Sage Journals: Discover world-class research

Abstract

Background

Chronic respiratory diseases (CRDs) are leading causes of morbidity and mortality worldwide, yet simple prognostic biomarkers are lacking. The systemic inflammation response index (SIRI) integrates neutrophil, monocyte and lymphocyte counts and has shown prognostic value in malignancies and cardiovascular disease, but its role in CRDs remains unclear. We hypothesized that higher baseline SIRI is independently and dose-dependently associated with increased all-cause mortality across the spectrum of CRDs.

Methods

We analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2001–2018, including 7923 adults with CRDs (asthma, emphysema or chronic bronchitis). SIRI was calculated from a single baseline complete blood count. Participants were followed until death or 26 April 2022 (median 7.4 years). Cox proportional-hazards models (three levels of adjustment) evaluated the association between SIRI tertiles and all-cause mortality. Sensitivity analyses included continuous SIRI, a clinically derived cut-off (12.98) and CRD-specific sub-cohorts.

Results

Higher SIRI tertiles were progressively associated with increased mortality: fully-adjusted hazard ratios (Model 3) were 1.19 (95% CI 1.02–1.39) and 1.88 (1.63–2.16) for tertiles two and three versus tertile 1 (P-trend <0.001). Each 1-unit rise in continuous SIRI conferred an 1.01% excess risk (HR 1.0101, 95% CI 1.0082–1.0121). The unitless cut-off value of ≥12.98 identified a high-risk group with an increased risk of all-cause mortality (HR 1.69, 95% CI 1.51–1.89). This association was consistent for all-cause mortality across the asthma (HR 1.83), emphysema (HR 1.70), and chronic bronchitis (HR 1.89) sub-cohorts.

Conclusions

SIRI is an independent, dose-dependent predictor of all-cause mortality in patients with CRDs, performing robustly across asthma, emphysema and chronic bronchitis. These findings support the use of SIRI as a readily available prognostic tool in CRD management.

Keywords

systemic immune-inflammation index mortality chronic respiratory diseases NHANES

Introduction

Chronic respiratory diseases (CRDs) encompass a range of pulmonary and airway pathologies, most notably chronic bronchitis, emphysema, and asthma.¹ The global health burden of CRDs is substantial, contributing significantly to worldwide morbidity and mortality.² Data from the Global Burden of Disease study reveals that CRDs are responsible for nearly nine million deaths annually, accounting for 7% of global deaths.³ The prevalence of these conditions has shown a marked increase, with an estimated 454.6 million cases diagnosed in 2019.^1,4 This growing epidemic not only has serious adverse effects on patients’ quality of life and mortality risks but also imposes a considerable strain on global healthcare systems, society, and economic resources.^5,6

The systemic inflammation response index (SIRI), a composite inflammatory biomarker calculated from peripheral blood neutrophil, monocyte, and lymphocyte counts,⁷ has been increasingly utilized as a prognostic indicator for survival and mortality across numerous diseases. The prognostic value of complete blood count (CBC)-derived cellular ratios, such as the neutrophil-to-lymphocyte ratio (NLR), is well-established in respiratory conditions.^8,9 In a prospective cohort of 298 postoperative colorectal patients, SIRI exhibited superior predictive performance for survival outcomes when compared to other conventional serum inflammatory markers.¹⁰ Similarly, a study by Wang et al. involving 52 patients with advanced lung adenocarcinoma demonstrated that low SIRI levels were associated with a prolonged progression-free survival.¹¹ This finding is supported by a meta-analysis which concluded that high SIRI levels are predictive of poor survival outcomes in breast cancer patients.¹² Furthermore, not only in chronic heart failure patients, SIRI can provide better prognostic discrimination correlating with mortality risk in CHF patients,¹³ but also in cardiovascular and cerebrovascular diseases.^14–17 Besides, elevated SIRI levels are also associated with the incidence and mortality rates of chronic kidney disease (CKD).¹⁸ SIRI integrates neutrophil, monocyte, and lymphocyte counts–key cellular mediators of chronic airway inflammation. In CRDs like asthma and COPD, neutrophils drive tissue damage through protease release and oxidative stress, while monocytes differentiate into alveolar macrophages that perpetuate inflammation and remodeling. Lymphopenia, conversely, reflects impaired immunoregulation. Thus, SIRI quantifies the net burden of these pathophysiological processes central to CRD progression.

