”, “interstitial lung disease”, and “spirometry” remain in active burst status. This suggests that current research frontiers are focused on characteristics of COPD and PTB, as well as interactive effects with other important respiratory diseases such as interstitial lung disease. Additionally, the importance of pulmonary function assessment in the management of COPD and PTB is also gradually gained attention. These research directions represent emerging hotspots reflecting the rapid advancement of knowledge in this field.

Figure 6 Keyword burst analysis of publications on COPD and PTB from 2011 to 2025. COPD, chronic obstructive pulmonary disease; PTB, pulmonary tuberculosis.

Discussion

Epidemiological associations

A multinational study shows that the overall prevalence of COPD is 8.8%, with individuals having a history of PTB being 3.78 times more likely to develop COPD compared to those without such history, with a 95% confidence interval (CI) ranging from 2.87 to 4.98 (19). Several other studies support this finding. For instance, a study from the UK Biobank showed that patients with a history of PTB had approximately 87% higher risk of developing COPD compared to those without a history of PTB (20). In a study conducted in eastern China, PTB increased the risk of developing COPD by 2.57 times. The risk of developing COPD was higher in older individuals, particularly among those with low educational attainment, high systemic inflammatory response index (SIRI), or low body mass index (BMI) (21,22). A study in South Korea further revealed that patients with a history of PTB had a significantly higher cumulative incidence of COPD than those without a history of PTB (P<0.001) (23). In addition to Mycobacterium tuberculosis, infections caused by non-tuberculous mycobacteria have also been shown to significantly elevate the risk of developing COPD, with adjusted risk increasing more than twofold in different populations (24).

Studies involving multiple countries have shown a positive correlation between the prevalence of previous PTB and the prevalence of COPD (r=0.1, P<0.001) (19). In China, age-standardized incidence, prevalence, mortality, and disability-adjusted life years of COPD are higher in western provinces such as Tibet, Xinjiang, Qinghai, Chongqing and Yunnan (25). These same regions also report higher PTB incidence and mortality, suggesting significant geospatial overlap between the two diseases (26). This overlapping geographical distribution provides a rationale for integrating public health control strategies for both conditions. In areas with a high burden of TB, active TB patients and TB survivors, especially those with a smoking history or persistent respiratory symptoms, should be considered at high risk for COPD, and spirometry is recommended after TB treatment completion or during regular follow-up to enable early COPD detection. Conversely, for newly diagnosed COPD patients, clinicians should maintain a high index of suspicion for concomitant active PTB, particularly in the presence of constitutional symptoms such as unexplained weight loss or afternoon fever, and initiate appropriate diagnostic workup.

Among newly diagnosed PTB patients in Xinjiang, China, the prevalence rate of TB-related obstructive pulmonary disease reached 43.4% (27). Similarly, among hospitalized PTB patients in Wuhan Jinyintan Hospital, 11.26% of them were diagnosed with TB-related obstructive lung disease (22). Among patients with COPD who visited the respiratory medicine outpatient department of the People’s Hospital of Tibet Autonomous Region, 45.1% of them showed radiologic signs of previous PTB on chest computed tomography (CT) scans (28). Among stable COPD patients who were treated at Beijing Tongren Hospital, affiliated to Capital Medical University, 45.0% of the patients were found to have previous PTB signs in their high-resolution CT (HRCT) examinations. However, only 18.2% had a clear history of PTB and had received anti-TB treatment, and 95.2% had radiologic evidence of upper lobe TB sequelae (29).

Mutual impacts between COPD and PTB

PTB may leave behind structural sequelae such as pulmonary fibrosis and bronchiectasis, which play a substantial role in the pathogenesis and progression of COPD (30). Studies have shown that up to 50% of patients develop post-TB pulmonary sequelae (30). The presence of TB-related structural damage can exacerbate respiratory impairment in patients with COPD, and the severity of airflow obstruction has been positively correlated with the frequency of PTB episodes (31). The studies found that patients with coexisting PTB and COPD have been found to exhibit worse pulmonary function compared to those with COPD alone. Specifically, their lung function indicators such as forced expiratory volume in one second (FEV₁) as a percentage of the predicted value, and the ratio of FEV₁ to forced vital capacity (FVC) were both significantly lower than those of patients without PTB. They also experience more severe airflow limitation and more severe clinical symptoms such as respiratory failure, coughing, shortness of breath, sputum production, recurrent wheezing as well as dyspnea. They also tend to have higher proportions of COPD Assessment Test (CAT) scores ≥10. They also experience more frequent acute exacerbations, higher hospitalization rates, more hospitalizations, longer duration of each acute episode, and longer hospital stays (32-34). Some studies have also shown that PTB history has also been identified as a risk factor for accelerated FEV₁ decline in patients with COPD, and for infections with potentially drug-resistant pathogens, with an odds ratio (OR) of 1.66 (35,36). Moreover, after being diagnosed with PTB, patients with COPD will have an increased incidence of anxiety and depression (37). In addition, the risk of lung cancer increases by approximately 1.24 times in COPD patients with concomitant PTB (38). In terms of treatment response, COPD patients with a history of TB tend to exhibit a poorer response to bronchodilators.

Conversely, individuals with COPD also have an increased risk of developing active TB. Studies estimate that COPD raises the risk of PTB by 1.44 to 3.14 times compared to the general population (39). The incidence of pulmonary lobar lesions and cavities in PTB patients with concomitant COPD is significantly higher than in those without COPD, suggesting that COPD may be a risk factor for cavitation in PTB and may worse pulmonary destruction (40). In univariate analyses, COPD has been shown to increase the risk of complications in PTB patients, including respiratory failure and pulmonary infections (41). Prognostically, patients with both PTB and COPD have a higher risk of death than those with PTB alone (42). A study from Denmark showed that patients with PTB alone lost an average of 7.07 years of life, whereas PTB patients who subsequently developed COPD lost an additional 4.86 years of life, indicating a substantial survival burden (42).

Immunologically, COPD patients infected with Mycobacterium tuberculosis exhibit a significant reduction in lymphocytes and CD4⁺ T lymphocytes, leading to impaired immune function and reduced ability to clear Mycobacterium tuberculosis (40). This immunosuppression increases the risk of developing active TB, with greater potential for pulmonary and extrapulmonary dissemination (43). Active TB, in turn, causes further pulmonary damage, exacerbating airflow limitation, increasing treatment complexity, and negatively impacting quality of life and long-term outcomes (31).

The clinical evidence of these bidirectional harms underscores the necessity for more proactive and integrated management strategies for patients with COPD-PTB comorbidity. Given their worse pulmonary function, higher risk of acute exacerbations, and poorer prognosis, clinicians should emphasize standardized COPD treatment, which includes ensuring correct inhaler technique and initiating pulmonary rehabilitation early, and implement closer follow-up plans to monitor disease progression. Furthermore, for COPD patients requiring inhaled corticosteroid therapy, particularly those residing in high-TB-burden areas or with a history of PTB, clinicians must carefully weigh the benefits against the potential risk of TB reactivation.

