Associations of inflammatory burden index with the prevalence and mortality of asthma in adults: a population based study

Introduction

Asthma is one of the most common chronic airway diseases worldwide, characterized by variable airflow obstruction leading to dyspnea and wheezing (1). In 2019, the World Health Organization estimated that approximately 260 million people worldwide were affected by this disease, resulting in 450,000 deaths that year (2,3). As a chronic inflammatory airway disease (CIAD), asthma not only significantly reduces patients’ quality of life but also imposes a substantial socioeconomic burden (4). Research indicates that early identification of inflammation-related biomarkers and intervention in modifiable risk factors (such as environmental exposure, obesity, etc.) can effectively delay disease progression and improve prognosis (5,6). Therefore, exploring novel inflammatory biomarkers holds significant importance for the prevention and management of asthma.

The core pathogenesis of asthma involves abnormal activation of various inflammatory cells and mediators (7,8). Th2 lymphocytes drive eosinophil recruitment and immunoglobulin E antibody secretion by B cells through the release of interleukin (IL)-4, IL-5, and IL-13 (7,9,10). Neutrophils play a dominant role in severe asthma and non-Th2 phenotypes, exacerbating oxidative stress and airway remodeling by releasing myeloperoxidase and neutrophil extracellular traps (11-13). Chronic airway inflammation ultimately develops in asthma patients in response to long-term influence of multiple cell types.

The inflammatory burden index (IBI) is a novel composite inflammatory biomarker that incorporates C-reactive protein (CRP), neutrophil count, and lymphocyte count (14,15). Studies have demonstrated that IBI can predict disease activity and mortality risk in patients with rheumatoid arthritis (16) and osteoarthritis (17). Furthermore, IBI has shown significant prognostic value in chronic inflammatory diseases such as cardiovascular diseases, diabetes, and cancer (14,18,19). However, no established relationship has been found between IBI and asthma. Although existing research has confirmed associations between composite inflammatory markers—including the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and neutrophil-to-lymphocyte ratio (NLR)—and mortality in asthma patients, these studies have not compared their predictive capabilities for asthma outcomes (20,21). Moreover, the predictive value of IBI in asthma and its comparative performance against other inflammatory biomarkers remain to be elucidated.

This study systematically evaluates the association between IBI and both asthma prevalence and all-cause mortality in adults, as well as comparing the predictive efficacy of IBI with other commonly used inflammatory markers [SII, SIRI, NLR, platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR)] for asthma-related outcomes, based on representative population data from the National Health and Nutrition Examination Survey (NHANES) 1999–2010 cycles. This study aims to provide a more precise inflammatory assessment tool for asthma risk stratification, thereby advancing the implementation of personalized intervention and management strategies. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1282/rc).

Methods

Study population

The data for this study were obtained from the NHANES, a database established by the Centers for Disease Control and Prevention (CDC) using a complex, multistage, stratified probability sampling design. NHANES encompasses comprehensive health data including demographic characteristics, dietary surveys, physical examinations, laboratory tests, and questionnaire responses. Its sampling weight design ensures national representativeness of research findings (22). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All study procedures were approved by the National Center for Health Statistics Ethics Review Board, with written informed consent obtained from all participants.

The study utilized data from five consecutive 2-year survey cycles conducted between 1999–2000 and 2009–2010. Among participants initially enrolled for this study, we excluded those aged <20 years and removed individuals with missing data regarding asthma status, IBI, or covariates.

Assessment of IBI and other indicators

The data for inflammatory markers in this study were obtained from the laboratory testing component of the NHANES. Complete blood count (CBC) parameters were measured using standardized automated hematology analyzers (Coulter DxH 800, Beckman Coulter), with all tests following the graded quality control procedures established by the Centers for CDC. CRP levels were determined using high-sensitivity immunoturbidimetric assays (Roche Cobas 6000 analyzer). The key metric in this study, the IBI, was calculated as: IBI = CRP×neutrophil count/lymphocyte count (14,15). Other evaluated inflammatory indices included: SII = platelet count×neutrophil count/lymphocyte count. SIRI = neutrophil count×monocyte count/lymphocyte count. NLR = neutrophil count/lymphocyte count. PLR = platelet count/lymphocyte count. MLR = monocyte count/lymphocyte count (20). Higher IBI values indicate greater inflammatory burden. For analytical purposes, IBI was stratified into quartiles (Q1, Q2, Q3, Q4), with the first quartile (Q1) representing low inflammatory burden and the fourth quartile (Q4) representing high inflammatory burden.

