Asthma is a chronic heterogeneous airway disease characterized by variable respiratory symptoms, airway inflammation, and reversible airflow limitation [1]. Despite major advances in asthma management, substantial heterogeneity exists regarding inflammatory pathways, clinical manifestations, disease severity, and therapeutic responsiveness. Among the recognized inflammatory phenotypes, eosinophilic asthma has received particular attention because of its association with severe exacerbations, impaired disease control, and eligibility for targeted biologic therapies [2–4].

In recent years, obesity has emerged as one of the most important modifiable risk factors for both asthma development and progression. Epidemiological and mechanistic studies have demonstrated that obesity is associated with increased asthma prevalence, poorer symptom control, greater healthcare utilization, and reduced responsiveness to inhaled corticosteroids (ICSs) [5–7]. Importantly, obesity-related asthma is increasingly recognized as a distinct phenotype characterized by systemic inflammation, metabolic dysregulation, and non-type 2 immune responses rather than classical eosinophilic airway inflammation. Recent evidence suggests that metabolic abnormalities may influence asthma phenotypes through pathways involving adipokine imbalance, oxidative stress, insulin resistance, mitochondrial dysfunction, and chronic low-grade inflammation [5–9].

Although body mass index (BMI) remains the most widely used measure of obesity, it does not adequately capture metabolic health or visceral adiposity [10]. Consequently, several metabolic indices have been developed to provide a more comprehensive assessment of insulin resistance and cardiometabolic dysfunction. Among these, homeostatic model assessment for insulin resistance (HOMA-IR), triglyceride-glucose (TyG) index, cholesterol-glucose (CHG) and metabolic score for insulin resistance (METS-IR) have demonstrated strong associations with metabolic syndrome, cardiovascular disease, and chronic inflammatory disorders [11–14]. However, unlike the other indices, HOMA-IR requires measurement of fasting insulin levels, which may not be routinely available in many clinical settings, particularly in preventive health services, primary care, rural healthcare facilities, and resource-limited environments. In contrast, TyG, CHG, and METS-IR rely on routinely obtained clinical and biochemical parameters, making them more practical and accessible tools for large-scale screening and risk assessment. Evaluating the ability of these readily available indices to characterize asthma phenotypes may therefore enhance their clinical utility and improve risk stratification in everyday practice. Emerging evidence further suggests that metabolic dysfunction contributes not only to asthma susceptibility but also to disease severity and phenotypic heterogeneity [8].

Nevertheless, data evaluating the relationship between metabolic dysfunction and eosinophilic asthma remain limited. Most previous studies have focused on obesity as an isolated risk factor without simultaneously examining multiple metabolic indices that may better reflect the biological consequences of excess adiposity. Understanding whether metabolic burden differs between eosinophilic and non-eosinophilic asthma phenotypes may provide valuable insights into disease mechanisms and identify potential therapeutic targets.

Therefore, the present study aimed to evaluate metabolic dysfunction using multiple insulin resistance-related indices in patients with asthma and to investigate their association with the eosinophilic asthma phenotype.

Research hypotheses:

Hypothesis 1. Patients with asthma exhibit greater metabolic dysfunction, as assessed by BMI, HOMA-IR, TyG, CHG, and METS-IR, compared with healthy controls.

Hypothesis 2. Greater metabolic dysfunction is associated with a lower likelihood of the eosinophilic asthma phenotype.

A total of 221 participants were included in the study, comprising 90 patients with asthma and 131 healthy controls. The mean age was significantly higher in the asthma group compared with the control group (54.27 ± 15.93 vs. 49.63 ± 14.10 years, p = 0.027) (Table 1).

Laboratory comparisons demonstrated significantly higher peripheral eosinophil counts in patients with asthma compared with controls (190 vs. 105 cells/µL, p = 0.029). The neutrophil-to-lymphocyte ratio (NLR) was also modestly but significantly elevated in the asthma group (p = 0.037), suggesting increased systemic inflammatory burden. Regarding metabolic parameters, HOMA-IR and METS-IR were significantly higher in patients with asthma than in controls (both p < 0.001), indicating increased insulin resistance and metabolic dysfunction (Table 2).

Patients receiving ICS-containing therapies had significantly lower TyG, METS-IR, and CHG indices compared with non-ICS users (all p < 0.05). These findings may suggest a lower metabolic burden among ICS users or reflect differences in disease phenotype and treatment patterns (Table 3).

Asthma patients were stratified according to predefined peripheral blood eosinophil thresholds. Using cutoffs of ≥ 150 and ≥ 300, eosinophilic phenotype prevalence was 58.9% and 27.8% Based on the predefined primary threshold of ≥ 300 cells/µL, 25 patients were classified as having eosinophilic asthma (Table 4).

Patients with eosinophilic asthma had significantly lower BMI compared with those with non-eosinophilic asthma (26.60 vs. 30.82 kg/m², p = 0.023). Similarly, METS-IR values were significantly lower in eosinophilic asthma patients (36.91 vs. 47.78, p = 0.005) (Table 5).

In the multivariable logistic regression analysis, including age, sex, ICS use, NLR, and METS-IR, none of the variables was independently associated with the eosinophilic asthma phenotype (Table 6). Although METS-IR demonstrated an inverse association with eosinophilic asthma [adjusted odds ratio (OR) 0.96, 95% CI 0.90–1.03], this association did not reach statistical significance (p = 0.254). No evidence of problematic multicollinearity was identified in the final model (Table 7).