Diagnosis

Currently, there is still no internationally standardized diagnostic criterion for COPD with coexisting PTB. Through a systematic review of relevant clinical studies published in recent years (Table 5), this study found that the number of studies in this area remains relatively limited, and the diagnostic criteria used in various studies show significant heterogeneity, particularly with regard to PTB diagnosis. Such diversity of these diagnostic criteria reflects the need to balance specificity and sensitivity of diagnosis in clinical research. However, it also complicates comparisons across studies and introduces the potential for bias.

Current clinical studies indicate that the diagnostic criteria for COPD in patients with concurrent COPD and PTB are consistent with those for COPD alone. The majority take objective pulmonary function testing as the core diagnostic basis, primarily using a FEV₁/FVC <0.7 after bronchodilator as the predominant diagnostic threshold. However, the diagnostic criteria for PTB are inconsistent, ranging from relying solely on patient medical history to requiring microbiological confirmation, and even combining imaging and immunological assays. Some studies have defined PTB cases exclusively based on self-reported history of disease or prior anti-TB treatment, a pragmatic but limited approach that risks misclassification. Lacking of uniformity in diagnostic criteria is a significant challenge in understanding disease burden, risk factors and prognosis, which highlights the necessity of establishing more standardized and feasible yet accurate diagnostic criteria in the future.

Therefore, in the diagnosis of concurrent COPD and PTB, for patients with chronic respiratory symptoms, confirmation of a PTB-related history (microbiologically confirmed or with a record of completed anti-TB treatment) or characteristic imaging sequelae (e.g., upper-lobe fibrosis, calcification, bronchiectasis) should be followed by spirometric confirmation of persistent airflow limitation. Concurrent chest imaging (at minimum radiography, ideally CT) is recommended to identify the co-existence of post-TB structural changes and imaging features of COPD/emphysema, which is particularly useful for symptomatic patients with borderline spirometric results or when spirometry is unavailable. In resource-constrained settings, a combination of symptoms and chest X-ray evidence of significant post-TB sequelae may serve as an initial screening strategy, with further assessment recommended when feasible. Future efforts could integrate biomarkers and quantitative imaging tools into this framework to advance toward a more precise and phenotype-driven diagnostic approach for COPD-PTB comorbidity.

Pulmonary function

In a study conducted by Oh et al., COPD patients with coexisting PTB (n=92) demonstrated lower FVC%, vital capacity (VC%), total lung capacity (TLC%), and functional residual capacity (FRC%) compared to patients with COPD alone (n=78; Table 6) (52). Jiang et al. reported that COPD patients with PTB (n=44) had significantly lower FVC%, FEV₁%, and FEV₁/FVC ratios compared to those with COPD alone (n=57; Table 6) (47). Similarly, in Li et al., patients with COPD and PTB (n=68) had lower forced expiratory flow (FEF) 25, FEF50, FEF75, and FEV₁/FVC than patients with COPD alone (n=68; Table 6) (54). However, Guiedem et al. found that the FVC%, FEV₁%, and FEV₁/FVC ratios in COPD patients with PTB (n=40) were found to be higher than those in patients with COPD alone (n=50; Table 6) (53).

The sample sizes across these studies were generally limited, which may affect the generalizability of the results. Furthermore, variations in pulmonary function outcomes may be influenced by additional factors. Differences in the type of post-TB sequelae among study populations could contribute to distinct functional impairments—for example, fibrotic-predominant lesions may mainly reduce lung volumes, while bronchiectasis-predominant changes are often associated with more pronounced airflow obstruction. Moreover, the diagnostic criteria for PTB were inconsistent across studies. Patients enrolled based solely on imaging sequelae may differ in the extent of lung structural damage and inflammatory burden from those with microbiologically confirmed active TB, potentially influencing pulmonary function measurements. The timing of functional assessment is also relevant, as acute inflammation during active TB infection may lead to transient functional decline, whereas impairment related to chronic sequelae tends to be persistent. Therefore, future studies should aim to expand sample sizes and enhance methodological rigor through multicenter collaborations to better control for confounding variables. We recommend stratifying patients based on imaging phenotypes of post-TB sequelae, TB activity status, and standardized diagnostic criteria. Such an approach would help clarify the characteristics and underlying mechanisms of pulmonary functional changes in patients with COPD-PTB comorbidity.

Biomarkers

Biomarkers for COPD with coexisting PTB are mostly indicators related to inflammation, such as interleukin (IL)-6, SIRI, C-reactive protein (CRP), tumor necrosis factor (TNF)-α, myeloperoxidase (MPO), IL-1α, IL-17, etc. (Table 7). Additionally, several studies have also focused on combined indicators of multiple biomarkers, including TNF-α with MPO, TNF-α with IL-6, and MPO with IL-6, which may enhance diagnostic and prognostic value (Table 7).

Beyond inflammatory mediators, some studies have also found that certain metabolites (e.g., aspartic acid, asparagine, serotonin), lipid indices [e.g., high-density lipoprotein cholesterol (HDL-C)], and pulmonary function markers (e.g., FEV₁/FVC ratio) may also serve as candidate biomarkers for COPD complicated by PTB (Table 7). Among them, IL-6 has been the most extensively studied; its levels are positively correlated with the frequency of exacerbations and disease stage, and have shown potential in predicting disease deterioration (Table 7).

Although associations have been observed between inflammatory biomarkers (e.g., IL-6, CRP) and disease severity or exacerbation risk in COPD-PTB comorbidity, translating these biomarkers into clinically useful tools faces significant challenges, primarily due to their insufficient disease specificity. Current evidence indicates that inflammatory markers such as IL-6 and CRP are elevated not only in COPD-PTB comorbidity but also in COPD alone or active PTB, reflecting a general state of systemic or airway inflammation rather than enabling effective differentiation between the comorbid condition and either disease alone (56). Furthermore, the levels of these markers are not static; they often fluctuate with exacerbations, infections, or therapeutic interventions. Therefore, in current clinical practice, dynamic monitoring of these inflammatory markers is more suitable for assessing disease stability in patients with comorbid COPD-PTB, predicting acute exacerbations, evaluating treatment responses, and guiding adjustments to management strategies.

To enhance clinical utility, future research should clearly distinguish between biomarkers for disease activity monitoring and those for diagnostic differentiation. For disease activity monitoring, priority should be given to validating biomarkers strongly linked to clinical outcomes. In the COPD population, existing studies have confirmed that enzymes related to inflammation and glutathione metabolism (e.g., mRNA expression profiles) in peripheral blood monocytes differ between COPD exacerbations and stable states, serving as markers of disease activity (57). Additionally, blood-based markers such as eosinophil counts and fibrinogen levels can predict COPD exacerbations and treatment responses (58). Large-scale cohort studies have also identified specific plasma proteins [e.g., chymotrypsin C (CTRC), oncostatin M (OSM), matrix metalloproteinase (MMP)-10)] associated with cardiopulmonary risks in COPD patients, with significant changes during exacerbations (59,60). Regarding TB-related biomarkers, evidence shows that levels of specific plasma-released proteins [e.g., fetuin-B (FETUB), γ-glutamyl hydrolase (GGH), serpin D1 (SERPIND1)] are significantly elevated in patients with active TB, aiding in distinguishing PTB patients from healthy individuals or those with other respiratory infections (61).