Assessment of asthma

After reviewing previous references (3,21,23), we based the asthma assessment on information collected from the questionnaire component of the National Health Interview Survey. NHANES utilized self-administered questionnaires to gather information regarding asthma and related symptoms. Participants were thus defined as having asthma if they answered “Yes” to the following question: “Has a doctor or other health professional ever told you that you have asthma?” The control group was defined as participants who answered “No” to this question.

Assessment of mortality

The mortality data were obtained by linking NHANES records with the most recent (2019) National Death Index data. The follow-up period was defined as the time interval from the date of interview conducted by Centers for CDC staff to the date of death.

Covariates

The covariate data involved in this study were all derived from sociodemographic information collected through standardized NHANES questionnaires and laboratory test results. Age was included in the analysis as a continuous variable. Gender was categorized into female and male. Race was divided into four categories: Mexican American, non-Hispanic White, non-Hispanic Black, and other race. Marital status was simplified into two groups: married and unmarried. Educational attainment was classified into three levels: less than high school, high school graduate, and above high school. Family economic status was measured by the poverty-to-income ratio (PIR) (20), which was calculated by dividing the total family income by the poverty threshold for the corresponding family size as defined by the U.S. Department of Health and Human Services, and was categorized into three levels: ≤1.0, 1.1–3.0, and >3.0. Smoking status was divided into three categories: never smokers were defined as those who had smoked fewer than 100 cigarettes in their lifetime, former smokers were those who had smoked more than 100 cigarettes but did not currently smoke, and current smokers were those who had smoked more than 100 cigarettes and currently smoked (20,24). Drinking status was categorized into non-drinkers (<12 drinks per year) and drinkers (≥12 drinks per year) (25). Based on guidance from the National Heart, Lung, and Blood Institute of the USA and the World Health Organization, body mass index (BMI) was classified into four groups: underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥30.0 kg/m²) (26,27). Hypertension was defined as systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg or based on a physician’s diagnosis (27). Diabetes was defined as meeting any of the following criteria: a confirmed diagnosis report, use of hypoglycemic drugs or insulin, HbA1c ≥6.5%, or fasting blood glucose ≥126 mg/dL (27). A family history of asthma was categorized into present or absent based on participants’ reports of asthma diagnoses among their first-degree relatives (21).

Statistical analysis

This study strictly adhered to the complex sampling design requirements of the NHANES database. To enhance the accuracy and validity of our findings, we incorporated Mobile Examination Center examination weights (WTMEC4YR, WTMEC2YR) in the analysis, employing weighted analytical methods to ensure nationally representative results. Continuous variables were expressed as weighted mean ± standard deviation (SD), while categorical variables were presented as frequencies (weighted percentages). Between-group differences were analyzed using weighted t-tests (for continuous variables) and weighted Chi-squared tests (for categorical variables).

Weighted multivariate logistic regression models were used in the cross-sectional analysis to calculate the odds ratio (OR) and 95% confidence interval (CI) for the association between the IBI and asthma risk. The predictive performance of IBI, SII, SIRI, NLR, PLR, and MLR for asthma was evaluated using receiver operating characteristic (ROC) curves. In the longitudinal analysis, weighted Cox proportional hazards models were employed to estimate hazard ratio (HR) and 95% CIs for the association between IBI and all-cause mortality risk in asthma patients. Kaplan-Meier survival curves were plotted, and between-group differences were assessed using the log-rank test. Time-dependent ROC analyses were conducted to evaluate the predictive value of inflammatory markers at different follow-up intervals (1-, 3-, and 5-year) and at quartiles of follow-up time (with cutoff points at 10.25, 12.75, and 16.17 years). Differences in the area under the curve (AUC) of inflammatory markers were compared using DeLong’s test. In the weighted multivariate regression analysis, we constructed three progressively adjusted regression models: Model 1 was not adjusted; Model 2 was adjusted for age, sex, marital status, and race; Model 3 was adjusted for age, sex, marital status, race, education level, PIR, drinking status, smoking status, BMI, diabetes, hypertension, and having a close relative with asthma. Restricted cubic splines (RCS) models with three knots were applied to explore potential nonlinear associations and were optimized based on the Akaike information criterion. Since the original IBI values exhibited a skewed distribution, a natural logarithm transformation (Ln-IBI) was applied, resulting in a normal distribution (Figure S1).

