The lymphocyte-eosinophil to neutrophil-monocyte ratio as a potential composite biomarker associated with asthma exacerbations in adults

Abstract

Background:

This study evaluates the lymphocyte-eosinophil to neutrophil-monocyte ratio (LENMR) as a novel inflammatory indicator of the exacerbation risk in adults with asthma.

Methods:

This cross-sectional study included 1344 adults with asthma from the 2007–2012 cycles of the National Health and Nutrition Examination Survey. The association between LENMR and asthma exacerbations was evaluated by using multivariable logistic regression with progressive adjustment for confounders. Subgroup analyses were conducted to assess the consistency of associations. Restricted cubic spline and threshold effect models were used to explore potential nonlinear relationships.

Results:

A higher LENMR was significantly associated with an increased risk of asthma exacerbations. In the fully adjusted model, the participants in the highest quartile had a 77% higher odds of exacerbation compared with the lowest quartile (odds ratio 1.77 [95% confidence interval, 1.19–2.65]; p = 0.007). Associations were consistent across subgroups. Restricted cubic spline analysis revealed a significant nonlinear relationship, with a threshold effect identified at LENMR = 0.31.

Conclusion:

An elevated LENMR is positively associated with asthma exacerbations under specific thresholds.

Asthma is a chronic airway disease characterized by persistent inflammation, bronchial hyperresponsiveness, and structural remodeling, which results in variable airflow limitation and respiratory symptoms.^1–3 Inflammatory phenotypes are broadly classified as type 2 (T2-high) and non–type 2 (T2-low). T2-high asthma is marked by eosinophilic inflammation mediated by T helper (Th) 2 cells and group 2 innate lymphoid cells, with interleukin (IL) 4, IL-5, and IL-13 contributing to airway hyperresponsiveness and mucus production.^4,5 In contrast, T2-low asthma is associated with neutrophilic or paucigranulocytic inflammation driven by Th1 and Th17 pathways, involving IL-8, IL-17, and tumor necrosis factor-α, and is often linked to more severe, corticosteroid-resistant disease.^3,6,7

Eosinophils are key effector cells in T2-high asthma, inducing airway injury through the release of cytotoxic granules and proinflammatory mediators. Elevated peripheral eosinophil counts are associated with increased exacerbation risk and enhanced response to biologic therapies that target type 2 inflammation, and are widely used as biomarkers in clinical monitoring.⁸ The neutrophil-to-lymphocyte ratio (NLR) reflects neutrophilic inflammation and has been associated with severe, corticosteroid-resistant asthma,^9,10 whereas the eosinophil-to-lymphocyte ratio (ELR) indicates eosinophilic activity and correlates with frequent exacerbations and poor disease control.^11,12 However, these indices represent single-pathway processes and may not adequately capture the complexity of asthma-related inflammation.

To address this limitation, the present study introduces the lymphocyte-eosinophil to neutrophil-monocyte ratio (LENMR), a composite marker designed to reflect both type 2 and non–type 2 inflammatory pathways. We aimed to examine the association between LENMR and the asthma exacerbation risk, offering preliminary insights into its potential clinical utility.

METHODS

Data Source and Study Population

The National Health and Nutrition Examination Survey (NHANES) is an ongoing, multistage program designed to assess the health and nutritional status of the U.S. population, providing a representative profile of the noninstitutionalized civilian population through a complex, stratified, multistage probability sampling method.¹³ Written informed consent was obtained from all participants before enrollment, and the study protocols were reviewed and approved by the institutional review board of the National Center for Health Statistics.¹⁴

This retrospective analysis was based on data from the 2007–2012 NHANES cycles.¹³ Participants were excluded if they met any of the following criteria: (1) age <18 years, (2) active asthma exacerbation, (3) missing data on asthma exacerbation status, or (4) incomplete complete blood cell count results. The detailed inclusion and exclusion process is illustrated in Fig. 1.

Figure 1.

Flowchart of participant selection and exclusion.