For diagnostic differentiation, identifying biomarker combinations that can specifically discriminate COPD-PTB comorbidity from either condition alone is crucial. Current research suggests that metabolic profiles characterized by upregulation of the arachidonic acid metabolism pathway and dysregulation of the tryptophan-kynurenine pathway may offer higher discriminatory value than single inflammatory factors (62). Integrating inflammatory indicators (e.g., IL-6, TNF-α) with such metabolic signatures, along with imaging features of parenchymal destruction and airway remodeling, holds promise for constructing more specific diagnostic models. Moreover, in patients with a history of PTB, worse lung function correlates with higher disease risk. This supports using pulmonary function tests for long-term monitoring, complementing biomarkers (19).

Therefore, key future directions include establishing clinically meaningful dynamic thresholds through prospective cohorts and advancing the development of multidimensional biomarker panels integrating inflammatory, metabolic, imaging, and functional information. This approach will not only more precisely elucidate the pathophysiological network of COPD-PTB comorbidity but also aims to translate these scientific insights into clinical tools. Systematic monitoring of key biomarkers can guide individualized treatment, thereby optimising patient management and improving overall prognosis.

Pathophysiological mechanisms

Le et al. found that cells treated with both cigarette smoke extract (CSE) and Bacillus Calmette-Guérin (BCG) exhibited higher levels of reactive oxygen species (ROS) and significantly increased apoptosis compared with cells exposed to CSE alone. Moreover, cells in this combined treatment group showed elevated expression of both M1- and M2-type macrophage-related cytokines, including inducible nitric oxide synthase (iNOS), interferon-γ (IFN-γ), TNF-α, and IL-10 (63). Meanwhile, expression of key signaling molecules such as phosphorylated Stat5 (p-Stat5) and Stat3 (p-Stat3), which regulate M1/M2 macrophage polarization, was also enhanced (63).

In animal models, mice exposed to both CSE and BCG exhibited greater weight loss compared to those exposed to CSE alone (63). Furthermore, bronchoalveolar lavage fluid (BALF) from both CSE and BCG exposure groups contained higher numbers of inflammatory cells, and lung tissue demonstrated increased counts of F4/80-positive macrophages, indicating that the infiltration of macrophages played an important role in this inflammatory environment (63). The relevant molecular detection results showed that the expression levels of iNOS and CD163 significantly increased in these mice, and there was an obvious co-localization phenomenon between iNOS or CD163 and F4/80 (63). Notably, the mice in the both CSE and BCG exposure group also displayed significantly higher expression levels of MMP-9 and MMP-12 in the lung tissue and BALF (63).

These findings indicate that combined exposure to CSE and BCG may significantly amplify inflammatory responses by activating both M1 and M2 macrophages signaling pathways. The resultant high levels of ROS, cytokines, and MMPs reflect the complexity of the inflammatory response. Research suggests that under dual influence of smoking and Mycobacterium tuberculosis infection, the immune response of the body may undergo significant changes, which may provide a plausible explanation for the observed clinical exacerbation. This finding holds significant theoretical importance for understanding exacerbation of clinical symptoms, decline in lung function, and poor prognosis in patients with COPD who are co-infected with PTB. In addition, these results offer valuable reference for the development of future treatment strategies and disease prevention.

Emerging imaging techniques in COPD and PTB

X-ray dark-field chest imaging

X-ray dark-field chest imaging is a novel radiological technique that has not yet been widely adopted in clinical practice. It is highly sensitive to the microstructure of examined tissues by measuring small-angle scattering at interfaces (64). In X-ray dark-field chest imaging, healthy alveoli exhibit relatively high signal intensity, whereas pathological changes such as emphysema and fibrosis, which disrupt alveolar structure, lead to a weakening of the dark-field signal (65). It is significantly superior to conventional chest radiography in detecting early emphysema and classifying its severity (64). Studies have shown that the signal intensity of dark-field imaging correlates significantly with the CT emphysema index, and the heterogeneity of signal distribution is also associated with the severity of emphysema (66). Compared to conventional CT, which primarily relies on density information, dark-field imaging demonstrates greater sensitivity in distinguishing early stages of emphysema, such as mild versus moderate disease, thereby enabling more refined staging (64). Furthermore, research indicates that the correlation between X-ray dark-field chest imaging and pulmonary diffusion capacity is stronger than that of CT-based parameters, with correlation coefficients of 0.62 and −0.27, respectively (66). This suggests that dark-field imaging not only reflects structural alterations but also indirectly assesses the extent of pulmonary functional impairment, thereby compensating for the inability of pulmonary function tests to localize pathological changes.

Currently, research on X-ray dark-field chest imaging has primarily focused on COPD, with no related studies conducted on PTB. Nevertheless, given its high value in detecting emphysema and pulmonary fibrosis, this technique holds promise for future application in screening for concurrent COPD and PTB. In patients with COPD-PTB comorbidity, PTB often leads to fibrosis or localized emphysema. Dark-field imaging may differentiate between these conditions through characteristic signal patterns—an ability that is challenging for conventional CT (67). Additionally, by utilizing its energy-dependent signal analysis capability, this technique could further distinguish fibrotic from emphysematous regions, which is important for evaluating structural changes induced by PTB (67). Since PTB is a risk factor for lung cancer in COPD patients, the high sensitivity of dark-field imaging to pulmonary microstructure may also facilitate the early detection of malignancy-associated alterations (68).

In terms of clinical practicality, the radiation dose of dark-field imaging is extremely low, approximately 0.035 mSv, which is only about 1% of that of chest CT. It also offers advantages such as lower cost and rapid scanning time, completing image acquisition in just 7 seconds (66). Operating at clinical dose levels, it provides diagnostic information with reduced radiation exposure compared to conventional CT (66). This is particularly important for COPD-PTB patients who require long-term follow-up, as it helps minimize cumulative radiation risk.

In summary, further exploration of the potential application of X-ray dark-field imaging in complex conditions such as COPD-PTB will not only enhance the early detection of PTB but also improve the overall management of COPD. In-depth research in this direction will provide new tools for clinical practice, allowing physicians to more comprehensively assess both structural and functional pulmonary changes and formulate more precise treatment strategies, ultimately leading to improved patient outcomes.

