Introduction

Chronic obstructive pulmonary disease (COPD) is now ranked as the third leading cause of death following cardiovascular disease and stroke,¹ which is characterized by persistent, progressive and exacerbated airflow obstruction along with chronic systemic inflammation.² Although smoking is a conventional risk factor for COPD, a history of pulmonary tuberculosis has been reported as a risk factor for chronic airflow obstruction, particularly in developing countries.^3–5 Even after adjusting for age, gender and smoking history, previous tuberculosis infection remains independently associated with airflow obstruction.^6,7 Globally, tuberculosis (TB) ranks as the second most deadly infectious disease after COVID-19. In 2022, an estimated 10.6 million individuals worldwide contracted tuberculosis.⁸ Unlike many other respiratory infections, TB tends to cause permanent damage or destruction to host tissues, transforming it from a treatable infectious ailment into a chronic condition.⁹ COPD could arise during the course of tuberculosis or following completion of tuberculosis treatment. The term of tuberculosis-associated obstructive pulmonary disease (TOPD) was first coined by Allwood et al¹⁰ Histological study in TOPD patients confirmed distortion in small pathways and blood vessels, indicating a phenotype other than smoking-associated COPD.^5,11

Although the pathogenesis of TOPD remains unclear, there has been a growing focus on inflammation in its development. Multiple studies have highlighted tuberculosis as a factor independently elevating the risk of COPD onset, with a direct association with inflammatory levels.^12–14 A study has shown that patients with TOPD have higher levels of IL-6 and CRP than those with smoking-associated COPD.² Furthermore, concentrations of sIL-2R, IL-6, and TNF-α were found to be elevated in TB patients with COPD in comparison to TB patients without COPD.¹⁵

Recently, in addition to the commonly used systemic inflammatory biomarkers Erythrocyte Sedimentation Rate (ESR) and C-reactive Protein (CRP), attention has turned to novel markers such as the Systemic Inflammatory Response Index (SIRI), C-reactive Protein-to-Lymphocyte Ratio (CLR), and Eosinophil Count-to-Lymphocyte Count Ratio (ELR). These markers have been found to be related to numerous diseases, including metabolic disorders, and inflammatory diseases.^16–18 TOPD may be associated with systemic inflammation,^2,19,20 however, there is limited knowledge about systemic inflammatory markers and risk factors for TOPD. Thus, the purpose of this study was to evaluate the correlation between systemic inflammatory markers and patients with TOPD, to help understand the pathogenesis of TOPD and to aid clinicians in early identification of high-risk TOPD patients, and to carry out appropriate prevention and treatment measures in time to alleviate the social and economic burden of TOPD.

Materials and Methods

Patients and Study Design

This cross-sectional study recruited patients with TB from January 2014 to January 2022 at the Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology. Considering that there are no diagnostic criteria for TOPD, referring to the Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD) criteria³ and combining the definition of post-tuberculosis lung disease (PTLD),⁹ TOPD in the present study was defined as evidence of chronic airflow limitation attributable at least in part to previous pulmonary tuberculosis.

Participants meeting the following inclusion criteria were enrolled in this study: (1) All patients with positive mycobacterial tuberculosis cultures or/and Xpert MTB/RIF (previous or current); (2) patients in TOPD group with airflow limitation (post-bronchodilator spirometry with FEV1/FVC<70%) and without any history of COPD or asthma or chronic cough, expectoration and wheezing before diagnosis of tuberculosis. (3) no acute exacerbation. (4) Patients in the non-TOPD (NTOPD) group without COPD, chronic bronchitis, bronchiectasis, severe interstitial disease and other diseases that could lead to airflow obstruction. The exclusion criteria were as follows: (1) age <18 years old; (2) no information available; (3) extrapulmonary TB.