Definitions of Asthma and Outcome

Asthma status was identified based on self-reported physician diagnosis collected via the NHANES Medical Conditions section.¹³ Participants responded to the standardized asthma question (MCQ010): “Has a doctor or other health professional ever told you that you have asthma?” The validity of self-reported asthma diagnoses and reports of exacerbations in NHANES has been confirmed in previous studies, which demonstrated high concordance with clinical records and national health surveys.^13,15–17

The outcome was defined as the occurrence of an asthma exacerbation within a 1-year period, determined based on participants’ self-reported responses to the standardized question (MCQ040) in the NHANES Medical Conditions Questionnaire (MCQ): “During the past 12 months, have you had an episode of asthma or an asthma attack?” Data were collected through structured, face-to-face interviews conducted by trained personnel by using a standardized questionnaire administered either during home visits or at mobile examination centers.

Definition of the LENMR

Complete blood cell counts were performed on venous blood samples by using the Beckman Coulter MAXM hematology analyzer (Beckman Coulter Inc., Brea, CA, USA) at the Mobile Examination Center. All specimens underwent preanalytical screening in accordance with NHANES exclusion criteria, and strict quality control protocols were applied throughout the collection, processing, and analysis stages to ensure data accuracy and consistency.¹³ The primary exposure variable, LENMR was calculated by using the following formula, with all cell counts expressed in units of ×10⁹/L:

Other Inflammatory Cell Ratios

Several inflammation-based hematologic indices have been associated with asthma exacerbation and disease control in previous studies.^{10,12,18–20} Derived from routine peripheral blood counts, these indices reflect systemic immune-inflammatory status. NLR and ELR were calculated by dividing the neutrophil and eosinophil counts, respectively, by the lymphocyte count. The eosinophil-to-neutrophil ratio (ENR) was calculated by dividing the eosinophil count by the neutrophil count, and the platelet-to-lymphocyte ratio (PLR) was calculated by dividing the platelet count by the lymphocyte count.

Covariables Assessment

Covariates in this study included age, gender, race, body mass index (BMI), smoking status, glucocorticoid use, eosinophils, fractional exhaled nitric oxide (FeNO), chronic obstructive pulmonary disease (COPD), diabetes, hypertension, chronic heart failure (CHF), coronary artery disease (CHD), and malignancy.

Statistical Analyses

Weighted analyses were conducted to ensure representativeness of the general population. Variables with a missing rate >30% were excluded, whereas those with ≤30% missing data were imputed by using multiple imputation (Supplemental Table S1). The normality of continuous variables was assessed by using the Kolmogorov-Smirnov test, and none of them followed a normal distribution (Supplemental Table S2). Continuous variables are expressed as median (interquartile range [IQR]), with p-values calculated by using the Wilcoxon rank sum test. Categorical variables are presented as percentages, with p-values determined by using the χ² test.

Table 1.

Baseline characteristics of participants stratified by asthma status^*

IQR = Interquartile range; BMI = body mass index; WBC = white blood cell count; ELR = eosinophil-to-lymphocyte ratio; NLR = neutrophil-to-lymphocyte ratio; PLR = platelet-to-lymphocyte ratio; ENR = eosinophil-to-neutrophil ratio; LENMR = lymphocyte-eosinophil to neutrophil-monocyte ratio; FeNO = fractional exhaled nitric oxide; COPD = chronic obstructive pulmonary disease; CHF = chronic heart failure; CHD = coronary artery disease.

^*Weighted estimates were calculated using NHANES sampling weights to account for the complex survey design. Values are presented as weighted frequencies (N) and weighted percentages (%).

^#Calculated by using t-tests.

^§Determined by using χ² tests.

Table 2.

Association between LENMR and asthma exacerbations across models

LENMR = Lymphocyte-eosinophil to neutrophil-monocyte ratio; OR = odds ratio; CI = confidence interval.

^*Unadjusted.

^#Adjusted for age, gender, race, body mass index, glucocorticoid use, and smoking status.

^§Further adjusted for chronic obstructive pulmonary disease, diabetes, hypertension, chronic heart failure, coronary artery disease, and malignancy.