Artificial intelligence (AI)-assisted CT

Since patients with COPD usually lack clear and characteristic symptoms, clinical diagnosis relies mainly on pulmonary function testing, the accuracy of which depends on patient cooperation (69). Therefore, this limitation frequently leads to misdiagnosis and underdiagnosis in clinical practice (69). AI has the potential to significantly improve the sensitivity and specificity of diagnoses. AI encompasses a series of technologies that simulate human intelligence, among which machine learning and deep learning being particularly widely used. Here, we have compiled relevant research findings on AI-assisted CT in COPD and PTB over the past year.

In COPD, AI-assisted CT has been widely applied in various aspects (Table 8), including disease staging and classification, small airway disease assessment, severity evaluation, detection of interstitial abnormalities and pulmonary fibrosis, as well as assessment of lipid metabolism and deposition changes. In addition, AI-assisted CT have also been applied to predict severity of COPD, risk of pneumonia, and mortality, etc.

Currently, AI-assisted CT also demonstrates promising application potential in diagnosis of PTB (Table 9), such as diagnosing co-infections with other pathogens, identifying specific causative organisms, predicting treatment success rates, and differentiating TB from other pulmonary diseases, including pulmonary mucormycosis, invasive pulmonary aspergillosis, talaromycosis, non-tuberculous mycobacterial lung diseases, TB-related fibrotic mediastinitis, endobronchial lung cancer, etc. Moreover, AI models have demonstrated potential in predicting the risk of pulmonary embolism.

Despite these advances, AI-assisted CT has not yet been applied to the diagnosis of COPD with concurrent PTB. In clinical practice, the application of AI-assisted CT technology to the assessment of COPD-PTB comorbidity demonstrates significant potential. AI tools enable rapid, automated identification and quantitative analysis of emphysema extent, fibrotic regions, and small airway disease from CT images, allowing clinicians to move beyond conventional subjective visual assessment and objectively quantify the relative contributions of both disease components in individual patients. Furthermore, this technological advancement not only assists radiologists in generating more precise diagnostic reports but, more importantly, provides pulmonologists with reliable bases for disease phenotype classification—such as distinguishing between “emphysema-dominant” or “fibrosis-dominant” subtypes. This capability offers objective reference points for developing personalized treatment plans, optimizing clinical decision-making, and evaluating prognosis. Particularly in resource-limited settings, this AI-assisted analytical approach significantly reduces diagnostic time while decreasing dependence on highly specialized radiologists, thereby enhancing the feasibility and accessibility of comprehensive comorbidity assessment (102).

Future research should focus on developing relevant AI models to enhance recognition and diagnosis capabilities of this complex disease. By integrating AI with imaging analytics, it may be possible to enhance diagnostic accuracy, improve early detection, and support tailored therapeutic strategies. This direction holds promise for advancing the early diagnosis and timely intervention of concurrent COPD and PTB.

Dual-energy computed tomography (DECT)

DECT can simultaneously provide high-resolution anatomical details and functional information on ventilation and perfusion in a single scan, including the location and pattern of defects (103,104). Recent studies have explored its clinical applications in COPD and PTB (Table 10). Our summary indicates that DECT is correlated with pulmonary perfusion imaging in patients with COPD and can be used effectively to assess regional ventilation. Importantly, DECT can generate virtual non-contrast (VNC) images from contrast-enhanced scans, which enables appropriate non-contrast imaging of COPD even when contrast enhancement is performed (107). In the diagnosis and management of COPD, ventilation/perfusion assessment using DECT facilitates early detection and staging of emphysema (111). When integrated with deep learning techniques, DECT can further improve the quantitative accuracy of emphysema and enhance the identification of early functional alterations, thereby enabling more refined disease stratification and personalized clinical management. Compared to conventional CT and pulmonary function tests, the unique advantage of DECT lies in its ability to simultaneously provide clear anatomical details and reflect pulmonary functional status. This integrated approach allows for early detection of pulmonary functional abnormalities and establishes a more accurate structure-function correlation.

In the diagnosis of PTB, DECT has been used to differentiate solitary PTB from solitary adenocarcinoma, with results showing good consistency with the manifestations of HRCT under certain parameter settings (109,110).

For patients with concurrent COPD and PTB, who often present with complex structural alterations and functional impairment, the unique clinical advantage of applying DECT lies in its capacity for simultaneous evaluation of structure and function within a single imaging session. In these individuals, pulmonary ventilation and perfusion may exhibit complex abnormalities due to emphysema, fibrosis, and bronchial distortion. DECT enables the acquisition of both detailed anatomical images and regional pulmonary blood flow maps in a single scan. This capability not only assists clinicians in intuitively understanding the functional basis of symptoms such as dyspnea and hypoxemia but also helps differentiate whether the underlying pathological mechanism is predominantly due to reduced pulmonary vascular bed or ventilation-perfusion mismatch. Furthermore, this information may provide valuable insights for assessing disease severity, guiding oxygen therapy, and formulating individualized rehabilitation plans, delivering critical data that are difficult to obtain directly through conventional CT or pulmonary function tests alone.

While DECT shows potential in assessing COPD and PTB individually, its application in patients with coexisting COPD and PTB remains largely unexplored. Future research should investigate DECT in this context, focusing on its utility for diagnosis, differential diagnosis, and prognostic prediction. Such studies may provide more robust imaging evidence to guide clinical decision-making and improve patient outcomes.

Challenges and future perspectives

Current limitations

The comorbidity of COPD and PTB poses a major challenge for respiratory disease due to their epidemiological overlap and bidirectional clinical interactions. Although research on comorbidity has increased in recent years, the number of available clinical and basic studies remains limited. At present, diagnosis relies on heterogeneous combinations of criteria, such as lung function, imaging, medical history, etc., lacking an internationally unified framework, which leads to reduced comparability and a high rate of underdiagnosis. Inflammatory biomarkers such as IL-6 and TNF-α lack specificity. For example, IL-6 levels are elevated not only in patients with COPD-PTB comorbidity but also in those with COPD alone or active PTB, making it difficult to reliably differentiate between these conditions. Moreover, biomarker levels fluctuate with disease activity, which limits the clinical utility of isolated measurements. Mechanistic research is also scarce, particularly regarding the synergistic lung injury caused by smoking and TB infection. Emerging imaging techniques face additional limitations. X-ray dark-field imaging, though promising for detecting emphysema and fibrosis, has not yet been widely implemented in clinical practice. AI-assisted CT carries risks of overfitting and limited generalizability in small-sample learning, and specialized algorithms for COPD and PTB differentiation are not yet available. DECT shows strong potential for simultaneous ventilation-perfusion assessment, but lacks multicenter validation in comorbidity scenarios.