Although there is emerging evidence that risk of all-cause mortality in asthma depends on the highest quartile of SIRI compared to the lowest,¹⁹ the research on the relationship between SIRI and mortality risk in CRDs has not been evaluated as far as we know. This gap is critical because CRDs exhibit unique immune phenotypes (e.g., neutrophilic vs eosinophilic inflammation) that may differentially influence SIRI’s predictive capacity. Therefore, the primary objective of this study was to determine the association between SIRI and all-cause mortality in a large, nationally representative cohort of US adults with CRDs. We hypothesized that a higher SIRI would be independently associated with increased mortality risk and that this association would be consistent across different types of CRDs, including asthma, chronic bronchitis, and emphysema.

Research design and methods

Study population

The NHANES provided cross-sectional data to assess nutritional and health status among the US non-institutionalized civilian population. Our study utilized data from the continuous NHANES survey from 11 survey cycles, conducted from 2001 to 2018 with a total of 91,351 participants. A total of 53,756 eligible participants aged 18 years and older were included in the study population. We next excluded patients without SIRI or CRDs disease data. Additionally, participants without records of age, sex, race, marital status, education level, body mass index, and family income-poverty ratio were excluded. Ultimately, there were 7923 participants were included in this study (Figure 1).

Figure 1.

Flow chart of study population selection.

This retrospective cohort study used the 2001–2018 NHANES cycles. SIRI was computed from the complete blood-count differential obtained at each participant’s single baseline mobile-examination-center visit; no repeated measurements were available. Mortality was ascertained via linkage to the National Death Index through 26 April 2022, yielding a median follow-up of 7.4 years (IQR 4.1–11.2) from blood draw to death or censoring.

Statistical analysis

SIRI was (neutro-phil count × monocyte count)/lymphocyte count. Participants were equally classified into three groups based on the values of SIRI. The adjusted hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated through using Cox proportional hazards regression model. This observational study used three models based on the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guideline.²⁰ Model I was adjusted for non. Model II was adjusted for age, sex, and race. Model III was adjusted for all variables in model II and included other risk factors for marital status, education level, body mass index, and family income-poverty ratio. Furthermore, Kaplan–Meier survival curves for all-cause mortality stratified by the values of SIRI index was generated. Smooth curve fitting (penalized spline method) examined the nonlinearity association between SIRI index and all-cause mortality was to be used. All data analyses were performed using R version 4.2.2, and p values less than 0.05 indicated statistical significance.

Results

Subject characteristics

The baseline characteristics of the cohort were analyzed based on the changes in SIRI tertiles, as shown in Table 1. This analysis included 7923 patients, with each tertile based on 2641 individual observations. Compared to the lowest SIRI tertile group, patients in the highest SIRI tertile had a higher average age, were mostly married and had a higher level of education, and there were more smokers. Additionally, the male-to-female ratio was similar in all SIRI groups, but the patients in the highest SIRI tertile group were predominantly non-Hispanic white. Furthermore, all baseline variables showed statistically significant differences between the SIRI groups (p < 0.05).

Table 1.

Demographic and clinical characteristics according to systemic inflammation response index.