To ensure the robustness of the results, this study implemented multidimensional validation: (I) subgroup analyses were conducted to examine potential effect modifications by demographic characteristics (e.g., age, sex, race), with interaction effects assessed using Wald tests; (II) multiple imputation by chained equations (MICE package) was employed to handle missing covariate data, with results pooled after five imputation cycles; (III) sensitivity analyses were performed to compare consistency between weighted and unweighted models; (IV) participants who died within two years of follow-up were excluded to verify result consistency. All analyses were performed using R software (version 4.4.3), with two-sided tests applied. P value <0.05 was considered statistically significant.

Results

Baseline characteristics of the study participants

Figure 1 presents the participant selection process. From 1999 to 2010, a total of 23,758 adult participants from NHANES were included and divided into two groups based on asthma status: the asthma and non-asthma group.

Figure 1 Flowchart of participant selection from NHANES 1999–2010. IBI, inflammatory burden index; NHANES, National Health and Nutrition Examination Survey.

Table 1 displays the baseline characteristics of the participants. The study population had a mean age of 46.49±16.62 years, with 48.66% being male and the majority being non-Hispanic White (73.03%). Among them, 2,955 (12.44%) had asthma. Compared to non-asthma participants, asthma patients were more likely to be younger, unmarried, non-Hispanic White females with lower income levels (P<0.001). They also had a higher proportion of current smokers, BMI, higher prevalence of hypertension and diabetes, and a greater likelihood of having a relative with asthma (P<0.05). Additionally, asthma patients exhibited significantly higher neutrophil counts and CRP levels compared to non-asthma participants (P<0.01). Among the six inflammatory markers (NLR, SIRI, SII, PLR, MLR, IBI), only IBI showed a significant difference between asthma and non-asthma groups (P<0.01).

Among adult asthma participants, 577 (20%) experienced all-cause mortality (Table S1). Compared to survivors, those who died were more likely to be older, unmarried (P<0.01), had lower education and income levels, a higher proportion of current smoker status (P<0.001), and had a higher prevalence of diabetes and hypertension (P<0.001). Furthermore, deceased asthma patients exhibited significantly higher levels of inflammatory markers (NLR, SIRI, SII, PLR, MLR, IBI) (P<0.01).

Associations between IBI and asthma

The logistic regression model demonstrated the association between IBI and asthma (Table 2). IBI was converted into a categorical variable (quartiles) for analysis. In Model 1, participants in the highest quartile of IBI had a 1.45-fold higher risk of asthma compared to those in the lowest quartile (95% CI: 1.25–1.67, P<0.001). In Model 2, this value was 1.51 (95% CI: 1.30–1.74, P<0.001), and in Model 3, it was 1.21 (95% CI: 1.04–1.41, P=0.02). Trend analysis revealed a significant correlation between IBI and asthma risk (P for trend <0.05). These results indicate that regardless of adjusting for confounding factors, higher IBI levels are associated with an increased likelihood of asthma.

Associations between IBI and all-cause mortality

The Cox proportional hazards model demonstrated the association between IBI and all-cause mortality (Table 3). Following the conversion of IBI into a categorical variable (quartiles) for analysis, a significant dose-response relationship was observed. In Model 1, when comparing participants in the lowest quartile, those in the highest IBI quartile had a 476% increased risk of all-cause mortality (95% CI: 3.42–6.63, P<0.001). Similar trends were observed after adjusting for confounders in both Models 2 and 3, with HR values of 2.98 (95% CI: 2.14–4.15, P<0.001) and 2.50 (95% CI: 1.79–3.50, P<0.001) respectively. Trend analysis revealed a significant correlation between IBI and all-cause mortality (P for trend <0.001). The Kaplan-Meier survival curves yielded consistent results (Figure S2). These findings indicate that higher IBI levels are significantly associated with increased all-cause mortality in adult asthma patients.