Future perspectives

Future research should focus on establishing a multimodal diagnostic framework that integrates clinical features, pulmonary function, radiomics, and molecular biomarkers. Within this framework, research on biomarkers must advance beyond correlational analysis to elucidate their specific clinical applicability. This entails not only identifying specific biomarkers capable of reliably distinguishing between COPD alone, PTB alone, and their comorbidity, but more critically, determining which indicators are suitable for longitudinally monitoring disease progression and providing early warning of acute exacerbations in patients with the comorbidity. Furthermore, prospective cohort studies are essential to validate dynamic thresholds for these biomarkers, thereby offering a robust foundation for dynamic, individualized therapeutic decision-making. Concurrently, significant efforts should be directed toward the clinical integration and validation of advanced imaging technologies. This includes developing AI-based quantitative tools tailored for comorbidity assessment to assist clinicians in efficiently and objectively quantifying structural alterations. It also involves exploring the unique value of functional imaging techniques, such as DECT, in evaluating the complex ventilation-perfusion abnormalities present in patients with COPD-PTB comorbidity. Integrating these imaging insights with clinical characteristics and trajectories of lung function decline will enable more precise patient risk stratification.

In summary, the complexity of COPD and PTB comorbidity demands innovation across the full spectrum from basic research to clinical application. Key future directions include the standardization of multimodal diagnostic approaches, the advancement of AI-driven personalized therapy, and the strengthening of multidisciplinary collaboration. These efforts will be crucial for improving diagnostic accuracy, differential capacity, and prognostic stratification, ultimately enhancing clinical outcomes for patients with COPD and PTB comorbidity.

Conclusions

This study provides an integrated bibliometric analysis combined with a comprehensive narrative synthesis focusing on the comorbidity of COPD and PTB. Over the past 15 years, global research output has shown a steady upward trend, with China and the United States emerging as major contributors. Citation network analysis highlights the continued influence of high-impact epidemiological studies, while keyword clustering and burst analyses reveal rapidly evolving research hotspots such as pulmonary function impairment, inflammatory biomarkers, radiomics-based imaging, and post-TB structural lung disease.

Epidemiological evidence consistently demonstrates that prior PTB substantially increases the risk of developing COPD, and that coexisting disease markedly worsens lung function, symptom burden, exacerbation frequency, and overall prognosis. Meanwhile, COPD increases susceptibility to active PTB and contributes to more severe disease presentations, forming a bidirectionally harmful clinical cycle. Biomarker research—particularly involving IL-6, TNF-α, MPO, and metabolomic indicators—shows potential but remains limited by methodological heterogeneity and insufficient multicenter validation. Mechanistic studies highlight the synergistic inflammatory and tissue-destructive effects of Mycobacterium tuberculosis and cigarette smoke exposure, providing biological explanations for clinical deterioration.

Advances in imaging—such as X-ray dark-field technology, AI-assisted CT interpretation, and DECT—offer promising avenues for early detection, structural assessment, and differential diagnosis, yet their application to COPD-PTB comorbidity remains scarce. Future research should focus on establishing unified diagnostic frameworks, validating disease-specific biomarkers, developing multimodal imaging-based evaluation systems, and leveraging AI-driven models to improve diagnostic precision and prognostic assessment.

Altogether, this study maps the knowledge landscape and emerging frontiers of COPD-PTB research. The findings emphasize the urgent need for standardized criteria, larger prospective cohorts, and cross-disciplinary collaboration to improve clinical recognition, individualized treatment, and long-term management of this complex comorbidity.

Acknowledgments

None.

Footnote

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Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2648/coif). The authors have no conflicts of interest to declare.

