Study of Blood Eosinophils and Plasma Periostin as Biomarkers for Response of COPD Patients to ICS/LABA Treatment

Study of Blood Eosinophils and Plasma Periostin as Biomarkers for Response of COPD Patients to ICS/LABA Treatment | 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 Study of Blood Eosinophils and Plasma Periostin as Biomarkers for Response of COPD Patients to ICS/LABA Treatment Sara Abdulbadie Abdulsattar, Mohamed Sayed Hantera, Hossam Abdel Mohsen Hodeb, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4892123/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Eosinophilic airway inflammation has been detected in up to 40% of chronic obstructive pulmonary disease (COPD) patients during stable periods of the disease and increased sputum eosinophil count was associated with better lung function, more future exacerbations, and symptoms that responded better to treatment with inhaled and oral corticosteroids. The aim of this work was to investigate the relationship of blood eosinophils and plasma Periostin with lung function changes related to ICS and long-acting beta2- agonist combination treatment in stable COPD patients for three months. Methods: This prospective experimental study was carried out on 50 COPD patients with post bronchodilator FEV1/ FVC ratio <70%. Patient collected from outpatient clinics of Chest Department Tanta University Hospitals, Tanta Chest Hospital and Mansoura Chest Hospital from February 2020 to February 2022.Patients subjected to complete history taking, clinical examinations including general and local examinations and laboratory investigations [complete blood count to assess eosinophilic count, arterial blood gases and plasma periostin] and spirometry were done for all patients pre and post treatment. All patients received three months of treatment with a fixed dose of combined inhalers of ICS and LABA and then they were classified into FEV1 responder and FEV1 non-responder according to improvement in FEV1 at least 12% and 200ml from base line of three months of combined treatment with ICS/LABA. Results: Blood eosinophil count had a significant positive correlation with smoking index (pack/year), negative correlations with FEV1% of predicted, FEV1 actual value (L), FEV1/FVC ratio. Plasma periostin concentration had significant positive correlation with blood eosinophil count, COPD grade, FVC actual value (L) and significant negative correlations with FEV1% of predicted, FEV1 actual value (L) and FEV1/FVC. Cut off values of blood eosinophil count >265cell/ µL, plasma Periostin concentration >15.747 ng/ml and FEV1% < 40.5% were associated with post treatment FEV1 response. Conclusions: COPD patients with poor lung functions regarding FEV1/FVC ratio, FEV1 actual value (L) and FEV1% of predicted as well as high blood eosinophil count and high plasma periostin concentration are predicted to have FEV1 response with fixed dose ICS/LABA combination treatment. Blood eosinophils Plasma Periostin COPD ICS/LABA combination Treatment Figures Figure 1 Introduction Chronic Obstructive Pulmonary Disease (COPD) is a complex pulmonary disorder characterized by persistent and often progressive obstruction of airflow due to abnormalities in the alveoli (emphysema) and/or airway (bronchitis, bronchiolitis). These abnormalities cause respiratory symptoms including dyspnea, cough, and sputum production, as well as exacerbations [ 1 ] . Pathological changes in the airways, lung parenchyma, and pulmonary vasculature are observed in patients with COPD. These changes encompass both structural and inflammatory modifications, which worsen in severity as airflow obstruction persists despite smoking cessation [ 2 ] . Eosinophilia and T helper type 2 (Th2) inflammation may also play a significant role in the development of COPD in a subset of patients, despite the widespread belief that neutrophils are the primary factor. Furthermore, a Th2-high signature has been observed in approximately 20% of smokers with COPD [ 3 ] . Elevated levels of eosinophils in the bloodstream are correlated with heightened eosinophil counts in the lungs and the prevalence of more pronounced indicators of type-2 inflammation in the airways among patients with COPD. These variations in airway inflammation may account for the differential response to ICS based on eosinophil count in the blood [ 4 , 5 ] Patients with frequent or severe exacerbations (two or more moderate exacerbations annually, or at least one exacerbation requiring hospitalization) and a blood eosinophil count of 300 cells/µl4 should receive initial treatment with ICS (as LAMA/LABA/ICS; triple therapy), according to the 2023 GOLD strategy. Furthermore, patients who exhibit persistent exacerbations despite receiving LAMA/LABA therapy and have blood eosinophil counts below 100 cells/µl may undergo triple therapy subsequent to a thorough evaluation of the anticipated benefits and associated risks. Although the recommendation has shifted away from LABA/ICS usage in patients with COPD and concurrent asthma, the administration of ICS remains obligatory [ 1 ] . Additionally, a number of studies have demonstrated a correlation among elevated sputum eosinophil counts and ameliorated lung function in patients with COPD who received ICS in conjunction with LABA [ 6 , 7 ] In recent trail, a significant correlation has been established not only among eosinophils in the sputum and those in the blood (8), but also among eosinophils and serum periostin, which can serve as an additional blood surrogate marker for eosinophilic airway inflammation [ 8 , 9 ] . Aim of the work: The purpose of this trail was to examine the correlation among plasma periostin and blood eosinophils and alterations in lung function associated with LABA and ICS combination therapy in stable COPD patients over a three-month period. Patients and Methods Fifty patients who were clinically stable COPD as defined by the Global Initiatives for Chronic Obstructive Lung Disease (GOLD 2020) [ 10 ] and had a post-bronchodilator FEV1/FVC ratio of less than 70% at the time of follow-up in the outpatient clinics of the Chest Department Tanta University Hospitals, Tanta Chest Hospital, and Mansoura