RCS analyses

We performed RCS analysis to evaluate the nonlinear relationship between IBI and both asthma risk and all-cause mortality (Figure 2). In the fully adjusted model, IBI showed a linear association with asthma risk (P for overall =0.001, P for nonlinear =0.07). Similarly, IBI exhibited a linear relationship with all-cause mortality (P for overall <0.001, P for nonlinear =0.15). Therefore, it is reasonable to conclude that both asthma prevalence and all-cause mortality in asthma patients increase with higher IBI levels.

Figure 2 Restricted cubic spline analysis of the association between IBI and asthma (A), as well as all-cause mortality in asthma patients (B). Adjusted for age, sex, marital status, race, education level, PIR, drinking status, smoking status, BMI, diabetes, hypertension, close relative with asthma. BMI, body mass index; CI, confidence interval; IBI, inflammatory burden index; PIR, poverty-income ratio.

ROC analysis

We conducted ROC analysis and calculated AUC values to compare the performance of IBI with other inflammatory markers (SII, SIRI, PLR, NLR, MLR) in predicting asthma and all-cause mortality. As shown in Figure 3, for asthma prediction, the AUC of IBI was 0.541 (95% CI: 0.529–0.552), which was higher than that of other inflammatory markers (SII, SIRI, NLR, PLR, MLR) (P<0.05) (Table 4). As illustrated in Figure 4, the predictive ability of all inflammatory markers for all-cause mortality gradually declined with prolonged follow-up intervals. The AUC of IBI for predicting 1-year all-cause mortality was 0.701, showing no statistically significant difference compared to other inflammatory markers (SII, SIRI, NLR, PLR, MLR) (P>0.05). However, the AUC of IBI progressively decreased as follow-up time increased. At a follow-up duration of 16.17 years, the AUC of IBI for all-cause mortality was 0.616, which was higher than that of SII, NLR, and PLR (P<0.01) (Figure S3) but showed no significant difference compared to SIRI and MLR (P>0.05).

Figure 3 The ROC curves and the AUC values of IBI, SII, SIRI, NLR, PLR and MLR in predicting asthma. AUC, area under the curve; IBI, inflammatory burden index; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic; SII, systemic immune-inflammation index; SIRI, systemic inflammatory response index.

Figure 4 The time-dependent ROC curve and the AUC values of IBI, SII, SIRI, NLR, PLR and MLR in predicting all-cause mortality in adults with asthma, with time cutoff values of 1 (A), 3 (B), 5 (C), 10.25 (D), 12.75 (E), and 16.17 (F) years. Data are presented as AUC (95% CI). AUC, area under the curve; IBI, inflammatory burden index; MLR, monocyte-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic; SII, systemic immune-inflammation index; SIRI, systemic inflammatory response index.

Subgroup analysis and sensitivity analysis

We conducted subgroup analyses and sensitivity analyses to verify the stability of the positive associations between IBI and asthma, as well as between IBI and all-cause mortality. The subgroup analyses revealed that the positive associations of IBI with asthma (Figure S4) and IBI with all-cause mortality (Figure S5) remained unchanged across stratified groups by age, gender, race, marital status, and PIR levels (P for interaction >0.05). Sensitivity analyses demonstrated that the positive associations between IBI with asthma and IBI with all-cause mortality remained robust after multiple imputation of covariates with missing values (Tables S2,S3), non-weighted analysis of the data (Tables S4,S5), and exclusion of participants who died within two years of follow-up (Table S6).

Discussion

We conducted a study involving 23,758 adult participants to investigate the association between IBI and the prevalence and mortality of asthma, as well as to evaluating the predictive performance of IBI compared to other inflammatory indicators in asthma. Our findings revealed that high levels of IBI were significantly associated with an increased risk of asthma prevalence and all-cause mortality in asthma patients. Time-dependent ROC analysis demonstrated that the AUC value of IBI was 0.701 at the 1-year follow-up, but its predictive performance exhibited a gradual decline with prolonged follow-up intervals. Notably, for long-term follow-up intervals (16.17 years), IBI showed superior predictive capability for all-cause mortality compared to SII, NLR, and PLR. IBI may be a better predictive indicator for assessing the mortality risk in individuals with asthma. Overall, our study underscores the importance of considering inflammatory status reflected by IBI as an independent risk factor for all-cause mortality in asthma patients.

