Impulse oscillometry in assessing small airway function in asthma

Impulse oscillometry in assessing small airway function in asthma | 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 Impulse oscillometry in assessing small airway function in asthma Wenwen Yu, Xiuting Huang, Yunlei Li, Xie Zhang, Legui Zheng, Jincong Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6011946/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Small airway dysfunction (SAD) can occur in asthma patients with normal spirometry, impacting symptoms and disease control. Small airway dysfunction is linked to more severe symptoms and worse spirometry, poor asthma control, nocturnal asthma, and exercise-induced asthma.Impulse oscillometry (IOS) assesses small airway function through a simple, noninvasive method that employs the forced oscillation technique. It requires minimal patient cooperation and is suitable for use in both children and adults. This method can assess obstructions in the large and small peripheral airways and has been used to measure bronchodilator response and bronchoprovocation testing. However, its utility in patients with SAD requires further investigation. Methods This study included 109 asthma patients with normal spirometry, who were divided into SAD (R5-R20 > 0.1 kPa·L⁻¹·s⁻¹) and normal groups (R5-R20 < 0.1 kPa·L⁻¹·s⁻¹) based on IOS results. Clinical data, IOS, and spirometry findings were compared between the two groups. Forty-three SAD patients were re-assessed after 3 months of treatment with standard inhaled corticosteroids and long-acting β₂ agonists. Findings SAD was identified in 56.9% of asthma patients with normal spirometry. The SAD group exhibited higher blood eosinophil counts (EOS), Z5%pred, R5%pred, R5-R20, and Fres (P<0.05), while FEV1%pred, MMEF%pred, MEF75%pred, MEF50%pred, MEF25%pred, and X5 were lower (P<0.05). Correlation analysis revealed that high R5-R20 correlated with higher Fres (ρ=0.812), R5%pred (ρ=0.637), and lower X5 (ρ=-0.643), MMEF%pred (ρ=-0.441), and FEV1%pred (ρ=-0.359). The AUCs for Fres, X5, MMEF%pred, and FEV1%pred were 0.940, 0.790, 0.702, and 0.684, respectively, with Fres demonstrating the highest diagnostic value for SAD (cutoff 14.385, 95% CI: 0.897-0.983, sensitivity 95.2%, specificity 83.0%). In the SAD group, significant reductions were observed in Z5%pred, R5%pred, R5-R20, Fres, and X5 after treatment (P<0.05). Interpretation Nearly 50% of asthma patients with normal spirometry exhibit small airway dysfunction, which can be detected using IOS. IOS provides higher sensitivity and specificity than spirometry for diagnosing SAD, with Fres being the most valuable parameter. Furthermore, IOS can effectively monitor asthma control. Bronchial asthma Impulse oscillometry Spirometry Small airway function Figures Figure 1 Figure 2 Introduction Bronchial asthma is a heterogeneous disease characterized by chronic airway inflammation and reversible expiratory airflow limitation, involving various inflammatory cells and mediators [ 1 ]. Approximately 3.7% of asthma patients progress to severe asthma, which is associated with increasing prevalence and poor control [ 2 ]. Traditionally, asthma was believed to primarily affect large airways; however, recent research indicates that 50–60% of asthma patients also exhibit small airway dysfunction [ 3 ], which is critical for understanding inflammation, airway hyperreactivity, and remodeling [ 4 ]. Small airway dysfunction is linked to more severe symptoms, poor asthma control, nocturnal asthma, and exercise-induced asthma [ 5 – 6 ], Research by Chan R et al. [ 7 ] found Patients with combined impairment of FVC and AX had significantly worse asthma control as higher ACQ, more severe exacerbations requiring OCS and worse spirometry than those with impaired FVC but preserved AX. Spirometry is widely used in asthma diagnosis, but its sensitivity for detecting small airway dysfunction is limited. Although a reduction in maximum mid-expiratory flow (MMEF) can indicate small airway obstruction, it requires forced exhalation and is subject to high variability [ 8 – 10 ]. In contrast, impulse oscillometry (IOS) assesses airway function during normal breathing and demonstrates greater sensitivity and specificity for small airway dysfunction, making it valuable for early detection and intervention [ 11 , 12 ]. Despite these advantages, IOS is not widely implemented in China, and research on its role in assessing small airways remains limited. The R5-R20 parameter of IOS reflects small airway resistance, with values exceeding 0.1 kPa·L⁻¹·s⁻¹ considered indicative of dysfunction [ 10 , 13 ]. This study aims to evaluate the utility of IOS in assessing small airway function and asthma control and to explore whether it can supplement or replace conventional spirometry in diagnosing small airway dysfunction. Methods Study design and participants This study included 109 asthma patients with normal spirometry evaluated at the respiratory departments of Yueqing People's Hospital and the Second Affiliated Hospital of Wenzhou Medical University between August 2018 and October 2022. The cohort comprised 56 males and 53 females, with a mean age of 40.83 ± 13.48 years. The patients were stratified into two groups based on R5-R20 values: 62 patients with small airway dysfunction (SAD group, R5-R20 > 0.1 kPa•L⁻¹•s⁻¹) and 47 patients with normal small airway function (Non-SAD group, R5-R20 < 0.1 kPa·L⁻¹·s⁻¹). These patient first underwent IOS, then FeNO, and finally spirometry . Within the SAD group, 43 patients received three months of therapy with standard inhaled corticosteroids and long-acting β₂ agonists, followed by follow-up and re-evaluation using impulse oscillometry (IOS). Nineteen patients were lost to follow-up during the study period. The subject flow diagram is illustrated in Figure 1. Data collection Clinical and demographic data were collected for both groups, including gender, age, BMI, asthma duration, and family history of asthma, as presented in Table 1. All patients underwent the following tests: Peripheral Blood Eosinophil Count: 4 ml of fasting venous blood was collected and sent to the laboratory within 2 hours for complete blood count, with the eosinophil absolute count recorded. Serum Total IgE Measurement: 4 ml of fasting venous blood was collected and sent to the laboratory within 2 hours for serum total IgE determination using a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA). FeNO Measurement: Exhaled nitric oxide (FeNO) levels were measured using the Swedish NIOX Niels system, following ATS/ERS guidelines. Patients were instructed to refrain from eating, drinking, and exercising for 2 hours before FeNO measurements. Online measurement method was used in this study. While seated upright, patients took a breath to empty the lungs, held a disposable bacterial filter in the mouth, and maintained smooth, slow exhalation into the test apparatus for at least 4 seconds to allow the airway compartment to be washed out and a reasonable plateau achieved. The average FeNO value for the 3-second plateau period was recorded. Repeated and reproducible exhalations were performed to obtain at least two FeNO plateau values that agree within 10% of each other. Spirometry (PFT): Pulmonary function was assessed using a Master Screen device (Jaeger, Germany) by trained technicians. Pre-BD spirometry was performed according to the standards of ATS/ERS guidelines. Key parameters included the first second as a percentage of predicted value (FEV1%pred), forced expiratory volume to forced vital capacity ratio (FEV1/FVC), maximum mid-expiratory flow as a percentage of predicted value (MMEF%pred), and forced expiratory flow at 75%, 50%, and 25% of forced vital capacity as a percentage of predicted value (MEF75%pred, MEF50%pred, MEF25%pred). Three measurements were taken, with the highest value recorded. Impulse Oscillometry (IOS): IOS was conducted using a Master Screen IOS device (Jaeger, Germany). Pre-BD IOS was performed in all subjects before spirometry. Each subject was asked to perform tidal breathing for 30–40 s via a mouthpiece that was connected to the IOS machine. After stable breathing, at least three cycles were recorded to measure total respiratory impedance as a percentage of predicted value (Z5%pred), total airway resistance as a percentage of predicted value (R5%pred), central airway resistance as a percentage of predicted value (R20%pred), peripheral airway resistance (R5-R20), resonance frequency (Fres), and peripheral elastic resistance (X5). In China, the reference normal values for IOS currently used in most hospitals were: R5 less than 120% of the predicted value; R20 less than 120% of the predicted value. X5 greater than the predicted value-0.2 kPa/(L.s); and Fres less than the predicted value +10 Hz. The study was approved by the Ethics Committee of Yueqing People's Hospital (Number: YQYY202200052), along with a waiver of informed consent. Data were collected retrospectively from patients involved prior to enrollment. Statistical analysis All data were analyzed using IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, NY, United States). Continuous data were tested for normality. Normally distributed data were expressed as mean ± SD (x±S), while non-normally distributed data were expressed as median (IQR). Independent t-tests were used for normally distributed data, while the Mann-Whitney U test was used for non-normally distributed data. Categorical data were compared using chi-square tests. ROC curve analysis was used to assess the diagnostic value of Fres, X5, MMEF, and FEV1%pred for small airway dysfunction. Pearson correlation analysis was conducted to examine the relationships between R5-R20 in IOS and lung function data. A P-value of less than 0.05 was considered statistically significant. