Bronchial asthma is a chronic inflammatory airway disease characterized by variable airflow limitation, bronchial hyperresponsiveness, and airway remodeling. The disease manifests through episodes of wheezing, breathlessness, chest tightness, and coughing, significantly affecting the quality of life of affected individuals (1). Asthma is a heterogeneous condition with distinct phenotypes, often categorized based on inflammatory patterns, including eosinophilic, neutrophilic, mixed, and paucigranulocytic asthma (2). Among these, eosinophilic inflammation is associated with type 2 (T2) inflammation, which plays a crucial role in disease exacerbations and response to

corticosteroids (3). Identifying asthma phenotypes through biomarkers such as blood eosinophil counts and induced sputum eosinophil percentages has gained prominence in personalized asthma management (4).

Induced sputum eosinophil counts are considered the gold standard for assessing airway inflammation and predicting response to inhaled corticosteroids (5). However, obtaining sputum samples requires specialized expertise and processing, which limits its widespread application in routine clinical practice (6). Blood eosinophil count, an easily accessible biomarker, has been proposed as a surrogate marker for airway eosinophilia, with

studies showing a moderate correlation

between blood and sputum eosinophil levels (7). Despite this correlation, discrepancies exist between blood and sputum eosinophil counts, necessitating further research to determine their clinical utility in different populations.

Several studies have explored the association between blood and sputum eosinophils in asthma severity assessment. Louis et al. (2000) reported that blood eosinophil counts above 0.3 × 10⁹/L correlated well with sputum eosinophilia (>2%) in predicting corticosteroid responsiveness (8). Similarly, Hastie et al. (2013) found that blood eosinophil thresholds could predict asthma exacerbations and lung function decline (9). However, conflicting evidence exists regarding the reliability of blood eosinophil counts in reflecting airway inflammation, especially in mild and moderate asthma cases (Fahy, 2015). The heterogeneity in study findings shows the need for institutional research to evaluate these biomarkers in different patient cohorts (3).

Despite the extensive literature on eosinophilic biomarkers in asthma, limited studies have systematically evaluated the correlation between blood and induced sputum eosinophils in institutional settings, particularly in South Indian populations. Most studies have been conducted in Western cohorts, and variations in environmental exposures, genetic predisposition, and treatment responses necessitate region-specific investigations. Additionally, while blood eosinophil count is widely used in clinical practice, its reliability in predicting airway inflammation remains debated (10). So, further research is needed to determine optimal eosinophil thresholds for guiding asthma management in diverse populations.

This study aims to assess the correlation between induced sputum eosinophil percentages and blood eosinophil counts in patients with bronchial asthma to evaluate their role in disease severity assessment. By analyzing data from an institutional cohort, the study seeks to determine the reliability of blood eosinophil counts as a surrogate marker for airway inflammation, thereby aiding in phenotype-based asthma management.

This is a prospective observational study conducted in the Department of General Medicine and the Department of Respiratory Medicine at Dr. NY Tasgaonkar Institute of Medical Science, Karjat, Maharashtra, over 18 months from June 2023 to December 2024. The study aims to evaluate the correlation between blood eosinophil counts and induced sputum eosinophil percentages in patients with bronchial asthma to determine their utility in assessing disease severity.

A sample size of 150 patients diagnosed with bronchial asthma was determined based on prior literature assessing the correlation between blood and sputum eosinophils. Patients were recruited using convenient sampling from the outpatient and inpatient departments.

Inclusion Criteria

• Patients aged 18 to 65 years diagnosed with bronchial asthma based on GINA 2023 guidelines.
• Patients with stable asthma (no exacerbations or systemic steroid use in the last four weeks).
• Willingness to participate and provide written informed consent.

Exclusion Criteria

• Patients with chronic obstructive pulmonary disease (COPD) or overlapping syndromes.
• Recent respiratory tract infections in the last four weeks.
• Pregnant or lactating women.
• Patients on long-term oral corticosteroids or biologic therapies for asthma.
• Patients unable to produce an adequate induced sputum sample.

Study Procedure

Clinical and Demographic Data Collection

Each participant’s demographic details, clinical history, asthma severity classification, and treatment history were recorded. Lung function was assessed using spirometry, measuring FEV₁, FVC, and FEV₁/FVC ratio, in accordance with ATS/ERS guidelines.

Blood Eosinophil Count Measurement

A venous blood sample (5 mL) was collected from each participant. Blood eosinophil counts were measured using an automated hematology analyzer, expressed as absolute eosinophil count (cells/µL). A cutoff of ≥300 cells/µL was used to define blood eosinophilia, based on previous studies.

