Skip to main content
Download PDF

Association between systemic immune-inflammation index and chronic obstructive pulmonary disease: a population-based study

BMC Pulmonary Medicine volume 23, Article number: 295 (2023) Cite this article

Abstract

Background

The Systemic Immune-Inflammation Index (SII) is a quantitative measurement of the systemic immune-inflammatory response in the human body. The SII has been shown to have prognostic value in various clinical settings, including critical illness, sepsis, and cancer. Its role in chronic obstructive pulmonary disease (COPD) remains unclear and requires further investigation.

Methods

We analyzed demographic data from 16,636 participants in the National Health and Nutrition Examination Survey. Logistic regression analysis was performed to assess the correlation between COPD, lung function, chronic respiratory symptoms and SII. We used Cox proportional hazards (PH) model to analyze the relationship between SII and mortality in COPD patients and healthy individuals. We used propensity score matching (PSM) method to match the COPD population with similar baseline levels with the normal population to further analyze the correlation between SII and COPD.

Results

We recruited 16,636 participants, ages 40 and above, for the study. A multivariable logistic regression analysis revealed that a higher SII level was independently associated with an elevated likelihood of COPD (Odds Ratio (OR) = 1.449; 95% Confidence Interval (CI): 1.252–1.676, P < 0.0001) after controlling for all other factors. Results of subgroup analysis showed a significant positive correlation between SII and COPD in different age groups, gender, Body Mass Index, smoking status, and those with a history of hypertension. The SII index had positive correlation with COPD after PSM (OR = 1.673; 95%CI: 1.443–1.938). After full adjustment, an increase in the SII is associated with a higher all-cause mortality rate. The hazard ratio (HR) with a 95% CI in the general population, COPD patients, and healthy individuals are 1.161 (1.088, 1.239), 1.282 (1.060, 1.550), and 1.129 (1.055, 1.207), respectively.

Conclusions

Higher SII levels are linked to higher prevalence of COPD. COPD patients with a higher SII levels have a higher risk of all-cause mortality. Additional large-scale, long-term studies are necessary to confirm these results.

Peer Review reports

Background

Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition characterized by progressive and irreversible airflow limitation [1]. According to the World Health Organization, COPD is estimated to affect over 330 million individuals globally and is projected to become the third leading cause of death by 2030 [2]. Globally, COPD is a major contributor to morbidity and mortality, significantly impacting patients’ quality of life and leading to increased healthcare utilization [3]. Although COPD can affect individuals of all ages, it is most commonly diagnosed in those over the age of 40 [4]. The disease is more prevalent in males, but the incidence in females is increasing, and the gender gap is narrowing. COPD is more prevalent in low- and middle-income countries, where exposure to indoor and outdoor air pollution is high [5]. The pathogenesis of COPD is complex and multifactorial, involving a combination of environmental and genetic factors [6]. Despite significant advancements in our understanding of the disease, further research is needed to better grasp the underlying mechanisms and develop effective interventions that can improve outcomes for COPD patients.

The Systemic Immune-Inflammation Index (SII) is a multi-marker index that provides a comprehensive measurement of the systemic immune-inflammatory response in the human body [7]. SII, based on lymphocyte, neutrophil, and platelet counts, predicts Hepatocellular Carcinoma (HCC) patient recurrence and survival post-surgery independently [7]. Studies show SII objectively reflects inflammation-immunity balance in malignant tumor patients [8, 9] and serves as a prognostic indicator in carcinoma research [10, 11]. Elevated SII levels have been associated with worse prognosis and higher mortality in patients with cancer and cardiovascular disease [12, 13]. Some studies have suggested that SII serves as a marker of chronic inflammation, indicated by increased neutrophil and platelet counts and decreased lymphocyte counts [7, 14]. There is still a lack of large sample studies on the association between SII and COPD.

COPD is distinguished by persistent airway inflammation and immune dysfunction [15]. Elevated levels of pro-inflammatory cytokines and oxidative stress have been observed in the airways of patients with COPD, indicating a persistent state of inflammation [16, 17]. Additionally, COPD is associated with alterations in the immune system, including changes in the balance between T-helper type 1 (Th1) and T-helper type 2 (Th2) cells, as well as changes in the number and function of immune cells such as macrophages and dendritic cells [18,19,20]. Studies demonstrate a close correlation between immunity and inflammation with the onset and progression of COPD. Given the SII is easily obtainable in clinical settings, exploring its correlation with COPD bears significant importance for the prevention and treatment of COPD. Our study uses the 1999–2010 National Health and Nutrition Examination Survey (NHANES) to investigate the correlation between SII and COPD.