Variables	SIRI			p-value
Variables	T1	T2	T3	p-value
Number of patients	2641	2641	2641
SIRI	0.67 ± 0.27	1.16 ± 0.36	2.27 ± 1.41	<0.001
Age (%)				<0.001
<60	1808 (68.46%)	1732 (65.58%)	1582 (59.90%)
≥60	833 (31.54%)	909 (34.42%)	1059 (40.10%)
Sex (%)				<0.001
Male	1106 (41.88%)	1097 (41.54%)	1131 (42.82%)
Female	1535 (58.12%)	1544 (58.46%)	1510 (57.18%)
Race (%)				<0.001
Mexican	204 (7.72%)	294 (11.13%)	261 (9.88%)
Hispanics	209 (7.91%)	229 (8.67%)	244 (9.24%)
Non-hispanic white	1068 (40.44%)	1420 (53.77%)	1591 (60.24%)
Non-hispanic black	933 (35.33%)	474 (17.95%)	356 (13.48%)
Others	227 (8.60%)	224 (8.48%)	189 (7.16%)
BMI (kg/m²)	29.2 ± 7.3	30.5 ± 7.8	31.2 ± 8.7	<0.001
Education levels (%)				0.015
≤High school	1195 (45.25%)	1220 (46.19%)	1371 (51.91%)
College	888 (33.62%)	857 (32.45%)	861 (32.60%)
> College	558 (21.13%)	564 (21.36%)	409 (15.49%)
Marital status (%)				0.001
Not married	2078 (78.68%)	2172 (82.24%)	2167 (82.05%)
Married or living with a partner	563 (21.32%)	469 (17.76%)	474 (17.95%)
Smoking status
Yes	1310 (49.60%)	1383 (52.37%)	1660 (62.85%)	<0.001
No	1331 (50.40%)	1258 (47.63%)	981 (37.15%)	<0.001
Asthma				<0.001
Yes	2122 (80.3%)	2063 (78.1%)	1935 (73.3%)
No	515 (19.5%)	574 (21.7%)	703 (26.6%)
Don’t know	4 (0.2%)	4 (0.2%)	3 (0.1%)
Emphysema				<0.001
Yes	196 (7.4%)	282 (10.7%)	441 (16.7%)
No	2442 (92.5%)	2352 (89.1%)	2188 (82.8%)
Don’t know	3 (0.1%)	7 (0.3%)	12 (0.5%)
Chronic bronchitis				<0.001
Yes	779 (29.5%)	886 (33.5%)	992 (37.6%)
No	1862 (70.5%)	1755 (66.5%)	1649 (62.4%)

Mean ± standardized differences (SD) for continuous variables, percentages (%) for categorical variables. SIRI, systemic inflammation response index.

Association between SIRI index and mortality

Table 2 displays the three quartiles of overall mortality rates among CRDs patients at different levels of SIRI. In the unadjusted Model 1, the hazard ratios (HRs) from the lowest SIRI tertile group to the highest SIRI tertile group were 1.00 (reference), 1.24 (1.07–1.44), and 2.15 (1.88–2.47), p < 0.001, indicating a significant association between SIRI levels and increased overall mortality risk among CRDs patients. In Model 2, after further adjusting for age, sex, and race, the HRs from the lowest SIRI tertile group to the highest SIRI tertile group were 1.00 (reference), 1.19 (1.02–1.38), and 1.94 (1.69–2.24), p < 0.001. The risk of overall mortality gradually increased with increasing SIRI. In Model 3, after adjusting for marital status, education level, and smoking status based on Model 2, the results of Model 3 were similar to those of Model 2. The HRs from the lowest SIRI tertile group to the highest SIRI tertile group were 1.00 (reference), 1.19 (1.02–1.39), and 1.88 (1.63–2.16), p < 0.001. Thus, the relationship between SIRI and overall mortality among CRDs patients remained consistent in different models of our study. We then used a smoothing curve to plot the relationship between SIRI levels and mortality risk among CRDs patients. We found an approximate linear relationship between SIRI and overall mortality risk and there appears to be an increasing trend in the risk of overall mortality with the increase of SIRI (Figure 2). Furthermore, we conducted Kaplan-Meier survival analysis for the overall mortality rates among the SIRI tertile groups. The curves showed that compared to the higher SIRI tertile groups, the lowest SIRI tertile group was associated with the lowest lifetime risk of overall mortality. As the SIRI tertiles increased, the risk of overall mortality also increased (Figure 3). Finally, we performed subgroup analysis on the association between SIRI and CRDs, and found that higher SIRI levels were significantly associated with an increased risk of all-cause mortality, regardless of age (<60 or ≥60), sex (male or female), race, education levels, smoking status, and marital status (p < 0.001) (Table 3).

Table 2.

Multivariate cox regression analysis of SIRI with all-cause mortality.

SIRI	Model 1 HR (95% CI) p-value	Model 2 HR (95% CI) p-value	Model 3 HR (95% CI) p-value
T1	Reference	Reference	Reference
T2	1.24 (1.07, 1.44) 0.0046	1.19 (1.02, 1.38) 0.0270	1.19 (1.02, 1.39) 0.0244
T3	2.15 (1.88, 2.47) <0.0001	1.94 (1.69, 2.24) <0.0001	1.88 (1.63, 2.16) <0.0001
P for trend	<0.001	<0.001	<0.001

SIRI: systemic inflammation response index; HR: hazard ratio; CI: confidence interval.

Model 1 adjust for none.

Model two adjust for age, sex, and race.

Model 3 adjust for age, sex, race, marital status, education level, smoking status.

Figure 2.