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors (YD and HY) had full access to all study data and bore final responsibility for the decision to submit for publication. Results General Characteristics of Subjects This study included 109 asthma patients, with 62 in the small airway dysfunction (SAD) group and 47 in the normal small airway function (Non-SAD) group. As shown in Table 1, demographic data including age, gender distribution, asthma duration, and family history of asthma were summarized. Notably, The SAD group had an average age of 42.06 ± 14.53 years, while the Non-SAD group had an average age of 39.21 ± 11.90 years. In the SAD group, 48.39% were male, with an asthma duration of 1.75 (1.15, 3.00) years, and 11.29% had a family history of asthma. In the Non-SAD group, 55.32% were male, with a similar asthma duration of 1.75 (1.17, 3.00) years, and 8.51% had a family history. There were no significant differences in demographics between the groups (all P > 0.05, Table 1). Turning to laboratory findings, the SAD group exhibited a higher peripheral blood eosinophil count (0.20 [0.13, 0.28] × 10⁹/L) compared to the Non-SAD group (0.13 [0.05, 0.23] × 10⁹/L, P 0.05). Furthermore, all patients completed spirometry and impulse oscillometry (IOS) testing (Table 1). Among the 109 patients, 29 (26.61%) had MMEF%pred < 60%, including 24 (38.71%) in the SAD group and 5 (10.64%) in the Non-SAD group. The spirometry results demonstrated that significant differences in FEV1%pred, MMEF%pred, MEF75%pred, MEF50%pred, and MEF25%pred between the groups (all P 0.05). Additionally, IOS parameters indicated that the SAD group had significantly higher Z5%pred, R20%pred, R5-R20, X5, and Fres values compared to the Non-SAD group (all P 0.05). In summary, the SAD group demonstrated distinct differences in eosinophil counts and spirometry parameters compared to the Non-SAD group, collectively suggesting a more severe airway obstruction and inflammatory response in patients with small airway dysfunction. Associations Between Spirometric and Impulse Oscillometry Measurements A correlation analysis of IOS and spirometry parameters in 109 patients demonstrated significant relationships among various measurements. Notably, R5-R20 was significantly positively correlated with Fres (ρ = 0.812, P < 0.001, Figure 2). On the other hand, R5-R20 exhibited negative correlations with X5, MMEF%pred, and FEV1%pred, with correlation coefficients of ρ = -0.643 (P < 0.001), ρ = -0.441 (P < 0.001), and ρ = -0.359 (P < 0.001), respectively (Figure 2). Further analysis revealed that Fres was negatively correlated with MMEF%pred, FEV1%pred, and X5, with correlation coefficients of r = -0.381 (P < 0.001), r = -0.317 (P < 0.001), and ρ = -0.572 (P < 0.001), respectively (Figure 2). Conversely, MMEF%pred showed positive correlations with FEV1%pred and X5, with correlation coefficients of r = 0.566 (P < 0.001) and ρ = 0.509 (P < 0.001), respectively (Figure 2). Impulse Oscillometry Measures Improved the Diagnostic Accuracy of Small Airway Dysfunction in Asthma Patients The diagnostic performance of IOS and spirometry for identifying small airway dysfunction in asthma patients was evaluated, as summarized in Table 2. Using a threshold of R5-R20 > 0.1 kPa•L⁻¹•s⁻¹, the AUC values for Fres, X5, MMEF%pred, and FEV1%pred were found to be 0.940, 0.790, 0.702, and 0.684, respectively (all P < 0.05). Among these parameters, Fres exhibited the highest diagnostic value, demonstrating a sensitivity of 95.2% and a specificity of 83.0%, with an optimal cutoff frequency of 14.385 Hz. These results indicate that IOS measures, particularly Fres, significantly enhance the diagnostic accuracy for detecting small airway dysfunction in asthma patients compared to traditional spirometry parameters. Comparison of IOS Indicators Before and After Treatment in the SAD Group A total of 43 patients in the SAD group were followed up after three months of treatment with standard inhaled corticosteroids and long-acting β2 agonists. The IOS parameters measured before and after treatment are presented in Table 3. Significant improvements were observed in several indicators: Z5%pred, R5%pred, R5-R20, and Fres all showed a decrease (P < 0.05), while X5 demonstrated an increase (P 0.05). These findings suggest that inhaler treatment effectively improves IOS parameters, indicating that changes in IOS can be utilized to assess control of small airway function in asthma patients. TABLE 1 Comparison of patient data between SAD group and Non-SAD group Characteristic SAD group (n=62) Non-SAD group (n=47) P-v alue Demographics and comorbidities Age (year) 42.06±14.53 39.21±11.90 0.263 BMI (kg/m 2 ) 24.29±4.01 23.67±3.59 0.411 Male sex (n,%) 30(48.39) 26(55.32) 0.473 Course of Disease (year) 1.75(1.15, 3.00) 1.75（1.17, 3.00） 0.792 Family History of Asthma (n,%) 7(11.29) 4(8.51) 0.633 Laboratory features EOS (×10 9 /L) 0.20（0.13, 0.28） 0.13(0.05, 0.23) 0.004 IgE (IU/ml) 42.2（25.08, 206.60） 38.00（24.81, 75.62） 0.457 FeNO (ppb) 26.00（15.75, 37.75） 23.00（15.00, 34.00） 0.664 Spirometry parameters FEV1 (% predicted) 93.47±13.39 102.17±11.26 < 0.001 FEV1/FVC (%) 82.11±7.63 83.64±6.41 0.271 MEF75 (% predicted) 88.76±20.12 100.55±16.26 0.001 MEF50 (% predicted) 76.19±21.97 89.49±22.52 0.003 MEF25 (% predicted) 61.26±26.92 78.21±23.28 0.001 MMEF (% predicted) 68.52±22.14 84.00±19.74 < 0.001 IOS metrics parameters Z5 (% predicted) 147.80±33.16 105.86±26.57 < 0.001 R5 (% predicted) 135.20±23.36 102.52±25.85 < 0.001 R20 (% predicted) 101.36±20.13 97.86±22.50 0.491 R5-R20 (kPa·L -1 ·s -1 ) 0.16（0.13, 0.20） 0.07（0.05, 0.08） < 0.001 X5 (kPa·L -1 ·s -1 ) -0.15（-0.21, -0.12） -0.10（-0.13, -0.08） < 0.001 Fres (Hz) 17.81±2.51 12.29±2.65 < 0.001 Data are presented as %, mean±SD or median (interquartile range). TABLE 2 Predictive values of IOS and spirometry measurements for small airway dysfunction diagnosis Variable AUC 95%CI Cut-off Sensitivity Specificity P -value Fres (Hz) 0.940 0.897-0.983 14.385 0.952 0.830 < 0.001 X5 (kPa·L -1 ·s -1 ) 0.790 0.706-0.875 -0.125 0.742 0.745 < 0.001 MMEF (% predicted) 0.702 0.604-0.800 66.5 0.830 0.565 < 0.001 FEV1 (% predicted) 0.684 0.584-0.783 94.5 0.809 0.532 0.001 AUC, area under the curve. TABLE 3 Comparison of IOS before and after treatment in the SAD group Variable Before Treatment (n=43) After Treatment (n=43) P -value Z5 (% predicted) 149.7±36.93 129.69±25.62 0.004 R5 (% predicted) 135.52±24.36 118.93±26.93 0.004 R20 (% predicted) 99.82±21.46 94.43±20.34 0.236 R5-R20 (kPa·L -1 ·s -1 ) 1.65（1.25, 2.14） 1.08（0.87, 1.76） < 0.001 X5 (kPa·L -1 ·s -1 ) -1.88±0.95 -1.48±0.68 0.029 Fres (Hz) 18.10±2.64 15.62±2.99 < 0.001 Data are presented as mean±SD or median (interquartile range). Discussion Small airways, defined as bronchi with a diameter of less than 2 mm, are often underestimated in asthma due to their size, challenges in obtaining tissue samples, and imaging difficulties. Nevertheless, recent research underscores their significant role in asthma pathology [ 14 , 15 ]. Approximately 50%-60% of asthma patients exhibit small airway dysfunction, which is closely associated with airway inflammation, remodeling, and symptoms such as poor asthma control, severe asthma, nocturnal asthma, and exercise-induced asthma [ 16 , 17 ]. Building on this evidence, our study suggests that changes in small airways may be linked to eosinophilic inflammation and can be effectively assessed using impulse oscillometry (IOS), which also serves as a tool for monitoring asthma control post-treatment. When considering diagnostic approaches, compared to conventional spirometry, IOS can differentiate between central and peripheral airway resistance and does not require special patient cooperation. Small airway dysfunction is typically diagnosed through reduced maximum mid-expiratory flow (MMEF) or increased R5-R20 on IOS [ 18 ]. Previous studies indicate that R5-R20 and Fres demonstrate higher diagnostic value for detecting small airway dysfunction than MMEF, suggesting that IOS could be an effective screening tool [ 9 ]. In line with these findings, our study categorized patients based on baseline R5-R20 levels: R5-R20 > 0.1 kPa·L⁻¹·s⁻¹ for the dysfunction group and R5-R20 < 0.1 kPa·L⁻¹·s⁻¹ for the normal group [ 10 , 13 ]. To contextualize the inflammatory mechanisms, asthma is characterized as a chronic airway inflammatory disease involving eosinophils, mast cells, T lymphocytes, and various inflammatory mediators. Supporting this, research by Casadeval et al. [ 19 ] found a positive correlation between peripheral blood eosinophil counts and sputum eosinophil counts, suggesting that peripheral eosinophil levels can reflect airway eosinophilic inflammation. Our study showed elevated peripheral eosinophil counts in the small airway dysfunction group, indicating a connection between small airway disease and eosinophilic inflammation [ 20 ]. Key markers of type 2 inflammation, such as fractional exhaled nitric oxide (FeNO) and eosinophil counts, are associated with small airway dysfunction and poor asthma control [ 21 ]. Additionally, studies have shown that eosinophil numbers in small airways are significantly higher than in larger airways, indicating more severe eosinophilic inflammation in small airways [ 22 ]. Reducing eosinophilic inflammation has been linked to improvements in small airway function and overall quality of life, including enhanced FEV1, decreased airway resistance, and lower FeNO levels [ 23 , 24 ]. Mechanistically, FeNO and peripheral blood eosinophil count are positively correlated, as nitric oxide produced by airway cells attracts eosinophils, worsening the inflammatory response [ 25 ]. However, our research showed no difference in FENO between the SAD group and the non-SAD group. The possible reason is that small airway eosinophilic inflammation may not significantly increase FeNO, especially when the airway