Data Collection

Baseline data, including demographic characteristics (age, sex and education level), history of treatment, smoking, resistance, complications (diabetes mellitus, hypertension) and routine laboratory parameters (leukocyte count, neutrophil count, serum albumin, C-reactive protein and erythrocyte sedimentation rate), were retrieved from the electronic medical record (EMR) system. Additionally, ratio inflammatory indicators, such as Systemic Inflammation Index (SII), Systemic Inflammatory Response Index (SIRI), neutrophils-to-lymphocyte ratio (NLR), monocyte count-to-lymphocyte count ratio (MLR), platelet count-to-lymphocyte count ratio (PLR), derived neutrophil-to-lymphocyte ratio (dNLR), C-reactive protein-to-serum albumin ratio (CAR), C-reactive protein to serum pre-albumin ratio (CPR), C-reactive protein-to-lymphocyte ratio (CLR), eosinophil count-to-lymphocyte count ratio (ELR), were calculated and included for subsequent analysis. The computing methods for these indicators are detailed in Supplementary Table 1.

Statistical Analysis

All statistical analyses were performed using SPSS version 26.0 and R software (version 4.2.3). Categorical variables were presented as frequencies and percentages and were analyzed using the Chi-square test and Fisher’s exact test. Meanwhile, continuous variables were presented as mean ± standard deviation (SD) or median (1st quartile, 3rd quartile) and compared by Student’s t-test or Mann–Whitney U-test based on data normality and distribution. Univariate and multivariable logistic regression analyses were executed to explore the relationship between influencing factors with the risk of the presence of TOPD. Receiver Operating Characteristic (ROC) curves were used to evaluate the diagnostic value of the novel inflammatory biomarkers selected by multivariate regression analysis, and patients were divided into four different groups according to their quartiles. The Chi-Square test was used to compare the incidence rate of TOPD in tuberculosis patients in subgroups of selected inflammatory biomarkers. The Jonckheere–Terpstra test was performed for trend analysis. In addition, 1:1 propensity score matching (PSM) was implemented using the nearest-neighbor method, with a caliper size of 0.02 to further identify the impact of selected inflammatory biomarkers on TOPD. The propensity score was computed using logistic regression analysis. P-value <0.05 based on a two-sided hypothesis was considered statistically significant.

Results

Clinical Data and Laboratory Indicators

A total of 737 patients were included in the present study, of whom 83 participants (11.26%) had TOPD. The baseline clinical and laboratory characteristics of participants are shown in Table 1 and Figure 1. Patients in the TOPD group exhibited a higher mean age compared to those in the NTOPD group (P<0.001). The proportion of males (P<0.001), people with low education levels (P<0.010), smokers (P<0.001) and patients with a history of irregular treatment (P=0.001) was significantly higher in the TOPD group compared to the NTOPD group. The levels of inflammatory indicators, including SII, SIRI, NLR, MLR, PLR, dNLR, CAR, CPR, CLR, ELR, ESR and CRP in TOPD group were significantly higher than those in NTOPD group (Figure 1A). No significant differences were observed in complications such as diabetes mellitus and hypertension (P>0.05).

Table 1 The Baseline Demographic and Clinical Characteristics of Study Participants

Figure 1 The comparison of inflammatory indicators between TOPD and NTOPD groups. (A) The comparison of 17 inflammatory indicators before PSM, where blue and red denote the TOPD and NTOPD groups, respectively. **P < 0.01 and ***P<0.001. (B) The comparison of 17 inflammatory indicators after PSM, where blue and red denote the TOPD and NTOPD groups, respectively. *P < 0.05, **P < 0.01 and ***P<0.001. Abbreviations: PSM, Propensity Score Matching; SII, Systemic Inflammation Index; SIRI, Systemic Inflammatory Response Index; NLR, Neutrophil count to lymphocyte count ratio; MLR, Monocyte count to lymphocyte count ratio; PLR, Platelet count to lymphocyte count ratio; dNLR, derived neutrophil-to-lymphocyte ratio; NHR, Neutrophil count to high-density lipoprotein ratio; MHR, Monocyte count to high-density lipoprotein ratio; PHR, Platelet count to high-density lipoprotein ratio; CAR, C-reactive protein to serum albumin ratio; CPR, C-reactive protein to serum pre-albumin ratio; CLR, C-reactive protein to lymphocyte count; ELR, Eosinophil count to lymphocyte count ratio; EMR, Eosinophil count to monocyte count ratio; ENR, Eosinophil count to neutrophil count ratio; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein.