The association between LENMR and asthma exacerbations was assessed by using multivariable logistic regression. Model 1 was unadjusted; model 2 was adjusted for age, gender, race, BMI, smoking status, and glucocorticoid use; and model 3 further accounted for COPD, diabetes, hypertension, CHF, CHD, and malignancy. LENMR was analyzed as a continuous variable to assess the linear relationship with exacerbation risk. The variable was subsequently categorized into quartiles (Q1–Q4) to explore potential nonlinear associations, with the lowest quartile (Q1) designated as the reference category. Trend tests were conducted by including the median value of each LENMR quartile as a continuous variable in the regression model to assess dose-response relationships. All results were reported as odds ratios (OR) with corresponding 95% confidence intervals (CI).

Multivariable logistic regression models were used to investigate the associations between inflammation-related biomarkers (FeNO, white blood cell count [WBC], neutrophils, lymphocytes, monocytes, eosinophils, ELR, NLR, PLR, and ENR) and the risk of asthma exacerbations across three progressively adjusted models (models 1–3). To assess the discriminatory capacity of LENMR in comparison with other hematologic indices (eosinophils, ENR, and NLR), receiver operating characteristic curve analysis was performed, and the area under the curve (AUC) was calculated to quantify overall discriminative ability.

Subgroup analyses were performed to examine the consistency of the association between LENMR and asthma exacerbations across categories stratified by age (<60 versus ≥60 years), gender, race (non-Hispanic White versus others), smoking status (ever- versus never-smokers), BMI (<30 versus ≥30 kg/m²), and the presence or absence of comorbidities. Restricted cubic spline (RCS) regression was used to flexibly model the potential nonlinear association between LENMR and asthma exacerbations. In addition, a threshold effect analysis was applied to detect inflection points in the relationship between LENMR and asthma exacerbations.

Statistical analyses were conducted using R (version 4.4.2; R Foundation for Statistical Computing, Vienna, Austria) and DecisionLinnc (version 1.1.1; Statsape Co., Ltd., Hangzhou, China).²¹ A two-sided p-value of <0.05 was considered statistically significant.

Ethical Considerations

The study was approved by the National Center for Health Statistics Research Ethics Review Board, Centers for Disease Control and Prevention, United States. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

RESULTS

Patient Baseline and Clinical Characteristics

As presented in Table 1, the exacerbation group differed significantly from the control group in several characteristics, including a higher proportion of women (67.15% versus 58.69%; p = 0.035), younger median age (45.00 versus 47.00 years; p = 0.002), a higher proportion of ever-smokers (57.06% versus 49.02%; p = 0.013), and a different distribution of glucocorticoid use (p = 0.004). In addition, the exacerbation group had higher eosinophils (0.20 vs. 0.20 ×10³/μL; p = 0.007), ENR (0.05 vs. 0.04; p = 0.020), and LENMR (0.21 vs. 0.16; p < 0.001), as well as lower NLR (2.00 vs. 2.11; p = 0.018). There were no significant differences in BMI, WBC, monocytes, ELR, PLR, FeNO, or comorbidities.

Association between the LENMR and Asthma Exacerbation

Logistic regression results that examined the association between LENMR and asthma exacerbations are presented in Table 2. LENMR, analyzed as a continuous variable, was significantly associated with the exacerbation risk in all models (model 1: p = 0.009; model 2: p = 0.014; model 3: p = 0.020). In the quartile analysis, ORs increased with higher LENMR quartiles compared with Q1. Model 3 showed ORs of 1.21 (95% CI, 0.82–1.78; p = 0.333) for Q2, 1.68 (95% CI, 1.16–2.43; p = 0.008) for Q3, and 1.77 (95% CI, 1.19–2.65; p = 0.007) for Q4. A significant linear trend was observed across quartiles in all models (model 1: p < 0.001; model 2: p = 0.002; model 3: p = 0.002).