Methods

Study data and population

The National Health and Nutrition Examination Survey (NHANES) is a continuous survey of the nutritional status of non-institutionalized Americans. It utilizes nationally representative samples and multi-stage sampling designs to monitor nutritional conditions biennially. The study protocol is approved by the Research Ethics Committee of the National Center for Health Statistics (NCHS). Further information can be found on the NCHS website. All participants in NHANES provided written informed consent. For our study, data from surveys conducted between 1999 and 2010 were analyzed, and demographic information from 35,479 participants was obtained. The lab test results were linked to other NHANES databases using the participant identifier SEQN (the unique sequence number for each participant). The final sample for analysis consisted of 16,636 participants (8,325 males and 8,311 females), after excluding participants with missing data.

Outcomes

In this study, the diagnosis of COPD relies on the ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) being less than 0.7 after bronchodilator administration, along with questionnaire surveys of participants and the use of COPD treatment-related drugs such as Long-Acting Muscarinic Antagonist (LAMA) and Long-Acting Beta2-Agonist (LABA). The questionnaire using a composite of three self-reported COPD outcomes (emphysema, chronic bronchitis, and COPD). Participants were considered to have COPD if they answered “yes” to the question, “Have you ever been told that you have emphysema/chronic bronchitis/chronic obstructive pulmonary disease?“ in a standardized medical condition questionnaire, which was administered during a personal interview.

We gathered the mortality data for every participant by connecting with the National Death Index (NDI) until December 31, 2019.

Calculation and assessment of SII index

SII defined as the product of peripheral platelet count, neutrophil count, and lymphocyte count divided by preoperative lymphocyte count: SII = (P * N) / L, where P, N, L represent peripheral platelet count, neutrophil count, and lymphocyte count respectively.

Covariates

The study considered various covariates, including demographic factors such as age, gender, and race (classified as non-Hispanic white, non-Hispanic black, Mexican American, or others), educational attainment (categorized as less than high school, high school, or more than high school), and family poverty income ratio (PIR; divided into < 1.85 and ≥ 1.85). Other health variables that were taken into account included smoking status (categorized as never, former, current), BMI (divided into under 25 kg/m2, and ≥ 25 kg/m2), history of hypertension, diabetes and cardiovascular disease (CVD), white blood cell (WBC) counts and C-reactive protein (CRP).

Statistical analysis

All statistical analyses were performed using appropriate NHANES sampling weights based on guidelines from the Centers for Disease Control and Prevention (CDC), to account for the complex multistage cluster survey design. Continuous variables were summarized as mean with standard error (SE), while categorical variables were presented as proportions. Given the skewed distribution of the SII index, its description included median and quartile values, and logarithmic transformation was applied in regression analysis. We used the chi-squared test for categorical variables and the Student’s t test for continuous variables to evaluate whether there were differences between COPD patients and normal people within the covariates. Using logistic regression, covariate-adjusted odds ratios (ORs) for the SII to COPD were calculated. In model 1, we adjusted age, gender, and race. Model 2 was adjusted for the covariates in model 1 as well as education levels, PIR, smoke status, BMI, history of diabetes, history of hypertension and history of CVD. Model 3 was adjusted for the covariates in Model 2 as well as WBC and CRP.

Subgroup analyses were performed to investigate whether age, gender, smoking status, BMI, history of hypertension, diabetes and CVD influenced the investigated correlations between SII and COPD outcomes. The p values for interactions were tested by the likelihood-ratio test. We used the Cox proportional hazards (PH) model to investigate the association between the SII and all-cause mortality in the general population, COPD patients, and healthy individuals. In the PSM analysis, the nearest method was employed to match COPD patients with normal individuals in a 1:1 ratio. Age, gender, race, PIR, education levels, smoking status, BMI, history of diabetes, hypertension and CVD, WBC, and CRP were adjusted as confounding variables during matching. The statistical analyses were conducted with SPSS 25.0 and R 4.1 software. P < 0.05 was considered statistically significant.