Restricted cubic spline analysis of the association between SIRI and all-cause mortality.

Figure 3.

Kaplan–meier survival analysis plot for all-cause mortality with SIRI categories.

Table 3.

Subgroup of association between systemic inflammation response index and all-cause mortality.

Subgroup	Q1	Q2	Q3	P for trend
Age				<0.001
<60	Reference	1.27 (0.92, 1.74) 0.1432	1.86 (1.38, 2.50) <0.0001
≥60	Reference	1.17 (0.99, 1.39) 0.0669	2.07 (1.78, 2.42) <0.0001
Sex				<0.001
Male	Reference	1.36 (1.09, 1.70) 0.0065	2.85 (2.34, 3.47) <0.0001
Female	Reference	1.16 (0.94, 1.42) 0.1656	1.64 (1.36, 1.99) <0.0001
Race				<0.001
Mexican	Reference	0.86 (0.49, 1.53) 0.6136	1.42 (0.83, 2.43) 0.1949
Hispanics	Reference	1.97 (0.95, 4.06) 0.0666	2.13 (1.06, 4.30) 0.0342
Non-hispanic white	Reference	1.20 (0.99, 1.46) 0.0667	2.11 (1.77, 2.52) <0.0001
Non-hispanic black	Reference	1.13 (0.82, 1.57) 0.4599	2.19 (1.64, 2.95) <0.0001
Others	Reference	1.59 (0.75, 3.40) 0.2299	2.10 (1.00, 4.38) 0.0490
Education levels				<0.001
≤High school	Reference	1.16 (0.96, 1.40) 0.1343	1.91 (1.61, 2.26) <0.0001
College	Reference	1.29 (0.97, 1.72) 0.0787	2.06 (1.58, 2.68) <0.0001
>College	Reference	1.73 (1.10, 2.72) 0.0172	3.38 (2.19, 5.20) <0.0001
Marital status (%)				<0.001
Not married	Reference	1.17 (1.00, 1.37) 0.0469	2.10 (1.82, 2.42) <0.0001
Married or living with a partner	Reference	1.98 (1.06, 3.67) 0.0309	2.77 (1.55, 4.95) 0.0006
Smoking status				<0.001
Yes	Reference	1.10 (0.92, 1.32) 0.2942	1.90 (1.62, 2.22) <0.0001
No	Reference	1.46 (1.11, 1.92) 0.0070	2.12 (1.62, 2.76) <0.0001

To examine whether the prognostic value of SIRI differs among major CRD entities, we repeated the Cox analyses within patients diagnosed with asthma, emphysema, and chronic bronchitis separately. After recalculating SIRI tertiles within each subtype, the highest-tertile group consistently exhibited the greatest mortality risk (Table 4). Among asthma patients, the fully-adjusted hazard ratio (Model 3) for T3 versus T1 was 1.83 (95% CI 1.53–2.21). In emphysema, the corresponding HR was 1.70 (95% CI 1.35–2.15), and in chronic bronchitis it reached 1.89 (95% CI 1.53–2.34) (all p < 0.001). These findings indicate that the positive, dose-response relationship between SIRI and all-cause mortality is robust and independent of the specific CRD diagnosis.

Table 4.

Association between SIRI (tertiles) and all-cause mortality in patients with asthma, emphysema, and chronic bronchitis.

	Model 1		Model 2		Model 3
	HR (95%CI)	p-value	HR (95%CI)	p-value	HR (95%CI)	p-value
Asthma	1.43 (1.3∼1.56)	<0.001	1.43 (1.31∼1.57)	<0.001	1.37 (1.25∼1.5)	<0.001
T1	Reference		Reference		Reference
T2	1.23 (1.02∼1.5)	0.033	1.25 (1.02∼1.52)	0.029	1.22 (1∼1.49)	0.046
T3	1.98 (1.66∼2.36)	<0.001	1.99 (1.65∼2.39)	<0.001	1.83 (1.53∼2.21)	<0.001
Emphysema	1.41 (1.26∼1.58)	<0.001	1.31 (1.17∼1.48)	<0.001	1.31 (1.17∼1.48)	<0.001
T1	Reference		Reference		Reference
T2	1.27 (1∼1.61)	0.046	1.2 (0.94∼1.53)	0.14	1.2 (0.94∼1.52)	0.148
T3	1.97 (1.57∼2.47)	<0.001	1.71 (1.35∼2.15)	<0.001	1.7 (1.35∼2.15)	<0.001
Chronic bronchitis	1.5 (1.35∼1.66)	<0.001	1.45 (1.31∼1.62)	<0.001	1.4 (1.26∼1.56)	<0.001
T1	Reference		Reference		Reference
T2	1.19 (0.95∼1.48)	0.135	1.2 (0.95∼1.5)	0.121	1.18 (0.94∼1.48)	0.159
T3	2.14 (1.75∼2.62)	<0.001	2.03 (1.64∼2.5)	<0.001	1.89 (1.53∼2.34)	<0.001

Model 1 adjust for none.

