Small Airway Dysfunction and Type 2 Biomarkers Predict Exacerbations in Mild, Well-Controlled Asthma: A Retrospective Cohort Study

Small Airway Dysfunction and Type 2 Biomarkers Predict Exacerbations in Mild, Well-Controlled Asthma: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Small Airway Dysfunction and Type 2 Biomarkers Predict Exacerbations in Mild, Well-Controlled Asthma: A Retrospective Cohort Study Guanhua Hou, Zishuo Wang, Yinan Xing, Wei Li, Limin Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7898079/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Small airway dysfunction (SAD) plays a pivotal but often overlooked role in asthma pathophysiology. Its contribution to exacerbation risk among patients with mild, well-controlled asthma remains unclear. Objective This study aimed to assess the prevalence and clinical significance of SAD and type 2 inflammation biomarkers in mild, well-controlled asthma, and to determine their independent and combined predictive value for acute exacerbations. Methods A retrospective cohort study was conducted in 250 adults with mild, well-controlled asthma. Lung function indices, blood eosinophil counts, and fractional exhaled nitric oxide (FeNO) levels were analyzed. Logistic regression and receiver operating characteristic (ROC) analyses were performed to evaluate predictors of exacerbations. Results SAD was identified in 40.4% of patients and was strongly associated with a higher exacerbation rate (73.3% vs. 32.9%, p < 0.001). SAD (adjusted OR = 17.91, 95% CI 7.03–49.39) and elevated eosinophils (aOR = 4.97, 95% CI 2.54–10.12) were independent predictors of exacerbations. Combined models incorporating FEF25–75%pred and eosinophil count achieved the highest discriminative performance (AUC = 0.769), surpassing any single biomarker. Conclusions Even in mild, well-controlled asthma, SAD and type 2 inflammation markers identify a high-risk phenotype susceptible to exacerbations. Integrating small airway function with inflammatory biomarkers enhances risk stratification, supporting precision monitoring and tailored therapeutic strategies in asthma management. small airway dysfunction type-2 inflammation mild asthma acute exacerbation biomarkers Figures Figure 1 Figure 2 Figure 3 Background Asthma is a heterogeneous, chronic inflammatory airway disease that affects more than 350 million people worldwide and imposes a substantial clinical and economic burden[ 1 ]. Although inhaled corticosteroids (ICS) and long-acting β₂-agonists (LABA) have markedly improved symptom control for most patients, 30–40% still experience recurrent acute exacerbations, which impair quality of life and increase mortality[ 2 ]. Conventional asthma assessment has focused on routine spirometric indices; however, growing evidence indicates that SAD—involving airways < 2 mm in diameter—plays a critical yet under-recognized role in asthma pathophysiology. Even among patients with normal spirometry, the prevalence of SAD can reach 50–60%, and it is significantly associated with poor symptom control and increased risk of exacerbations[ 3 , 4 ]. Recent studies show that, in mild, well-controlled asthma, the presence of SAD is linked to a substantially higher risk of exacerbations, representing an overlooked high-risk phenotype[ 5 , 6 ]. In addition, blood eosinophil count and FeNO—biomarkers of type-2 (T2) inflammation—provide prognostic information for predicting asthma attacks[ 7 ]. FeNO, a noninvasive marker of Th2-type airway inflammation, is associated with lower lung function and a higher risk of future exacerbations; its predictive performance improves when combined with other indicators[ 8 ]. Approximately 75% of patients receiving anti-IL-5/IL-5Rα therapy exhibit both elevated blood eosinophils (≥ 300 cells/µL) and FeNO (≥ 25 ppb)[ 9 ]. However, the interplay between SAD and inflammatory biomarkers—and their combined prognostic value for clinical outcomes—remains incompletely understood. The 2025 GINA strategy revises management of mild asthma, emphasizing that consistent anti-inflammatory therapy is needed even for mild symptoms[ 1 , 10 ]. Accordingly, identifying high-risk subgroups within mild asthma is essential for precision care and optimal resource allocation. Data remain scarce regarding the prevalence and clinical features of SAD—and its relationship to exacerbations—specifically among patients with mild, well-controlled asthma. Prior work has focused predominantly on moderate-to-severe or poorly controlled disease, with limited systematic evaluation of patients who are well controlled yet potentially at risk. Heterogeneity in the definition of SAD and limited prospective validation further constrain clinical translation. We therefore aimed to (i) determine the prevalence and clinical features of SAD and T2 biomarkers in mild, well-controlled asthma; (ii) evaluate their independent associations with acute exacerbations; and (iii) assess the incremental discrimination gained by combining small-airway indices with T2 biomarkers for risk stratification. Methods We conducted a single-centre retrospective cohort of adults with physician-diagnosed asthma who attended the Department of Respiratory and Critical Care Medicine at Henan Provincial People’s Hospital between January 2022 and March 2024. Asthma diagnosis and control were determined according to GINA criteria; control status over the preceding four weeks was categorized as well-controlled, partly controlled, or uncontrolled based on daytime and nocturnal symptoms, activity limitation, and SABA use. Mild asthma was defined as GINA steps 1–2 (low-dose ICS or ICS/LABA)[ 1 ]. In total, 250 de-identified cases were extracted from an existing database. The protocol was approved by the institutional review board and adhered to the Declaration of Helsinki. Inclusion criteria comprised: (1) Chinese citizens aged 18–75 years; (2) a diagnosis of asthma by a respiratory specialist according to the Global Initiative for Asthma (GINA) strategy, with typical symptoms and signs and objective evidence of variable airflow limitation; (3) availability of complete clinical data, including spirometry and biomarker measurements; and (4) provision of written informed consent and voluntary participation. Exclusion criteria were any of the following: (1) coexisting active respiratory diseases (e.g., chronic obstructive pulmonary disease, bronchiectasis, or interstitial lung disease); (2) respiratory tract infection within the preceding 4 weeks; (3) current use of medications likely to materially affect lung function or inflammatory biomarkers (e.g., systemic corticosteroids); (4) pregnancy or lactation; (5) severe cardiovascular disease, malignancy, or other systemic disorders; or (6) incomplete clinical data or poor compliance with testing procedures. Spirometry and small-airway assessments were performed using MasterScreen® following ATS/ERS recommendations and GLI reference values [ 11 – 13 ]. SAD was defined as at least two of three indices—FEF50%, FEF75%, and FEF25–75%—being < 65% of predicted[ 14 ], in the setting of otherwise normal conventional ventilatory measures; patients were classified into SAD and non-SAD groups accordingly. Biomarkers of T2 inflammation included blood eosinophil counts (Sysmex XN-9100) and FeNO (NIOX VERO®) measured per ATS/ERS guidance[ 15 , 16 ]. Exacerbations were defined, consistent with GINA[ 1 ], as progressive worsening of respiratory symptoms and lung function requiring additional reliever therapy or treatment escalation (e.g., increased ICS or oral corticosteroids) or unscheduled medical care.Covariates included age, sex, body-mass index, allergy history, asthma duration, and smoking status (ever/current defined as > 100 lifetime cigarettes). Analyses were conducted in R (v4.3.2) with a two-sided α = 0.05. Descriptive statistics were reported as mean ± SD or median (IQR) and counts (percentages). Between-group comparisons used independent-samples t -tests or Mann–Whitney U tests for continuous variables and χ² or Fisher’s exact tests for categorical variables. Multivariable logistic regression evaluated independent associations of SAD and inflammatory markers with exacerbations, reporting adjusted odds ratios (aOR), 95% confidence intervals, and p values; model fit was assessed with the Hosmer–Lemeshow test, and multicollinearity with variance-inflation factors (VIF < 5). Discriminative performance was assessed by receiver-operating-characteristic (ROC) curve analysis of single markers (FEF25–75% predicted, FEV1% predicted, FEV1/FVC, FeNO, and blood eosinophils) and combined models (inflammation + lung function); the area under the curve (AUC) was calculated. Results Of 250 adults with mild, well-controlled asthma, 101 (40.4%) had SAD;(see Table 1 ). There were no significant between-group differences in demographics: age, disease duration, sex, and BMI were comparable between the SAD and non-SAD groups (all p > 0.05). Pulmonary function indices were significantly lower in the SAD group; for example, the median FVC% predicted was 101.5% versus 104.6% in the non-SAD group ( p = 0.002). Similarly, FEV1% predicted (84.7% vs 99.8%), FEV1/FVC (72.8% vs 77.9%), and FEV1/FVC% predicted (87.2% vs 96.3%) were all lower in the SAD group ( p < 0.001). Regarding inflammatory markers, peripheral blood eosinophil counts were higher in the SAD group (median 0.33 vs 0.27×10⁹/L; p = 0.004). FeNO also tended to be higher in the SAD group: although the difference in median FeNO did not reach significance, the proportion with FeNO ≥ 25 ppb was significantly greater in the SAD group than in the non-SAD group (46.5% vs 32.2%; p = 0.031). In addition, a history of allergy was more frequent in the SAD group than in the non-SAD group (36.6% vs 25.5%), a difference that approached statistical significance ( p = 0.081). During follow-up, a significantly higher proportion of patients in the SAD group experienced acute exacerbations than in the non-SAD group (73.3% vs 32.9%; p < 0.001). Table 1 Clinical and pulmonary function characteristics of patients with mild, well-controlled asthma with vs without SAD Characteristic Non-SAD (n = 149) SAD (n = 101) P value Age, years 47.00 (21.00) 51.00 (21.00) 0.259 BMI, kg/m² 25.78 (5.85) 25.89 (6.08) 0.727 Disease duration, years 9.00 (22.00) 9.00 (22.00) 0.639 FeNO, ppb 19.00 (19.00) 23.00 (20.00) 0.074 FVC% predicted 104.60 (14.60) 101.50 (18.40) 0.002 FEV1% predicted 99.80 (16.10) 84.70 (13.50) < 0.001 FEV1/FVC, % 77.87 (6.87) 72.82 (6.82) < 0.001 FEV1/FVC% predicted 96.30 (9.70) 87.20 (11.60) < 0.001 Blood eosinophils, ×10⁹/L 0.27 (0.34) 0.33 (0.40) 0.004 Sex — male / female, n (%) 56 (37.6) / 93 (62.4) 43 (42.6) / 58 (57.4) 0.509 Smoking history, n (%) 25 (16.8) 18 (17.8) 0.965 Allergy history, n (%) 38 (25.5) 37 (36.6) 0.081 Blood eosinophils ≥ 300 cells/µL, n (%) 73 (49.0) 51 (50.5) 0.917 FeNO ≥ 25 ppb, n (%) 48 (32.2) 47 (46.5) 0.031 Acute exacerbations, n (%) 49 (32.9) 74 (73.3) < 0.001 Patients were stratified by the occurrence of acute exacerbations: 123 (49.2%) experienced at least one event and were compared with 127 without exacerbations (see Table 2 ). The groups were similar in age, sex, disease duration, and smoking history. Median BMI was significantly