Results

The study recruited 16,636 participants, with ages over 40. The sample included 8325 males (50.042%) and 8311 females (49.958%) (Fig. 1). The participants were classified into ethnic groups, with Mexican Americans accounting for 18.328%, non-Hispanic white people 53.889%, non-Hispanic black people 18.532%, and others 9.251%. Table 1 illustrated characteristics of participants. The age of COPD patients was higher than that of normal people, and the difference was statistically significant(P < 0.0001). Furthermore, COPD group and normal group had significant differences in these variables: race, education levels, PIR, smoking status, history of diabetes, hypertension and CVD, WBC counts, CRP, and SII index(P < 0.01).

Fig. 1
figure 1

Flowchart of the sample selection from NHANES 1999–2010

Full size image
Table 1 Basic characteristics of participants (n = 16,636) in the NHANES 1999–2010
Full size table

Table 2 displays the correlation between SII index and COPD. The results show a positive association between SII index and COPD. The correlation between SII and COPD was significant in both the crude model (OR = 1.673; 95%CI: 1.443–1.938, P < 0.0001), model 1 (OR = 1.595; 95%CI: 1.384–1.837, P < 0.0001), model 2 (OR = 1.482; 95%CI: 1.286–1.708, P < 0.0001), and model 3 (OR = 1.449; 95%CI: 1.252–1.676, P < 0.0001). Tertile categorization of SII was performed in sensitivity analysis. Compared to participants in the lowest tertile of SII, those in the highest tertile had a 43.1% increased risk of COPD in model 3(OR = 1.431; 95%CI: 1.161–1.765, P = 0.001). The prevalence of COPD was found to increase with an increase in SII (P for trend < 0.001).

Table 2 Association Between Systemic Immune-Inflammation Index and COPD
Full size table

We selected years with data on lung function and chronic respiratory symptoms, and analyzed the association between the SII and both lung function and chronic respiratory symptoms (Table S1, S2). In the unadjusted (crude) model, an increase in SII was associated with a significant decrease in FEV1 (beta = -87.599, 95% CI: -155.153 to -20.044, P = 0.013).This association remained significant after adjusting for potential confounders in model 1 (beta = -92.878, 95% CI: -149.868 to -35.888, P = 0.002) and model 2 (beta = -59.813, 95% CI: -112.957 to -6.669, P = 0.030).However, in the fully adjusted model 3, the association was attenuated and no longer statistically significant (beta = -10.058, 95% CI: -71.424 to 51.309, P = 0.730). As for FVC, in the unadjusted (crude) model, an increase in SII was significantly associated with a reduction in FVC (beta = -80.389, 95% CI: -159.942 to -0.836, P = 0.048). This relationship remained significant in the adjusted model 1 (beta = -84.635, 95% CI: -142.460 to -26.810, P = 0.006), but was no longer statistically significant in model 2 (beta = -45.724, 95% CI: -100.353 to 8.905, P = 0.095) or the fully adjusted model 3 (beta = 19.503, 95% CI: -43.074 to 82.079, P = 0.515).

We investigated the relationship between the SII and the presence of chronic respiratory symptoms, including frequent cough, frequent phlegm, and wheezing in the past year (Table S3). In the unadjusted model, an increase in SII was significantly associated with an increased odds of having any chronic respiratory symptom (OR = 1.567, 95% CI: 1.326 to 1.851, P < 0.0001). This relationship remained significant in model 1 (OR = 1.584, 95% CI: 1.326 to 1.891, P < 0.0001) and model 2 (OR = 1.385, 95% CI: 1.162 to 1.651, P = 0.001), but was marginally non-significant in the fully adjusted model 3 (OR = 1.218, 95% CI: 0.999 to 1.485, P = 0.051). In Table 3, subgroup analyses on COPD and various confounding factors are presented. The results of subgroup analysis showed that the positive association between SII and COPD was significant in different age groups, for both male and female participants, for BMI, and for those with a history of hypertension. Interestingly, stronger association between SII and COPD was observed in participants who were former or current smokers, with the greatest differences noted in the smoking status subgroup (P for interaction = 0.025).