Model 2 adjust for age, sex, and race.

Model 3 adjust for age, sex, race, marital status, education level, smoking status.

As detailed in Supplemental Table 1, we evaluated the prognostic performance of systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) through multivariable Cox regression. In unadjusted analyses (Model 1), all inflammatory indices exhibited significant associations with mortality, with MLR showing the strongest effect (T3 HR = 3.05, p < 0.001). However, after adjustment for age, sex, race, and sociodemographic confounders (Model 3), NLR (T3 HR = 1.88, 95% CI: 1.63–2.17) and MLR (T3 HR = 1.80, 95% CI: 1.55–2.09) retained significant but attenuated risks, while PLR demonstrated weaker predictive capacity (T3 HR = 1.24, 95% CI: 1.09–1.42). Then, we determined the optimal prognostic cut-off for SIRI by maximally selected rank statistics, identifying a single threshold of 12.98 that best stratified mortality risk. Participants with SIRI ≥12.98 exhibited a consistently higher hazard of all-cause death—HR 1.97 (95% CI 1.76–2.19) unadjusted, attenuating to HR 1.69 (95% CI 1.51–1.89) after full adjustment—confirming the clinical relevance of this cut-off (Supplemental Table 2). Beyond tertile stratification, treating SIRI as a continuous variable yielded a consistent dose-response pattern: each 1-unit increase in SIRI was associated with an 1.01% rise in all-cause mortality after full adjustment (Model 3 HR 1.0101, 95% CI 1.0082–1.0121, p < 0.001; Supplemental Table 3).

Discussion

This study is the first to establish the SIRI as a novel, independent, and dose-dependent predictor of all-cause mortality in a large, nationally representative cohort of patients with CRDs. Utilizing data from the NHANES database (2001–2018), we identified a significant linear association between elevated SIRI levels and increased risk of all-cause mortality. Crucially, this association remained robust after extensive adjustment for sociodemographic and clinical confounders, indicating that SIRI may serve as a readily available and potent prognostic tool for risk stratification in CRD patients. A key novel insight from our analysis is the consistency of this association across the major CRD entities—asthma, emphysema, and chronic bronchitis—suggesting SIRI captures a universal inflammatory risk pathway common to these diseases. Recent studies by Buonacera et al. and Regolo et al. further highlight that sustained neutrophilia and systemic inflammation—core components of SIRI—are key drivers of respiratory failure and mortality, thereby reinforcing its prognostic relevance across chronic respiratory diseases.^21–23

The prognostic value of complete blood count (CBC)-derived inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), has been increasingly recognized in CRDs.¹⁹ The prognostic significance of these indices has been well-documented. A cross-sectional NHANES study by Zhu et al. linked elevated NLR levels with higher all-cause mortality in asthma.²⁴ This association was corroborated by Shi et al., who confirmed a correlation between NLR and the severity and prognosis of asthma in both adults and children.^25,26 Furthermore, Xuanqi et al. showed that PLR can predict the frequency of acute exacerbations in patients with stable chronic obstructive pulmonary disease (COPD),²⁷ and both NLR and PLR have been identified as prognostic predictors for mortality in COPD.²⁸ A higher SII has been associated with a poorer prognosis in combined cohorts of COPD and asthma patients.²⁹ While these studies lay a foundation for the role of systemic inflammation, our findings uniquely demonstrate that SIRI—integrating three key immune cell lineages (neutrophils, monocytes, and lymphocytes)—provides a superior and more holistic reflection of the dysfunctional immune response in CRDs, which is strongly predictive of mortality.