lower in the exacerbation group than in the non-exacerbation group (25.25 vs 26.37 kg/m²; p = 0.002). Pulmonary function was consistently poorer in the exacerbation group, with lower FVC% predicted, FEV1% predicted, FEV1/FVC, and FEV1/FVC% predicted (all p < 0.05). Small-airway impairment was more pronounced: FEF25–75% predicted and FEF50% predicted were significantly reduced (50.8% vs 64.0% and 58.5% vs 66.2%, respectively; p < 0.001), whereas FEF25% predicted did not differ ( p = 0.926). A history of allergy was more frequent in the exacerbation group (38.2% vs 22.0%; p = 0.008). Elevated blood eosinophils (≥ 300 cells/µL) were also more common (63.4% vs 36.2%; p < 0.001). Although the proportion with FeNO ≥ 25 ppb was similar between groups (40.7% vs 35.4%; p = 0.472), the median FeNO level was slightly higher in the exacerbation group (21 vs 19 ppb; p = 0.032). The prevalence of SAD reached 60.2% in the exacerbation group compared with 21.3% in the non-exacerbation group ( p < 0.001), highlighting the strong link between small-airway dysfunction and exacerbation risk. Table 2 Clinical, inflammatory, and small-airway characteristics by exacerbation status in mild, well-controlled asthma Characteristic No exacerbation (n = 127) Exacerbation (n = 123) P value Age, years 48.00 (19.00) 48.00 (22.00) 0.422 BMI, kg/m² 26.37 (4.64) 25.25 (6.96) 0.002 Disease duration, months 6.00 (34.00) 6.00 (22.00) 0.171 FeNO, ppb 19.00 (17.50) 21.00 (62.00) 0.032 FVC% predicted 105.90 (12.85) 102.00 (16.70) 0.007 FEV1% predicted 95.50 (17.90) 90.00 (19.10) 0.001 FEV1/FVC, % 76.41 (6.89) 75.02 (10.02) 0.018 FEV1/FVC% predicted 93.90 (8.90) 91.00 (16.80) 0.022 FEF25–75% predicted, % 64.00 (31.55) 50.80 (35.60) < 0.001 FEF50% predicted, % 66.20 (16.30) 58.50 (34.15) < 0.001 FEF25% predicted, % 47.20 (23.90) 45.40 (34.25) 0.926 Sex — male, n (%) 80 (63.0) 71 (57.7) 0.470 Smoking history, n (%) 20 (15.7) 23 (18.7) 0.652 Allergy history, n (%) 28 (22.0) 47 (38.2) 0.008 Blood eosinophils ≥ 300 cells/µL, n (%) 46 (36.2) 78 (63.4) < 0.001 FeNO ≥ 25 ppb, n (%) 45 (35.4) 50 (40.7) 0.472 SAD, n (%) 27 (21.3) 74 (60.2) < 0.001 In univariable analyses, SAD, blood eosinophils ≥ 300 cells/µL, airflow obstruction (FEV1/FVC < 70%), and lower mid-expiratory flows were associated with higher odds of exacerbation, whereas BMI ≥ 25 kg/m² was inversely associated; FeNO ≥ 25 ppb showed no association. In the multivariable model adjusting for age, sex, allergy history, and smoking status, SAD remained the strongest predictor (adjusted odds ratio [aOR] 17.91, 95% CI 7.03–49.39, p < 0.001). Blood eosinophils ≥ 300 cells/µL were independently associated with exacerbations (aOR 4.97, 95% CI 2.54–10.12, p < 0.001), as was FEV1/FVC < 70% (aOR 3.84, 95% CI 1.49–10.38, p = 0.006). Higher BMI was protective (aOR 0.41, 95% CI 0.20–0.79, p = 0.009). A higher FEF25–75% predicted was associated with lower risk (aOR 0.98 per 1%-predicted increase, 95% CI 0.96–0.99, p = 0.022). FeNO ≥ 25 ppb (aOR 0.67, 95% CI 0.33–1.30, p = 0.237), FEF75% predicted (aOR 1.03, 95% CI 0.99–1.06, p = 0.083), and FEF50% predicted (aOR 1.02, 95% CI 0.99–1.05, p = 0.196) were not independently associated with exacerbations.Overall, these findings identify a high-risk phenotype characterized by SAD, eosinophilic inflammation, and airflow obstruction, while indicating modest protective effects of higher BMI and better small-airway flow. Table 3 Multivariable logistic regression of factors associated with acute exacerbations Variable Unadjusted OR (95% CI) p value Adjusted OR (95% CI) p value BMI ≥ 25 kg/m² 0.56 (0.33–0.92) 0.024 0.41 (0.20–0.79) 0.009 SAD (yes vs no) 5.59 (3.24–9.92) < 0.001 17.91 (7.03–49.39) < 0.001 FeNO ≥ 25 ppb 1.25 (0.75–2.09) 0.396 0.67 (0.33–1.30) 0.237 Blood eosinophils ≥ 300 cells/µL 3.55 (2.04–6.28) < 0.001 4.97 (2.54–10.12) < 0.001 FEV1/FVC < 70% 2.57 (1.37–4.99) 0.004 3.84 (1.49–10.38) 0.006 FEF25–75% predicted 0.98 (0.97–0.99) 0.004 0.98 (0.96–0.99) 0.022 FEF75% predicted 0.98 (0.93–1.00) 0.062 1.03 (0.99–1.06) 0.083 FEF50% predicted 0.98 (0.97–0.99) 0.002 1.02 (0.99–1.05) 0.196 Notes: Adjusted models control for age, sex, allergy history, and smoking history. For categorical predictors, the reference categories are: BMI < 25 kg/m²; no SAD; FeNO < 25 ppb; blood eosinophils < 300 cells/µL; FEV1/FVC ≥ 70%. ROC curve analyses revealed substantial heterogeneity in the performance of single markers. Blood eosinophil count achieved the highest AUC (0.750), indicating better discrimination, whereas FeNO showed the lowest AUC (0.579), only slightly above 0.5 and therefore of limited predictive value (see Fig. 1 ). The AUCs for other lung-function indices ranged from 0.587 to 0.653, indicating modest discrimination at best (see Fig. 2 ). Combined models outperformed single markers, underscoring the incremental value of integrating inflammatory biomarkers with lung-function measures. In particular, combinations that included blood eosinophils markedly improved model performance (see Fig. 3 ). The eosinophils + FEF25–75% predicted model produced the highest AUC (0.769), surpassing eosinophils alone (0.750) and FEF25–75% predicted alone (0.653). Pairing eosinophils with FEV1% predicted or with FEV1/FVC also improved discrimination (AUCs 0.754 and 0.757 vs 0.617 and 0.587, respectively). Although FeNO-based combinations (e.g., FeNO + FEF25–75% predicted, AUC 0.716) performed better than FeNO alone, they remained less discriminative than the corresponding eosinophil-based models. Discussion In patients with mild, well-controlled asthma, we investigated the clinical significance of SAD and T2 inflammatory biomarkers—blood eosinophil count and FeNO—and their associations with acute exacerbations. We found that, in this population traditionally considered at low risk, SAD was common (~ 40% of patients) and was linked to a substantially higher subsequent exacerbation rate than in those without SAD. Elevated blood eosinophil counts were also associated with increased risk, and FeNO levels were modestly higher among patients who experienced exacerbations. To our knowledge, this is the first evidence that even mild, well-controlled asthma contains a high-risk phenotype prone to exacerbations; a combined evaluation of SAD and inflammatory biomarkers can identify this subgroup and has important implications for individualized management. Our findings suggest that SAD may represent a latent “danger signal” in patients with mild asthma. As the distal “end units” of the airway tree, the small airways can harbor inflammation and remodeling even when symptoms appear well controlled[ 17 ]. Once this “silent zone” is involved, airway reserve declines, making acute airflow limitation more likely in the presence of triggers (e.g., infections or allergens) and helping to explain why patients with SAD can experience severe deterioration after exposure despite otherwise mild day-to-day symptoms[ 18 ]. Additionally, our data indicate that higher blood eosinophil counts are closely associated with acute exacerbations, consistent with the paradigm that eosinophilic airway inflammation drives worsening: peripheral eosinophilia often reflects active T2 inflammation in the airways, promoting mucosal edema and hyperresponsiveness and thereby increasing attack risk[ 19 ]. By contrast, FeNO levels were only slightly higher in the exacerbation group, likely reflecting suppression by the widespread use of inhaled corticosteroids. Moreover, FeNO mainly indexes epithelial T2 inflammation, whereas eosinophil counts partly reflect the systemically mobilizable pool of inflammatory cells; these signals may therefore be asynchronous in mild asthma[ 20 ]. Notably, combining small-airway indices with inflammatory markers provided superior risk prediction: single metrics had limited sensitivity and specificity, whereas the coexistence of small-airway impairment with eosinophilia or elevated FeNO was associated with a substantially higher risk of exacerbation. This incremental effect points to interplay between distal airway damage and persistent T2 inflammation, with synergistic effects that increase susceptibility to triggers. Overall, we propose a mechanistic model in which abnormalities of small-airway structure and function create a “fragile” state and active eosinophilic inflammation supplies the “spark,” together markedly elevating the likelihood of acute exacerbations. Numerous recent high-quality studies likewise underscore the pivotal role of small-airway pathology in asthma; our findings are concordant with—and extend—this literature. In a multicentre analysis of mild asthma, Galant and colleagues reported that ~ 36% of well-controlled patients had SAD and that SAD independently predicted exacerbations over the subsequent year; after adjustment for age, smoking, baseline FEV1, and other covariates, its presence more than doubled the risk of exacerbations[ 21 ]. Although our study differed in population selection and in the operational definition of SAD, we similarly observed small-airway impairment in approximately one-third to one-half of patients with mild disease and confirmed the predictive value of SAD for exacerbations, supporting the notion that SAD represents an under-recognized high-risk phenotype in mild asthma[ 5 ]. Notably, the foundational ATLANTIS report found that the overall prevalence of SAD reached 50–60% across asthma severity strata and exceeded 90% when broader criteria integrating multiple diagnostic modalities were used[ 17 ]. Although these figures exceed our observed prevalence (~ 40%), the difference likely reflects variation in SAD definitions and enrollment criteria and indirectly supports our conclusion that distal airway involvement is widespread in asthma. Consistent with this, our results align with the impulse oscillometry (IOS)–based study by Gao and colleagues, which documented a high prevalence of SAD (~ 75%) and, via structural equation modeling, identified SAD as a central determinant of poor asthma control and diminished quality of life[ 4 ]. Taken together, these studies underscore that, regardless of how mild asthma may appear clinically, “silent” disease in the distal airways can covertly drive deterioration. Our work advances this literature by concentrating on mild asthma and incorporating a combined assessment of inflammatory biomarkers, thereby expanding the means to identify high-risk phenotypes—an important innovation of the present study. With respect to inflammatory biomarkers, our findings both corroborate and extend prior literature. A substantial body of evidence indicates that peripheral blood eosinophil count and FeNO are important predictors of exacerbation risk in asthma[ 22 , 23 ]. Using a UK primary-care database, Price[ 19 ]reported that patients with either high FeNO (≥ 50 ppb) or eosinophilia (≥ 300 cells/µL) had increased annual exacerbation rates, and that concomitant elevations in both were associated with a markedly greater rate—approximately 3.67 times that of patients without either elevation. In line with these observations, we found that eosinophilia combined with SAD conferred a far higher likelihood of acute exacerbations than either risk factor alone. This “SAD + eosinophilia” phenotype may represent a novel high-risk