is not exhausted [ 26 ]. Small airways contribute significantly to total airway resistance, accounting for 10%-25% [ 27 ]. Previous research [ 28 , 29 ] has shown that IOS parameters such as R5-R20, X5, and MMEF can detect small airway abnormalities even when conventional spirometry results are normal. In our study, despite normal FEV1, FVC, and FEV1/FVC ratios, the small airway dysfunction group demonstrated lower spirometry parameters (MMEF, MEF50%, MEF25%) compared to the normal group, consistent with prior findings. We also observed significantly higher values of R5%pred, R5-R20, Fres, and X5 in the dysfunction group. These results confirm that IOS parameters correlate well with conventional spirometry and can identify small airway dysfunction even when spirometry results are normal. The negative values of R5%pred, R5-R20, Fres, and X5 were significantly higher in the small airway dysfunction group. X5 reflects peripheral elastic resistance, which increases notably with small airway obstruction, while Fres, as a measure of resonance frequency, is a sensitive indicator of airway viscous resistance that can rise even with mild peripheral airway obstruction [ 11 , 30 ]. Our findings suggest that small airway dysfunction can exist in asthma patients without significant changes in conventional lung function. Elevated R5%pred, R5-R20, Fres, and X5 negative values serve as indicators of small airway abnormalities, aligning with observations by Liu et al. [ 31 ]Previous studies have also shown correlations between IOS parameters (R5-R20, Fres, X5) and conventional lung function (MMEF%pred) in asthma patients [ 32 ]. Our data extend these observations, revealing positive correlations between IOS parameters and MMEF%pred, suggesting that IOS can reliably detect small airway dysfunction, even when conventional lung function appears normal. Moreover, IOS demonstrates greater sensitivity and specificity compared to conventional tests for identifying small airway disease [ 33 , 34 ]. Fres, in particular, proved to be the most effective parameter for diagnosing small airway dysfunction, with an optimal cutoff of 14.385 Hz. When conventional lung function tests yield normal results, a Fres value above this threshold should raise suspicion for small airway dysfunction. Previous studies have established that normal Fres values range from greater than 30 Hz in children to 8–12 Hz in adults [ 11 ]. Conditions such as airway obstruction and interstitial lung disease can lead to increased Fres. Liu et al. [ 30 ] also noted that combining FeNO with Fres enhances the diagnostic accuracy for small airway dysfunction, as Fres reflects both elastic and viscous airway resistance, while FeNO indicates eosinophilic inflammation. Therapeutically, in the small airway dysfunction group, treatment with low-dose inhaled corticosteroids and long-acting β2 agonists resulted in improved IOS parameters (Z5%pred, R5%pred, R5-R20, X5, Fres). This indicates that small airway function can improve with appropriate treatment, and these changes can be monitored through pre- and post-treatment IOS comparisons. Previous studies report that inhaled corticosteroids combined with long-acting β2 agonists target small airways effectively, improving asthma control and reducing symptoms and exacerbations [ 35 ]. Additionally, triple therapy (ICS + LABA + LAMA) has been shown to enhance clinical control and small airway function in severe asthma [ 36 ]. However, further research is necessary to assess the role of IOS in evaluating asthma treatment and control. Several limitations should be acknowledged: First, our study had a relatively high loss-to-follow-up rate (30.6%). Second, we focused primarily on laboratory and spirometry data, without clinical symptom assessments such as asthma control tests. Finally, peripheral eosinophil count was used as a proxy for eosinophilic inflammation, which is less precise than sputum analysis. In summary, IOS parameters correlate well with conventional spirometry but offer higher sensitivity and specificity for detecting small airway dysfunction. Fres, in particular, has significant diagnostic value and can complement conventional spirometry in evaluating small airway dysfunction in asthma. Conclusion This study highlights the significant prevalence of small airway dysfunction (SAD) among asthma patients with normal spirometry, affecting nearly 57% of the cohort. The findings demonstrate that impulse oscillometry (IOS) is a valuable tool for detecting SAD, offering greater sensitivity and specificity compared to traditional spirometry. Specifically, the Fres parameter emerged as the most effective diagnostic indicator for SAD. Furthermore, our results indicate that IOS can effectively monitor changes in small airway function following treatment with standard inhaled corticosteroids and long-acting β₂ agonists, suggesting its utility in managing asthma control. These insights underscore the importance of incorporating IOS into routine clinical practice for a comprehensive evaluation of asthma patients, particularly those with normal spirometry but persistent symptoms. Future studies should explore the long-term implications of SAD on asthma management and patient quality of life. Declarations Acknowledgements We wish to acknowledge all the participants, medical-, nursing-, and technical-staff who has been involved in collecting the samples for this study. Author contributions W. Y., X. H., H. Y. performed the research, collected and analyzed the data and wrote the paper. Y.L., X.Z., L. Z., and J.W. contributed to sample collection. A. C. , Y. S.and Y.D.contributed to supervision of this study and revision of the manuscript. All authors reviewed the manuscript. Funding This work was financially supported by the Science and Technology Plan Project of Wenzhou Municipality (Y20220340). Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Declarations Ethics approval and consent to participate The present study was conducted in accordance with the amended Declaration of Helsinki. The Ethics Committee of Affiliated Yueqing Hospital of Wenzhou Medical University approved the protocol of this retrospective study (approval number: YQYY202200052). Consent to publish Every participant consent to publish. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. References Westerhof G A, C H,de Nijs S B,et al, Clinical predictors of remission and persistence of adult-onset asthma[J]. J Allergy Clin Immunol, 2018. 141(1):104-109. Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention, 2023. http://www.ginaasthma.org/. Usmani O S, S D, Spinola M,et al, The prevalence of small airways disease in adult asthma: A systematic literature review[J]. Respir Med, 2016. 116:19-27. Postma D S, B C, Baldi S,et al, Exploring the relevance and extent of small airways dysfunction in asthma (ATLANTIS): baseline data from a prospective cohort study[J]. Lancet Respir Med, 2019. 7(5):402-416. Manoharan A, A W J, Lipworth J,et al, Small airway dysfunction is associated with poorer asthma control[J]. Eur Respir J, 2014. 44(5): 1353-5. Cottini M, L A, Lombardi C,et al, Clinical Characterization and Predictors of IOS-Defined Small-Airway Dysfunction in Asthma[J]. J Allergy Clin Immunol Pract, 2020. 8(3): 997-1004. Chan R, L B, Forced Vital Capacity and Low Frequency Reactance Area Measurements Are Associated with Asthma Control and Exacerbations[J]. Lung, 2022. 200(3): 301-303. Brashier, B, Salvi S, Measuring lung function using sound waves: role of the forced oscillation technique and impulse oscillometry system[J]. Breathe (Sheff), 2015. 11(1): 57-65. Su Z Q, Guan W J,Li S Y,et al, Significances of spirometry and impulse oscillometry for detecting small airway disorders assessed with endobronchial optical coherence tomography in COPD[J]. Int J Chron Obstruct Pulmon Dis, 2018. 13: 3031-3044. Shi Y, Aledia A S,Galant S P,George S C,Peripheral airway impairment measured by oscillometry predicts loss of asthma control in children[J]. J Allergy Clin Immunol, 2013. 131(3): 718-23. King G G., Bates J Berger, K I,P Calverley, et al, Technical standards for respiratory oscillometry[J]. Eur Respir J, 2020. 55(2). Oostveen E,M D,Lorino H,et al,The forced oscillation technique in clinical practice: methodology, recommendations and future developments[J]. Eur Respir J, 2003. 22(6): 1026-41. Manoharan A, Anderson W J,J Lipworth,Lipworth B J., Assessment of spirometry and impulse oscillometry in relation to asthma control[J]. Lung, 2015. 193(1):47-51. Chiu H Y.H., Y. H,Su, K. C, et al, Small Airway Dysfunction by Impulse Oscillometry in Symptomatic Patients with Preserved Pulmonary Function[J]. J Allergy Clin Immunol Pract, 2020. 8(1):229-235. van der Wiel, E., ten Hacken N H,Postma D S,et al., Small-airways dysfunction associates with respiratory symptoms and clinical features of asthma: a systematic review[J]. J Allergy Clin Immunol, 2013. 131(3): 646-57. Kole TM, V.B.E., Kraft M, et al. , Predictors and associations of the persistent airflow limitation phenotype in asthma: a post-hoc analysis of the ATLANTIS study. Lancet Respir Med, 2023. 11(1):55-64. Kraft M, R.M., Hallmark B, et al, The role of small airway dysfunction in asthma control and exacerbations: a longitudinal, observational analysis using data from the ATLANTIS study. Lancet Respir Med, 2022. 10(7): 661-668. Foy B. H, S.M., Bordas R,et al, Lung Computational Models and the Role of the Small Airways in Asthma[J]. Am J Respir Crit Care Med, 2019. 200(8): 982-991. Mortimer, K., Lesosky, M,García-Marcos, L,et al., The burden of asthma, hay fever and eczema in adults in 17 countries: GAN Phase I study[J]. Eur Respir J, 2022. 60(3). Casadevall, C., Quero, S,Millares, L,et al., Relationship between Respiratory Microbiome and Systemic Inflammatory Markers in COPD: A Pilot Study. Int J Mol Sci, 2024. 