In this retrospective cross-sectional study, we investigated the relationship between metabolic dysfunction and eosinophilic asthma using multiple insulin resistance-related indices. Three principal findings emerged. First, patients with asthma exhibited significantly greater metabolic dysfunction and systemic inflammatory burden than healthy controls. Second, eosinophilic and non-eosinophilic asthma phenotypes differed significantly in BMI and METS-IR values, with patients with non-eosinophilic asthma demonstrating a greater obesity-related metabolic burden. Third, although these differences remained evident in univariate analyses, the associations were attenuated after adjustment for potential confounding factors in the multivariable logistic regression model.

The association between obesity and asthma has been extensively documented [6, 7]. However, contemporary evidence indicates that obesity-related asthma should not be viewed merely as asthma occurring in individuals with excess body weight. Rather, it represents a biologically distinct phenotype characterized by metabolic abnormalities, systemic inflammation, altered immune responses, and reduced corticosteroid responsiveness. Obesity-related airway disease is frequently associated with neutrophilic or paucigranulocytic inflammation and lower expression of classical type-2 inflammatory biomarkers [5]. Our findings are consistent with this concept, as patients with non-eosinophilic asthma exhibited higher BMI and METS-IR values in the univariate analyses, although these associations were attenuated after multivariable adjustment.

Several biological mechanisms may explain these observations. Adipose tissue functions as an active endocrine organ that produces adipokines, pro-inflammatory cytokines, and mediators involved in insulin resistance. Chronic metabolic inflammation may alter airway immune responses through increased production of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), leptin, oxidative stress, and dysregulated macrophage activation, together with reduced adiponectin levels [5–7]. Rather than promoting classical type-2 eosinophilic inflammation, these metabolic alterations predominantly activate innate immune pathways and are thought to favor neutrophilic or paucigranulocytic airway inflammation. In addition, obesity-related metabolic dysfunction may impair airway function through mechanisms independent of eosinophilic inflammation, including reduced lung compliance, increased airway closure, altered respiratory mechanics, and airway remodeling associated with insulin resistance and mitochondrial dysfunction. Consequently, obesity-related metabolic abnormalities may be more closely linked to non-eosinophilic asthma, a phenotype frequently characterized by corticosteroid resistance, persistent symptoms, and poorer clinical outcomes. These findings suggest that metabolic dysfunction may contribute to asthma phenotype differentiation rather than uniformly increasing eosinophilic inflammation, although the underlying mechanisms could not be directly evaluated in the present study.

Although METS-IR was not independently associated with eosinophilic asthma after adjustment, it consistently showed lower values among eosinophilic patients in the univariate analyses. Unlike HOMA-IR, TyG, and CHG, METS-IR integrates anthropometric measures of adiposity together with glucose and lipid metabolism, thereby providing a more comprehensive representation of obesity-related metabolic burden [9]. Similarly, BMI directly reflects excess adiposity, whereas HOMA-IR, TyG, and CHG primarily reflect insulin resistance through glucose- and lipid-based parameters. Because obesity-related asthma is driven not only by insulin resistance but also by excess adiposity, systemic inflammation, and metabolic dysregulation, indices incorporating anthropometric components may better capture the biological processes underlying asthma phenotypic heterogeneity. Although the independent association was attenuated after adjustment, the consistent differences observed in univariate analyses suggest that composite metabolic indices such as METS-IR may provide complementary information for characterizing the complex interaction between metabolism and asthma phenotypes.

The clinical implications of these findings are noteworthy. Patients with high metabolic burden may represent a subgroup with obesity-related non-eosinophilic asthma, a phenotype often characterized by poorer symptom control, lower responsiveness to ICSs, and fewer therapeutic options than eosinophilic disease. Identification of metabolic dysfunction in routine clinical practice may therefore facilitate more personalized management strategies emphasizing weight reduction, physical activity, dietary modification, and optimization of metabolic health in addition to conventional pharmacological therapy. These findings may help clinicians identify patients who require closer metabolic assessment and integrated management of obesity-related comorbidities alongside standard asthma care.

Our study has several limitations. The retrospective cross-sectional design precludes causal inference, and reverse causality cannot be excluded. Eosinophilic phenotype was defined using peripheral blood eosinophil counts rather than sputum eosinophils or fractional exhaled nitric oxide. Historical eosinophil counts were not consistently available; therefore, persistence of eosinophilia over time could not be confirmed. Given the known temporal variability of blood eosinophil counts, some degree of phenotype misclassification is possible. In addition, information regarding asthma severity, pulmonary function parameters, physical activity, dietary habits, and cumulative corticosteroid exposure was not available. Nevertheless, this study is among the first to simultaneously evaluate multiple metabolic indices in relation to eosinophilic asthma phenotype while including a healthy control group for comparison.

Patients with asthma exhibited greater metabolic dysfunction than healthy controls. Although eosinophilic asthma was associated with lower BMI and METS-IR values in univariate analyses, these associations were not confirmed after adjustment for potential confounders. Larger prospective studies are warranted to further investigate the relationship between metabolic dysfunction and asthma phenotypes.