Table 3 Subgroup analysis for the association between SII and COPD
Full size table

At the census day of 31 December 2019, 5193 participants were determined as deceased (31.2%). The median follow-up duration was 146 months (range 0–249 months). We examined the association between the SII and all-cause mortality among three different groups: the general population, patients with chronic obstructive pulmonary disease (COPD), and individuals without COPD (Table 4). The associations were assessed using Cox proportional hazards (PH) models, and the results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs).Among the whole population, after adjusting for potential confounders in model 3, an increase in SII from the first (Q1) to the third quantile (Q3) was associated with a 16.5% increase in the risk of all-cause mortality (HR = 1.165, 95% CI: 1.072–1.267).Among patients with COPD, after full adjustment in model 3, an increase in SII from Q1 to Q3 was associated with a 34.1% increase in the risk of all-cause mortality (HR = 1.341, 95% CI: 1.041–1.727).Among individuals without COPD, after full adjustment in model 3, an increase in SII from Q1 to Q3 was associated with a 13.1% increase in the risk of all-cause mortality (HR = 1.131, 95% CI: 1.036–1.235).These results suggest that an increase in SII is associated with a higher risk of all-cause mortality among both the general population and patients with COPD, highlighting the potential role of SII in predicting mortality. Due to substantial disparities in variables and subject numbers between COPD and normal groups, we conducted a 1:1 PSM analysis (Fig. 2). A total of 2112 participants were enrolled and divided into COPD and normal groups. The baseline characteristics of each group after propensity score matching are presented in Table 5. The baseline characteristics of all participants were comparable after PSM. We found significant differences in SII between COPD and normal groups after PSM and that SII was higher in COPD than in normal group (P = 0.002). We then analyzed the association of SII with COPD and its association in different subgroups after PSM (Fig. 3). The SII index had positive correlation with COPD after PSM (OR = 1.673; 95%CI: 1.443–1.938). In the subgroup of patients aged 60 years or more, SII had a stronger association with COPD than in the subgroup of patients aged less than 60 years. SII has a stronger association with COPD in female than in male. Interestingly, we found that SII had a stronger association with COPD among people with lower education levels. SII was also more strongly associated with COPD in the poor population.

Fig. 2
figure 2

Propensity score matching analysis of the standardized mean difference results for the different variables

Full size image
Fig. 3
figure 3

Subgroup analysis for the association between SII and COPD after PSM

Full size image
Table 4  h (95% CIs) for all-cause mortality according to SII among whole population, COPD patients and normal people
Full size table
Table 5 Basic characteristics of participants after PSM Analysis
Full size table

Discussion

To the best of our knowledge, this is the first study to have demonstrated a connection between SII and outcomes associated with COPD based on data from a representative national sample. Our findings demonstrate a positive correlation between SII and COPD, even after adjusting for various covariates among the US population.

Extensive research has explored the relationship between immunity and COPD. Studies indicate elevated levels of CD8+ T lymphocytes in the blood and airway tissues of COPD patients, alongside increased numbers of activated CD4+ and CD8+ cells expressing nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), signal transducer and activator of transcription 4 (STAT-4), interferon-γ (IFN-γ), and perforin [21, 22]. Stable COPD patients exhibit an increase in sputum CD8+ cells compared to control smokers with normal lung function and non-smokers [23]. Studies have shown that in stable COPD patients, the bronchial mucosa is dominated by T lymphocytes, particularly CD8+ cells [21, 22, 24]. The reduced apoptosis of CD8+ T lymphocytes contributes to their accumulation in the airway submucosa of smokers with COPD [25]. In contrast, studies indicate that the T cell-mediated immune response may be altered or weakened in severe COPD patients [26]. Furthermore, neutrophil accumulation in the sputum of stable COPD patients has been linked to the heightened expression of macrophage inflammatory protein 1α (MIP-1α) in the bronchial epithelium of those with severe disease, as compared to those with mild/moderate COPD [21, 22, 24]. Elevated levels of neutrophils have been observed in the small airways of COPD patients, as the severity of the disease increases, compared to smokers with normal lung function as controls [27]. There was a study indicate that platelet activation may serve as a novel connection between COPD, inflammation, and cardiovascular disease [28]. Platelet activation has been shown to predict adverse outcomes in patients with stable coronary disease and identify individuals at risk for recurrent cardiovascular events following percutaneous coronary intervention [29, 30]. The interplay between platelets and inflammatory cells leads to the release of chemokines, thereby promoting the accumulation of immune mediators, a crucial factor in the formation of atherosclerotic plaques. Studies indicate that platelet activation contributes to structural changes in the pulmonary vasculature, which may be implicated in the pathogenesis of various forms of pulmonary arterial hypertension [31]. These studies have shown that neutrophils, platelets and lymphocytes are closely related to the biological mechanism of COPD.