25(15):8467. Menzies-Gow, A.M., A. H.Brightling, C. E., Clinical utility of fractional exhaled nitric oxide in severe asthma management[J]. Eur Respir J, 2020. 55(3). Abdo M, P.F., Kirsten A-M,et al. , Longitudinal Impact of Sputum Inflammatory Phenotypes on Small Airway Dysfunction and Disease Outcomes in Asthma[J]. J Allergy Clin Immunol Pract, 2022. 10(6):1545-1553. Hamid, Q., Song, Y,Kotsimbos, T. C,et al, Inflammation of small airways in asthma[J]. J Allergy Clin Immunol, 1997. 100(1):44-51. Abdo M, W.H., Veith V, et al, Small airway dysfunction as predictor and marker for clinical response to biological therapy in severe eosinophilic asthma: a longitudinal observational study[J]. Respir Res, 2020. 21(1): 278. Gao, F., Lei, J,Zhu, H,Zhao, L,et al., Small airway dysfunction links asthma exacerbations with asthma control and health-related quality of life[J]. Respir Res, 2024. 25(1): 306. Li L, Zhang H, Holloway J W, et al. Pubertal onset with adulthood lung function mediated by height growth in adolescence[J]. European Respiratory Society, 2020. 6(4): 00535-2020. Bafadhel, M., Peterson, S,De Blas, M. A,et al., Predictors of exacerbation risk and response to budesonide in patients with chronic obstructive pulmonary disease: a post-hoc analysis of three randomised trials. Lancet Respir Med, 2018. 6(2): 117-126. Carr, T.F., Altisheh, R,Zitt, M., Small airways disease and severe asthma[J]. World Allergy Organ J, 2017. 10(1):20. Galant, S.P., Morphew, T., Adding oscillometry to spirometry in guidelines better identifies uncontrolled asthma, future exacerbations, and potential targeted therapy[J]. Ann Allergy Asthma Immunol, 2024. 132(1): 21-29. Cottini, M., Licini, A,Lombardi, C,Bagnasco, D,et al., Small airway dysfunction and poor asthma control: a dangerous liaison.[J] Clin Mol Allergy, 2021. 19(1): 7. Liu, Z., Lin, L,Liu, X., Clinical application value of impulse oscillometry in geriatric patients with COPD[J]. Int J Chron Obstruct Pulmon Dis, 2017. 12: 897-905. Liu, L., Liu, W,Liu, C,et al., Study on small airway function in asthmatics with fractional exhaled nitric oxide and impulse oscillometry[J]. Clin Respir J, 2018. 12(2):483-490. Desiraju, K., Agrawal, A., Impulse oscillometry: The state-of-art for lung function testing[J]. Lung India, 2016. 33(4): 410-6. Liwsrisakun, C., Chaiwong, W,Pothirat, C., Comparative assessment of small airway dysfunction by impulse oscillometry and spirometry in chronic obstructive pulmonary disease and asthma with and without fixed airflow obstruction[J]. Front Med (Lausanne), 2023. 10: 1181188. Cottini, M., Lombardi, C,Micheletto, C., Small airway dysfunction and bronchial asthma control : the state of the art[J]. Asthma Res Pract, 2015. 1:13. Carpagnano, G.E., Portacci, A. and S. Dragonieri, Montagnolo, F,et al., Managing Small Airway Disease in Patients with Severe Asthma: Transitioning from the "Silent Zone" to Achieving "Quiet Asthma"[J]. J Clin Med, 2024. 13(8): 2320. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6011946","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443503483,"identity":"08cf0ec0-4942-4c49-b111-e4de4ce0cdcc","order_by":0,"name":"Wenwen Yu","email":"","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenwen","middleName":"","lastName":"Yu","suffix":""},{"id":443503485,"identity":"9ffea889-ea0c-4c64-9ada-31b9f94f01a9","order_by":1,"name":"Xiuting Huang","email":"","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiuting","middleName":"","lastName":"Huang","suffix":""},{"id":443503488,"identity":"7dd5920b-2f9d-45f0-a141-fffbb344aa9d","order_by":2,"name":"Yunlei Li","email":"","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunlei","middleName":"","lastName":"Li","suffix":""},{"id":443503489,"identity":"752bf567-7259-4186-8915-9926ee27c76a","order_by":3,"name":"Xie Zhang","email":"","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xie","middleName":"","lastName":"Zhang","suffix":""},{"id":443503490,"identity":"21a9435b-7cfa-4cc6-ac16-bd22d905d64c","order_by":4,"name":"Legui Zheng","email":"","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Legui","middleName":"","lastName":"Zheng","suffix":""},{"id":443503493,"identity":"e43455fd-84e7-4cc0-816f-7a7a0d36b056","order_by":5,"name":"Jincong Wang","email":"","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jincong","middleName":"","lastName":"Wang","suffix":""},{"id":443503495,"identity":"c9aa3ea4-72d4-4874-84e6-eda77d8c8d21","order_by":6,"name":"Ali Chen","email":"","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Chen","suffix":""},{"id":443503498,"identity":"98aa0e09-9724-45b7-bf9e-a7ad17e2af7e","order_by":7,"name":"Yubo Shi","email":"","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yubo","middleName":"","lastName":"Shi","suffix":""},{"id":443503501,"identity":"bd9f5efc-4df4-4e2a-938a-50d72792eb1a","order_by":8,"name":"Yuanrong Dai","email":"","orcid":"","institution":"the Second Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanrong","middleName":"","lastName":"Dai","suffix":""},{"id":443503502,"identity":"6339172d-58e1-4370-85a3-16b9bdaa27f9","order_by":9,"name":"Hua Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACNvkDiY//GEgw27c3EKmFT4LhsQFPgQ27Ac8BIrXISTA+E+D5kMZvIJFArMOkm9MYJAwOS5tLPt54g6HGJpqwFpljaQ8MDA4bW85OK7ZgOJaW20BQC0NOukGCweFkhts5ZhKMDYeJ0ZL/TeKAweH6hptniNUikZAm2WCQxmxwg4dYLTwHko0ZDGyYJXuAfkkgxi/y7Q2Jjxn+SDDzsx/eeONDjQ1hLciA+KhB0kKqjlEwCkbBKBgZAAAlNTxn4iZDogAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Yueqing Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hua","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2025-02-12 05:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6011946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6011946/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80800434,"identity":"37efb37a-bb22-4259-be60-bd8ebcfbb33d","added_by":"auto","created_at":"2025-04-17 08:27:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe scheme of the study flowcharts.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6011946/v1/115e6d6688e7b8860d16c474.jpeg"},{"id":80800437,"identity":"9db24c9d-9cf2-4c3c-a81b-c0609d8e71f8","added_by":"auto","created_at":"2025-04-17 08:27:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations Between Spirometric and Impulse Oscillometry Measurements\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6011946/v1/00c1c183806f6cdfd9d1dadd.jpeg"},{"id":82147583,"identity":"07c86191-27dc-4a04-b912-26622420cf7c","added_by":"auto","created_at":"2025-05-07 07:01:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6011946/v1/2ed2b0a6-6a88-43e0-897c-4ab27ff53f09.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impulse oscillometry in assessing small airway function in asthma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBronchial asthma is a heterogeneous disease characterized by chronic airway inflammation and reversible expiratory airflow limitation, involving various inflammatory cells and mediators [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately 3.7% of asthma patients progress to severe asthma, which is associated with increasing prevalence and poor control [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Traditionally, asthma was believed to primarily affect large airways; however, recent research indicates that 50\u0026ndash;60% of asthma patients also exhibit small airway dysfunction [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which is critical for understanding inflammation, airway hyperreactivity, and remodeling [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Small airway dysfunction is linked to more severe symptoms, poor asthma control, nocturnal asthma, and exercise-induced asthma [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Research by Chan R et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] found Patients with combined impairment of FVC and AX had significantly worse asthma control as higher ACQ, more severe exacerbations requiring OCS and worse spirometry than those with impaired FVC but preserved AX.\u003c/p\u003e \u003cp\u003eSpirometry is widely used in asthma diagnosis, but its sensitivity for detecting small airway dysfunction is limited. Although a reduction in maximum mid-expiratory flow (MMEF) can indicate small airway obstruction, it requires forced exhalation and is subject to high variability [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, impulse oscillometry (IOS) assesses airway function during normal breathing and demonstrates greater sensitivity and specificity for small airway dysfunction, making it valuable for early detection and intervention [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite these advantages, IOS is not widely implemented in China, and research on its role in assessing small airways remains limited. The R5-R20 parameter of IOS reflects small airway resistance, with values exceeding 0.1 kPa\u0026middot;L⁻\u0026sup1;\u0026middot;s⁻\u0026sup1; considered indicative of dysfunction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This study aims to evaluate the utility of IOS in assessing small airway function and asthma control and to explore whether it can supplement or replace conventional spirometry in diagnosing small airway dysfunction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy \u003c/strong\u003e\u003cstrong\u003edesign\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eparticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 109 asthma patients with normal spirometry evaluated at the respiratory departments of Yueqing People\u0026apos;s Hospital and the Second Affiliated Hospital of Wenzhou Medical University between August 2018 and October 2022. The cohort comprised 56 males and 53 females, with a mean age of 40.83 \u0026plusmn; 13.48 years. The patients were stratified into two groups based on R5-R20 values: 62 patients with small airway dysfunction (SAD group, R5-R20 \u0026gt; 0.1 kPa\u0026bull;L⁻\u0026sup1;\u0026bull;s⁻\u0026sup1;) and 47 patients with normal small airway function (Non-SAD group, R5-R20 \u0026lt; 0.1 kPa\u0026middot;L⁻\u0026sup1;\u0026middot;s⁻\u0026sup1;). These patient first underwent IOS, then FeNO, and finally spirometry\u003cem\u003e. \u003c/em\u003eWithin the SAD group, 43 patients received three months of therapy with standard inhaled corticosteroids and long-acting \u0026beta;₂ agonists, followed by follow-up and re-evaluation using impulse oscillometry (IOS). Nineteen patients were lost to follow-up during the study period. The subject flow diagram is illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and demographic data were collected for both groups, including gender, age, BMI, asthma duration, and family history of asthma, as presented in Table 1. All patients underwent the following tests:\u003c/p\u003e\n\n\u003cp\u003ePeripheral Blood Eosinophil Count: 4 ml of fasting venous blood was collected and sent to the laboratory within 2 hours for complete blood count, with the eosinophil absolute count recorded.