subtype, whose recognition has tangible implications for individualized clinical management. Regarding FeNO, a subgroup analysis by Pavord et al. in a trial of patients with mild asthma similarly found no significant predictive value of baseline FeNO for exacerbations[ 24 ]. Nevertheless, FeNO is a sensitive marker of eosinophilic airway inflammation and retains clear clinical relevance: multiple randomized trials and meta-analyses indicate that FeNO-guided therapy can reduce exacerbations [ 25 ], and in moderate-to-severe asthma, higher FeNO levels often signal poorer control and more frequent attacks[ 26 ]. In this context, we speculate that the lack of a significant difference between groups in the proportion with elevated FeNO (≥ 25 ppb; 40.7% vs 35.4%, p = 0.472) may reflect the predominance of well-controlled patients receiving low-dose inhaled corticosteroids. Notably, the proportion with elevated FeNO (≥ 25 ppb) was significantly greater in the SAD group than in the non-SAD group (46.5% vs 32.2%, p = 0.031), indicating that small-airway impairment in mild asthma often coexists with residual T2 inflammation. This is consistent with the findings reported by Gao et al. among patients with asthma who experienced exacerbations: older age, obesity, and elevated FeNO were independent predictors of SAD[ 4 ]. Taken together, our study is, to our knowledge, the first to integrate small-airway functional indices with inflammatory biomarkers for exacerbation risk stratification, showing that the combined assessment outperforms any single marker. This multidimensional “physiology + inflammation” framework provides new insights into asthma and highlights the innovative nature of our work. Approaching asthma through the prism of phenotypic heterogeneity, our study adds meaningful insight into risk characteristics among patients with mild disease and carries important academic value. Historically, research and management have focused on moderate-to-severe or difficult-to-treat asthma, while mildly symptomatic, well-controlled patients have received less attention. Yet the principle that mild does not equate to low risk is gaining consensus [ 10 , 27 ], and our findings provide new empirical support. By situating the “SAD with high-inflammation” phenotype within the high-risk category, these results help refine the asthma phenotypic spectrum and prompt a re-examination of risk-management strategies for mild asthma.From a clinical perspective, evaluation in seemingly well-controlled patients should extend beyond large-airway indices such as FEV1 to encompass small-airway assessments—MMEF/FEF25–75% and IOS parameters—as well as inflammatory biomarkers[ 28 ]. Incorporating these measures into routine follow-up may enable early identification of subgroups characterized by SAD and a high inflammatory burden and, in turn, justify targeted intensification of therapy. For pharmacologic management, extrafine-particle ICS that effectively reach the small airways can be prioritized; in mild asthma, use of such formulations has been associated with a markedly lower exacerbation rate than non-use (27.7% vs 55.3%) [ 5 ], supporting the notion of a small-airways treatable trait. For patients with prominent eosinophilic inflammation, periodic monitoring of FeNO and blood counts can inform ICS dose adjustment and help assess adherence [ 29 ]; in highly eosinophilic phenotypes, early initiation of anti-IL-5 or other biologics may be warranted to prevent severe exacerbations[ 30 ]. Looking ahead, mobile health and remote-monitoring technologies—enabling at-home exhaled NO or peak-flow measurements and sensor-based surveillance of respiratory resistance—could provide the infrastructure for precise monitoring and individualized intervention in high-risk mild asthma. This study has several limitations. First, it was a single-centre, retrospective analysis from one regional medical centre with a relatively limited sample size, raising the possibility of selection bias and constraining generalizability. Multicentre, large-scale prospective studies are therefore needed to confirm the robustness and broader applicability of our results. Second, SAD was assessed using ventilatory flow indices, which have modest sensitivity[ 28 ]; compared with more sophisticated methods—impulse oscillometry, multiple-breath nitrogen washout, or advanced lung imaging—our approach may underestimate the true prevalence of SAD. Future work should adopt more sensitive small-airway assessments and develop standardized criteria for defining SAD.Third, inflammatory biomarkers were obtained only at baseline. Because blood eosinophil counts and FeNO fluctuate with seasonality, treatment adherence, and other factors, single time-point measurements may not fully capture risk [ 22 ]. Longitudinal monitoring could clarify the temporal coupling between biomarker variability and exacerbations—for example, whether “FeNO surges” herald imminent attacks. Fourth, while we focused on two readily accessible T2 biomarkers, non-eosinophilic pathways may also contribute to risk; we did not evaluate sputum cytology or airway neutrophilia, which may have led to under-recognition of risk in non-T2–predominant patients[ 31 ]. Future studies should incorporate broader inflammatory phenotyping to delineate the roles of distinct endotypes in mild-asthma exacerbations.Finally, despite all patients meeting criteria for mild, well-controlled disease, treatment regimens varied (as-needed ICS vs daily maintenance), and we did not perform in-depth stratification by regimen or adherence, which may confound risk attribution. Subsequent research should better control for baseline therapy and compare the predictive utility of SAD and inflammatory markers across treatment strategies. Notwithstanding these limitations, our conclusions remained robust in multivariable analyses, indicating that they do not fundamentally challenge our central hypothesis. Rather, they highlight clear directions for future work—larger and more rigorously designed studies to validate and extend these findings. Overall, we adopt a cautiously optimistic stance: the conclusions appear sound but warrant confirmation in broader populations. Conclusions This study shows that SAD and inflammatory phenotypes materially influence the risk of acute exacerbations in mild, well-controlled asthma. Even in disease traditionally considered “mild,” a high-risk subgroup is evident: concomitant small-airway impairment and persistent T2 inflammation define a phenotype prone to exacerbations. In keeping with evidence that clinical severity and acute risk do not invariably align, our integrated evaluation of physiological and inflammatory indices provides an incremental method for identifying high-risk patients. Collectively, these findings broaden the asthma phenotypic spectrum, highlight the centrality of the small airways in risk stratification, and offer practical guidance for more precise monitoring and intervention in patients with mild asthma. Future directions Looking ahead, several avenues warrant focused investigation: (1) prospective longitudinal cohorts to validate the predictive performance of SAD and eosinophilic biomarkers for both first and recurrent exacerbations, and to quantify the clinical benefit of targeting these risks (e.g., intensifying small-airway–directed therapy and anti-inflammatory treatment); (2) deployment of advanced functional imaging and biomarker technologies to clarify causal links and mechanisms between SAD and inflammatory imbalance, including the distal-airway pathways that precipitate acute attacks; (3) expansion of study populations across races, regions, and age groups—pediatric and geriatric cohorts included—to assess the generalizability of this high-risk phenotype; and (4) machine-learning–enabled integration of small-airway indices, FeNO, hematologic counts, and clinical features to develop comprehensive, clinically useful prediction models for exacerbation risk in mild asthma. Collectively, these efforts will strengthen the emerging “physiology + inflammation” framework for asthma management and underpin precision medicine and comprehensive disease control. As this agenda advances, we anticipate substantial improvements in risk screening and individualized intervention for patients with mild asthma, with tangible reductions in acute exacerbations and better long-term outcomes. Abbreviations SAD, small airway dysfunction; FeNO, fractional exhaled nitric oxide;BMI, body mass index; FEF25–75, forced expiratory flow between 25% and 75% of FVC; FEF25, forced expiratory flow at 25% of FVC; FEF50, forced expiratory flow at 50% of FVC; FEF75, forced expiratory flow at 75% of FVC; FEV1/FVC, ratio of forced expiratory volume in one second to forced vital capacity; FEV1, forced expiratory volume in one second; FVC, forced vital capacity; SABA, short-acting β₂-agonist; ICS, inhaled corticosteroids; LABA, long-acting β₂-agonist. OR, odds ratio; CI, confidence interval; T2,type-2; BEC ,blood eosinophil count; IOS, impulse oscillometry; Declarations Ethics approval and consent to participate: The study was approved by the Medical Ethics Committee of Henan Provincial People’s Hospital(Approval Number: No. 2025-024), and every patient provided written informed consent. Consent for publication: Not applicable. This manuscript does not contain data from any individual person (including individual details, images or videos). Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This study is supported by the Provincial–Ministerial Co-construction Project of the Henan Medical Science and Technology Research Program (SBGJ202302003). Authors' contributions: Guanhua Hou conceived and designed the study, Guanhua Hou drafted the manuscript, Wei Li assisted with the preparation of tables, and Yinan Xing and Zishuo Wang assisted with statistical analysis. Limin Zhao critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements: We would like to thank Hongjian ZHao for his suggestions when we had difficulties in conducting this study. References GINA Strategy Report. Global Initiative for Asthma - GINA. https://ginasthma.org/2025-gina-strategy-report/. Accessed 19 Aug 2025. Porsbjerg C, Melén E, Lehtimäki L, Shaw D. Asthma. The Lancet. 2023;401:858–73. https://doi.org/10.1016/S0140-6736(22)02125-0. Postma DS, Brightling C, Baldi S, Van den Berge M, Fabbri LM, Gagnatelli A, et al. Exploring the relevance and extent of small airways dysfunction in asthma (ATLANTIS): baseline data from a prospective cohort study. 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J Allergy Clin Immunol Pract. 2024;12:2705-2716.e6. https://doi.org/10.1016/j.jaip.2024.05.034. 程丽, 蒋毅, 岳倩如, 安若丽, 马红霞. 呼出气一氧化氮和外周血嗜酸粒细胞计数对嗜酸粒细胞型哮喘的诊断价值. 中华全科医师杂志. 2020;19:502–6. https://doi.org/10.3760/cma.j.cn114798-20200410-00437. Murugesan N, Saxena D, Dileep A, Adrish M, Hanania NA. Update on the Role of FeNO in Asthma Management. Diagnostics (Basel). 2023;13:1428. https://doi.org/10.3390/diagnostics13081428. Pham DD, Lee J-H, Kwon H-S, Song W-J, Cho YS, Kim H, et al. Concurrent Blood Eosinophils and FeNO as Biological Therapy Indicators in Severe Asthma: Findings From the Precision Medicine Intervention in Severe Asthma Study. J Allergy Clin Immunol Pract. 