Comparative Evaluation of LENMR and Other Inflammatory Biomarkers

In the fully adjusted model (model 3), eosinophils, NLR, and ENR exhibited significant associations with asthma exacerbations. The eosinophil count showed a positive association (OR 2.77 [95% CI, 1.25–6.14]; p = 0.014), NLR displayed an inverse relationship (OR 0.83 [95% CI, 0.73–0.94]; p = 0.004), and ENR demonstrated a positive association (OR 52.89 [95% CI, 3.17–883.42]; p = 0.007), shown in Table 3. To further evaluate their discriminatory capacity, receiver operating characteristic curve analysis was conducted. As shown in Fig. 2, LENMR yielded the highest AUC (0.671), outperforming ENR (0.642), NLR (0.624), and eosinophils (0.618).

Table 3.

Association between other inflammatory biomarkers and asthma exacerbations across models

OR = Odds ratio; CI = confidence interval; FeNO = fractional exhaled nitric oxide; WBC = white blood cell count; ELR = eosinophil-to-lymphocyte ratio; NLR = neutrophil-to-lymphocyte ratio; PLR = platelet-to-lymphocyte ratio; ENR = eosinophil-to-neutrophil ratio.

^*Unadjusted.

^#Adjusted for age, gender, race, body mass index, glucocorticoid use, and smoking status.

^§Further adjusted for chronic obstructive pulmonary disease, diabetes, hypertension, chronic heart failure, coronary artery disease, and malignancy.

Figure 2.

Comparison of ROC curves for inflammatory biomarkers in relation to asthma exacerbations.

Subgroup Analysis

Subgroup analysis (Fig. 3) showed that the association between LENMR and the asthma exacerbation risk was consistent across all categories examined. The analysis stratified participants by age (<60 versus ≥60 years), gender, race (non-Hispanic White versus others), BMI (<30 versus ≥30 kg/m²), smoking status, and glucocorticoid use, along with comorbidities. No significant interactions were found (all p-values for interaction > 0.05).

Figure 3.

Subgroup analysis of the association between LENMR and asthma exacerbations.

RCS and Threshold Effect Analysis

The RCS analysis (Fig. 4) revealed a significant nonlinear relationship between LENMR and the risk of asthma exacerbations in model 3 (p-value for non-linearity = 0.007). Threshold analysis (Table 4) identified an inflection point at 0.31. Below this value, LENMR was strongly associated with an increased risk of exacerbations (OR 11.23 [95% CI, 3.17–40.10]; p < 0.001), whereas no significant association was observed above the threshold (OR 1.05 [95% CI, 0.57–1.93]; p = 0.881). The log-likelihood ratio test favored the threshold model over the linear model (p = 0.010).

Figure 4.

Restricted cubic spline analysis of the association between LENMR and asthma exacerbation risk.

Table 4.

Threshold and saturation effects of LENMR on the risk of asthma exacerbations

LENMR = Lymphocyte-eosinophil to neutrophil-monocyte ratio; CI = confidence interval; OR = odds ratio.

^*A threshold effect was observed for LENMR < 0.31, with a statistically significant association with asthma exacerbations (OR 11.23 [95% CI, 3.17–40.10]; p < 0.001).

^#For LENMR ≥ 0.31, the association was not statistically significant (OR 1.05 [95% CI, 0.57–1.93]; p = 0.881).

^§The log-likelihood ratio test, which compared the linear and threshold models, showed a statistically significant result (p = 0.010).

Sensitivity Analyses

Sensitivity analyses were conducted to assess the stability of the results across different modeling strategies. First, the association between LENMR and asthma exacerbations was examined by using progressively adjusted models. Second, LENMR was analyzed as a continuous variable, by quartiles, and continuously within quartile strata. Finally, subgroup analyses were performed according to key covariates. Results remained consistent across all approaches, which supports the robustness of the findings.

DISCUSSION

This study demonstrated a significant association between LENMR and the risk of asthma exacerbations in a nationally representative adult population. After adjustment for demographic, clinical, and comorbidity factors, individuals in the highest LENMR quartile exhibited a 56% greater risk of exacerbations compared with those in the lowest quartile. Analysis of these findings suggests that LENMR captures broader inflammatory processes relevant to asthma pathogenesis and may enhance clinical risk stratification.