Several studies have indicated the prognostic value of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and the proportion of lymphocytes in COPD patients. Studies indicate that during acute exacerbation of COPD (AECOPD), inflammation severity significantly increases, resulting in higher levels of NLR and PLR. These markers of inflammation might be used to predict the prognosis of COPD patients [32]. Compared with NLR and PLR, SII more comprehensively integrates the relationship between neutrophils, lymphocytes, and platelets. Although SII is a novel marker, studies have explored its relation to lung diseases. Notably, SII holds potential as a prognostic predictor for patients with metastatic non-small-cell lung cancer (NSCLC). Low SII was linked with a longer progression-free survival and overall survival [33]. Another study suggests that a SII value above 500 in patients with connective tissue disorders can indicate pulmonary interstitial involvement [34]. A study found that SII values exceeding 851.51 × 109/L were an independent risk factor for venous thromboembolism (VTE) related to lung cancer [35]. Our study found that SII had positive correlation with COPD after adjusted with various covariates. We used PSM to match the COPD population and the normal population with the same baseline characteristics, and then found that SII was still associated with COPD.

We observed an association between the SII and lung function. In the initial unadjusted model, we found that an increase in SII was significantly associated with a decrease in FEV1, which is a measure of the amount of air forcefully exhaled from the lungs in one second. This suggests that as SII increases, lung expiratory capacity significantly decreases. Even after adjusting for potential confounding factors in models 1 and 2, this association remained statistically significant, indicating that these factors did not substantially alter the relationship between SII and FEV1. However, when we further accounted for all potential confounding factors in the fully adjusted model 3, the association between SII and FEV1 was notably weakened and no longer statistically significant. This implies that other factors may be playing a moderating role in the relationship between SII and lung function. Similar trends were observed in relation to FVC, which measures the maximum amount of air that can be forcibly inhaled after a full exhalation. In the unadjusted model, an increase in SII was significantly associated with a reduction in FVC. The association remained statistically significant in model 1, even after adjusting for potential confounding factors. However, in models 2 and the fully adjusted model 3, the association between SII and FVC lost its statistical significance. This indicates that after considering all potential confounding factors, the relationship between SII and FVC partially disappears. We also investigated the relationship between SII and chronic respiratory symptoms, including frequent cough, frequent phlegm, and wheezing in the past year. In the unadjusted model, an increase in SII was significantly associated with an increased likelihood of experiencing any chronic respiratory symptom. This significant association persisted in models 1 and 2, but in the fully adjusted model 3, the relationship became marginally non-significant. This suggests that part of the association between SII and chronic respiratory symptoms can be explained by certain potential confounding factors. This study reveals a complex relationship between SII and lung function as well as chronic respiratory symptoms. While an unadjusted analysis shows significant associations, accounting for potential confounding factors diminishes or eliminates the statistical significance. These findings indicate that SII alone may not be the sole contributor to changes in lung function and respiratory symptoms, and other biological and environmental factors likely play a role. Further research will be essential to better understand the underlying mechanisms and potential therapeutic implications related to SII, lung function, and respiratory health.

In our study, we found a significant association between the SII and all-cause mortality rates. Our findings suggest a similar predictive role for SII in mortality among both COPD patients and the general population. Increased SII may reflect a heightened state of systemic inflammation and immune activation, which are crucial drivers of disease progression and adverse health outcomes. Chronic inflammation, as indicated by elevated SII, may precipitate tissue damage and organ dysfunction, thus increasing the risk of mortality. However, the exact mechanisms through which SII impacts mortality warrant further investigation. Moreover, prospective studies and randomized trials are needed to validate these associations and explore the therapeutic implications of modulating SII.