2025;13:1681-1689.e2. https://doi.org/10.1016/j.jaip.2025.03.011. Golam SM, Janson C, Beasley R, FitzGerald JM, Harrison T, Chipps B, et al. The burden of mild asthma: Clinical burden and healthcare resource utilisation in the NOVELTY study. Respir Med. 2022;200:106863. https://doi.org/10.1016/j.rmed.2022.106863. 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Eur Respir J. 2023;62:2201519. https://doi.org/10.1183/13993003.01519-2022. 中华医学会, 中华医学会杂志社, 中华医学会全科医学分会, 中华医学会呼吸病学分会肺功能学组, 中华医学会《中华全科医师杂志》编辑委员会, 中国呼吸系统疾病基层诊疗与管理指南制订专家组. 中国常规肺功能检查基层指南（2024年）. 中华全科医师杂志. 2025;24:121–37. https://doi.org/10.3760/cma.j.cn114798-20240618-00555. Dweik RA, Boggs PB, Erzurum SC, Irvin CG, Leigh MW, Lundberg JO, et al. An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FENO) for clinical applications. Am J Respir Crit Care Med. 2011;184:602–15. https://doi.org/10.1164/rccm.9120-11ST. American Thoracic Society, European Respiratory Society. ATS/ERS recommendations for standardized procedures for the online and offline measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide, 2005. Am J Respir Crit Care Med. 2005;171:912–30. https://doi.org/10.1164/rccm.200406-710ST. Cottini M, Lombardi C, Passalacqua G, Bagnasco D, Berti A, Comberiati P, et al. Small Airways: The “Silent Zone” of 2021 GINA Report? Front Med (Lausanne). 2022;9:884679. https://doi.org/10.3389/fmed.2022.884679. Kaplan A. The Myth of Mild: Severe Exacerbations in Mild Asthma: An Underappreciated, but Preventable Problem. Adv Ther. 2021;38:1369–81. https://doi.org/10.1007/s12325-020-01598-2. Price DB, Bosnic-Anticevich S, Pavord ID, Roche N, Halpin DMG, Bjermer L, et al. Association of elevated fractional exhaled nitric oxide concentration and blood eosinophil count with severe asthma exacerbations. Clinical and Translational Allergy. 2019;9:41. https://doi.org/10.1186/s13601-019-0282-7. Marcos MC, Cisneros Serrano C. What is the added value of FeNO as T2 biomarker? Front Allergy. 2022;3:957106. https://doi.org/10.3389/falgy.2022.957106. Galant SP, Kuks PJM, Kole TM, Kraft M, Siddiqui S, Fabbri LM, et al. Assessment of the role of small airway dysfunction in relation to exacerbation risk in patients with well controlled asthma (ATLANTIS): an observational study. Lancet Respir Med. 2025;:S2213-2600(25)00283-8. https://doi.org/10.1016/S2213-2600(25)00283-8. Lindsley A, Lugogo N, Reeh K, Spahn J, Parnes J. Asthma Biologics Across the T2 Spectrum of Inflammation in Severe Asthma: Biomarkers and Mechanism of Action. JAA. 2025;Volume 18:33–57. https://doi.org/10.2147/JAA.S496630. Bacharier LB, Pavord ID, Maspero JF, Jackson DJ, Fiocchi AG, Mao X, et al. Blood eosinophils and fractional exhaled nitric oxide are prognostic and predictive biomarkers in childhood asthma. J Allergy Clin Immunol. 2024;154:101–10. https://doi.org/10.1016/j.jaci.2023.09.044. Pavord ID, Holliday M, Reddel HK, Braithwaite I, Ebmeier S, Hancox RJ, et al. Predictive value of blood eosinophils and exhaled nitric oxide in adults with mild asthma: a prespecified subgroup analysis of an open-label, parallel-group, randomised controlled trial. Lancet Respir Med. 2020;8:671–80. https://doi.org/10.1016/S2213-2600(20)30053-9. Korevaar DA, Damen JA, Heus P, Moen MJ, Spijker R, van Veen IH, et al. Effectiveness of FeNO-guided treatment in adult asthma patients: A systematic review and meta-analysis. Clin Exp Allergy. 2023;53:798–808. https://doi.org/10.1111/cea.14359. Busse WW, Wenzel SE, Casale TB, FitzGerald JM, Rice MS, Daizadeh N, et al. Baseline FeNO as a prognostic biomarker for subsequent severe asthma exacerbations in patients with uncontrolled, moderate-to-severe asthma receiving placebo in the LIBERTY ASTHMA QUEST study: a post-hoc analysis. Lancet Respir Med. 2021;9:1165–73. https://doi.org/10.1016/S2213-2600(21)00124-7. Beasley R, Holliday M, Reddel HK, Braithwaite I, Ebmeier S, Hancox RJ, et al. Controlled Trial of Budesonide-Formoterol as Needed for Mild Asthma. N Engl J Med. 2019;380:2020–30. https://doi.org/10.1056/NEJMoa1901963. Beinart D, Goh ESY, Boardman G, Chung LP. Small airway dysfunction measured by impulse oscillometry is associated with exacerbations and poor symptom control in patients with asthma treated in a tertiary hospital subspecialist airways disease clinic. Front Allergy. 2024;5:1403894. https://doi.org/10.3389/falgy.2024.1403894. Petsky HL, Cates CJ, Kew KM, Chang AB. Tailoring asthma treatment on eosinophilic markers (exhaled nitric oxide or sputum eosinophils): a systematic review and meta-analysis. Thorax. 2018;73:1110–9. https://doi.org/10.1136/thoraxjnl-2018-211540. Farne HA, Wilson A, Milan S, Banchoff E, Yang F, Powell CV. Anti-IL-5 therapies for asthma. Cochrane Database Syst Rev. 2022;7:CD010834. https://doi.org/10.1002/14651858.CD010834.pub4. Hudey SN, Ledford DK, Cardet JC. Mechanisms of non-type 2 asthma. Curr Opin Immunol. 2020;66:123–8. https://doi.org/10.1016/j.coi.2020.10.002. Additional Declarations No competing interests reported. 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10:44:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive performance of spirometric indices for acute exacerbations of asthma\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7898079/v1/efa71be5599ed87f644e2dbc.png"},{"id":99292445,"identity":"d9dd6165-8cbf-4d1b-9a8a-507504bc7767","added_by":"auto","created_at":"2025-12-31 10:44:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive performance of combined spirometric indices and biomarkers for acute exacerbations of asthma\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7898079/v1/4bc831aca8d4c8afadb2296b.png"},{"id":99787859,"identity":"d48ea242-4c2a-46e6-a2a3-4bb29bdb65d0","added_by":"auto","created_at":"2026-01-08 12:39:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":940589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7898079/v1/a77919f3-25cc-487f-9679-57b65f769284.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Small Airway Dysfunction and Type 2 Biomarkers Predict Exacerbations in Mild, Well-Controlled Asthma: A Retrospective Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eAsthma is a heterogeneous, chronic inflammatory airway disease that affects more than 350\u0026nbsp;million people worldwide and imposes a substantial clinical and economic burden[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although inhaled corticosteroids (ICS) and long-acting β₂-agonists (LABA) have markedly improved symptom control for most patients, 30\u0026ndash;40% still experience recurrent acute exacerbations, which impair quality of life and increase mortality[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventional asthma assessment has focused on routine spirometric indices; however, growing evidence indicates that SAD\u0026mdash;involving airways\u0026thinsp;\u0026lt;\u0026thinsp;2 mm in diameter\u0026mdash;plays a critical yet under-recognized role in asthma pathophysiology. Even among patients with normal spirometry, the prevalence of SAD can reach 50\u0026ndash;60%, and it is significantly associated with poor symptom control and increased risk of exacerbations[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent studies show that, in mild, well-controlled asthma, the presence of SAD is linked to a substantially higher risk of exacerbations, representing an overlooked high-risk phenotype[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, blood eosinophil count and FeNO\u0026mdash;biomarkers of type-2 (T2) inflammation\u0026mdash;provide prognostic information for predicting asthma attacks[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. FeNO, a noninvasive marker of Th2-type airway inflammation, is associated with lower lung function and a higher risk of future exacerbations; its predictive performance improves when combined with other indicators[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Approximately 75% of patients receiving anti-IL-5/IL-5Rα therapy exhibit both elevated blood eosinophils (\u0026ge;\u0026thinsp;300 cells/\u0026micro;L) and FeNO (\u0026ge;\u0026thinsp;25 ppb)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the interplay between SAD and inflammatory biomarkers\u0026mdash;and their combined prognostic value for clinical outcomes\u0026mdash;remains incompletely understood. The 2025 GINA strategy revises management of mild asthma, emphasizing that consistent anti-inflammatory therapy is needed even for mild symptoms[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Accordingly, identifying high-risk subgroups within mild asthma is essential for precision care and optimal resource allocation.\u003c/p\u003e \u003cp\u003eData remain scarce regarding the prevalence and clinical features of SAD\u0026mdash;and its relationship to exacerbations\u0026mdash;specifically among patients with mild, well-controlled asthma. Prior work has focused predominantly on moderate-to-severe or poorly controlled disease, with limited systematic evaluation of patients who are well controlled yet potentially at risk. Heterogeneity in the definition of SAD and limited prospective validation further constrain clinical translation.\u003c/p\u003e \u003cp\u003eWe therefore aimed to (i) determine the prevalence and clinical features of SAD and T2 biomarkers in mild, well-controlled asthma; (ii) evaluate their independent associations with acute exacerbations; and (iii) assess the incremental discrimination gained by combining small-airway indices with T2 biomarkers for risk stratification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e We conducted a single-centre retrospective cohort of adults with physician-diagnosed asthma who attended the Department of Respiratory and Critical Care Medicine at Henan Provincial People\u0026rsquo;s Hospital between January 2022 and March 2024. Asthma diagnosis and control were determined according to GINA criteria; control status over the preceding four weeks was categorized as well-controlled, partly controlled, or uncontrolled based on daytime and nocturnal symptoms, activity limitation, and SABA use. Mild asthma was defined as GINA steps 1\u0026ndash;2 (low-dose ICS or ICS/LABA)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In total, 250 de-identified cases were extracted from an existing database. The protocol was approved by the institutional review board and adhered to the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e comprised: (1) Chinese citizens aged 18\u0026ndash;75 years; (2) a diagnosis of asthma by a respiratory specialist according to the Global Initiative for Asthma (GINA) strategy, with typical symptoms and signs and objective evidence of variable airflow limitation; (3) availability of complete clinical data, including spirometry and biomarker measurements; and (4) provision of written informed consent and voluntary participation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion criteria\u003c/b\u003e were any of the following: (1) coexisting active respiratory diseases (e.g., chronic obstructive pulmonary disease, bronchiectasis, or interstitial lung disease); (2) respiratory tract infection within the preceding 4 weeks; (3) current use of medications likely to materially affect lung function or inflammatory biomarkers (e.g., systemic corticosteroids); (4) pregnancy or lactation; (5) severe cardiovascular disease, malignancy, or other systemic disorders; or (6) incomplete clinical data or poor compliance with testing procedures.