\u003c/p\u003e\n\n\u003cp\u003eSerum Total IgE Measurement: 4 ml of fasting venous blood was collected and sent to the laboratory within 2 hours for serum total IgE determination using a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA).\u003c/p\u003e\n\n\u003cp\u003eFeNO Measurement: Exhaled nitric oxide (FeNO) levels were measured using the Swedish NIOX Niels system, following ATS/ERS guidelines. Patients were instructed to refrain from eating, drinking, and exercising for 2 hours before FeNO measurements. Online measurement method was used in this study. While seated upright, patients took a breath to empty the lungs, held a disposable bacterial filter in the mouth, and maintained smooth, slow exhalation into the test apparatus for at least 4 seconds to allow the airway compartment to be washed out and a reasonable plateau achieved. The average FeNO value for the 3-second plateau period was recorded. Repeated and reproducible exhalations were performed to obtain at least two FeNO plateau values that agree within 10% of each other.\u003c/p\u003e\n\n\u003cp\u003eSpirometry (PFT): Pulmonary function was assessed using a Master Screen device (Jaeger, Germany) by trained technicians. Pre-BD spirometry was performed according to the standards of ATS/ERS guidelines. Key parameters included the first second as a percentage of predicted value (FEV1%pred), forced expiratory volume to forced vital capacity ratio (FEV1/FVC), maximum mid-expiratory flow as a percentage of predicted value (MMEF%pred), and forced expiratory flow at 75%, 50%, and 25% of forced vital capacity as a percentage of predicted value (MEF75%pred, MEF50%pred, MEF25%pred). Three measurements were taken, with the highest value recorded.\u003c/p\u003e\n\n\u003cp\u003eImpulse Oscillometry (IOS): IOS was conducted using a Master Screen IOS device (Jaeger, Germany). Pre-BD IOS was performed in all subjects before spirometry. Each subject was asked to perform tidal breathing for 30\u0026ndash;40 s via a mouthpiece that was connected to the IOS machine. After stable breathing, at least three cycles were recorded to measure total respiratory impedance as a percentage of predicted value (Z5%pred), total airway resistance as a percentage of predicted value (R5%pred), central airway resistance as a percentage of predicted value (R20%pred), peripheral airway resistance (R5-R20), resonance frequency (Fres), and peripheral elastic resistance (X5). In China, the reference normal values for IOS currently used in most hospitals were: R5 less than 120% of the predicted value; R20 less than 120% of the predicted value. X5 greater than the predicted value-0.2 kPa/(L.s); and Fres less than the predicted value +10 Hz.\u003c/p\u003e\n\n\u003cp\u003eThe study was approved by the Ethics Committee of Yueqing People\u0026apos;s Hospital (Number: YQYY202200052), along with a waiver of informed consent. Data were collected retrospectively from patients involved prior to enrollment.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were analyzed using IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, NY, United States). Continuous data were tested for normality. Normally distributed data were expressed as mean \u0026plusmn; SD (x\u0026plusmn;S), while non-normally distributed data were expressed as median (IQR). Independent t-tests were used for normally distributed data, while the Mann-Whitney U test was used for non-normally distributed data. Categorical data were compared using chi-square tests. ROC curve analysis was used to assess the diagnostic value of Fres, X5, MMEF, and FEV1%pred for small airway dysfunction. Pearson correlation analysis was conducted to examine the relationships between R5-R20 in IOS and lung function data. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRole of the funding source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors (YD and HY) had full access to all study data and bore final responsibility for the decision to submit for publication.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral Characteristics of Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 109 asthma patients, with 62 in the small airway dysfunction (SAD) group and 47 in the normal small airway function (Non-SAD) group. As shown in Table 1, demographic data including age, gender distribution, asthma duration, and family history of asthma were summarized. Notably, The SAD group had an average age of 42.06 \u0026plusmn; 14.53 years, while the Non-SAD group had an average age of 39.21 \u0026plusmn; 11.90 years. In the SAD group, 48.39% were male, with an asthma duration of 1.75 (1.15, 3.00) years, and 11.29% had a family history of asthma. In the Non-SAD group, 55.32% were male, with a similar asthma duration of 1.75 (1.17, 3.00) years, and 8.51% had a family history. There were no significant differences in demographics between the groups (all P \u0026gt; 0.05, Table 1).\u003c/p\u003e\n\u003cp\u003eTurning to laboratory findings, the SAD group exhibited a higher peripheral blood eosinophil count (0.20 [0.13, 0.28] \u0026times; 10⁹/L) compared to the Non-SAD group (0.13 [0.05, 0.23] \u0026times; 10⁹/L, P \u0026lt; 0.05). However, no significant differences were found in serum total IgE or FeNO levels between the two groups (P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eFurthermore, all patients completed spirometry and impulse oscillometry (IOS) testing (Table 1). Among the 109 patients, 29 (26.61%) had MMEF%pred \u0026lt; 60%, including 24 (38.71%) in the SAD group and 5 (10.64%) in the Non-SAD group. The spirometry results demonstrated that significant differences in FEV1%pred, MMEF%pred, MEF75%pred, MEF50%pred, and MEF25%pred between the groups (all P \u0026lt; 0.05), while no difference was observed in FEV1/FVC (P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eAdditionally, IOS parameters indicated that the SAD group had significantly higher Z5%pred, R20%pred, R5-R20, X5, and Fres values compared to the Non-SAD group (all P \u0026lt; 0.05), with no difference in R20%pred (P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eIn summary, the SAD group demonstrated distinct differences in eosinophil counts and spirometry parameters compared to the Non-SAD group, collectively suggesting a more severe airway obstruction and inflammatory response in patients with small airway dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations Between Spirometric and Impulse Oscillometry Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA correlation analysis of IOS and spirometry parameters in 109 patients demonstrated significant relationships among various measurements. Notably, R5-R20 was significantly positively correlated with Fres (\u0026rho; = 0.812, P \u0026lt; 0.001, Figure 2). On the other hand, R5-R20 exhibited negative correlations with X5, MMEF%pred, and FEV1%pred, with correlation coefficients of \u0026rho; = -0.643 (P \u0026lt; 0.001), \u0026rho; = -0.441 (P \u0026lt; 0.001), and \u0026rho; = -0.359 (P \u0026lt; 0.001), respectively (Figure 2).\u003c/p\u003e\n\u003cp\u003eFurther analysis revealed that Fres was negatively correlated with MMEF%pred, FEV1%pred, and X5, with correlation coefficients of r = -0.381 (P \u0026lt; 0.001), r = -0.317 (P \u0026lt; 0.001), and \u0026rho; = -0.572 (P \u0026lt; 0.001), respectively (Figure 2). Conversely, MMEF%pred showed positive correlations with FEV1%pred and X5, with correlation coefficients of r = 0.566 (P \u0026lt; 0.001) and \u0026rho; = 0.509 (P \u0026lt; 0.001), respectively (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpulse Oscillometry Measures Improved the Diagnostic Accuracy of Small Airway Dysfunction in Asthma Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic performance of IOS and spirometry for identifying small airway dysfunction in asthma patients was evaluated, as summarized in Table 2. Using a threshold of R5-R20 \u0026gt; 0.1 kPa\u0026bull;L⁻\u0026sup1;\u0026bull;s⁻\u0026sup1;, the AUC values for Fres, X5, MMEF%pred, and FEV1%pred were found to be 0.940, 0.790, 0.702, and 0.684, respectively (all P \u0026lt; 0.05). Among these parameters, Fres exhibited the highest diagnostic value, demonstrating a sensitivity of 95.2% and a specificity of 83.0%, with an optimal cutoff frequency of 14.385 Hz. These results indicate that IOS measures, particularly Fres, significantly enhance the diagnostic accuracy for detecting small airway dysfunction in asthma patients compared to traditional spirometry parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of IOS Indicators Before and After Treatment in the SAD Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 43 patients in the SAD group were followed up after three months of treatment with standard inhaled corticosteroids and long-acting \u0026beta;2 agonists. The IOS parameters measured before and after treatment are presented in Table 3. Significant improvements were observed in several indicators: Z5%pred, R5%pred, R5-R20, and Fres all showed a decrease (P \u0026lt; 0.05), while X5 demonstrated an increase (P \u0026lt; 0.05). However, no significant change was noted in R20%pred (P \u0026gt; 0.05). These findings suggest that inhaler treatment effectively improves IOS parameters, indicating that changes in IOS can be utilized to assess control of small airway function in asthma patients.