In the anti-inflammatory treatment of asthma, suppressing airway inflammation represents the core objective (28,29). After anti-inflammatory therapy, a significant negative correlation is observed between blood neutrophil count and the lung function parameter FEV₁/FVC in asthma patients (30), indicating that anti-inflammatory treatment can reduce neutrophil counts and improve airway obstruction. As a primary treatment modality, inhaled corticosteroids (ICS) primarily alleviate symptoms by suppressing airway inflammation (28). Furthermore, ICS therapy may also mitigate systemic inflammatory responses by inhibiting the elevation of oxidative stress and inflammatory markers under external environmental stimuli (31). Therefore, as IBI serves as a comprehensive indicator of systemic inflammatory burden, anti-inflammatory treatment in asthma patients may lead to a reduction in IBI levels. This observation suggests that monitoring IBI could have potential clinical value in reflecting the efficacy of anti-inflammatory therapy. Changes in treatment regimens during follow-up, such as adjustments in ICS dosage or combination therapies, may dynamically alter IBI levels, thereby potentially attenuating its accuracy in predicting long-term mortality risk. These factors may partially explain the relatively conservative predictive value of IBI observed in this study. Future research should explore the dynamic changes of IBI under different treatment strategies and its relationship with clinical outcomes in prospective cohorts with detailed therapeutic and phenotypic information.

In recent years, composite inflammatory indices based on CBC have garnered increasing attention for their value in assessing chronic inflammatory diseases. As composite markers, the SII and SIRI integrate multidimensional information such as neutrophils, lymphocytes, monocytes, and platelets, providing a more comprehensive reflection of systemic inflammatory status (21,32). Classical indices such as NLR and PLR have demonstrated superior discriminatory ability in predicting the risk of diabetic kidney disease compared to other markers (33). In contrast to SIRI and aggregate index of systemic inflammation, the MLR exhibits higher clinical value in predicting cardiovascular diseases (34). Cohort data reveal that SII and SIRI are significantly associated with mortality in asthma patients (20,35). Notably, SIRI holds unique predictive value for asthma-related stroke risk, outperforming the traditional SII marker (with an AUC difference of 0.07) (36). A cohort study suggests that among CBC-derived composite inflammatory markers, MLR demonstrates the highest predictive value for asthma mortality (20). These findings collectively highlight the unique utility of composite inflammatory indices in disease prediction.

The IBI is a novel composite inflammatory marker integrating CRP and NLR and demonstrates unique clinical value in assessing the association between systemic inflammation and chronic inflammatory diseases (14,37). IBI has been linked to mortality risk in rheumatoid arthritis and osteoarthritis (16,17). Research indicates that IBI is associated with the prevalence of CIAD. Higher levels of IBI are linked to increased all-cause mortality and respiratory disease mortality in CIAD patients (37). However, these studies did not establish IBI’s predictive value for asthma incidence nor clarify its comparative performance against other composite inflammatory markers. Our current study reveals that elevated IBI levels are significantly associated with increased asthma prevalence and higher all-cause mortality in asthma patients. Although its predictive ability is modest (AUC =0.541), IBI outperforms other composite inflammatory markers (SII, SIRI, NLR, PLR, MLR) in asthma prediction. At a 1-year follow-up, IBI’s AUC for all-cause mortality in asthma patients was 0.701, slightly lower than MLR (AUC =0.731), but without statistical significance (P>0.05). IBI’s predictive ability for all-cause mortality declined over extended follow-up period. However, at the 16.17-year follow-up, IBI exhibited the highest predictive value (AUC =0.616) compared to other inflammatory markers. Notably, IBI’s predictive performance varies significantly across different disease populations. IBI demonstrates strong predictive efficacy for in-hospital mortality in severe fever with thrombocytopenia syndrome (SFTS) (AUC =0.799) (38), likely because SFTS, as an acute viral infection, involves dramatic fluctuations in pro-inflammatory cytokines that IBI can effectively capture. In contrast, IBI shows weaker predictive performance for Intensive Care Unit/hospital mortality in sepsis patients (AUC =0.58) (39), possibly due to sepsis patients often presenting with complex pathophysiological changes such as multi-organ dysfunction and metabolic disturbances, which may dilute the predictive power of pure inflammatory markers. Among males aged 40 and above, IBI maintains relatively stable predictive ability for all-cause mortality, with AUC values of 0.622, 0.634, and 0.632 at 5-, 10-, and 15-year follow-ups (40), respectively. In contrast, the predictive performance of IBI declines steadily in asthma patients. This difference may stem from the fact that mortality risk in middle-aged and elderly males is primarily driven by chronic degenerative diseases (e.g., cardiovascular diseases), whereas asthma-related mortality is more likely dominated by acute respiratory events. The sudden and unpredictable nature of such events may explain IBI’s stronger short-term predictive performance but gradual attenuation over time.