\u003c/p\u003e \u003cp\u003eSpirometry and small-airway assessments were performed using MasterScreen\u0026reg; following ATS/ERS recommendations and GLI reference values [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. SAD was defined as at least two of three indices\u0026mdash;FEF50%, FEF75%, and FEF25\u0026ndash;75%\u0026mdash;being \u0026lt;\u0026thinsp;65% of predicted[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], in the setting of otherwise normal conventional ventilatory measures; patients were classified into SAD and non-SAD groups accordingly. Biomarkers of T2 inflammation included blood eosinophil counts (Sysmex XN-9100) and FeNO (NIOX VERO\u0026reg;) measured per ATS/ERS guidance[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Exacerbations were defined, consistent with GINA[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], as progressive worsening of respiratory symptoms and lung function requiring additional reliever therapy or treatment escalation (e.g., increased ICS or oral corticosteroids) or unscheduled medical care.Covariates included age, sex, body-mass index, allergy history, asthma duration, and smoking status (ever/current defined as \u0026gt;\u0026thinsp;100 lifetime cigarettes).\u003c/p\u003e \u003cp\u003eAnalyses were conducted in R (v4.3.2) with a two-sided α\u0026thinsp;=\u0026thinsp;0.05. Descriptive statistics were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) and counts (percentages). Between-group comparisons used independent-samples \u003cem\u003et\u003c/em\u003e-tests or Mann\u0026ndash;Whitney U tests for continuous variables and χ\u0026sup2; or Fisher\u0026rsquo;s exact tests for categorical variables. Multivariable logistic regression evaluated independent associations of SAD and inflammatory markers with exacerbations, reporting adjusted odds ratios (aOR), 95% confidence intervals, and \u003cem\u003ep\u003c/em\u003e values; model fit was assessed with the Hosmer\u0026ndash;Lemeshow test, and multicollinearity with variance-inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5). Discriminative performance was assessed by receiver-operating-characteristic (ROC) curve analysis of single markers (FEF25\u0026ndash;75% predicted, FEV1% predicted, FEV1/FVC, FeNO, and blood eosinophils) and combined models (inflammation\u0026thinsp;+\u0026thinsp;lung function); the area under the curve (AUC) was calculated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf 250 adults with mild, well-controlled asthma, 101 (40.4%) had SAD;(see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were no significant between-group differences in demographics: age, disease duration, sex, and BMI were comparable between the SAD and non-SAD groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Pulmonary function indices were significantly lower in the SAD group; for example, the median FVC% predicted was 101.5% versus 104.6% in the non-SAD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Similarly, FEV1% predicted (84.7% vs 99.8%), FEV1/FVC (72.8% vs 77.9%), and FEV1/FVC% predicted (87.2% vs 96.3%) were all lower in the SAD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding inflammatory markers, peripheral blood eosinophil counts were higher in the SAD group (median 0.33 vs 0.27\u0026times;10⁹/L; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). FeNO also tended to be higher in the SAD group: although the difference in median FeNO did not reach significance, the proportion with FeNO\u0026thinsp;\u0026ge;\u0026thinsp;25 ppb was significantly greater in the SAD group than in the non-SAD group (46.5% vs 32.2%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). In addition, a history of allergy was more frequent in the SAD group than in the non-SAD group (36.6% vs 25.5%), a difference that approached statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.081). During follow-up, a significantly higher proportion of patients in the SAD group experienced acute exacerbations than in the non-SAD group (73.3% vs 32.9%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and pulmonary function characteristics of patients with mild, well-controlled asthma with vs without SAD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-SAD (n\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSAD (n\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.00 (21.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.00 (21.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.78 (5.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.89 (6.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 (22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 (22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeNO, ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.00 (19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.00 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104.60 (14.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101.50 (18.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.80 (16.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.70 (13.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.87 (6.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.82 (6.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.30 (9.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.20 (11.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood eosinophils, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex \u0026mdash; male / female, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (37.6) / 93 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (42.6) / 58 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllergy history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood eosinophils\u0026thinsp;\u0026ge;\u0026thinsp;300 cells/\u0026micro;L, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeNO\u0026thinsp;\u0026ge;\u0026thinsp;25 ppb, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute exacerbations, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePatients were stratified by the occurrence of acute exacerbations: 123 (49.2%) experienced at least one event and were compared with 127 without exacerbations (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The groups were similar in age, sex, disease duration, and smoking history. Median BMI was significantly lower in the exacerbation group than in the non-exacerbation group (25.25 vs 26.37 kg/m\u0026sup2;; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Pulmonary function was consistently poorer in the exacerbation group, with lower FVC% predicted, FEV1% predicted, FEV1/FVC, and FEV1/FVC% predicted (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Small-airway impairment was more pronounced: FEF25\u0026ndash;75% predicted and FEF50% predicted were significantly reduced (50.8% vs 64.0% and 58.5% vs 66.2%, respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas FEF25% predicted did not differ (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.926). A history of allergy was more frequent in the exacerbation group (38.2% vs 22.0%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Elevated blood eosinophils (\u0026ge;\u0026thinsp;300 cells/\u0026micro;L) were also more common (63.4% vs 36.2%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the proportion with FeNO\u0026thinsp;\u0026ge;\u0026thinsp;25 ppb was similar between groups (40.7% vs 35.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.472), the median FeNO level was slightly higher in the exacerbation group (21 vs 19 ppb; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). The prevalence of SAD reached 60.2% in the exacerbation group compared with 21.3% in the non-exacerbation group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting the strong link between small-airway dysfunction and exacerbation risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical, inflammatory, and small-airway characteristics by exacerbation status in mild, well-controlled asthma\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo exacerbation (n\u0026thinsp;=\u0026thinsp;127)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExacerbation (n\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.00 (19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.00 (22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.37 (4.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.25 (6.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration, months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00 (34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00 (22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeNO, ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.00 (17.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.00 (62.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105.90 (12.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102.00 (16.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.50 (17.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.00 (19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.41 (6.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.02 (10.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.90 (8.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.00 (16.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEF25\u0026ndash;75% predicted, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.00 (31.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.80 (35.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEF50% predicted, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.20 (16.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.50 (34.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEF25% predicted, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.20 (23.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.40 (34.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex \u0026mdash; male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllergy history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood eosinophils\u0026thinsp;\u0026ge;\u0026thinsp;300 cells/\u0026micro;L, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeNO\u0026thinsp;\u0026ge;\u0026thinsp;25 ppb, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn univariable analyses, SAD, blood eosinophils\u0026thinsp;\u0026ge;\u0026thinsp;300 cells/\u0026micro;L, airflow obstruction (FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70%), and lower mid-expiratory flows were associated with higher odds of exacerbation, whereas BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2; was inversely associated; FeNO\u0026thinsp;\u0026ge;\u0026thinsp;25 ppb showed no association. In the multivariable model adjusting for age, sex, allergy history, and smoking status, SAD remained the strongest predictor (adjusted odds ratio [aOR] 17.91, 95% CI 7.03\u0026ndash;49.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Blood eosinophils\u0026thinsp;\u0026ge;\u0026thinsp;300 cells/\u0026micro;L were independently associated with exacerbations (aOR 4.97, 95% CI 2.54\u0026ndash;10.