Asthma is recognized as a heterogeneous disease with distinct inflammatory phenotypes. T2-high asthma is characterized by eosinophilic inflammation mediated by cytokines, including IL-4, IL-5, and IL-13, and typically responds well to corticosteroids and biologic therapies.^18,19 The blood eosinophil count remains a widely used marker of T2 inflammation, and ELR has been investigated as an additional surrogate indicator. An elevated baseline ELR has been shown to predict a more favorable response to omalizumab in patients with severe allergic asthma, which highlights its potential utility in identifying individuals likely to benefit from biologic therapy.¹¹ In contrast, non-T2 asthma, which is associated with neutrophilic or mixed granulocytic inflammation, is more common in corticosteroid-resistant or severe cases and is linked to poor symptom control and progressive airway remodeling.^6,22,23 NLR that reflects neutrophilic inflammation, has been associated with greater asthma severity, lower lung function, and an increased risk of exacerbations in both adult and pediatric populations.^10,12,20 However, both ELR and NLR are limited by their focus on a single inflammatory pathway and are unable to identify patients with overlapping or atypical inflammatory profiles.

The eosinophil count and FeNO value are well-established markers of type 2 airway inflammation and have been consistently associated with poorer asthma control and increased exacerbation risk in recent studies.^24–26 In our analysis, eosinophil count remained independently associated with acute exacerbations after adjustment for demographic and clinical variables (OR 2.77 [95% CI, 1.25–6.14]; p = 0.014), whereas FeNO showed no significant association (OR 1.01 [95% CI, 1.00–1.01]; p = 0.246), as shown in Table 3. Furthermore, the eosinophil count demonstrated lower discriminative performance compared with several inflammatory cell ratios in Fig. 2, which underscores the potential value of integrated hematologic indices in risk stratification for asthma exacerbations.

Several inflammation-related hematologic biomarkers have been investigated in previous studies for their relevance to asthma prognosis, particularly in evaluating systemic inflammatory responses and predicting disease control or exacerbation.^20–22 Among these, ELR and ENR demonstrated stronger associations with asthma control and exacerbation risk compared with eosinophil count alone, especially in individuals who presented with mixed eosinophilic and neutrophilic inflammatory profiles.¹² NLR and PLR have likewise been associated with asthma severity and disease control across varied populations.^10,20 The present study found that the eosinophil count, NLR, ENR, and LENMR were independently associated with acute exacerbations after adjustment for age, gender, race, BMI, glucocorticoid use, smoking status, and comorbidities (model 3). Among these biomarkers, LENMR demonstrated the highest discriminatory performance (AUC = 0.671), exceeding that of ENR (AUC = 0.642) and NLR (AUC = 0.624), which indicates its potential value in stratifying the risk of asthma exacerbations in clinical settings.

In this context, our study assessed LENMR as a novel composite marker of systemic inflammation. LENMR was significantly associated with asthma exacerbation risk, independent of demographic and clinical factors, and remained consistent across subgroups defined by age, gender, BMI, smoking status, glucocorticoid use, and comorbidities. The reproducibility of these findings suggests that LENMR may serve as a useful biomarker for asthma risk assessment. RCS and threshold analyses identified a nonlinear relationship, with the risk rising steeply at lower LENMR levels and plateauing at higher ranges. This potential threshold effect indicates that LENMR may be most predictive within specific intervals. Accounting for such nonlinear patterns could enhance the asthma risk prediction. Further studies are needed to validate these findings and explore the underlying biologic mechanisms.

This study has several limitations. First, the cross-sectional design precludes causal inference and prospective longitudinal studies or real-world cohort validations are needed to determine whether LENMR possesses sufficient predictive specificity to inform clinical decision-making. Second, the dataset lacked information on asthma phenotypes and the use of biologic therapies, which limited the ability to assess the biomarker's performance across distinct clinical subtypes or treatment responses. Third, although the use of nationally representative sampling weights improves generalizability within the U.S. adult population, external validation in geographically and ethnically diverse cohorts is necessary to confirm the robustness and applicability of these findings.

CONCLUSION

Under certain thresholds, an elevated LENMR is associated with increased risk of asthma exacerbations, which supports its potential utility as a composite biomarker for risk stratification in asthma.