Our study has several key strengths. Firstly, the statistical analysis is comprehensive, incorporating a large sample size representative of the national population, precise measurement of SII index, and thorough evaluation of COPD outcomes. Secondly, we considered several influential confounding factors such as demographics and lifestyle habits to eliminate any biases in our results. Finally, we used PSM to match populations with similar baseline characteristics. PSM can increase the efficiency of the statistical analysis by reducing the number of confounders that need to be controlled for in the analysis, compared to traditional regression-based approaches. Our study also has a few limitations. Firstly, the sample population is limited to Americans and cannot be generalized to other populations. Secondly, the age range of participants (over 40 years old) excludes young people and adolescents. Lastly, the cross-sectional design of the study does not allow us to establish a causal link between SII exposure and COPD.

Conclusions

Our findings showed that higher SII levels are linked to higher prevalence of COPD. COPD patients with a higher SII levels have a higher risk of all-cause mortality. However, additional large-scale, long-term studies are necessary to confirm these results.

Data Availability

The datasets analyzed during the current study are publicly available in the National Health Nutrition Survey (NHANES), https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

AECOPD:

Acute exacerbation of COPD

BMI:

Body Mass Index

CDC:

Centers for Disease Control and Prevention

CI:

Confidence interval

COPD:

Chronic obstructive pulmonary disease

CRP:

C-reactive protein

CVD:

Cardiovascular Disease

DM:

Diabetic Mellitus

FEV1:

Forced expiratory volume in one second

FVC:

Forced vital capacity

HCC:

Hepatocellular Carcinoma

HR:

Hazard Ratio

IFG:

Impaired fasting glucose

IFN-γ:

Interferon-γ

LABA:

Long-Acting Beta2-Agonist

LAMA:

Long-Acting Muscarinic Antagonist

MIP-1α:

Macrophage inflammatory protein 1α

NCHS:

National Center for Health Statistics

NF-κB:

Nuclear factor kappa-light-chain-enhancer of activated B cells

NHANES:

National Health and Nutrition Examination Survey

NLR:

Neutrophil-to-lymphocyte ratio

NSCLC:

Non-small-cell lung cancer

OR:

Odds ratio

PIR:

Poverty income ratio

PLR:

Platelet-to-lymphocyte ratio

PSM:

Propensity score matching

SE:

Standard error

SII:

Systemic Immune-Inflammation Index

STAT-4:

Signal transducer and activator of transcription 4

Th1:

T-helper type 1

Th2:

T-helper type 2

VTE:

Venous thromboembolism

WBC:

White blood cell

References

  1. Global regional. National deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990–2015: a systematic analysis for the global burden of Disease Study 2015. Lancet Respir Med. 2017;5(9):691–706.

    Article  Google Scholar 

  2. World Health Organization. www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd). Accessed 20 Mar 2023.

  3. Lareau SC, Fahy B, Meek P, Wang A. Chronic obstructive Pulmonary Disease (COPD). Am J Respir Crit Care Med. 2019;199(1):P1–P2.

    Article  PubMed  Google Scholar 

  4. Agustí A, Vogelmeier C, Faner R. COPD 2020: changes and challenges. Am J Physiol Lung Cell Mol Physiol. 2020;319(5):L879–83.

    Article  PubMed  Google Scholar 

  5. Christenson SA, Smith BM, Bafadhel M, Putcha N. Chronic obstructive pulmonary disease. Lancet. 2022;399(10342):2227–42.

    Article  PubMed  Google Scholar 

  6. Kaur M, Chandel J, Malik J, Naura AS. Particulate matter in COPD pathogenesis: an overview. Inflamm Res. 2022;71(7–8):797–815.

    Article  PubMed  CAS  Google Scholar 

  7. Hu B, Yang X-R, Xu Y, Sun Y-F, Sun C, Guo W, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014;20(23):6212–22.

    Article  PubMed  CAS  Google Scholar 

  8. Chen J-H, Zhai E-T, Yuan Y-J, Wu K-M, Xu J-B, Peng J-J, Chen C-Q, He Y-L, Cai S-R. Systemic immune-inflammation index for predicting prognosis of colorectal cancer. World J Gastroenterol. 2017;23(34):6261–72.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Miao Y, Yan Q, Li S, Li B, Feng Y. Neutrophil to lymphocyte ratio and platelet to lymphocyte ratio are predictive of chemotherapeutic response and prognosis in epithelial ovarian cancer patients treated with platinum-based chemotherapy. Cancer Biomark. 2016;17(1):33–40.