This study demonstrates that IBI exhibits superior predictive ability for both asthma incidence and mortality in asthma patients compared to other composite inflammatory indices, which may be attributed to the inclusion of CRP. As a biomarker reflecting systemic inflammatory status, CRP can assess disease severity and predict disease progression and outcomes (41). Studies have shown that CRP is negatively correlated with lung function in asthma patients and can indicate the severity of airway inflammation (42). While complete blood cell counts are susceptible to influences from age and medications (43,44), CRP may provide a more stable reflection of systemic inflammation, potentially explaining IBI’s enhanced predictive value over other composite inflammatory markers. IBI represents the dynamic equilibrium among CRP, neutrophils, and lymphocytes which play a crucial role in asthma pathophysiology. Dysregulation of IBI may contribute to the progression of inflammation, exacerbation of airway hyperresponsiveness, and ultimately increased mortality risk in asthma patients. The integration of CRP in IBI appears to provide a more comprehensive assessment of inflammatory burden, potentially accounting for its better predictive performance in asthma outcomes compared to indices relying solely on cellular components.

There are several limitations in this study. First, some of the basic characteristics in this study were obtained through questionnaires or face-to-face interviews, making recall bias inevitable. Second, this is a retrospective observational study and thus cannot establish causality. Although confounding variables were adjusted for in this research, other potential or unmeasured confounders, such as dietary and sleep habits, remain difficult to eliminate. Third, the sample consisted solely of U.S. adults, which limits the applicability of these findings to children or populations in other regions, particularly Asia. Fourth, due to the lack of data on clinicians’ mortality risk assessments in the database, it was not possible to compare the IBI score with clinical risk evaluations. Fifth, due to the lack of detailed treatment data (such as medication type, dosage, and adherence), we were unable to analyze whether treatment interventions could reduce IBI and whether such a reduction could further translate into a decrease in mortality risk. In the future, prospective studies should monitor changes in IBI following treatment interventions to evaluate its potential as a modifiable risk marker. Future research should explore the use of more precise diagnostic tools or the combination of multiple diagnostic methods to improve the accuracy of IBI diagnosis.

Conclusions

The findings of this study demonstrate that elevated levels of IBI are significantly associated with increased asthma prevalence and all-cause mortality. IBI exhibits superior predictive value for adult asthma prevalence compared to other inflammatory composite markers (SII, SIRI, NLR, PLR, MLR), and shows better predictive value for mortality in adult asthma patients than SII, NLR, and PLR. IBI can serve as an effective predictive indicator to identify the mortality risk of asthma in a simple and cost-effective manner. These findings contribute to the growing evidence supporting the potential utility of IBI in predicting asthma prognosis and may inform clinical decision-making for the management of asthma in patients. However, further prospective clinical trials are required to validate the potential role of IBI in asthma pathology.