Although asthma, COPD, chronic bronchitis, and other major CRDs have distinct pathophysiological mechanisms, they all share a common feature: unresolved chronic inflammatory responses.^30–32 Evidence suggests that elevated and activated neutrophil levels are linked to symptom relief and treatment outcomes in symptomatic asthma patients.³³ Furthermore, monocytes in the peripheral blood are also activated in asthma patients.³⁴ On one hand, these monocytes can differentiate into macrophages or dendritic cells, thereby promoting the inflammatory process and contributing to lung function destruction.^35–37 On the other hand, lymphocytes, which play a regulatory role in allergic inflammation associated with asthma,³⁸ may have a diminished response due to the increase in neutrophil and monocyte counts. In COPD patients, the levels of CD8 + T lymphocytes in the blood are elevated compared to healthy individuals.^39,40 However, studies also indicate that T cell-mediated immune responses may be diminished in severe COPD patients.⁴¹ Tzu-Tao et al. further confirmed that the percentage of peripheral blood lymphocytes is related to the worsening of COPD symptoms.⁴² Additionally, research has shown a relationship between an increased peripheral blood neutrophil count and higher rates of COPD exacerbation and mortality.^43,44 Moreover, reactive oxidative stress (ROS), which is closely linked to acute exacerbation of COPD,^45–47 can induce chemotaxis and increase the count of peripheral blood monocytes. This suggests that an increased peripheral blood monocyte count may also be associated with acute exacerbations of COPD. Based on these findings, it is reasonable to speculate that other CRDs may exhibit similar changes in peripheral blood inflammatory cells. Therefore, utilizing the SIRI, which incorporates these cell counts, is a suitable approach for predicting the mortality rate of CRD patients.

This study has several notable strengths. First, to our knowledge, this is the first study to investigate the association between SIRI levels and mortality risk specifically in patients with CRDs. Second, with 7923 participants included in our analysis, the large sample size provides robust statistical power and enhances the credibility of our findings. Third, our statistical models were adjusted for several potential confounders and risk factors, which further strengthens the reliability of our results.

However, several limitations of our study should be considered. The data were sourced from the cross-sectional NHANES database, which, by its nature, provides only a snapshot of health status at a specific time point. This design limits our ability to establish causality or track the long-term relationship between CRDs and mortality. The limitations of the NHANES database also extended to key clinical and laboratory parameters. Inconsistent and incomplete measurement of CRP/hs-CRP across NHANES cycles precluded their inclusion in our models; future studies with standardized inflammatory markers are warranted to refine prognostic assessments. Additionally, the lack of data on critical clinical parameters such as oxygen therapy, pulmonary function tests, and disease-specific medications prevented further adjustment for these potential confounders. The absence of P/F ratio data also restricted our direct assessment of hypoxemic severity, suggesting that future studies should prioritize collecting arterial blood gas data to validate SIRI’s prognostic utility, particularly in respiratory failure subgroups. Future research could also validate SIRI against P/F ratios in mechanically ventilated patients. Lastly, the unavailability of comprehensive data on dyspnea and exercise capacity in NHANES prevented the use of established clinical indices, such as the BODE index, which could serve as a valuable complement to SIRI in assessing CRD prognosis.

In summary, our study provides evidence that higher SIRI levels are relevant to the increased all-cause mortality in patients with CRDs. However, Further research is warranted to confirm our findings.

Supplemental Material

Supplemental Material - Association between the systemic inflammation response index and all-cause mortality in patients with chronic respiratory diseases: A nationwide cohort study from the NHANES 2001–2018

Supplemental Material for Association between the systemic inflammation response index and all-cause mortality in patients with chronic respiratory diseases: A nationwide cohort study from the NHANES 2001–201 by Danyan Huang, Chunfeng Xing, Yongwen Feng, Pan Jiang, Jibo Li, Haoda Liang, Feng Xu and Dandan He in Chronic Respiratory Disease

Supplemental Material

Supplemental Material - Association between the systemic inflammation response index and all-cause mortality in patients with chronic respiratory diseases: A nationwide cohort study from the NHANES 2001–2018

Supplemental Material

Supplemental Material - Association between the systemic inflammation response index and all-cause mortality in patients with chronic respiratory diseases: A nationwide cohort study from the NHANES 2001–2018

Footnotes

ORCID iD

Feng Xu

Author contributions

Danyan Huang, Yongwen Feng and Jibo Li contributed equally to study conceptualization, data curation, and initial manuscript drafting. Pan Jiang and Haoda Liang performed additional statistical analyses, generated the figures and tables, and critically revised the manuscript. Chunfeng Xing led the sensitivity analyses, conducted all subgroup analyses, and substantially revised the Methods and Discussion sections during the revision phase. Dandan He, as the corresponding author, coordinated the response to reviewers’ comments, supplied missing data, refined the statistical models, and conducted the final scientific and linguistic review of the revised manuscript. Feng Xu and Dandan He jointly supervised the overall study and verified the accuracy of all revisions. All authors read and approved the final submitted version.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Supported by Sanming Project of Medicine in Shenzhen Guangming (szgmtd2025003) and grants from Shenzhen Science and Technology Program (JCYJ20240813152030039).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The NHANES dataset is publicly available online, accessible at .

