Asthma remains one of the most prevalent chronic respiratory diseases worldwide, affecting an estimated 262 million people and causing over 450,000 deaths annually.1 While asthma management has advanced significantly with the development of inhaled corticosteroids (ICS) and biologic therapies, approximately 5-10% of patients suffer from severe asthma that remains uncontrolled despite maximal standard treatment.2 This treatment-resistant subgroup accounts for disproportionate healthcare costs and disease burden, highlighting the critical need for precise severity stratification tools.3

The recognition of asthma as a heterogeneous disease has led to paradigm shifts in its classification and management. Current guidelines

emphasize phenotyping based on inflammatory profiles, particularly distinguishing Type 2 (T2)-high from T2-low asthma.4 Among T2-high biomarkers, eosinophilic inflammation has emerged as a key therapeutic target, with several monoclonal antibodies (e.g., anti-IL-5, anti-IL-4/13) demonstrating efficacy specifically in eosinophilic asthma.5

Current guidelines from the Global Initiative for Asthma (GINA) acknowledge multiple potential biomarkers including fractional exhaled nitric oxide (FeNO), blood eosinophil count (AEC), serum IgE, and sputum eosinophils, but provide limited guidance on their comparative utility.6 this uncertainty is reflected in clinical practice variations, with some centers relying primarily on AEC while others emphasize sputum analysis.7

Previous studies have produced conflicting results about biomarker performance. The 2020 European Respiratory Society task force found sputum eosinophils superior for predicting exacerbations8, while primary care studies often favor AEC due to its practicality9. Notably, few studies have directly compared all three major eosinophilic biomarkers (sputum, AEC, IgE) against standardized severity classifications in the same patient cohort.10

This study aims to address these evidence gaps by systematically evaluating:

• The correlation of induced sputum eosinophils, AEC, and serum IgE with asthma severity classified by GINA criteria
• The relative diagnostic accuracy of each biomarker for identifying severe asthma
• Practical cutoff values that could guide clinical decision-making

Our findings will provide much-needed evidence to optimize biomarker selection in both specialized and general practice settings, potentially improving phenotyping accuracy and targeted treatment allocation.

We conducted a cross-sectional observational study at Sahyadri Super Speciality Hospital, Nashik between July 2024 to Dec 2024. The study population comprised 120 consecutive adult asthma patients (aged 18–65 years) recruited from the asthma outpatient clinic.

We excluded patients who:

• Had experienced exacerbations within the preceding 4 weeks
• Were current smokers or had >10 pack-year smoking history
• Had other chronic respiratory diseases (e.g., COPD, bronchiectasis)
• Were pregnant or breastfeeding

A control group of 40 healthy volunteers with normal spirometry and no history of respiratory disease was included for biomarker comparison.

Clinical Assessment

All participants underwent:

1. Structured interviews documenting:

• Asthma duration and medication use
• Exacerbation history (previous 12 months)
• Comorbidities (allergic rhinitis, atopic dermatitis)

1. Physical examinations including:

• Anthropometric measurements (height, weight, BMI)
• Auscultation for wheezing

1. Asthma severity classification according to 2023 GINA guidelines⁶:

• Mild:Controlled with Step 1–2 treatment
• Moderate:Requiring Step 3 therapy
• Severe:Uncontrolled despite Step 4–5 treatment

Biomarker Measurements

1. Sputum Induction and Analysis

• Sputum was induced using hypertonic saline (4.5%) via ultrasonic nebulizer following ERS guidelines¹¹
• Samples were processed with dithiothreitol (DTT) and cytospin slides were stained with Wright-Giemsa
• Two blinded pathologists counted 400 non-squamous cells to determine eosinophil percentage

1. Hematological Tests

• Absolute eosinophil count (AEC) was measured from venous blood using automated hematology analyzers (Sysmex XN-1000)
• Serum total IgE levels were quantified by chemiluminescent immunoassay (Roche Diagnostics)

1. Pulmonary Function Tests

• Spirometry was performed pre- and post-bronchodilator (400 µg salbutamol) per ATS/ERS standards¹²
• Fractional exhaled nitric oxide (FeNO) was measured using NIOX Vero® (50 mL/sec flow)

Statistical Analysis

We analyzed data using SPSS version 26 (IBM Corp.)  Continuous variables were reported as mean ± SD (normal distribution) or median [IQR] (non-normal) Group comparisons used ANOVA/Kruskal-Wallis tests with post-hoc corrections Correlation analyses employed Spearman’s rank test (ρ) Diagnostic accuracy was assessed via ROC curves with Youden’s index determining optimal cutoffs Multivariable logistic regression adjusted for age, sex, BMI, and ICS dose A two-tailed p-value <0.05 was considered statistically significant.

Table 1: Baseline Characteristics of Study Participants

*Data presented as mean ± SD or median [IQR]. *p<0.05 vs mild, *p<0.01 vs moderate (post-hoc Dunnett's test).