\u003c/p\u003e\n\u003cp\u003eTABLE 1 Comparison of patient data between SAD group and Non-SAD group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eSAD group (n=62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eNon-SAD group (n=47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eP-v\u003c/em\u003ealue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eDemographics and comorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e42.06\u0026plusmn;14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e39.21\u0026plusmn;11.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e24.29\u0026plusmn;4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e23.67\u0026plusmn;3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eMale sex (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e30(48.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e26(55.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eCourse of Disease (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.75(1.15, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e1.75（1.17, 3.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eFamily History of Asthma (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e7(11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e4(8.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eLaboratory features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eEOS (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.20（0.13, 0.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.13(0.05, 0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eIgE (IU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e42.2（25.08, 206.60）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e38.00（24.81, 75.62）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eFeNO (ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e26.00（15.75, 37.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e23.00（15.00, 34.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eSpirometry parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eFEV1 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e93.47\u0026plusmn;13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e102.17\u0026plusmn;11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eFEV1/FVC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e82.11\u0026plusmn;7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e83.64\u0026plusmn;6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eMEF75 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e88.76\u0026plusmn;20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e100.55\u0026plusmn;16.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eMEF50 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e76.19\u0026plusmn;21.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e89.49\u0026plusmn;22.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eMEF25 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e61.26\u0026plusmn;26.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e78.21\u0026plusmn;23.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eMMEF (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e68.52\u0026plusmn;22.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e84.00\u0026plusmn;19.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eIOS metrics parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eZ5 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e147.80\u0026plusmn;33.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e105.86\u0026plusmn;26.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eR5 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e135.20\u0026plusmn;23.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e102.52\u0026plusmn;25.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eR20 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e101.36\u0026plusmn;20.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e97.86\u0026plusmn;22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eR5-R20 (kPa\u0026middot;L\u003csup\u003e-1\u003c/sup\u003e\u0026middot;s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.16（0.13, 0.20）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.07（0.05, 0.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eX5 (kPa\u0026middot;L\u003csup\u003e-1\u003c/sup\u003e\u0026middot;s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e-0.15（-0.21, -0.12）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e-0.10（-0.13, -0.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eFres (Hz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e17.81\u0026plusmn;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e12.29\u0026plusmn;2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as %, mean\u0026plusmn;SD or median (interquartile range).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2\u003c/strong\u003e\u003cstrong\u003ePredictive values of IOS and spirometry measurements for small airway dysfunction diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCut-off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFres (Hz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.897-0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e14.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eX5 (kPa\u0026middot;L\u003csup\u003e-1\u003c/sup\u003e\u0026middot;s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.706-0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMMEF (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.604-0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e66.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFEV1 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.584-0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC, area under the curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Comparison of IOS before and after treatment in the SAD group\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eBefore Treatment (n=43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eAfter Treatment (n=43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eZ5 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e149.7\u0026plusmn;36.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e129.69\u0026plusmn;25.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eR5 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e135.52\u0026plusmn;24.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e118.93\u0026plusmn;26.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eR20 (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e99.82\u0026plusmn;21.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e94.43\u0026plusmn;20.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eR5-R20 (kPa\u0026middot;L\u003csup\u003e-1\u003c/sup\u003e\u0026middot;s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e1.65（1.25, 2.14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.08（0.87, 1.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eX5 (kPa\u0026middot;L\u003csup\u003e-1\u003c/sup\u003e\u0026middot;s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e-1.88\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e-1.48\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eFres (Hz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e18.10\u0026plusmn;2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e15.62\u0026plusmn;2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean\u0026plusmn;SD or median (interquartile range).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSmall airways, defined as bronchi with a diameter of less than 2 mm, are often underestimated in asthma due to their size, challenges in obtaining tissue samples, and imaging difficulties. Nevertheless, recent research underscores their significant role in asthma pathology [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Approximately 50%-60% of asthma patients exhibit small airway dysfunction, which is closely associated with airway inflammation, remodeling, and symptoms such as poor asthma control, severe asthma, nocturnal asthma, and exercise-induced asthma [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Building on this evidence, our study suggests that changes in small airways may be linked to eosinophilic inflammation and can be effectively assessed using impulse oscillometry (IOS), which also serves as a tool for monitoring asthma control post-treatment.