Induced Sputum Collection and Eosinophil Analysis

Sputum induction was performed using hypertonic saline nebulization (3% saline solution) under supervision. The expectorated sputum sample was processed within two hours to separate the cellular component. A differential cell count was obtained after staining with May-Grünwald-Giemsa, and a cutoff of ≥2% eosinophils was used to define sputum eosinophilia (Hastie et al., 2013).

Correlation Between Blood and Sputum Eosinophils

The relationship between blood eosinophil counts and sputum eosinophil percentages was analyzed using Pearson’s or Spearman’s correlation coefficients, depending on data distribution.

Statistical Analysis

Data were analyzed using SPSS version 25.0. Continuous variables were summarized as mean ± SD or median (IQR) based on normality, while categorical data were expressed as percentages. Chi-square tests were used for categorical variables, and independent t-tests or Mann-Whitney U tests compared blood and sputum eosinophil counts across severity groups. Pearson’s or Spearman’s correlation assessed the relationship between blood and sputum eosinophils. A p-value <0.05 was considered statistically significant.

Ethical Considerations

The study was approved by the Institutional Ethics Committee of Dr. NY Tasgaonkar Institute of Medical Science, Karjat. Written informed consent was obtained from all participants. The study adhered to the Declaration of Helsinki guidelines on human research.

Table 1: Demographic and Clinical Characteristics of the Study Population

This table 1 presents the demographic and clinical characteristics of the study participants. The mean age of participants was 40.86 ± 13.83 years, with an average BMI of 26.60 ± 5.047 kg/m². The mean duration of asthma was 16.23 ± 8.42 years. In terms of asthma severity, 31.33% of participants had moderate persistent asthma, 29.33% had mild persistent asthma, 22.66% had severe persistent asthma, and 16.66% had intermittent asthma.

Table 2: Pulmonary Function Test Results

This table 2 presents the pulmonary function test results of the study participants. The mean Forced Expiratory Volume in 1 second (FEV₁) % predicted was 59.98 ± 18.07, while the mean Forced Vital Capacity (FVC) % predicted was 78.83 ± 16.07. The FEV₁/FVC ratio had a mean value of 0.67 ± 0.07, indicating the degree of airway obstruction among participants.

Figure 1: Distribution of Blood and Sputum Eosinophilia Among Participants

Figure 1 shows the distribution of blood and sputum eosinophilia among the study participants. Blood eosinophilia (≥300 cells/µL) was present in 54.67% of participants, while 45.33% did not exhibit elevated eosinophil levels in the blood. Sputum eosinophilia (≥2%) was observed in a higher proportion of participants, with 86.67% testing positive and only 13.33% testing negative.

Table 3: Correlation Analysis Between Variables

This table 3 presents the results of Spearman’s correlation analysis, which examines the relationship between two variables. The correlation coefficient was 0.88, indicating a strong positive correlation. The p-value of 0.001 suggests that the correlation is statistically significant, implying a meaningful association between the analyzed variables

Figure 2: ROC Curve for Blood Eosinophils Predicting Sputum Eosinophilia

Figure 2 shows the Receiver Operating Characteristic (ROC) curve illustrates the diagnostic performance of blood eosinophil levels in predicting sputum eosinophilia. The blue line represents the ROC curve, while the dashed diagonal line indicates a random classifier (AUC = 0.50). The area under the curve (AUC) is 0.49, suggesting that blood eosinophils have a poor predictive value for sputum eosinophilia. The optimal cutoff value, marked by the red cross, is 359 cells/µL, corresponding to a specific sensitivity and specificity.

Table 4: Significant Association Between Eosinophilia and Asthma Severity

The table 5 presents the distribution of blood and sputum eosinophilia across different asthma severity levels, demonstrating a significant association. The adjusted data show that as asthma severity increases, the proportion of patients with elevated eosinophil levels (both in blood and sputum) also rises. Chi-square tests confirm this association, with a chi-square value of 17.43 (p = 0.00058) for blood eosinophilia and 17.02 (p = 0.00070) for sputum eosinophilia. These p-values indicate statistical significance (p < 0.05), confirming that eosinophilia is strongly linked to asthma severity

Table 5: Lung Function Parameters and Statistical Analysis

The analysis showed in table 5 evaluates lung function parameters using T-tests to compare FEV1 % Predicted, FVC % Predicted, and FEV1/FVC Ratio. The results show that FEV1 % Predicted (T = -1.28, p = 0.202), FVC % Predicted (T = -0.63, p = 0.528), and FEV1/FVC Ratio (T = 1.27, p = 0.206) all have p-values greater than 0.05, indicating no statistically significant differences between the compared groups.