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References

1. Xu J, Zeng Q, Li S, et al. Inflammation mechanism and research progress of COPD. Front Immunol 2024;15:1404615. [Crossref] [PubMed]
2. Trajman A, Campbell JR, Kunor T, et al. Tuberculosis. Lancet 2025;405:850-66. [Crossref] [PubMed]
3. Stuck L, Klinkenberg E, Abdelgadir Ali N, et al. Prevalence of subclinical pulmonary tuberculosis in adults in community settings: an individual participant data meta-analysis. Lancet Infect Dis 2024;24:726-36. [Crossref] [PubMed]
4. Gai X, Allwood B, Sun Y. Advances in the awareness of tuberculosis-associated chronic obstructive pulmonary disease. Chin Med J Pulm Crit Care Med 2024;2:250-6. [Crossref] [PubMed]
5. Alkhanani MF. Assessing the Impact of Air Quality and Socioeconomic Conditions on Respiratory Disease Incidence. Trop Med Infect Dis 2025;10:56. [Crossref] [PubMed]
6. Amipara AG, Rangari A, Ghewade B. Diagnosis and Management of Tuberculous Pleural Effusion in a Patient With Chronic Obstructive Pulmonary Disease: A Case Report. Cureus 2024;16:e64505. [Crossref] [PubMed]
7. Auld SC, Barczak AK, Bishai W, 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;210:979-93. [Crossref] [PubMed]
8. Zenner D, Martineau AR. Tuberculosis: an under-recognized cause of COPD? Solving the post-TB lung disease puzzle, one piece at a time. Int J Epidemiol 2025;54:dyaf033.
9. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2095-128. [Crossref] [PubMed]
10. Vos T, Flaxman AD, Naghavi M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2163-96. [Crossref] [PubMed]
11. GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017;390:1151-210. [Crossref] [PubMed]
12. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2100-32. [Crossref] [PubMed]
13. Racanelli AC, Kikkers SA, Choi AMK, et al. Autophagy and inflammation in chronic respiratory disease. Autophagy 2018;14:221-32. [Crossref] [PubMed]
14. Smith NL, Denning DW. Underlying conditions in chronic pulmonary aspergillosis including simple aspergilloma. Eur Respir J 2011;37:865-72. [Crossref] [PubMed]
15. Blanc PD, Annesi-Maesano I, Balmes JR, et al. The Occupational Burden of Nonmalignant Respiratory Diseases. An Official American Thoracic Society and European Respiratory Society Statement. Am J Respir Crit Care Med 2019;199:1312-34.
16. Andréjak C, Nielsen R, Thomsen VØ, et al. Chronic respiratory disease, inhaled corticosteroids and risk of non-tuberculous mycobacteriosis. Thorax 2013;68:256-62. [Crossref] [PubMed]
17. Byrne AL, Marais BJ, Mitnick CD, et al. Tuberculosis and chronic respiratory disease: a systematic review. Int J Infect Dis 2015;32:138-46. [Crossref] [PubMed]
18. Meghji J, Mortimer K, Agusti A, et al. Improving lung health in low-income and middle-income countries: from challenges to solutions. Lancet 2021;397:928-40. [Crossref] [PubMed]
19. Kamenar K, Hossen S, Gupte AN, et al. Previous tuberculosis disease as a risk factor for chronic obstructive pulmonary disease: a cross-sectional analysis of multicountry, population-based studies. Thorax 2022;77:1088-97. [Crossref] [PubMed]
20. Zeng Z, Chen H, Shao Z, et al. Associations of prior pulmonary tuberculosis with the incident COPD: a prospective cohort study. Ther Adv Respir Dis 2024;18:17534666241239455. [Crossref] [PubMed]
21. Wang J, Yu L, Yang Z, et al. Development of chronic obstructive pulmonary disease after a tuberculosis episode in a large, population-based cohort from Eastern China. Int J Epidemiol 2025;54:dyae174. [Crossref] [PubMed]
22. Hu S, Yu Q, Liu F, et al. A Novel Inflammatory Indicator for Tuberculosis-Associated Obstructive Pulmonary Disease (TOPD): The Systemic Inflammatory Response Index (SIRI). J Inflamm Res 2024;17:4219-28. [Crossref] [PubMed]
23. Kim T, Choi H, Kim SH, 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. [Crossref] [PubMed]
24. Kim BG, Shin SH, Lee SK, et al. Risk of incident chronic obstructive pulmonary disease during longitudinal follow-up in patients with nontuberculous mycobacterial pulmonary disease. Respir Res 2024;25:333. [Crossref] [PubMed]
25. Yin P, Wu J, Wang L, et al. The Burden of COPD in China and Its Provinces: Findings From the Global Burden of Disease Study 2019. Front Public Health 2022;10:859499. [Crossref] [PubMed]
26. Public Health Science Data Center. Annual Statistics of Pulmonary Tuberculosis by Region in China, 2020. Available online: https://www.phsciencedata.cn/Share/ky_sjml.jsp?id=f90892b6-c000-48fe-a73e-a4c6db172385
27. Chang W, Li Z, Liang Q, et al. The Incidence, Risk Factors, and Predictive Model of Obstructive Disease in Post-Tuberculosis Patients. Int J Chron Obstruct Pulmon Dis 2024;19:2457-66. [Crossref] [PubMed]
28. Liang Y, Yangzom D, Tsokyi L, et al. Clinical and Radiological Features of COPD Patients Living at ≥3000 m Above Sea Level in the Tibet Plateau. Int J Chron Obstruct Pulmon Dis 2021;16:2445-54. [Crossref] [PubMed]
29. Jin J, Li S, Yu W, et al. Emphysema and bronchiectasis in COPD patients with previous pulmonary tuberculosis: computed tomography features and clinical implications. Int J Chron Obstruct Pulmon Dis 2018;13:375-84. [Crossref] [PubMed]
30. Gai X, Allwood B, Sun Y. Post-tuberculosis lung disease and chronic obstructive pulmonary disease. Chin Med J (Engl) 2023;136:1923-8. [Crossref] [PubMed]
31. Kayongo A, Nyiro B, Siddharthan T, et al. Mechanisms of lung damage in tuberculosis: implications for chronic obstructive pulmonary disease. Front Cell Infect Microbiol 2023;13:1146571. [Crossref] [PubMed]
32. Wang Y, Li Z, Li F. Impact of Previous Pulmonary Tuberculosis on Chronic Obstructive Pulmonary Disease: Baseline Results from a Prospective Cohort Study. Comb Chem High Throughput Screen 2023;26:93-102. [Crossref] [PubMed]
33. Chen S, Kuhn M, Prettner K, et al. The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020-50: a health-augmented macroeconomic modelling study. Lancet Glob Health 2023;11:e1183-93. [Crossref] [PubMed]
34. Xing Z, Sun T, Janssens JP, et al. Airflow obstruction and small airway dysfunction following pulmonary tuberculosis: a cross-sectional survey. Thorax 2023;78:274-80. [Crossref] [PubMed]
35. Bae J, Lee HJ, Choi KY, et al. Risk factors of acute exacerbation and disease progression in young patients with COPD. BMJ Open Respir Res 2024;11:e001740. [Crossref] [PubMed]
36. Ji HW, Yu S, Sim YS, et al. Clinical Significance of Various Pathogens Identified in Patients Experiencing Acute Exacerbations of COPD: A Multi-center Study in South Korea. Tuberc Respir Dis (Seoul) 2025;88:292-302. [Crossref] [PubMed]
37. Chandra E, Anand S, Gupta A, et al. Prevalence and impact of anxiety and depression in patients with chronic obstructive pulmonary disease: a comparative analysis of post-tuberculosis and non-tuberculosis chronic obstructive pulmonary disease cases. Monaldi Arch Chest Dis 2025; Epub ahead of print. [Crossref]
38. Park HY, Kang D, Shin SH, et al. Pulmonary Tuberculosis and the Incidence of Lung Cancer among Patients with Chronic Obstructive Pulmonary Disease. Ann Am Thorac Soc 2022;19:640-8. [Crossref] [PubMed]