Acknowledgments

We appreciate the people who contributed to the NHANES data we studied.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1282/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1282/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1282/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Porsbjerg C, Melén E, Lehtimäki L, et al. Asthma. Lancet 2023;401:858-73. [Crossref] [PubMed]
2. Kelly A, Lavender P. Epigenetic Approaches to Identifying Asthma Endotypes. Allergy Asthma Immunol Res 2024;16:130-41. [Crossref] [PubMed]
3. Lu C, Zhu Y. The dietary inflammatory index and asthma prevalence: a cross-sectional analysis from NHANES. Front Nutr 2024;11:1485399. [Crossref] [PubMed]
4. Zhao W, Sun Y, Zhu B. Association of oxidative balance scores with cardiovascular and all cause mortality in patients with asthma. Sci Rep 2025;15:3901. [Crossref] [PubMed]
5. Björkander S, Klevebro S, Hernandez-Pacheco N, et al. Obese asthma phenotypes display distinct plasma biomarker profiles. Clin Transl Allergy 2023;13:e12238. [Crossref] [PubMed]
6. Kim J, Chung SJ, Kim WJ. Biomarkers of the relationship of particulate matter exposure with the progression of chronic respiratory diseases. Korean J Intern Med 2024;39:25-33. [Crossref] [PubMed]
7. Hammad H, Lambrecht BN. The basic immunology of asthma. Cell 2021;184:1469-85. [Crossref] [PubMed]
8. Qin Z, Chen Y, Wang Y, et al. Immunometabolism in the pathogenesis of asthma. Immunology 2024;171:1-17. [Crossref] [PubMed]
9. Ji T, Li H. T-helper cells and their cytokines in pathogenesis and treatment of asthma. Front Immunol 2023;14:1149203. [Crossref] [PubMed]
10. O'Keefe A, Connors L, Ling L, et al. Asthma. Allergy Asthma Clin Immunol 2025;20:81. [Crossref] [PubMed]
11. Yamasaki A, Okazaki R, Harada T. Neutrophils and Asthma. Diagnostics (Basel) 2022;12:1175. [Crossref] [PubMed]
12. Hudey SN, Ledford DK, Cardet JC. Mechanisms of non-type 2 asthma. Curr Opin Immunol 2020;66:123-8. [Crossref] [PubMed]
13. Li Y, Yang T, Jiang B. Neutrophil and neutrophil extracellular trap involvement in neutrophilic asthma: A review. Medicine (Baltimore) 2024;103:e39342. [Crossref] [PubMed]
14. Xie H, Ruan G, Wei L, et al. The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non-small cell lung cancer. J Cachexia Sarcopenia Muscle 2023;14:869-78. [Crossref] [PubMed]
15. Li W, Zhao D, Li W. Inflammatory burden index predicts long term mortality in a nationally representative population from NHANES. Sci Rep 2025;15:25034. [Crossref] [PubMed]
16. Zhai J, Yuan B, Liu T, et al. Association between the inflammatory burden index and rheumatoid arthritis and its all-cause mortality: data from NHANES 1999-2018. Front Med (Lausanne) 2024;11:1421497. [Crossref] [PubMed]
17. Xiong Z, Xu W, Wang Y, et al. Inflammatory burden index: associations between osteoarthritis and all-cause mortality among individuals with osteoarthritis. BMC Public Health 2024;24:2203. [Crossref] [PubMed]
18. Yu S, Li J, Chen H, et al. Association of the inflammatory burden index with the risk of pre-diabetes and diabetes mellitus: a cross-sectional study. BMC Endocr Disord 2025;25:82. [Crossref] [PubMed]
19. Wang Y, Huan C, Wang G, et al. The inflammation burden index can predict the cardiac injury following antitumour therapy in lung cancer patients with diabetes. Sci Rep 2025;15:11291. [Crossref] [PubMed]
20. Ke J, Qiu F, Fan W, 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. [Crossref] [PubMed]
21. Tian T, Xie M, Sun G. Association of systemic immune-inflammation index with asthma and asthma-related events: a cross-sectional NHANES-based study. Front Med (Lausanne) 2024;11:1400484. [Crossref] [PubMed]
22. Zipf G, Chiappa M, Porter KS, et al. National health and nutrition examination survey: plan and operations, 1999-2010. Vital Health Stat 1 2013;1-37.
23. Qianqian X, Qingmei Z, Jin J, et al. Obesity indicators and female asthma risk: the mediating role of white blood cell count based on NHANES data analysis. BMC Pulm Med 2025;25:399. [Crossref] [PubMed]
24. Ji T, Lv Y, Yang J, et al. Association between depression and asthma: insight from observational and genetic evidence. BMC Psychiatry 2025;25:786. [Crossref] [PubMed]