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70% (aOR 3.84, 95% CI 1.49\u0026ndash;10.38, p\u0026thinsp;=\u0026thinsp;0.006). Higher BMI was protective (aOR 0.41, 95% CI 0.20\u0026ndash;0.79, p\u0026thinsp;=\u0026thinsp;0.009). A higher FEF25\u0026ndash;75% predicted was associated with lower risk (aOR 0.98 per 1%-predicted increase, 95% CI 0.96\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.022). FeNO\u0026thinsp;\u0026ge;\u0026thinsp;25 ppb (aOR 0.67, 95% CI 0.33\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.237), FEF75% predicted (aOR 1.03, 95% CI 0.99\u0026ndash;1.06, p\u0026thinsp;=\u0026thinsp;0.083), and FEF50% predicted (aOR 1.02, 95% CI 0.99\u0026ndash;1.05, p\u0026thinsp;=\u0026thinsp;0.196) were not independently associated with exacerbations.Overall, these findings identify a high-risk phenotype characterized by SAD, eosinophilic inflammation, and airflow obstruction, while indicating modest protective effects of higher BMI and better small-airway flow.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression of factors associated with acute exacerbations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.33\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.20\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAD (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.59 (3.24\u0026ndash;9.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.91 (7.03\u0026ndash;49.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeNO\u0026thinsp;\u0026ge;\u0026thinsp;25 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25 (0.75\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.33\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood eosinophils\u0026thinsp;\u0026ge;\u0026thinsp;300 cells/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.55 (2.04\u0026ndash;6.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.97 (2.54\u0026ndash;10.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.57 (1.37\u0026ndash;4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.84 (1.49\u0026ndash;10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEF25\u0026ndash;75% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.96\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEF75% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.93\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.99\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEF50% predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.99\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Adjusted models control for age, sex, allergy history, and smoking history. For categorical predictors, the reference categories are: BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2;; no SAD; FeNO\u0026thinsp;\u0026lt;\u0026thinsp;25 ppb; blood eosinophils\u0026thinsp;\u0026lt;\u0026thinsp;300 cells/\u0026micro;L; FEV1/FVC\u0026thinsp;\u0026ge;\u0026thinsp;70%.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eROC curve analyses revealed substantial heterogeneity in the performance of single markers. Blood eosinophil count achieved the highest AUC (0.750), indicating better discrimination, whereas FeNO showed the lowest AUC (0.579), only slightly above 0.5 and therefore of limited predictive value (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The AUCs for other lung-function indices ranged from 0.587 to 0.653, indicating modest discrimination at best (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Combined models outperformed single markers, underscoring the incremental value of integrating inflammatory biomarkers with lung-function measures. In particular, combinations that included blood eosinophils markedly improved model performance (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The eosinophils\u0026thinsp;+\u0026thinsp;FEF25\u0026ndash;75% predicted model produced the highest AUC (0.769), surpassing eosinophils alone (0.750) and FEF25\u0026ndash;75% predicted alone (0.653). Pairing eosinophils with FEV1% predicted or with FEV1/FVC also improved discrimination (AUCs 0.754 and 0.757 vs 0.617 and 0.587, respectively). Although FeNO-based combinations (e.g., FeNO\u0026thinsp;+\u0026thinsp;FEF25\u0026ndash;75% predicted, AUC 0.716) performed better than FeNO alone, they remained less discriminative than the corresponding eosinophil-based models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn patients with mild, well-controlled asthma, we investigated the clinical significance of SAD and T2 inflammatory biomarkers\u0026mdash;blood eosinophil count and FeNO\u0026mdash;and their associations with acute exacerbations. We found that, in this population traditionally considered at low risk, SAD was common (~\u0026thinsp;40% of patients) and was linked to a substantially higher subsequent exacerbation rate than in those without SAD. Elevated blood eosinophil counts were also associated with increased risk, and FeNO levels were modestly higher among patients who experienced exacerbations. To our knowledge, this is the first evidence that even mild, well-controlled asthma contains a high-risk phenotype prone to exacerbations; a combined evaluation of SAD and inflammatory biomarkers can identify this subgroup and has important implications for individualized management.\u003c/p\u003e \u003cp\u003eOur findings suggest that SAD may represent a latent \u0026ldquo;danger signal\u0026rdquo; in patients with mild asthma. As the distal \u0026ldquo;end units\u0026rdquo; of the airway tree, the small airways can harbor inflammation and remodeling even when symptoms appear well controlled[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Once this \u0026ldquo;silent zone\u0026rdquo; is involved, airway reserve declines, making acute airflow limitation more likely in the presence of triggers (e.g., infections or allergens) and helping to explain why patients with SAD can experience severe deterioration after exposure despite otherwise mild day-to-day symptoms[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, our data indicate that higher blood eosinophil counts are closely associated with acute exacerbations, consistent with the paradigm that eosinophilic airway inflammation drives worsening: peripheral eosinophilia often reflects active T2 inflammation in the airways, promoting mucosal edema and hyperresponsiveness and thereby increasing attack risk[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. By contrast, FeNO levels were only slightly higher in the exacerbation group, likely reflecting suppression by the widespread use of inhaled corticosteroids. Moreover, FeNO mainly indexes epithelial T2 inflammation, whereas eosinophil counts partly reflect the systemically mobilizable pool of inflammatory cells; these signals may therefore be asynchronous in mild asthma[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Notably, combining small-airway indices with inflammatory markers provided superior risk prediction: single metrics had limited sensitivity and specificity, whereas the coexistence of small-airway impairment with eosinophilia or elevated FeNO was associated with a substantially higher risk of exacerbation. This incremental effect points to interplay between distal airway damage and persistent T2 inflammation, with synergistic effects that increase susceptibility to triggers. Overall, we propose a mechanistic model in which abnormalities of small-airway structure and function create a \u0026ldquo;fragile\u0026rdquo; state and active eosinophilic inflammation supplies the \u0026ldquo;spark,\u0026rdquo; together markedly elevating the likelihood of acute exacerbations.\u003c/p\u003e \u003cp\u003eNumerous recent high-quality studies likewise underscore the pivotal role of small-airway pathology in asthma; our findings are concordant with\u0026mdash;and extend\u0026mdash;this literature. In a multicentre analysis of mild asthma, Galant and colleagues reported that ~\u0026thinsp;36% of well-controlled patients had SAD and that SAD independently predicted exacerbations over the subsequent year; after adjustment for age, smoking, baseline FEV1, and other covariates, its presence more than doubled the risk of exacerbations[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although our study differed in population selection and in the operational definition of SAD, we similarly observed small-airway impairment in approximately one-third to one-half of patients with mild disease and confirmed the predictive value of SAD for exacerbations, supporting the notion that SAD represents an under-recognized high-risk phenotype in mild asthma[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, the foundational ATLANTIS report found that the overall prevalence of SAD reached 50\u0026ndash;60% across asthma severity strata and exceeded 90% when broader criteria integrating multiple diagnostic modalities were used[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although these figures exceed our observed prevalence (~\u0026thinsp;40%), the difference likely reflects variation in SAD definitions and enrollment criteria and indirectly supports our conclusion that distal airway involvement is widespread in asthma. Consistent with this, our results align with the impulse oscillometry (IOS)\u0026ndash;based study by Gao and colleagues, which documented a high prevalence of SAD (~\u0026thinsp;75%) and, via structural equation modeling, identified SAD as a central determinant of poor asthma control and diminished quality of life[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Taken together, these studies underscore that, regardless of how mild asthma may appear clinically, \u0026ldquo;silent\u0026rdquo; disease in the distal airways can covertly drive deterioration. Our work advances this literature by concentrating on mild asthma and incorporating a combined assessment of inflammatory biomarkers, thereby expanding the means to identify high-risk phenotypes\u0026mdash;an important innovation of the present study.\u003c/p\u003e \u003cp\u003eWith respect to inflammatory biomarkers, our findings both corroborate and extend prior literature. A substantial body of evidence indicates that peripheral blood eosinophil count and FeNO are important predictors of exacerbation risk in asthma[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Using a UK primary-care database, Price[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]reported that patients with either high FeNO (\u0026ge;\u0026thinsp;50 ppb) or eosinophilia (\u0026ge;\u0026thinsp;300 cells/\u0026micro;L) had increased annual exacerbation rates, and that concomitant elevations in both were associated with a markedly greater rate\u0026mdash;approximately 3.67 times that of patients without either elevation. In line with these observations, we found that eosinophilia combined with SAD conferred a far higher likelihood of acute exacerbations than either risk factor alone. This \u0026ldquo;SAD\u0026thinsp;+\u0026thinsp;eosinophilia\u0026rdquo; phenotype may represent a novel high-risk subtype, whose recognition has tangible implications for individualized clinical management. Regarding FeNO, a subgroup analysis by Pavord et al. in a trial of patients with mild asthma similarly found no significant predictive value of baseline FeNO for exacerbations[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Nevertheless, FeNO is a sensitive marker of eosinophilic airway inflammation and retains clear clinical relevance: multiple randomized trials and meta-analyses indicate that FeNO-guided therapy can reduce exacerbations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and in moderate-to-severe asthma, higher FeNO levels often signal poorer control and more frequent attacks[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this context, we speculate that the lack of a significant difference between groups in the proportion with elevated FeNO (\u0026ge;\u0026thinsp;25 ppb; 40.7% vs 35.