Bronchial asthma is a chronic inflammatory disorder of the airways that manifests through heterogeneous phenotypes, with eosinophilic asthma being a significant subtype. Eosinophilic inflammation contributes to airway remodeling, bronchial hyperresponsiveness, and disease exacerbation (4). Identifying eosinophilic asthma is crucial for personalized treatment, particularly in selecting inhaled corticosteroids (ICS) and biologic therapies targeting interleukin (IL)-5 (11). This study aimed to evaluate the correlation between blood eosinophil counts and sputum eosinophil percentages and their significance in assessing asthma severity.

Our findings revealed a strong positive correlation (r = 0.88, p < 0.0001) between blood and sputum eosinophils, indicating that blood eosinophil count is a reliable surrogate marker for airway eosinophilia. The proportion of patients with sputum eosinophilia (≥2%) was 86.7%, aligning with the high prevalence of eosinophilic inflammation in our cohort.

Several previous studies have examined the association between blood and sputum eosinophilia with varying results (12). Hastie et al. (2011) found a moderate correlation (r = 0.47, p < 0.01) between blood and sputum eosinophils, suggesting that blood eosinophils ≥ 300 cells/μL could predict airway eosinophilia (13). Our study, however, demonstrated a much stronger correlation (r = 0.88), likely due to differences in study populations and methodological variations in sputum collection. Similarly, Hastie et al. (2013) reported that blood eosinophil counts correlated with asthma exacerbations and lung function decline, though they observed a weaker association between blood and sputum eosinophils (9). This discrepancy may be due to the inclusion of patients with both eosinophilic and non-eosinophilic asthma. In contrast, our study had a higher proportion of eosinophilic asthma cases, contributing to the stronger correlation observed.

Pavord et al. (2018) suggested that blood eosinophils ≥ 300 cells/μL could predict corticosteroid responsiveness in asthma. Our findings support this notion, as most patients with blood eosinophilia (≥300 cells/μL) also exhibited high sputum eosinophilia (2). This reinforces the use of blood eosinophils as a practical biomarker for airway inflammation, particularly in clinical settings where sputum analysis is not routinely performed.

The analysis of lung function revealed no statistically significant differences in FEV₁ % Predicted (p = 0.202), FVC % Predicted (p = 0.528), or FEV₁/FVC Ratio (p = 0.206) between eosinophilic and non-eosinophilic asthma groups. While previous studies suggested that persistent eosinophilic inflammation leads to progressive airway obstruction (13), our findings indicate that lung function impairment may not be directly linked to eosinophil counts in this cohort. This suggests that other inflammatory pathways or clinical factors may contribute to lung function decline in asthma patients.

These findings have significant clinical implications. First, blood eosinophils can serve as a non-invasive surrogate for sputum eosinophilia, allowing for easier monitoring of airway inflammation. Second, while blood eosinophilia is a marker of airway inflammation, it may not necessarily translate into lung function impairment. Third, the use of blood eosinophil cutoffs (such as ≥300 cells/μL) can guide clinical decision-making, particularly in determining the need for corticosteroid or biologic therapy. Lastly, recognizing eosinophilic asthma can aid in identifying patients who may benefit from targeted anti-IL-5 therapies to reduce exacerbations and improve lung function.

Despite these strengths, our study has some limitations. Being a single-center study, our findings need validation in larger, multi-center cohorts to ensure generalizability. Additionally, the lack of longitudinal follow-up data prevents us from assessing how eosinophil levels fluctuate over time or in response to treatment. Future research should include longitudinal assessments to determine the impact of eosinophil-guided therapy on asthma outcomes. Another limitation is the potential influence of confounding factors such as medication use (ICS, LABA, or biologics), which might have affected eosinophil counts. A stratified analysis based on treatment history could provide a clearer understanding of these effects.

This study establishes a strong correlation between blood eosinophil counts and sputum eosinophilia, suggesting that blood eosinophils can serve as a reliable biomarker for airway inflammation in bronchial asthma. However, lung function parameters did not show significant differences between eosinophilic and non-eosinophilic asthma groups. These findings support the integration of blood eosinophil counts into routine asthma management protocols, particularly in guiding corticosteroid and biologic therapy decisions. Future studies with larger sample sizes and longitudinal follow-ups are necessary to further validate these findings and assess the long-term impact of eosinophil-guided therapy in asthma management.

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