The severe asthma group had significantly higher BMI (p=0.003), longer disease duration (p<0.001), and lower lung function (FEV₁% predicted, p<0.001) compared to milder groups. FeNO levels showed a stepwise increase with severity (p<0.001), supporting T2 inflammation progression.

Table 2: Biomarker Profiles across Severity Groups

        *Data as median [IQR]. *p<0.01 vs mild, *p<0.001 vs moderate (Kruskal-Wallis with Bonferroni correction).

All biomarkers increased significantly with asthma severity (p<0.01). Sputum eosinophils showed the greatest intergroup differences (12.4% in severe vs 2.1% in mild, p<0.001). AEC ≥300 cells/μL identified 82% of severe cases, while IgE had wider overlap between groups.

Table 3: Diagnostic Accuracy for Severe Asthma

                   AUC = Area Under ROC Curve; Comparisons by DeLong's test.

Sputum eosinophils demonstrated superior discrimination for severe asthma (AUC 0.88), significantly outperforming AEC (p=0.004) and IgE (p<0.001). At the 6.5% cutoff, sputum analysis had 89% sensitivity and 92% specificity.

Table 4: Multivariable Predictors of Severe Asthma

                                               Adjusted for age, sex, and ICS dose.

In logistic regression, sputum eosinophilia (OR 8.2, p<0.001) was the strongest independent predictor of severe asthma, followed by FeNO ≥50 ppb (OR 4.7). AEC retained significance but with lower effect size (OR 3.1).

This study provides three key findings with clinical implications for asthma management: (1) sputum eosinophils demonstrated superior performance in stratifying asthma severity compared to peripheral blood markers, (2) a 6.5% sputum eosinophil cutoff effectively discriminated severe asthma (89% sensitivity, 92% specificity), and (3) central obesity (BMI ≥30 kg/m²) emerged as an independent severity predictor, suggesting metabolic-inflammatory interactions in severe disease.

Sputum Eosinophils as the Gold Standard

Our results confirm sputum eosinophil quantification as the most reliable biomarker for asthma severity assessment, aligning with the 2022 ERS task force recommendations.¹³ The strong correlation between sputum eosinophilia and GINA severity steps (ρ=0.62, p<0.001) supports its pathophysiological relevance, as airway eosinophilia directly reflects T2 inflammation's intensity.¹⁴ The 6.5% cutoff we identified matches previous reports linking ≥3% eosinophils with exacerbation risk¹⁵, but extends this by establishing a higher threshold specifically for severe asthma phenotyping.

Blood vs. Airway Biomarkers

While AEC showed moderate correlation with severity (ρ=0.51), its lower diagnostic accuracy (AUC 0.72 vs. 0.88 for sputum) echoes concerns about systemic vs. local inflammation discordance.¹⁶ Notably, 28% of severe asthma patients had AEC <300 cells/μL, potentially misclassifying them as non-eosinophilic without sputum analysis. This finding challenges the increasing reliance on AEC for biologic therapy eligibility decisions¹⁷ and supports maintaining sputum induction capabilities in severe asthma clinics.

The Limited Role of Serum IgE

Serum IgE's poor specificity (68% at ≥250 IU/mL) reflects its multifactorial determinants, including atopy, parasitic infections, and non-asthma allergic conditions.¹⁸ Our data suggest IgE should not be used alone for severity assessment, though it may retain value when combined with other markers in specific phenotypes (e.g., allergic asthma).¹⁹

 Metabolic Comorbidities and Asthma Severity

The independent association between obesity (BMI ≥30) and severe asthma (OR 2.9, p=0.01) parallels recent evidence about adipose tissue-driven inflammation.²⁰ this raises important questions about whether:

• Mechanical effects(reduced lung volumes from abdominal fat)²¹
• Metabolic inflammation(leptin-mediated TH2 polarization)²²
• Microbiome alterations(gut-lung axis dysbiosis)²³

contribute synergistically to severity. Weight loss interventions improve both metabolic and respiratory parameters in obese severe asthma patients.^{24, 25}

This study confirms sputum eosinophils as the optimal biomarker for asthma severity stratification, with a 6.5% cutoff effectively identifying severe disease. While blood eosinophils (AEC ≥450 cells/μL) offer a practical alternative, their lower accuracy reinforces the value of airway-specific assessment where feasible. Obesity (BMI ≥30) independently predicted severity, highlighting the need for integrated metabolic-respiratory management. These findings support:

1. Prioritizing sputum analysis in severe asthma workups
2. Cautious interpretation of AEC results
3. Addressing obesity in refractory asthma

For precision care, sputum-guided approaches should be implemented when resources allow, while AEC remains useful in primary settings. Future studies should explore cost-effective biomarker integration and metabolic-inflammatory interactions.

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