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.472) may reflect the predominance of well-controlled patients receiving low-dose inhaled corticosteroids. Notably, the proportion with elevated FeNO (\u0026ge;\u0026thinsp;25 ppb) was significantly greater in the SAD group than in the non-SAD group (46.5% vs 32.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), indicating that small-airway impairment in mild asthma often coexists with residual T2 inflammation. This is consistent with the findings reported by Gao et al. among patients with asthma who experienced exacerbations: older age, obesity, and elevated FeNO were independent predictors of SAD[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Taken together, our study is, to our knowledge, the first to integrate small-airway functional indices with inflammatory biomarkers for exacerbation risk stratification, showing that the combined assessment outperforms any single marker. This multidimensional \u0026ldquo;physiology\u0026thinsp;+\u0026thinsp;inflammation\u0026rdquo; framework provides new insights into asthma and highlights the innovative nature of our work.\u003c/p\u003e \u003cp\u003eApproaching asthma through the prism of phenotypic heterogeneity, our study adds meaningful insight into risk characteristics among patients with mild disease and carries important academic value. Historically, research and management have focused on moderate-to-severe or difficult-to-treat asthma, while mildly symptomatic, well-controlled patients have received less attention. Yet the principle that mild does not equate to low risk is gaining consensus [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and our findings provide new empirical support. By situating the \u0026ldquo;SAD with high-inflammation\u0026rdquo; phenotype within the high-risk category, these results help refine the asthma phenotypic spectrum and prompt a re-examination of risk-management strategies for mild asthma.From a clinical perspective, evaluation in seemingly well-controlled patients should extend beyond large-airway indices such as FEV1 to encompass small-airway assessments\u0026mdash;MMEF/FEF25\u0026ndash;75% and IOS parameters\u0026mdash;as well as inflammatory biomarkers[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Incorporating these measures into routine follow-up may enable early identification of subgroups characterized by SAD and a high inflammatory burden and, in turn, justify targeted intensification of therapy. For pharmacologic management, extrafine-particle ICS that effectively reach the small airways can be prioritized; in mild asthma, use of such formulations has been associated with a markedly lower exacerbation rate than non-use (27.7% vs 55.3%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], supporting the notion of a small-airways treatable trait. For patients with prominent eosinophilic inflammation, periodic monitoring of FeNO and blood counts can inform ICS dose adjustment and help assess adherence [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; in highly eosinophilic phenotypes, early initiation of anti-IL-5 or other biologics may be warranted to prevent severe exacerbations[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Looking ahead, mobile health and remote-monitoring technologies\u0026mdash;enabling at-home exhaled NO or peak-flow measurements and sensor-based surveillance of respiratory resistance\u0026mdash;could provide the infrastructure for precise monitoring and individualized intervention in high-risk mild asthma.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it was a single-centre, retrospective analysis from one regional medical centre with a relatively limited sample size, raising the possibility of selection bias and constraining generalizability. Multicentre, large-scale prospective studies are therefore needed to confirm the robustness and broader applicability of our results. Second, SAD was assessed using ventilatory flow indices, which have modest sensitivity[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; compared with more sophisticated methods\u0026mdash;impulse oscillometry, multiple-breath nitrogen washout, or advanced lung imaging\u0026mdash;our approach may underestimate the true prevalence of SAD. Future work should adopt more sensitive small-airway assessments and develop standardized criteria for defining SAD.Third, inflammatory biomarkers were obtained only at baseline. Because blood eosinophil counts and FeNO fluctuate with seasonality, treatment adherence, and other factors, single time-point measurements may not fully capture risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Longitudinal monitoring could clarify the temporal coupling between biomarker variability and exacerbations\u0026mdash;for example, whether \u0026ldquo;FeNO surges\u0026rdquo; herald imminent attacks. Fourth, while we focused on two readily accessible T2 biomarkers, non-eosinophilic pathways may also contribute to risk; we did not evaluate sputum cytology or airway neutrophilia, which may have led to under-recognition of risk in non-T2\u0026ndash;predominant patients[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Future studies should incorporate broader inflammatory phenotyping to delineate the roles of distinct endotypes in mild-asthma exacerbations.Finally, despite all patients meeting criteria for mild, well-controlled disease, treatment regimens varied (as-needed ICS vs daily maintenance), and we did not perform in-depth stratification by regimen or adherence, which may confound risk attribution. Subsequent research should better control for baseline therapy and compare the predictive utility of SAD and inflammatory markers across treatment strategies. Notwithstanding these limitations, our conclusions remained robust in multivariable analyses, indicating that they do not fundamentally challenge our central hypothesis. Rather, they highlight clear directions for future work\u0026mdash;larger and more rigorously designed studies to validate and extend these findings. Overall, we adopt a cautiously optimistic stance: the conclusions appear sound but warrant confirmation in broader populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study shows that SAD and inflammatory phenotypes materially influence the risk of acute exacerbations in mild, well-controlled asthma. Even in disease traditionally considered \u0026ldquo;mild,\u0026rdquo; a high-risk subgroup is evident: concomitant small-airway impairment and persistent T2 inflammation define a phenotype prone to exacerbations. In keeping with evidence that clinical severity and acute risk do not invariably align, our integrated evaluation of physiological and inflammatory indices provides an incremental method for identifying high-risk patients. Collectively, these findings broaden the asthma phenotypic spectrum, highlight the centrality of the small airways in risk stratification, and offer practical guidance for more precise monitoring and intervention in patients with mild asthma.\u003c/p\u003e\n\u003ch3\u003eFuture directions\u003c/h3\u003e\n\u003cp\u003eLooking ahead, several avenues warrant focused investigation: (1) prospective longitudinal cohorts to validate the predictive performance of SAD and eosinophilic biomarkers for both first and recurrent exacerbations, and to quantify the clinical benefit of targeting these risks (e.g., intensifying small-airway\u0026ndash;directed therapy and anti-inflammatory treatment); (2) deployment of advanced functional imaging and biomarker technologies to clarify causal links and mechanisms between SAD and inflammatory imbalance, including the distal-airway pathways that precipitate acute attacks; (3) expansion of study populations across races, regions, and age groups\u0026mdash;pediatric and geriatric cohorts included\u0026mdash;to assess the generalizability of this high-risk phenotype; and (4) machine-learning\u0026ndash;enabled integration of small-airway indices, FeNO, hematologic counts, and clinical features to develop comprehensive, clinically useful prediction models for exacerbation risk in mild asthma. Collectively, these efforts will strengthen the emerging \u0026ldquo;physiology\u0026thinsp;+\u0026thinsp;inflammation\u0026rdquo; framework for asthma management and underpin precision medicine and comprehensive disease control. As this agenda advances, we anticipate substantial improvements in risk screening and individualized intervention for patients with mild asthma, with tangible reductions in acute exacerbations and better long-term outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSAD, small airway dysfunction; FeNO, fractional exhaled nitric oxide;BMI, body mass index; FEF25\u0026ndash;75, forced expiratory flow between 25% and 75% of FVC; FEF25, forced expiratory flow at 25% of FVC; FEF50, forced expiratory flow at 50% of FVC; FEF75, forced expiratory flow at 75% of FVC; FEV1/FVC, ratio of forced expiratory volume in one second to forced vital capacity; FEV1, forced expiratory volume in one second; FVC, forced vital capacity; SABA, short-acting \u0026beta;₂-agonist; ICS, inhaled corticosteroids; LABA, long-acting \u0026beta;₂-agonist. OR, odds ratio; CI, confidence interval; T2,type-2; BEC ,blood eosinophil count; IOS, impulse oscillometry;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate: \u003c/strong\u003eThe study was approved by the Medical Ethics Committee of Henan Provincial People\u0026rsquo;s Hospital(Approval Number: No. 2025-024), and every patient provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication: \u003c/strong\u003eNot applicable. This manuscript does not contain data from any individual person (including individual details, images or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials: \u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eThis study is supported by the Provincial\u0026ndash;Ministerial Co-construction Project of the Henan Medical Science and Technology Research Program (SBGJ202302003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e Guanhua Hou conceived and designed the study, Guanhua Hou drafted the manuscript, Wei Li assisted with the preparation of tables, and Yinan Xing and Zishuo Wang assisted with statistical analysis. Limin Zhao critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements: \u003c/strong\u003eWe would like to thank Hongjian ZHao for his suggestions when we had difficulties in conducting this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGINA Strategy Report. Global Initiative for Asthma - GINA. https://ginasthma.org/2025-gina-strategy-report/. Accessed 19 Aug 2025.