\u003c/p\u003e \u003cp\u003eWhen considering diagnostic approaches, compared to conventional spirometry, IOS can differentiate between central and peripheral airway resistance and does not require special patient cooperation. Small airway dysfunction is typically diagnosed through reduced maximum mid-expiratory flow (MMEF) or increased R5-R20 on IOS [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Previous studies indicate that R5-R20 and Fres demonstrate higher diagnostic value for detecting small airway dysfunction than MMEF, suggesting that IOS could be an effective screening tool [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In line with these findings, our study categorized patients based on baseline R5-R20 levels: R5-R20\u0026thinsp;\u0026gt;\u0026thinsp;0.1 kPa\u0026middot;L⁻\u0026sup1;\u0026middot;s⁻\u0026sup1; for the dysfunction group and R5-R20\u0026thinsp;\u0026lt;\u0026thinsp;0.1 kPa\u0026middot;L⁻\u0026sup1;\u0026middot;s⁻\u0026sup1; for the normal group [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo contextualize the inflammatory mechanisms, asthma is characterized as a chronic airway inflammatory disease involving eosinophils, mast cells, T lymphocytes, and various inflammatory mediators. Supporting this, research by Casadeval et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] found a positive correlation between peripheral blood eosinophil counts and sputum eosinophil counts, suggesting that peripheral eosinophil levels can reflect airway eosinophilic inflammation. Our study showed elevated peripheral eosinophil counts in the small airway dysfunction group, indicating a connection between small airway disease and eosinophilic inflammation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Key markers of type 2 inflammation, such as fractional exhaled nitric oxide (FeNO) and eosinophil counts, are associated with small airway dysfunction and poor asthma control [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, studies have shown that eosinophil numbers in small airways are significantly higher than in larger airways, indicating more severe eosinophilic inflammation in small airways [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Reducing eosinophilic inflammation has been linked to improvements in small airway function and overall quality of life, including enhanced FEV1, decreased airway resistance, and lower FeNO levels [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Mechanistically, FeNO and peripheral blood eosinophil count are positively correlated, as nitric oxide produced by airway cells attracts eosinophils, worsening the inflammatory response [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, our research showed no difference in FENO between the SAD group and the non-SAD group. The possible reason is that small airway eosinophilic inflammation may not significantly increase FeNO, especially when the airway is not exhausted [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSmall airways contribute significantly to total airway resistance, accounting for 10%-25% [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous research [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] has shown that IOS parameters such as R5-R20, X5, and MMEF can detect small airway abnormalities even when conventional spirometry results are normal. In our study, despite normal FEV1, FVC, and FEV1/FVC ratios, the small airway dysfunction group demonstrated lower spirometry parameters (MMEF, MEF50%, MEF25%) compared to the normal group, consistent with prior findings. We also observed significantly higher values of R5%pred, R5-R20, Fres, and X5 in the dysfunction group. These results confirm that IOS parameters correlate well with conventional spirometry and can identify small airway dysfunction even when spirometry results are normal. The negative values of R5%pred, R5-R20, Fres, and X5 were significantly higher in the small airway dysfunction group. X5 reflects peripheral elastic resistance, which increases notably with small airway obstruction, while Fres, as a measure of resonance frequency, is a sensitive indicator of airway viscous resistance that can rise even with mild peripheral airway obstruction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings suggest that small airway dysfunction can exist in asthma patients without significant changes in conventional lung function. Elevated R5%pred, R5-R20, Fres, and X5 negative values serve as indicators of small airway abnormalities, aligning with observations by Liu et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]Previous studies have also shown correlations between IOS parameters (R5-R20, Fres, X5) and conventional lung function (MMEF%pred) in asthma patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our data extend these observations, revealing positive correlations between IOS parameters and MMEF%pred, suggesting that IOS can reliably detect small airway dysfunction, even when conventional lung function appears normal.\u003c/p\u003e \u003cp\u003eMoreover, IOS demonstrates greater sensitivity and specificity compared to conventional tests for identifying small airway disease [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Fres, in particular, proved to be the most effective parameter for diagnosing small airway dysfunction, with an optimal cutoff of 14.385 Hz. When conventional lung function tests yield normal results, a Fres value above this threshold should raise suspicion for small airway dysfunction. Previous studies have established that normal Fres values range from greater than 30 Hz in children to 8\u0026ndash;12 Hz in adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conditions such as airway obstruction and interstitial lung disease can lead to increased Fres. Liu et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] also noted that combining FeNO with Fres enhances the diagnostic accuracy for small airway dysfunction, as Fres reflects both elastic and viscous airway resistance, while FeNO indicates eosinophilic inflammation.\u003c/p\u003e \u003cp\u003eTherapeutically, in the small airway dysfunction group, treatment with low-dose inhaled corticosteroids and long-acting β2 agonists resulted in improved IOS parameters (Z5%pred, R5%pred, R5-R20, X5, Fres). This indicates that small airway function can improve with appropriate treatment, and these changes can be monitored through pre- and post-treatment IOS comparisons. Previous studies report that inhaled corticosteroids combined with long-acting β2 agonists target small airways effectively, improving asthma control and reducing symptoms and exacerbations [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, triple therapy (ICS\u0026thinsp;+\u0026thinsp;LABA\u0026thinsp;+\u0026thinsp;LAMA) has been shown to enhance clinical control and small airway function in severe asthma [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, further research is necessary to assess the role of IOS in evaluating asthma treatment and control.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged: First, our study had a relatively high loss-to-follow-up rate (30.6%). Second, we focused primarily on laboratory and spirometry data, without clinical symptom assessments such as asthma control tests. Finally, peripheral eosinophil count was used as a proxy for eosinophilic inflammation, which is less precise than sputum analysis.\u003c/p\u003e \u003cp\u003eIn summary, IOS parameters correlate well with conventional spirometry but offer higher sensitivity and specificity for detecting small airway dysfunction. Fres, in particular, has significant diagnostic value and can complement conventional spirometry in evaluating small airway dysfunction in asthma.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the significant prevalence of small airway dysfunction (SAD) among asthma patients with normal spirometry, affecting nearly 57% of the cohort. The findings demonstrate that impulse oscillometry (IOS) is a valuable tool for detecting SAD, offering greater sensitivity and specificity compared to traditional spirometry. Specifically, the Fres parameter emerged as the most effective diagnostic indicator for SAD. Furthermore, our results indicate that IOS can effectively monitor changes in small airway function following treatment with standard inhaled corticosteroids and long-acting β₂ agonists, suggesting its utility in managing asthma control. These insights underscore the importance of incorporating IOS into routine clinical practice for a comprehensive evaluation of asthma patients, particularly those with normal spirometry but persistent symptoms. Future studies should explore the long-term implications of SAD on asthma management and patient quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to acknowledge all the participants, medical-, nursing-, and technical-staff who has been involved in collecting the samples for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW. Y., X. H., H. Y. performed the research, collected and analyzed the data and wrote the paper. Y.L., X.Z., L. Z., and J.W. contributed to sample collection. A. C. , Y. S.and Y.D.contributed to supervision of this study and revision of the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the Science and Technology Plan Project of Wenzhou Municipality (Y20220340).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe present study was conducted in accordance with the amended Declaration of Helsinki. The Ethics Committee of Affiliated Yueqing Hospital of Wenzhou Medical University approved the protocol of this retrospective study (approval number: YQYY202200052).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvery participant consent to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWesterhof G A, C H,de Nijs S B,et al, Clinical predictors of remission and persistence of adult-onset asthma[J]. J Allergy Clin Immunol, 2018. 141(1):104-109.\u003c/li\u003e\n\u003cli\u003eGlobal Initiative for Asthma. Global Strategy for Asthma Management and Prevention, 2023. http://www.ginaasthma.org/.\u003c/li\u003e\n\u003cli\u003eUsmani O S, S D, Spinola M,et al, The prevalence of small airways disease in adult asthma: A systematic literature review[J]. Respir Med, 2016. 116:19-27.\u003c/li\u003e\n\u003cli\u003ePostma D S, B C, Baldi S,et al, Exploring the relevance and extent of small airways dysfunction in asthma (ATLANTIS): baseline data from a prospective cohort study[J]. Lancet Respir Med, 2019. 7(5):402-416.\u003c/li\u003e\n\u003cli\u003eManoharan A, A W J, Lipworth J,et al, Small airway dysfunction is associated with poorer asthma control[J]. Eur Respir J, 2014. 44(5): 1353-5.\u003c/li\u003e\n\u003cli\u003eCottini M, L A, Lombardi C,et al, Clinical Characterization and Predictors of IOS-Defined Small-Airway Dysfunction in Asthma[J]. J Allergy Clin Immunol Pract, 2020. 