Age, gender, resistance, irregular treatment, educational level, smoking and complications were used as matching variables for PSM, and 69 TOPD patients and 69 NTOPD patients were successfully matched. After PSM, there was no significant difference in the above variables between the two groups (P>0.05) (Table 1). Furthermore, Figure 1B showed that SII, SIRI, NLR, MLR, dNLR, CAR, CLR, CRP remained significantly different between the TOPD group and NTOPD group, whereas PLR, CPR, ELR and ESR were not significantly different between the two groups after PSM.

Evaluation of the Impact of Inflammatory Indicators on TOPD

Before PSM, on univariate analysis, age, gender, irregular treatment, educational level, smoking, SII, SIRI, NLR, MLR PLR, dNLR, CAR, CLR, ELR, ESR, CRP were significantly associated with TOPD (P<0.01). Age, irregular treatment, SIRI, CLR and ELR were independent influencing factors of TOPD on multivariate analysis (P<0.01), as given in Table 2.

Table 2 Univariate and Multivariate Logistic Analysis of Factors Associated with TOPD Before PSM

To further explore the impact of inflammatory indicators on TOPD, univariate and multivariate Logistic Analysis were performed on significant factors (SII, SIRI, NLR, MLR, dNLR, CAR, CLR, CRP) after PSM (Table 3). The univariate logistic regression analysis showed that SII, SIRI, NLR, MLR, dNLR were significantly associated with TOPD (P<0.05). And SIRI remained independently significantly associated with an increased risk of TOPD on multivariable analysis. After adjusting for confounding factors, SIRI still showed significant statistical differences, indicating the robustness of SIRI as a predictor of TOPD in TB patients.

Table 3 Univariate and Multivariate Logistic Analysis of Significant Factors Associated with TOPD After PSM

ROC curve analysis was performed to assess the efficiency of inflammatory indicators. As shown in Figure 2, the AUC of SIRI, CLR and CRP were 0.702, 0.665 and 0.640 before PSM, whereas, their AUC were 0.668, 0.601 and 0.608 after PSM. The AUC of ELR and ESR were 0.596 and 0.607 before PSM, however, there was no significant difference in the diagnostic effect of ELR and ESR in predicting the outcome (P>0.05). SIRI was found to be superior in diagnosing TOPD than CLR and CRP before and after PSM. As shown in Supplementary Tables 2 and 3, the optimum truncation Value for SIRI to predict TOPD was 3.19 (sensitivity: 0.801, specificity: 0.506) and 3.38 (sensitivity: 0.783, specificity: 0.493) before and after PSM, respectively.

Figure 2 Receiver operating characteristic (ROC) curve analysis of SIRI, CLR, ELR, ESR and CRP before Propensity Score Matching (PSM) (A) and after PSM (B). Abbreviations: SIRI, Systemic Inflammatory Response Index; CLR, C-reactive protein to lymphocyte count; ELR, Eosinophil count to lymphocyte count ratio; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein. Note: There was no significant difference in the diagnostic effect of ELR and ESR in predicting the outcome after PSM (P>0.05).

The Prevalence of TOPD Among Different Quartiles of SIRI

Patients were divided into four different groups based on the SIRI quartiles. As illustrated in Figure 3, the prevalence of TOPD among tuberculosis patients exhibited a rising trend corresponding to increasing SIRI values both before and after PSM and Jonckheere–Terpstra test showed that the trend was statistically significant (P<0.001), which suggested that tuberculosis patients with higher SIRI values are at greater risk of TOPD.

Figure 3 The prevalence of TOPD patients among different quartiles of SIRI before Propensity Score Matching (PSM) (A) and after PSM (B). Abbreviation: SIRI, Systemic Inflammatory Response Index.