    Article  PubMed  CAS  Google Scholar 

  10. Fu H, Zheng J, Cai J, Zeng K, Yao J, Chen L, et al. Systemic Immune-Inflammation index (SII) is useful to predict survival outcomes in patients after liver transplantation for Hepatocellular Carcinoma within Hangzhou Criteria. Cell Physiol Biochem. 2018;47(1):293–301.

    Article  PubMed  CAS  Google Scholar 

  11. Lu Y, Xin D, Wang F. Predictive significance of preoperative systemic Immune-Inflammation index determination in postoperative liver metastasis of Colorectal Cancer. Onco Targets Ther. 2019;12:7791–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Fest J, Ruiter R, Mulder M, Groot Koerkamp B, Ikram MA, Stricker BH, van Eijck CHJ. The systemic immune-inflammation index is associated with an increased risk of incident cancer-A population-based cohort study. Int J Cancer. 2020;146(3):692–8.

    Article  PubMed  CAS  Google Scholar 

  13. Fani L, van der Willik KD, Bos D, Leening MJG, Koudstaal PJ, Rizopoulos D, et al. The association of innate and adaptive immunity, subclinical atherosclerosis, and cardiovascular disease in the Rotterdam Study: a prospective cohort study. PLoS Med. 2020;17(5):e1003115.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Zhong J-H, Huang D-H, Chen Z-Y. Prognostic role of systemic immune-inflammation index in solid tumors: a systematic review and meta-analysis. Oncotarget. 2017;8(43):75381–8.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Brightling C, Greening N. Airway inflammation in COPD: progress to precision medicine. Eur Respir J 2019; 54(2).

  16. Benjamin JT, Plosa EJ, Sucre JM, van der Meer R, Dave S, Gutor S et al. Neutrophilic inflammation during lung development disrupts elastin assembly and predisposes adult mice to COPD. J Clin Invest 2021; 131(1).

  17. Barnes PJ. Inflammatory endotypes in COPD. Allergy. 2019;74(7):1249–56.

    Article  PubMed  Google Scholar 

  18. Chen Y, Kumar RK, Thomas PS, Herbert C. Th1/17-Biased Inflammatory Environment Associated with COPD Alters the Response of Airway Epithelial Cells to Viral and Bacterial Stimuli. Mediators Inflamm. 2019; 2019:7281462.

  19. Tetley TD. Macrophages and the pathogenesis of COPD. Chest. 2002;121(5 Suppl):156S–9.

    Article  PubMed  CAS  Google Scholar 

  20. Paplinska-Goryca M, Misiukiewicz-Stepien P, Proboszcz M, Nejman-Gryz P, Gorska K, Krenke R. The expressions of TSLP, IL-33, and IL-17A in Monocyte Derived dendritic cells from Asthma and COPD patients are related to epithelial-macrophage interactions. Cells 2020; 9(9).

  21. Barnes PJ. Cellular and molecular mechanisms of chronic obstructive pulmonary disease. Clin Chest Med. 2014;35(1):71–86.

    Article  PubMed  Google Scholar 

  22. Hogg JC, Timens W. The pathology of chronic obstructive pulmonary disease. Annu Rev Pathol. 2009;4:435–59.

    Article  PubMed  CAS  Google Scholar 

  23. Chrysofakis G, Tzanakis N, Kyriakoy D, Tsoumakidou M, Tsiligianni I, Klimathianaki M, Siafakas NM. Perforin expression and cytotoxic activity of sputum CD8 + lymphocytes in patients with COPD. Chest. 2004;125(1):71–6.

    Article  PubMed  Google Scholar 

  24. Caramori G, Casolari P, Giuffrè S, Barczyk A, Adcock I, Papi A. COPD pathology in the small airways. Panminerva Med. 2011;53(1):51–70.

    PubMed  CAS  Google Scholar 

  25. Siena L, Gjomarkaj M, Elliot J, Pace E, Bruno A, Baraldo S, Saetta M, Bonsignore MR, James A. Reduced apoptosis of CD8 + T-lymphocytes in the airways of smokers with mild/moderate COPD. Respir Med. 2011;105(10):1491–500.

    Article  PubMed  Google Scholar 

  26. Di Stefano A, Capelli A, Lusuardi M, Caramori G, Balbo P, Ioli F, et al. Decreased T lymphocyte infiltration in bronchial biopsies of subjects with severe chronic obstructive pulmonary disease. Clin Exp Allergy. 2001;31(6):893–902.