8(3): 997-1004.\u003c/li\u003e\n\u003cli\u003eChan R, L B, Forced Vital Capacity and Low Frequency Reactance Area Measurements Are Associated with Asthma Control and Exacerbations[J]. Lung, 2022. 200(3): 301-303.\u003c/li\u003e\n\u003cli\u003eBrashier, B, Salvi S, Measuring lung function using sound waves: role of the forced oscillation technique and impulse oscillometry system[J]. Breathe (Sheff), 2015. 11(1): 57-65.\u003c/li\u003e\n\u003cli\u003eSu Z Q, Guan W J,Li S Y,et al, Significances of spirometry and impulse oscillometry for detecting small airway disorders assessed with endobronchial optical coherence tomography in COPD[J]. Int J Chron Obstruct Pulmon Dis, 2018. 13: 3031-3044.\u003c/li\u003e\n\u003cli\u003eShi Y, Aledia A S,Galant S P,George S C,Peripheral airway impairment measured by oscillometry predicts loss of asthma control in children[J]. J Allergy Clin Immunol, 2013. 131(3): 718-23.\u003c/li\u003e\n\u003cli\u003eKing G G., Bates J Berger, K I,P Calverley, et al, Technical standards for respiratory oscillometry[J]. Eur Respir J, 2020. 55(2).\u003c/li\u003e\n\u003cli\u003eOostveen E,M D,Lorino H,et al,The forced oscillation technique in clinical practice: methodology, recommendations and future developments[J]. Eur Respir J, 2003. 22(6): 1026-41.\u003c/li\u003e\n\u003cli\u003eManoharan A, Anderson W J,J Lipworth,Lipworth B J., Assessment of spirometry and impulse oscillometry in relation to asthma control[J]. Lung, 2015. 193(1):47-51.\u003c/li\u003e\n\u003cli\u003eChiu H Y.H., Y. H,Su, K. C, et al, Small Airway Dysfunction by Impulse Oscillometry in Symptomatic Patients with Preserved Pulmonary Function[J]. J Allergy Clin Immunol Pract, 2020. 8(1):229-235.\u003c/li\u003e\n\u003cli\u003evan der Wiel, E., ten Hacken N H,Postma D S,et al., Small-airways dysfunction associates with respiratory symptoms and clinical features of asthma: a systematic review[J]. J Allergy Clin Immunol, 2013. 131(3): 646-57.\u003c/li\u003e\n\u003cli\u003eKole TM, V.B.E., Kraft M, et al. , Predictors and associations of the persistent airflow limitation phenotype in asthma: a post-hoc analysis of the ATLANTIS study. Lancet Respir Med, 2023. 11(1):55-64.\u003c/li\u003e\n\u003cli\u003eKraft M, R.M., Hallmark B, et al, The role of small airway dysfunction in asthma control and exacerbations: a longitudinal, observational analysis using data from the ATLANTIS study. Lancet Respir Med, 2022. 10(7): 661-668.\u003c/li\u003e\n\u003cli\u003eFoy B. H, S.M., Bordas R,et al, Lung Computational Models and the Role of the Small Airways in Asthma[J]. Am J Respir Crit Care Med, 2019. 200(8): 982-991.\u003c/li\u003e\n\u003cli\u003eMortimer, K., Lesosky, M,Garc\u0026iacute;a-Marcos, L,et al., The burden of asthma, hay fever and eczema in adults in 17 countries: GAN Phase I study[J]. Eur Respir J, 2022. 60(3).\u003c/li\u003e\n\u003cli\u003eCasadevall, C., Quero, S,Millares, L,et al., Relationship between Respiratory Microbiome and Systemic Inflammatory Markers in COPD: A Pilot Study. Int J Mol Sci, 2024. 25(15):8467.\u003c/li\u003e\n\u003cli\u003eMenzies-Gow, A.M., A. H.Brightling, C. E., Clinical utility of fractional exhaled nitric oxide in severe asthma management[J]. Eur Respir J, 2020. 55(3).\u003c/li\u003e\n\u003cli\u003eAbdo M, P.F., Kirsten A-M,et al. , Longitudinal Impact of Sputum Inflammatory Phenotypes on Small Airway Dysfunction and Disease Outcomes in Asthma[J]. J Allergy Clin Immunol Pract, 2022. 10(6):1545-1553.\u003c/li\u003e\n\u003cli\u003eHamid, Q., Song, Y,Kotsimbos, T. C,et al, Inflammation of small airways in asthma[J]. J Allergy Clin Immunol, 1997. 100(1):44-51.\u003c/li\u003e\n\u003cli\u003eAbdo M, W.H., Veith V, et al, Small airway dysfunction as predictor and marker for clinical response to biological therapy in severe eosinophilic asthma: a longitudinal observational study[J]. Respir Res, 2020. 21(1): 278.\u003c/li\u003e\n\u003cli\u003eGao, F., Lei, J,Zhu, H,Zhao, L,et al., Small airway dysfunction links asthma exacerbations with asthma control and health-related quality of life[J]. Respir Res, 2024. 25(1): 306.\u003c/li\u003e\n\u003cli\u003eLi L, Zhang H, Holloway J W, et al. Pubertal onset with adulthood lung function mediated by height growth in adolescence[J]. European Respiratory Society, 2020. 6(4): 00535-2020.\u003c/li\u003e\n\u003cli\u003eBafadhel, M., Peterson, S,De Blas, M. A,et al., Predictors of exacerbation risk and response to budesonide in patients with chronic obstructive pulmonary disease: a post-hoc analysis of three randomised trials. Lancet Respir Med, 2018. 6(2): 117-126.\u003c/li\u003e\n\u003cli\u003eCarr, T.F., Altisheh, R,Zitt, M., Small airways disease and severe asthma[J]. World Allergy Organ J, 2017. 10(1):20.\u003c/li\u003e\n\u003cli\u003eGalant, S.P., Morphew, T., Adding oscillometry to spirometry in guidelines better identifies uncontrolled asthma, future exacerbations, and potential targeted therapy[J]. Ann Allergy Asthma Immunol, 2024. 132(1): 21-29.\u003c/li\u003e\n\u003cli\u003eCottini, M., Licini, A,Lombardi, C,Bagnasco, D,et al., Small airway dysfunction and poor asthma control: a dangerous liaison.[J] Clin Mol Allergy, 2021. 19(1): 7.\u003c/li\u003e\n\u003cli\u003eLiu, Z., Lin, L,Liu, X., Clinical application value of impulse oscillometry in geriatric patients with COPD[J]. Int J Chron Obstruct Pulmon Dis, 2017. 12: 897-905.\u003c/li\u003e\n\u003cli\u003eLiu, L., Liu, W,Liu, C,et al., Study on small airway function in asthmatics with fractional exhaled nitric oxide and impulse oscillometry[J]. Clin Respir J, 2018. 12(2):483-490.\u003c/li\u003e\n\u003cli\u003eDesiraju, K., Agrawal, A., Impulse oscillometry: The state-of-art for lung function testing[J]. Lung India, 2016. 33(4): 410-6.\u003c/li\u003e\n\u003cli\u003eLiwsrisakun, C., Chaiwong, W,Pothirat, C., Comparative assessment of small airway dysfunction by impulse oscillometry and spirometry in chronic obstructive pulmonary disease and asthma with and without fixed airflow obstruction[J]. Front Med (Lausanne), 2023. 10: 1181188.\u003c/li\u003e\n\u003cli\u003eCottini, M., Lombardi, C,Micheletto, C., Small airway dysfunction and bronchial asthma control : the state of the art[J]. Asthma Res Pract, 2015. 1:13.\u003c/li\u003e\n\u003cli\u003eCarpagnano, G.E., Portacci, A. and S. Dragonieri, Montagnolo, F,et al., Managing Small Airway Disease in Patients with Severe Asthma: Transitioning from the \u0026quot;Silent Zone\u0026quot; to Achieving \u0026quot;Quiet Asthma\u0026quot;[J]. J Clin Med, 2024. 13(8): 2320.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bronchial asthma, Impulse oscillometry, Spirometry, Small airway function","lastPublishedDoi":"10.21203/rs.3.rs-6011946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6011946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmall airway dysfunction (SAD) can occur in asthma patients with normal spirometry, impacting symptoms and disease control. Small airway dysfunction is linked to more severe symptoms and worse spirometry, poor asthma control, nocturnal asthma, and exercise-induced asthma.Impulse oscillometry (IOS) assesses small airway function through a simple, noninvasive method that employs the forced oscillation technique. It requires minimal patient cooperation and is suitable for use in both children and adults. This method can assess obstructions in the large and small peripheral airways and has been used to measure bronchodilator response and bronchoprovocation testing. However, its utility in patients with SAD requires further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 109 asthma patients with normal spirometry, who were divided into SAD (R5-R20 \u0026gt; 0.1 kPa·L⁻¹·s⁻¹) and normal groups (R5-R20 \u0026lt; 0.1 kPa·L⁻¹·s⁻¹) based on IOS results. Clinical data, IOS, and spirometry findings were compared between the two groups. Forty-three SAD patients were re-assessed after 3 months of treatment with standard inhaled corticosteroids and long-acting β₂ agonists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSAD was identified in 56.9% of asthma patients with normal spirometry. The SAD group exhibited higher blood eosinophil counts (EOS), Z5%pred, R5%pred, R5-R20, and Fres (P\u0026lt;0.05), while FEV1%pred, MMEF%pred, MEF75%pred, MEF50%pred, MEF25%pred, and X5 were lower (P\u0026lt;0.05). Correlation analysis revealed that high R5-R20 correlated with higher Fres (ρ=0.812), R5%pred (ρ=0.637), and lower X5 (ρ=-0.643), MMEF%pred (ρ=-0.441), and FEV1%pred (ρ=-0.359). The AUCs for Fres, X5, MMEF%pred, and FEV1%pred were 0.940, 0.790, 0.702, and 0.684, respectively, with Fres demonstrating the highest diagnostic value for SAD (cutoff 14.385, 95% CI: 0.897-0.983, sensitivity 95.2%, specificity 83.0%). In the SAD group, significant reductions were observed in Z5%pred, R5%pred, R5-R20, Fres, and X5 after treatment (P\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNearly 50% of asthma patients with normal spirometry exhibit small airway dysfunction, which can be detected using IOS. IOS provides higher sensitivity and specificity than spirometry for diagnosing SAD, with Fres being the most valuable parameter. Furthermore, IOS can effectively monitor asthma control.\u003c/p\u003e","manuscriptTitle":"Impulse oscillometry in assessing small airway function in asthma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 08:27:51","doi":"10.21203/rs.3.rs-6011946/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"795552ce-8c46-46ed-b780-465c13e62584","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T06:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 08:27:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6011946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6011946","identity":"rs-6011946","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}