25. Fu Y, Li L, Gao J, et al. J-shaped association of dietary catechins intake with the prevalence of osteoarthritis and moderating effect of physical activity: an American population-based cohort study. Front Immunol 2023;14:1287856. [Crossref] [PubMed]
26. Liu S, Zhang H, Lan Z. Associations of obesity with chronic inflammatory airway diseases and mortality in adults: a population-based investigation. BMC Public Health 2024;24:1300. [Crossref] [PubMed]
27. Li G, Ji H, Jiang Q, et al. Association between physical activity and depression in adult prescription opioid users: A cross-sectional analysis based on NHANES 2007-2018. Gen Hosp Psychiatry 2024;89:1-7. [Crossref] [PubMed]
28. Tattersall MC, Jarjour NN, Busse PJ. Systemic Inflammation in Asthma: What Are the Risks and Impacts Outside the Airway? J Allergy Clin Immunol Pract 2024;12:849-62. [Crossref] [PubMed]
29. Jia N, Jin M, Liu Y, et al. The management of type 2 inflammatory respiratory diseases: a Chinese expert consensus [2024]. J Thorac Dis 2025;17:1807-31.
30. Li Y, Wang J, Zhao Q, et al. The impact of inflammatory cells on lung function in asthmatics in a cross sectional retrospect study. Heliyon 2024;10:e33691. [Crossref] [PubMed]
31. Chaiwong W, Liwsrisakun C, Inchai J, et al. Biomarkers of Oxidative Stress, Systemic Inflammation and Thrombosis in Adult Asthmatic Patients Treated with Inhaled Corticosteroids During Exposure to Fine Particulate Matter. J Clin Med 2025;14:2360. [Crossref] [PubMed]
32. Islam MM, Satici MO, Eroglu SE. Unraveling the clinical significance and prognostic value of the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic immune-inflammation index, systemic inflammation response index, and delta neutrophil index: An extensive literature review. Turk J Emerg Med 2024;24:8-19. [Crossref] [PubMed]
33. Li X, Wang L, Liu M, et al. Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study. Front Endocrinol (Lausanne) 2023;14:1285509. [Crossref] [PubMed]
34. Li X, Zhang L, Du Y, et al. Association between monocyte-to-lymphocyte ratio and cardiovascular diseases: insights from NHANES data. Diabetol Metab Syndr 2025;17:98. [Crossref] [PubMed]
35. Xu F, Jiang H, Li F, et al. Association between the systemic inflammation response index and mortality in the asthma population. Front Med (Lausanne) 2024;11:1446364. [Crossref] [PubMed]
36. Cheng W, Bu X, Xu C, et al. Higher systemic immune-inflammation index and systemic inflammation response index levels are associated with stroke prevalence in the asthmatic population: a cross-sectional analysis of the NHANES 1999-2018. Front Immunol 2023;14:1191130. [Crossref] [PubMed]
37. Zhu N, Lin S, Wang L, et al. Elevated inflammatory burden index increases mortality in adults with chronic inflammatory airway diseases: a nationwide cohort study. BMC Pulm Med 2024;24:399. [Crossref] [PubMed]
38. Yang Z, Wang L, Hong B, et al. Inflammatory Burden Index as a Predictor of In-Hospital Mortality in Patients With Severe Fever With Thrombocytopenia Syndrome. J Med Virol 2025;97:e70225. [Crossref] [PubMed]
39. Zhong Z, Fan M, Lv L, et al. Inflammatory burden index as a predictor of mortality in septic patients: a retrospective study using the MIMIC-IV database. BMC Infect Dis 2025;25:552. [Crossref] [PubMed]
40. Deng J, Hua J, Zeng T, et al. Associations between inflammatory burden index, prostate cancer, and mortality among middle-aged and elderly individuals. World J Urol 2024;42:538. [Crossref] [PubMed]
41. Rizo-Téllez SA, Sekheri M, Filep JG. C-reactive protein: a target for therapy to reduce inflammation. Front Immunol 2023;14:1237729. [Crossref] [PubMed]
42. Allam MH, Said AF, El Samie Omran AA, et al. High sensitivity C-reactive protein: its correlation with sputum cell counts in bronchial asthma. Respir Med 2009;103:1878-84. [Crossref] [PubMed]
43. Zhang J, Zhang X, Xiao B, et al. Analyzing the causal role of blood cells in aging: a Mendelian randomization study. Biogerontology 2024;26:7. [Crossref] [PubMed]
44. Bayrak S, Tatar C, Cakar E, et al. Evaluation of the predictive power of laboratory markers in the diagnosis of acute appendicitis in the elderly. North Clin Istanb 2019;6:293-301. [Crossref] [PubMed]