39. Qi F, Yang H, Han Y, et al. Coexistent Pulmonary Tuberculosis and Lung Cancer: An Analysis of Incidence Trends, Financial Burdens and Influencing Factors. Cancer Innov 2025;4:e70009. [Crossref] [PubMed]
40. Zhang Y, Zhang Y, Ma N, et al. Correlation between elderly patients with COPD and the impact on immunity in tuberculosis patients: A retrospective study. Medicine (Baltimore) 2024;103:e40140. [Crossref] [PubMed]
41. Tan J, Shi X, Pi Y, et al. Nutritional scores predict the prognosis of patients with pulmonary tuberculosis. Front Nutr 2024;11:1454207. [Crossref] [PubMed]
42. Sy KTL, Horváth-Puhó E, Sørensen HT, et al. Burden of Chronic Obstructive Pulmonary Disease Attributable to Tuberculosis: A Microsimulation Study. Am J Epidemiol 2023;192:908-15. [Crossref] [PubMed]
43. Sershen CL, Salim T, May EE. Investigating the comorbidity of COPD and tuberculosis, a computational study. Front Syst Biol 2023;3:940097. [Crossref] [PubMed]
44. Jiang Z, Dai Y, Chang J, et al. The Clinical Characteristics, Treatment and Prognosis of Tuberculosis-Associated Chronic Obstructive Pulmonary Disease: A Protocol for a Multicenter Prospective Cohort Study in China. Int J Chron Obstruct Pulmon Dis 2024;19:2097-107. [Crossref] [PubMed]
45. Kim DJ, Oh JY, Rhee CK, et al. Metabolic Fingerprinting Uncovers the Distinction Between the Phenotypes of Tuberculosis Associated COPD and Smoking-Induced COPD. Front Med (Lausanne) 2021;8:619077. [Crossref] [PubMed]
46. Mp S, Mohanty Mohapatra M, Mahesh Babu V, et al. Metabolic Syndrome in Post-Pulmonary Tuberculosis-Associated Obstructive Airway Disease: A Cross-Sectional Analytical Study. Cureus 2022;14:e23640. [Crossref] [PubMed]
47. Jiang M, Pang N, Wang J, et al. Characteristics of Serum Autoantibody Repertoire and Immune Subgroup Variation of Tuberculosis-Associated Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis 2023;18:2867-86. [Crossref] [PubMed]
48. Swain S, Pothal S, Behera A, et al. Treatment outcome among Post TB obstructive airways diseases and COPD: A prospective cohort study. J Family Med Prim Care 2021;10:3411-6. [Crossref] [PubMed]
49. Huang HL, Cheng MH, Lee MR, et al. Prevalence and treatment outcomes of latent tuberculosis infection among older patients with chronic obstructive pulmonary disease in an area with intermediate tuberculosis burden. Emerg Microbes Infect 2025;14:2497302. [Crossref] [PubMed]
50. Ali B, Iqbal T, Iqbal Q, et al. Chronic Obstructive Pulmonary Diseases in Post Tuberculosis Patients. Pakistan Journal of Chest Medicine 2023;29:13-7.
51. Park HJ, Byun MK, Lee J, et al. Airflow obstruction and chronic obstructive pulmonary disease are common in pulmonary tuberculosis even without sequelae findings on chest X-ray. Infect Dis (Lond) 2023;55:533-42. [Crossref] [PubMed]
52. Oh JY, Lee YS, Min KH, et al. Difference in systemic inflammation and predictors of acute exacerbation between smoking-associated COPD and tuberculosis-associated COPD. Int J Chron Obstruct Pulmon Dis 2018;13:3381-7. [Crossref] [PubMed]
53. Guiedem E, Pefura-Yone EW, Ikomey GM, et al. Cytokine profile in the sputum of subjects with post-tuberculosis airflow obstruction and in those with tobacco related chronic obstructive pulmonary disease. BMC Immunol 2020;21:52. [Crossref] [PubMed]
54. Li J, Ye M, Wang H, et al. The exacerbating effects of stable pulmonary tuberculosis on the deterioration of inflammatory response, coagulation function, and pulmonary function in COPD: A propensity score-matched retrospective study. J Clin Tuberc Other Mycobact Dis 2025;40:100545. [Crossref] [PubMed]
55. Jiang E, Fu Y, Wang Y, et al. The role and clinical significance of myeloperoxidase (MPO) and TNF-α in prognostic evaluation of T-COPD. BMC Pulm Med 2025;25:192. [Crossref] [PubMed]
56. Vivekanandan MM, Adankwah E, Aniagyei W, et al. Plasma cytokine levels characterize disease pathogenesis and treatment response in tuberculosis patients. Infection 2023;51:169-79. [Crossref] [PubMed]
57. Oit-Wiscombe I, Virág L, Kilk K, et al. Pattern of Expression of Genes Involved in Systemic Inflammation and Glutathione Metabolism Reveals Exacerbation of COPD. Antioxidants (Basel) 2024;13:953. [Crossref] [PubMed]
58. Mahler DA, Halpin DMG. Peak Inspiratory Flow as a Predictive Therapeutic Biomarker in COPD. Chest 2021;160:491-8. [Crossref] [PubMed]
59. Cardenas EI, Andelid K, Pournaras N, et al. Serum proteomics links the cardiorespiratory biomarkers CTRC, OSM, and MMP-10 to exacerbation severity and number in patients with COPD. Clin Sci (Lond) 2025;139:449-62. [Crossref] [PubMed]
60. Ngo D, Pratte KA, Flexeder C, et al. Systemic Markers of Lung Function and Forced Expiratory Volume in 1 Second Decline across Diverse Cohorts. Ann Am Thorac Soc 2023;20:1124-35. [Crossref] [PubMed]
61. Schiff HF, Walker NF, Ugarte-Gil C, et al. Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers. JCI Insight 2024;9:e173273. [Crossref] [PubMed]
62. Huang Z, Zhao YL, Wei J, et al. Mulberry (Morus alba l.) leaf improves arachidonic acid metabolism disorder in chronic obstructive pulmonary disease. Phytomedicine 2025;148:157392. [Crossref] [PubMed]
63. Le Y, Cao W, Zhou L, et al. Infection of Mycobacterium tuberculosis Promotes Both M1/M2 Polarization and MMP Production in Cigarette Smoke-Exposed Macrophages. Front Immunol 2020;11:1902. [Crossref] [PubMed]
64. Urban T, Sauter AP, Frank M, et al. Dark-Field Chest Radiography Outperforms Conventional Chest Radiography for the Diagnosis and Staging of Pulmonary Emphysema. Invest Radiol 2023;58:775-81. [Crossref] [PubMed]
65. Drexel F, Sideri-Lampretsa V, Bast H, et al. Deformable image registration of dark-field chest radiographs for functional lung assessment. Med Phys 2025;52:e18023. [Crossref] [PubMed]
66. Willer K, Fingerle AA, Noichl W, et al. X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study. Lancet Digit Health 2021;3:e733-44. [Crossref] [PubMed]
67. Taphorn K, Mechlem K, Sellerer T, et al. Direct Differentiation of Pathological Changes in the Human Lung Parenchyma With Grating-Based Spectral X-ray Dark-Field Radiography. IEEE Trans Med Imaging 2021;40:1568-78. [Crossref] [PubMed]
68. Guo P, Zhang L, Men L, et al. Analytical Reconstruction of Human-Scale Dark-Field CT. IEEE Trans Med Imaging 2026;45:125-34. [Crossref] [PubMed]
69. Wang RY, Sun W, Luo ZJ, et al. Application of artificial intelligence in combination with CT radiomics in chronic obstructive pulmonary disease. Zhonghua Jie He He Hu Xi Za Zhi 2025;48:186-90. [Crossref] [PubMed]
70. Chaudhary MFA, Awan HA, Gerard SE, et al. Deep Learning Estimation of Small Airway Disease from Inspiratory Chest Computed Tomography: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 2025;211:1185-95. [Crossref] [PubMed]
71. Shiraishi Y, Tanabe N, Sakamoto R, et al. Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study. BMC Pulm Med 2024;24:200. [Crossref] [PubMed]
72. Zhao M, Wu Y, Li Y, et al. Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images. BMC Pulm Med 2024;24:294. [Crossref] [PubMed]
73. Zhu Z, Zhao S, Li J, et al. Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease. Respir Res 2024;25:167. [Crossref] [PubMed]