    Article  PubMed  Google Scholar 

  27. Hogg JC. Pathophysiology of airflow limitation in chronic obstructive pulmonary disease. Lancet. 2004;364(9435):709–21.

    Article  PubMed  Google Scholar 

  28. Maclay JD, McAllister DA, Johnston S, Raftis J, McGuinnes C, Deans A, Newby DE, Mills NL, MacNee W. Increased platelet activation in patients with stable and acute exacerbation of COPD. Thorax. 2011;66(9):769–74.

    Article  PubMed  Google Scholar 

  29. Christie DJ, Kottke-Marchant K, Gorman RT. Hypersensitivity of platelets to adenosine diphosphate in patients with stable cardiovascular disease predicts major adverse events despite antiplatelet therapy. Platelets. 2008;19(2):104–10.

    Article  PubMed  CAS  Google Scholar 

  30. Gianetti J, Parri MS, Sbrana S, Paoli F, Maffei S, Paradossi U, Berti S, Clerico A, Biagini A. Platelet activation predicts recurrent ischemic events after percutaneous coronary angioplasty: a 6 months prospective study. Thromb Res. 2006;118(4):487–93.

    Article  PubMed  CAS  Google Scholar 

  31. Humbert M, Morrell NW, Archer SL, Stenmark KR, MacLean MR, Lang IM, et al. Cellular and molecular pathobiology of pulmonary arterial hypertension. J Am Coll Cardiol. 2004;43(12 Suppl S):13S–24S.

    Article  PubMed  CAS  Google Scholar 

  32. Gan WQ, Man SFP, Senthilselvan A, Sin DD. Association between chronic obstructive pulmonary disease and systemic inflammation: a systematic review and a meta-analysis. Thorax. 2004;59(7):574–80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Liu J, Li S, Zhang S, Liu Y, Ma L, Zhu J, et al. Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio can predict clinical outcomes in patients with metastatic non-small-cell lung cancer treated with nivolumab. J Clin Lab Anal. 2019;33(8):e22964.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Ruta VM, Man AM, Alexescu TG, Motoc NS, Tarmure S, Ungur RA et al. Neutrophil-To-Lymphocyte ratio and systemic Immune-Inflammation index-biomarkers in interstitial lung disease. Med (Kaunas). 2020; 56(8).

  35. Zhang L, Liu X, Yang R, Yang Y, Chen X. The diagnostic value of the systemic Immune-Inflammation index for venous thromboembolism in Lung Cancer Patients: a retrospective study. Mediators Inflamm. 2022;2022:9215311.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the staff and the participants of the NHANES study for their valuable contributions.

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 2042022kf1215), Chinese foundation for hepatitis prevention and control-TianQing liver disease research fund subject (Grant No. TGQB20210109), the Open Funds of Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province (Grant No. KFJJ-202005, KFJJ-201907), the Open Research Program of the State Key Laboratory of Virology of China (Grant No. 2023KF004, 2022KF003, 2021KF002, 2021KF006), and the Key research and development project of Hubei Province (Grant No. 2022BCA009).

Author information

Author notes

  1. Chenglin Ye and Li Yuan contributed equally to this work.

Authors and Affiliations

  1. Department of Clinical Laboratory, institute of translational medicine, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, PR China

    Chenglin Ye & Chengliang Zhu

  2. Department of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, 430060, Hubei, PR China

    Li Yuan

  3. State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, 430072, Hubei, PR China

    Kailang Wu

  4. Department of Pharmacy, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, PR China

    Bingzheng Shen

Authors
  1. Chenglin Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kailang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bingzheng Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengliang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.Y. and L.Y. designed the study and performed the analysis. All authors interpreted the results. C.Y. drafted the manuscript. C.Z., B.S., and K.W. conceived and designed the study and revised the manuscript. All authors critically revised the manuscript for intellectual content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chengliang Zhu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Ethics approval and consent to participate is not applicable for this study, because this study uses publicly available, existing data.

Consent for publication

Not Applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, C., Yuan, L., Wu, K. et al. Association between systemic immune-inflammation index and chronic obstructive pulmonary disease: a population-based study. BMC Pulm Med 23, 295 (2023). https://doi.org/10.1186/s12890-023-02583-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12890-023-02583-5

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

Contact us