Supplemental Material

Supplemental material for this article is available online.

References

GBD 2019 Chronic Respiratory Diseases Collaborators . Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the global burden of disease study 2019. eClinicalMedicine 2023; 59: 101936.

The

. GBD 2017: a fragile world. Lancet 2018; 392(10159): 1683.

Prevalence and attributable health burden of chronic respiratory diseases . 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet Respir Med 2020; 8(6): 585–596.

Global, regional, and 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.

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet 2020; 396(10258): 1204–1222.

Chen

Kuhn

Prettner

, 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 Global Health 2023; 11(8): e1183–e1193.

Jin

Chen

, et al. The associations of two novel inflammation indexes, SII and SIRI with the risks for cardiovascular diseases and all-cause mortality: a 10-year Follow-Up study in 85,154 individuals. J Inflamm Res 2021; 14: 131–140.

de Jager

CPC

Wever

Gemen

EFA

, et al. The neutrophil-lymphocyte count ratio in patients with community-acquired pneumonia. PLoS One 2012; 7(10): e46561.

Cataudella

Giraffa

Di Marca

, et al. Neutrophil-to-lymphocyte ratio: an emerging marker predicting prognosis in elderly adults with community-acquired pneumonia. J Am Geriatr Soc 2017; 65(8): 1796–1801.

10.

Cao

Zheng

, et al. Levels of systemic inflammation response index are correlated with tumor-associated bacteria in colorectal cancer. Cell Death Dis 2023; 14(1): 69.

11.

Wang

. Prognostic significance of SIRI in patients with late-stage lung adenocarcinoma receiving EGFR-TKI treatment. Curr Probl Cancer 2024; 50: 101099.

12.

Zhang

Cheng

. Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis. Ann Med 2024; 56(1): 2337729.

13.

Zhu

Wang

Zhou

, et al. The associations of two novel inflammation biomarkers, SIRI and SII, with mortality risk in patients with chronic heart failure. J Inflamm Res 2024; 17: 1255–1264.

14.

Zhao

Dong

Qin

, et al. Inflammation index SIRI is associated with increased all-cause and cardiovascular mortality among patients with hypertension. Front Cardiovasc Med 2022; 9: 1066219.

15.

Zhang

Xing

Zhou

, et al. The predictive role of systemic inflammation response index (SIRI) in the prognosis of stroke patients. Clin Interv Aging 2021; 16: 1997–2007.

16.

Wang

Wen

Jiang

, et al. The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage. Front Immunol 2023; 14: 1115031.

17.

Xia

, et al. Systemic immune inflammation index (SII), system inflammation response index (SIRI) and risk of all-cause mortality and cardiovascular mortality: a 20-year follow-up cohort study of 42,875 US adults. J Clin Med 2023; 12(3): 1128.

18.

Huang

Mai

Zhao

, et al. Association of systemic immune-inflammation index and systemic inflammation response index with chronic kidney disease: observational study of 40,937 adults. Inflamm Res 2024; 73(4): 655–667.

19.

Qiu

Fan

, et al. Associations of complete blood cell count-derived inflammatory biomarkers with asthma and mortality in adults: a population-based study. Front Immunol 2023; 14: 1205687.

20.

von Elm

Altman

Egger

, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007; 147(8): 573–577.

21.

Buonacera

Stancanelli

Colaci

, et al. Neutrophil to lymphocyte ratio: an emerging marker of the relationships between the immune system and diseases. Int J Mol Sci 2022; 23: 3636.

22.

Regolo

Vaccaro

Sorce

, et al. Neutrophil-to-Lymphocyte ratio (NLR) is a promising predictor of mortality and admission to intensive care unit of COVID-19 patients. J Clin Med 2022; 11: 2235.

23.