74. Moslemi A, Hague CJ, Hogg JC, et al. Classifying Future Healthcare Utilization in COPD Using Quantitative CT Lung Imaging and Two-Step Feature Selection via Sparse Subspace Learning with the CanCOLD Study. Acad Radiol 2024;31:4221-30. [Crossref] [PubMed]
75. Lee AN, Hsiao A, Hasenstab KA. Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning. Radiol Cardiothorac Imaging 2024;6:e240005. [Crossref] [PubMed]
76. Lukhumaidze L, Hogg JC, Bourbeau J, et al. Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning. Acad Radiol 2025;32:543-55. [Crossref] [PubMed]
77. Sharma M, Kirby M, McCormack DG, et al. Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity. Acad Radiol 2024;31:2567-78. [Crossref] [PubMed]
78. Deng X, Li W, Yang Y, et al. COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images. Med Biol Eng Comput 2024;62:1733-49. [Crossref] [PubMed]
79. van der Veer T, Andrinopoulou ER, Braunstahl GJ, et al. Association between automatic AI-based quantification of airway-occlusive mucus plugs and all-cause mortality in patients with COPD. Thorax 2025;80:105-8. [Crossref] [PubMed]
80. Ibad HA, Hathaway QA, Bluemke DA, et al. CT-derived pectoralis composition and incident pneumonia hospitalization using fully automated deep-learning algorithm: multi-ethnic study of atherosclerosis. Eur Radiol 2024;34:4163-75. [Crossref] [PubMed]
81. Feng S, Zhang R, Zhang W, et al. Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning. Respiration 2025;104:1-14. [Crossref] [PubMed]
82. Zhang T, Pang H, Wu Y, et al. InspirationOnly: synthesizing expiratory CT from inspiratory CT to estimate parametric response map. Med Biol Eng Comput 2025;63:2277-94. [Crossref] [PubMed]
83. Touloumes N, Gagianas G, Bradley J, et al. ScreenDx, an artificial intelligence-based algorithm for the incidental detection of pulmonary fibrosis. Am J Med Sci 2025;369:705-11. [Crossref] [PubMed]
84. Dorosti T, Schultheiss M, Hofmann F, et al. Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging. Comput Biol Med 2025;185:109533. [Crossref] [PubMed]
85. Zhang T, Pang H, Wu Y, et al. BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis. Comput Methods Programs Biomed 2025;259:108516. [Crossref] [PubMed]
86. Almeida SD, Norajitra T, Lüth CT, et al. Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT. Eur Radiol 2024;34:4379-92. [Crossref] [PubMed]
87. D Almeida S. How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection. Insights Imaging 2024;15:198.
88. Salhöfer L, Holtkamp M, Bonella F, et al. Fully automatic quantification of pulmonary fat attenuation volume by CT: an exploratory pilot study. Eur Radiol Exp 2024;8:139. [Crossref] [PubMed]
89. Ma G, Dou Y, Dang S, et al. Effect of adaptive statistical iterative reconstruction-V algorithm and deep learning image reconstruction algorithm on image quality and emphysema quantification in COPD patients under ultra-low-dose conditions. Br J Radiol 2025;98:535-43. [Crossref] [PubMed]
90. Nadeem SA, Zhang X, Nagpal P, et al. Automated CT-based decoupling of the effects of airway narrowing and wall thinning on airway counts in chronic obstructive pulmonary disease. Br J Radiol 2025;98:150-9. [Crossref] [PubMed]
91. Liu Y, Zhang W, Sun M, et al. The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model. BMC Pulm Med 2024;24:515. [Crossref] [PubMed]
92. Shao J, Ma J, Yu Y, et al. A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections. Innovation (Camb) 2024;5:100648. [Crossref] [PubMed]
93. Kim HJ, Kwak N, Yoon SH, et al. Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study. Sci Rep 2024;14:13162. [Crossref] [PubMed]
94. Li Y, Chen D, Zhang Y, et al. Performance of Chest CT-Based Artificial Intelligence Models in Distinguishing Pulmonary Mucormycosis, Invasive Pulmonary Aspergillosis, and Pulmonary Tuberculosis. Med Mycol 2025;myae123.
95. Zhou L, Wang Y, Zhu W, et al. A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms. Ann Med 2024;56:2401613. [Crossref] [PubMed]
96. Zhou Y, Lin P, Xia L, et al. An enhancing diagnostic pulmonary diseases diagnostic method for differentiating talaromycosis from tuberculosis. iScience 2025;28:111867. [Crossref] [PubMed]
97. Gharamaleki OG, Colijn C, Sekirov I, et al. Early prediction of Mycobacterium tuberculosis transmission clusters using supervised learning models. Sci Rep 2024;14:27652. [Crossref] [PubMed]
98. Li HL, Zhi RZ, Liu HS, et al. Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from mycobacterium tuberculosis. Front Public Health 2025;13:1470072. [Crossref] [PubMed]
99. Zhang S, He C, Wan Z, et al. Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism. Med Biol Eng Comput 2024;62:1589-600. [Crossref] [PubMed]
100. Yang J, Deng L, Jing M, et al. Added value of spectral computed tomography quantitative parameters for differentiating tuberculosis-associated fibrosing mediastinitis from endobronchial lung cancer: initial results. Clin Radiol 2024;79:526-35. [Crossref] [PubMed]
101. Kong H, Li Y, Shen Y, et al. Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms. Eur J Med Res 2024;29:618. [Crossref] [PubMed]
102. Jiang W, Zhang H, Li Z, et al. AI-Powered Chest X-Ray for Diagnosing Pulmonary Tuberculosis in County and Township Health Care Facilities in Yichang: Retrospective, Real-World Study. J Med Internet Res 2025;27:e83041. [Crossref] [PubMed]
103. Zhang S, Simard M, Lapointe A, et al. Evaluation of Radiation Dose Effect on Lung Function Using Iodine Maps Derived From Dual-Energy Computed Tomography. Int J Radiat Oncol Biol Phys 2024;120:894-903. [Crossref] [PubMed]
104. Moore J, Remy J, Altschul E, et al. Thoracic Applications of Spectral CT Scan. Chest 2024;165:417-30. [Crossref] [PubMed]
105. Borgheresi A, Cesari E, Agostini A, et al. Pulmonary emphysema: the assessment of lung perfusion with Dual-Energy CT and pulmonary scintigraphy. Radiol Med 2024;129:1622-32. [Crossref] [PubMed]
106. Hwang HJ, Lee SM, Seo JB, et al. Visual and Quantitative Assessments of Regional Xenon-Ventilation Using Dual-Energy CT in Asthma-Chronic Obstructive Pulmonary Disease Overlap Syndrome: A Comparison with Chronic Obstructive Pulmonary Disease. Korean J Radiol 2020;21:1104-13. [Crossref] [PubMed]
107. Steinhardt M, Marka AW, Ziegelmayer S, et al. Comparison of Virtual Non-Contrast and True Non-Contrast CT Images Obtained by Dual-Layer Spectral CT in COPD Patients. Bioengineering (Basel) 2024;11:301. [Crossref] [PubMed]
108. Kim M, Doganay O, Hwang HJ, et al. Lobar Ventilation in Patients with COPD Assessed with the Full-Scale Airway Network Flow Model and Xenon-enhanced Dual-Energy CT. Radiology 2021;298:201-9. [Crossref] [PubMed]
109. Zhang G, Li S, Yang K, et al. The value of dual-energy spectral CT in differentiating solitary pulmonary tuberculosis and solitary lung adenocarcinoma. Front Oncol 2022;12:1000028. [Crossref] [PubMed]
110. Khan AU, Khanduri S, Tarin Z, et al. Dual-Energy Computed Tomography Lung in patients of Pulmonary Tuberculosis. J Clin Imaging Sci 2020;10:39. [Crossref] [PubMed]
111. Song J, Hwang EJ, Yoon SH, et al. Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases. Invest Radiol 2026;61:167-74. [Crossref] [PubMed]