\u003c/li\u003e\n\u003cli\u003ePorsbjerg C, Mel\u0026eacute;n E, Lehtim\u0026auml;ki L, Shaw D. Asthma. The Lancet. 2023;401:858\u0026ndash;73. https://doi.org/10.1016/S0140-6736(22)02125-0.\u003c/li\u003e\n\u003cli\u003ePostma DS, Brightling C, Baldi S, Van den Berge M, Fabbri LM, Gagnatelli A, et al. Exploring the relevance and extent of small airways dysfunction in asthma (ATLANTIS): baseline data from a prospective cohort study. 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J Allergy Clin Immunol Pract. 2024;12:2705-2716.e6. https://doi.org/10.1016/j.jaip.2024.05.034.\u003c/li\u003e\n\u003cli\u003e程丽, 蒋毅, 岳倩如, 安若丽, 马红霞. 呼出气一氧化氮和外周血嗜酸粒细胞计数对嗜酸粒细胞型哮喘的诊断价值. 中华全科医师杂志. 2020;19:502\u0026ndash;6. https://doi.org/10.3760/cma.j.cn114798-20200410-00437.\u003c/li\u003e\n\u003cli\u003eMurugesan N, Saxena D, Dileep A, Adrish M, Hanania NA. Update on the Role of FeNO in Asthma Management. Diagnostics (Basel). 2023;13:1428. https://doi.org/10.3390/diagnostics13081428.\u003c/li\u003e\n\u003cli\u003ePham DD, Lee J-H, Kwon H-S, Song W-J, Cho YS, Kim H, et al. Concurrent Blood Eosinophils and FeNO as Biological Therapy Indicators in Severe Asthma: Findings From the Precision Medicine Intervention in Severe Asthma Study. J Allergy Clin Immunol Pract. 2025;13:1681-1689.e2. https://doi.org/10.1016/j.jaip.2025.03.011.\u003c/li\u003e\n\u003cli\u003eGolam SM, Janson C, Beasley R, FitzGerald JM, Harrison T, Chipps B, et al. The burden of mild asthma: Clinical burden and healthcare resource utilisation in the NOVELTY study. Respir Med. 2022;200:106863. https://doi.org/10.1016/j.rmed.2022.106863.\u003c/li\u003e\n\u003cli\u003eStanojevic S, Kaminsky DA, Miller MR, Thompson B, Aliverti A, Barjaktarevic I, et al. ERS/ATS technical standard on interpretive strategies for routine lung function tests. Eur Respir J. 2022;60:2101499. https://doi.org/10.1183/13993003.01499-2021.\u003c/li\u003e\n\u003cli\u003eHall GL, Filipow N, Ruppel G, Okitika T, Thompson B, Kirkby J, et al. Official ERS technical standard: Global Lung Function Initiative reference values for static lung volumes in individuals of European ancestry. Eur Respir J. 2021;57:2000289. https://doi.org/10.1183/13993003.00289-2020.\u003c/li\u003e\n\u003cli\u003eBhakta NR, McGowan A, Ramsey KA, Borg B, Kivastik J, Knight SL, et al. European Respiratory Society/American Thoracic Society technical statement: standardisation of the measurement of lung volumes, 2023 update. Eur Respir J. 2023;62:2201519. https://doi.org/10.1183/13993003.01519-2022.\u003c/li\u003e\n\u003cli\u003e中华医学会, 中华医学会杂志社, 中华医学会全科医学分会, 中华医学会呼吸病学分会肺功能学组, 中华医学会《中华全科医师杂志》编辑委员会, 中国呼吸系统疾病基层诊疗与管理指南制订专家组. 中国常规肺功能检查基层指南（2024年）. 中华全科医师杂志. 2025;24:121\u0026ndash;37. https://doi.org/10.3760/cma.j.cn114798-20240618-00555.\u003c/li\u003e\n\u003cli\u003eDweik RA, Boggs PB, Erzurum SC, Irvin CG, Leigh MW, Lundberg JO, et al. An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FENO) for clinical applications. Am J Respir Crit Care Med. 2011;184:602\u0026ndash;15. https://doi.org/10.1164/rccm.9120-11ST.\u003c/li\u003e\n\u003cli\u003eAmerican Thoracic Society, European Respiratory Society. ATS/ERS recommendations for standardized procedures for the online and offline measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide, 2005. Am J Respir Crit Care Med. 2005;171:912\u0026ndash;30. https://doi.org/10.1164/rccm.200406-710ST.\u003c/li\u003e\n\u003cli\u003eCottini M, Lombardi C, Passalacqua G, Bagnasco D, Berti A, Comberiati P, et al. Small Airways: The \u0026ldquo;Silent Zone\u0026rdquo; of 2021 GINA Report? Front Med (Lausanne). 2022;9:884679. https://doi.org/10.3389/fmed.2022.884679.\u003c/li\u003e\n\u003cli\u003eKaplan A. The Myth of Mild: Severe Exacerbations in Mild Asthma: An Underappreciated, but Preventable Problem. Adv Ther. 2021;38:1369\u0026ndash;81. https://doi.org/10.1007/s12325-020-01598-2.\u003c/li\u003e\n\u003cli\u003ePrice DB, Bosnic-Anticevich S, Pavord ID, Roche N, Halpin DMG, Bjermer L, et al. Association of elevated fractional exhaled nitric oxide concentration and blood eosinophil count with severe asthma exacerbations. Clinical and Translational Allergy. 2019;9:41. https://doi.org/10.1186/s13601-019-0282-7.\u003c/li\u003e\n\u003cli\u003eMarcos MC, Cisneros Serrano C. What is the added value of FeNO as T2 biomarker? Front Allergy. 2022;3:957106. https://doi.org/10.3389/falgy.2022.957106.\u003c/li\u003e\n\u003cli\u003eGalant SP, Kuks PJM, Kole TM, Kraft M, Siddiqui S, Fabbri LM, et al. Assessment of the role of small airway dysfunction in relation to exacerbation risk in patients with well controlled asthma (ATLANTIS): an observational study. Lancet Respir Med. 2025;:S2213-2600(25)00283-8. https://doi.org/10.1016/S2213-2600(25)00283-8.\u003c/li\u003e\n\u003cli\u003eLindsley A, Lugogo N, Reeh K, Spahn J, Parnes J. Asthma Biologics Across the T2 Spectrum of Inflammation in Severe Asthma: Biomarkers and Mechanism of Action. JAA. 2025;Volume 18:33\u0026ndash;57. https://doi.org/10.2147/JAA.S496630.\u003c/li\u003e\n\u003cli\u003eBacharier LB, Pavord ID, Maspero JF, Jackson DJ, Fiocchi AG, Mao X, et al. Blood eosinophils and fractional exhaled nitric oxide are prognostic and predictive biomarkers in childhood asthma. J Allergy Clin Immunol. 2024;154:101\u0026ndash;10. https://doi.org/10.1016/j.jaci.2023.09.044.\u003c/li\u003e\n\u003cli\u003ePavord ID, Holliday M, Reddel HK, Braithwaite I, Ebmeier S, Hancox RJ, et al. Predictive value of blood eosinophils and exhaled nitric oxide in adults with mild asthma: a prespecified subgroup analysis of an open-label, parallel-group, randomised controlled trial. Lancet Respir Med. 2020;8:671\u0026ndash;80. https://doi.org/10.1016/S2213-2600(20)30053-9.\u003c/li\u003e\n\u003cli\u003eKorevaar DA, Damen JA, Heus P, Moen MJ, Spijker R, van Veen IH, et al. Effectiveness of FeNO-guided treatment in adult asthma patients: A systematic review and meta-analysis. Clin Exp Allergy. 2023;53:798\u0026ndash;808. https://doi.org/10.1111/cea.14359.\u003c/li\u003e\n\u003cli\u003eBusse WW, Wenzel SE, Casale TB, FitzGerald JM, Rice MS, Daizadeh N, et al. Baseline FeNO as a prognostic biomarker for subsequent severe asthma exacerbations in patients with uncontrolled, moderate-to-severe asthma receiving placebo in the LIBERTY ASTHMA QUEST study: a post-hoc analysis. Lancet Respir Med. 2021;9:1165\u0026ndash;73. https://doi.org/10.1016/S2213-2600(21)00124-7.\u003c/li\u003e\n\u003cli\u003eBeasley R, Holliday M, Reddel HK, Braithwaite I, Ebmeier S, Hancox RJ, et al. Controlled Trial of Budesonide-Formoterol as Needed for Mild Asthma. N Engl J Med. 2019;380:2020\u0026ndash;30. https://doi.org/10.1056/NEJMoa1901963.\u003c/li\u003e\n\u003cli\u003eBeinart D, Goh ESY, Boardman G, Chung LP. Small airway dysfunction measured by impulse oscillometry is associated with exacerbations and poor symptom control in patients with asthma treated in a tertiary hospital subspecialist airways disease clinic. Front Allergy. 2024;5:1403894. https://doi.org/10.3389/falgy.2024.1403894.\u003c/li\u003e\n\u003cli\u003ePetsky HL, Cates CJ, Kew KM, Chang AB. Tailoring asthma treatment on eosinophilic markers (exhaled nitric oxide or sputum eosinophils): a systematic review and meta-analysis. Thorax. 2018;73:1110\u0026ndash;9. https://doi.org/10.1136/thoraxjnl-2018-211540.\u003c/li\u003e\n\u003cli\u003eFarne HA, Wilson A, Milan S, Banchoff E, Yang F, Powell CV. Anti-IL-5 therapies for asthma. Cochrane Database Syst Rev. 2022;7:CD010834. https://doi.org/10.1002/14651858.CD010834.pub4.\u003c/li\u003e\n\u003cli\u003eHudey SN, Ledford DK, Cardet JC. Mechanisms of non-type 2 asthma. Curr Opin Immunol. 2020;66:123\u0026ndash;8. https://doi.org/10.1016/j.coi.2020.10.002.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"small airway dysfunction, type-2 inflammation, mild asthma, acute exacerbation, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7898079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7898079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSmall airway dysfunction (SAD) plays a pivotal but often overlooked role in asthma pathophysiology. Its contribution to exacerbation risk among patients with mild, well-controlled asthma remains unclear.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to assess the prevalence and clinical significance of SAD and type 2 inflammation biomarkers in mild, well-controlled asthma, and to determine their independent and combined predictive value for acute exacerbations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted in 250 adults with mild, well-controlled asthma. Lung function indices, blood eosinophil counts, and fractional exhaled nitric oxide (FeNO) levels were analyzed. Logistic regression and receiver operating characteristic (ROC) analyses were performed to evaluate predictors of exacerbations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSAD was identified in 40.4% of patients and was strongly associated with a higher exacerbation rate (73.3% vs. 32.9%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SAD (adjusted OR\u0026thinsp;=\u0026thinsp;17.91, 95% CI 7.03\u0026ndash;49.39) and elevated eosinophils (aOR\u0026thinsp;=\u0026thinsp;4.97, 95% CI 2.54\u0026ndash;10.12) were independent predictors of exacerbations. Combined models incorporating FEF25\u0026ndash;75%pred and eosinophil count achieved the highest discriminative performance (AUC\u0026thinsp;=\u0026thinsp;0.769), surpassing any single biomarker.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEven in mild, well-controlled asthma, SAD and type 2 inflammation markers identify a high-risk phenotype susceptible to exacerbations. Integrating small airway function with inflammatory biomarkers enhances risk stratification, supporting precision monitoring and tailored therapeutic strategies in asthma management.\u003c/p\u003e","manuscriptTitle":"Small Airway Dysfunction and Type 2 Biomarkers Predict Exacerbations in Mild, Well-Controlled Asthma: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 10:44:04","doi":"10.21203/rs.3.rs-7898079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T07:44:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T03:15:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-11T23:42:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19232389974100467889944968856478117769","date":"2026-01-09T13:58:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306258994698146757882094953403553859656","date":"2026-01-08T23:39:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-29T10:02:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-19T09:10:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-22T06:53:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-22T06:52:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-10-19T10:57:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5dc46c4b-7f2c-4d2b-b10e-80f8af23e1af","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T12:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 10:44:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7898079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7898079","identity":"rs-7898079","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}