Regolo

Sorce

Vaccaro

, et al. Assessing humoral immuno-inflammatory pathways associated with respiratory failure in COVID-19 patients. J Clin Med 2023; 12: 4057.

24.

Zhu

Lin

, et al. Naples prognostic score as a novel prognostic prediction indicator in adult asthma patients: a population-based study. World Allergy Organ J 2023; 16(10): 100825.

25.

Wawryk-Gawda

Żybowska

Ostrowicz

. The neutrophil to lymphocyte ratio in children with bronchial asthma. J Clin Med 2023; 12(21): 6869.

26.

Huang

Zhan

, et al. The neutrophil to lymphocyte ratio as a novel predictor of asthma and its exacerbation: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci 2020; 24(22): 11719–11728.

27.

Liu

Feng

, et al. The combination of hemogram indexes to predict exacerbation in stable chronic obstructive pulmonary disease. Front Med 2020; 7: 572435.

28.

Zinellu

Mangoni

, et al. Clinical significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in acute exacerbations of COPD: present and future. Eur Respir Rev 2022; 31(166): 220095.

29.

Benz

Wijnant

SRA

Trajanoska

, et al. Sarcopenia, systemic immune-inflammation index and all-cause mortality in middle-aged and older people with COPD and asthma: a population-based study. ERJ Open Res 2022; 8(1): 0628–2021.

30.

Mah

Ritchie

Finney

. Selected updates on chronic obstructive pulmonary disease. Curr Opin Pulm Med 2024; 30(2): 136–140.

31.

Qin

Chen

Wang

, et al. Immunometabolism in the pathogenesis of asthma. Immunology 2024; 171(1): 1–17.

32.

Zhang

Wurzel

Perret

, et al. Chronic bronchitis in children and adults: definitions, pathophysiology, prevalence, risk factors, and consequences. J Clin Med 2024; 13(8): 2413.

33.

Monteseirín

. Neutrophils and asthma. J Investig Allergol Clin Immunol 2009; 19(5): 340–354.

34.

Eguíluz-Gracia

Malmstrom

Dheyauldeen

, et al. Monocytes accumulate in the airways of children with fatal asthma. Clin Exp Allergy 2018; 48(12): 1631–1639.

35.

Han

Chen

, et al. Upregulated expression of substance P and NK1R in blood monocytes and B cells of patients with allergic rhinitis and asthma. Clin Exp Immunol 2022; 210(1): 39–52.

36.

Rastogi

Fraser

, et al. Inflammation, metabolic dysregulation, and pulmonary function among obese urban adolescents with asthma. Am J Respir Crit Care Med 2015; 191(2): 149–160.

37.

Zhang

Tang

, et al. Epithelial exosomal contactin-1 promotes monocyte-derived dendritic cell-dominant T-cell responses in asthma. J Allergy Clin Immunol 2021; 148(6): 1545–1558.

38.

Schuijs

Willart

Hammad

, et al. Cytokine targets in airway inflammation. Curr Opin Pharmacol 2013; 13(3): 351–361.

39.

Barnes

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

40.

Hogg

Timens

. The pathology of chronic obstructive pulmonary disease. Annu Rev Pathol 2009; 4: 435–459.

41.

Di Stefano

Capelli

Lusuardi

, 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.

42.

Chen

Lee

Chang

, et al. Prediction value of neutrophil and eosinophil count at risk of COPD exacerbation. Ann Med 2023; 55(2): 2285924.

43.

Chen

, et al. Suppressor of variegation 3–9 homologue 1 impairment and neutrophil-skewed systemic inflammation are associated with comorbidities in COPD. BMC Pulm Med 2021; 21(1): 276.

44.

Lonergan

Dicker

Crichton

, et al. Blood neutrophil counts are associated with exacerbation frequency and mortality in COPD. Respir Res 2020; 21(1): 166.

45.

Maclay

McAllister

Johnston

, et al. Increased platelet activation in patients with stable and acute exacerbation of COPD. Thorax 2011; 66(9): 769–774.

46.

Shen

Yang

Guo

, et al. Increased serum ox-LDL levels correlated with lung function, inflammation, and oxidative stress in COPD. Mediat Inflamm 2013; 2013: 972347.

47.

Barnes

. Cellular and molecular mechanisms of asthma and COPD. Clin Sci (Lond) 2017; 131(13): 1541–1558.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.49 MB

0.47 MB

0.46 MB