Discussion

Although the exact mechanism of obstructive airways in post-TB patients remains unclear, the development of TOPD might be influenced by mechanisms of chronic inflammation. Several studies have indicated that the inflammatory response following tuberculosis infection drives leukocyte aggregation, tissue destruction and remodeling, forming lung tissue granulomas and lung cavities, leading to chronic airway obstruction and chronic inflammation.^21,22 Previous studies demonstrated that inflammatory cytokines levels remained high and lung remodeling and chronic inflammatory response may persist after curing tuberculosis.^23–25 Immunologic mechanisms may also be involved in subsequent systemic inflammation after treatment of tuberculosis.^5,14,19 Additionally, pulmonary inflammation serves as root cause of systemic inflammation, as it can release cytokines such as TNF-a, IL-6, IL-8, etc. These pro-inflammatory mediators may be released into the systemic circulation when passing through the pulmonary circulation and contribute to the activation of systemic inflammatory cells.²⁶ Therefore, systemic inflammatory markers can be used to predict TOPD to facilitate the early identification of high-risk TOPD patients, and enable the implementation of appropriate treatments to control the systemic inflammatory response of TOPD.

Leukocyte, platelets, high-density lipoproteins and albumin play roles in the inflammatory response, making their ratio crucial for the indirect assessment of inflammatory status.^17,27–30 For example, NLR, PLR, dNLR, MLR, SII and SIRI are useful indicators of systemic inflammation and disease severity in psoriasis.³¹ MHR, NHR, CLR, and CAR could be used for assessing type 2 diabetes mellitus in active pulmonary tuberculosis.¹⁶ In the present study, we observed that the levels of SII, SIRI, NLR, MLR, dNLR, CAR, CLR and CRP in the TOPD group were higher than those in the NTOPD group before and after PSM, suggesting that TOPD patients have higher levels of inflammation than NTOPD patients. Univariate and multivariate logistic regression analysis before and after PSM showed an association between SIRI and TOPD. Moreover, we also found that age and irregular treatment were independent risk factors for TOPD, which is roughly the same as that reported in some previous studies.^32,33 The results of multivariate regression of ELR and CLR before and after PSM in this study were different. A possible reason for this is that the single index of peripheral blood is easily affected by individual differences and confounding factors, and the composite index can better reflect the clinical status of TOPD patients than the single index. Additionally, when this study population was divided by SIRI quartiles, the prevalence of TOPD increased with the increase of SIRI level, indicating that TB patients with higher values of SIRI are more likely to be high-risk group for TOPD. In this study, we found that SIRI was elevated in TOPD patients, and SIRI had good predictive efficacy and robustness, which could help in understanding the inflammatory response involved in TOPD.

SIRI can reflect the state of neutrophils, lymphocytes and monocytes simultaneously, which more comprehensively reflects the inflammatory state and immune balance of the body than NLR or MLR alone. The possible reasons for the increase of SIRI in TOPD patients are that inflammation induces the increase of neutrophils,³⁴ and COPD leads to abnormal respiration, which makes the tissues and organs in a hypoxic state, affects the development and maturation of immune cells, and the lymphocyte count gradually decreases.³⁵ Furthermore, previous studies have shown that COPD and TB have different degrees of lymphocyte immune dysfunction.^36,37 Alex Kayongo et al have described immune pathways potentially driving the immunopathogenesis of TOPD, involving interactions of neutrophils, monocyte-derived macrophages, and lymphocytes with airway epithelial cells, fibroblasts, and extracellular matrix, ultimately causing persistent lung inflammation, tissue necrosis, and airway remodeling.³⁶

Nevertheless, the current study is not exempt from some limitations. Firstly, the present study was retrospective, there may have been some unavoidable bias in the study. Secondly, all enrolled patients were from a single center, which might limit the extrapolation of our findings to other regions. Therefore, future research will need to confirm and improve our conclusions through larger samples and multi-center prospective studies.

Conclusion

In conclusion, the levels of inflammatory indicators were higher in patients with TOPD than in those with NTOPD, and SIRI may be a simple and useful inflammatory index for assessing TOPD. Additionally, TB patients with higher values of SIRI are more likely to be high-risk group for TOPD. Therefore, TB patients could be monitored for systemic inflammation and much more attention should be paid to SIRI in the examination of TB to identify high-risk patients with TOPD early, and to carry out appropriate prevention and treatment measures in time to reduce the social and economic burden of TOPD.