Chest Hospital participated in this prospective experimental study. The trail was conducted from February 2020 to February 2022 with the authorization of the Ethical Committee of the Faculty of Medicine, Tanta University in Tanta, Egypt (approval code: 33652/1/20). Written informed assent was obtained from each patient. Patients who meet the following inclusion criteria: age exceeding 40 years, a history of cigarette consumption exceeding 10 pack years, and a post-bronchodilator FEV1/FVC ratio below 70%. Pregnant women, patients with a recent history of COPD exacerbation within one month, and those with concurrent lung diseases including interstitial lung disease, pneumonia, active pulmonary tuberculosis, pulmonary embolism, or lung cancer were excluded from the study. Every individual underwent the following procedures: comprehensive history taking, clinical examination encompassing general and local chest examinations, radiological assessment (chest x-ray), and laboratory investigations (including eosinophilic count assessment via arterial blood gases (ABG), complete blood count (CBC) and plasma periostin). Regarding eosinophilic count and plasma periostin concentration, blood samples about (3cm,2cm for doing CBC and assessing eosinophilic count and 1cm to detect plasma Periostin concentration) were collected after 2 weeks of washout period ( After cessation of ICS for 2 weeks, an inhaled LABA, or LAMA for 2 days, an inhaled short acting B2 for 12 hours) and recollected again after 3 months of treatment with a fixed dose of combined inhalers of ICS and LABA (50mcg salmetrol/500mcg fluticasone or 9mcg formotrol/320mcg budesonide, twice daily) when they separated into their components and transferred to the Clinical Pathology Department; Tanta University Hospitals on dry ice, and kept in -80°c freezers until use. plasma samples were thawed for periostin and measured using a commercially available ELISA kit according to the manufactors instructions. The Human Periostin ELISA (Enzyme-Linked Immunosorbent Assay) kit is an in vitro enzyme-linked immunosorbent assay for the quantitative measurement of Human Periostin in Cell Culture Supernatants, Serum, Plasma. from Biokit for Scientific Research [ 11 ] . Procedure of Spirometry Spirometry was done according to ATS guidelines 2019 [ 12 ] . The procedure was explained to the patients who were informed about the activities that should be avoided before the technique. Without shoes on, the subjects' age and gender were documented, their weight was estimated to the nearest kilogram, and their height was measured to the nearest centimeter. The hands of the physician were cleansed. The patient was situated in a seated position, maintaining a modest elevation of the head. A nose clip was utilized to close the patient's nose, and a mouthpiece was inserted into his oral cavity. He was instructed to firmly clasp his lips around the mouthpiece. At total lung capacity (TLC), the patient was instructed to inhale maximally with a pause of less than one second, followed by an exhalation of maximal force in a single continuous sequence until no further air was emitted for a minimum of six seconds. The procedure step was repeated as necessary for a minimum of three satisfied maneuvers and rest among every trial for 10 minutes. The patient was prepared 10–15 minutes before the procedure by inhalation of 400 mcg of salbutamol. The presence of a post-bronchodilator FEV1/FVC ratio less than 0.70 confirm non fully reversible airflow obstruction [ 13 ] . Then patients were subdivided into 4 subgroups according to post bronchodilator FEV1%. FEV1% ≥ 80% predicted; GOLD 1 – mild airflow obstruction, 50% ≤ FEV1 < 80% predicted; GOLD 2 – moderate airflow obstruction, 30% ≤ FEV1 < 50% predicted GOLD 3 – severe airflow obstruction, FEV1% < 30% predicted; GOLD 4 - very severe airflow obstruction). After three months of treatment with a fixed dose of ICS/LABA, spirometry was repeated again, and patients were classified into two groups according to improvement in FEV1 of at least 12% and 200ml from baseline into: FEV1 non- responders who did not record this criterion and FEV1 responders who recorded this criterion. Statistical analysis For statistical analysis, SPSS v26 (IBM Inc., Chicago, IL, USA) was utilized. Histograms and the Shapiro-Wilks test were utilized to assess the normality of the data distribution. The quantitative parametric variables were expressed as the mean and standard deviation (SD), and the unpaired Student's t-test was utilized to compare the two groups. Quantitative non-parametric data were analyzed using the Mann Whitney test and were presented as the median and interquartile range (IQR). Frequency and percentage (%) were used to present qualitative variables, which were analyzed utilizing the chi-square test or Fisher's exact test. The Spearman Correlation Test (rs) was employed to examine the direction and strength of the relationship among two nonparametric variables. The area under the curve (AUC) gauged the efficacy of the test as a whole. A p-value less than 0.05 was deemed to be statistically significant. Results The included 50 patients were subdivided at the end of the trail into two groups according to improvement in FEV1 of at least 12% and 200ml from baseline into: FEV1 non- responders included 39 (78%) patients and FEV1 responders included 11 (22%) patients. Regarding demographic data, age, sex, BMI, smoking status, smoking index (pack/year) and co morbidities were insignificantly different among the two studied groups. COPD grade differed significantly among the two groups (p = 0.009) with regard to baseline COPD criteria Table 1 Table 1 Comparison between FEV1 non- responder and FEV1 responder groups regarding demographic data and baseline COPD criteria FEV1 non-responder (n = 39) FEV1 responder (n = 11) P Age (years) 60.0 (50.0–67.0) 61.0 (53.0–66.0) 0.879 Gender Male 30 (76.9%) 9 (81.8%) 1.000 Female 9 (23.1%) 2 (18.2%) BMI (kg/m2) 22.4 (20.8–28.0) 23.0 (20.8–26.0) 0.963 Smoking status Passive smoker 6 (15.4%) 2 (18.2%) 0.393 Active smoker 21 (53.8%) 8 (72.7%) Ex- smoker 12 (30.8%) 1 (9.1%) Smoking index (pack/ year) 45.0 (39.0–54.0) 47.0 (39.0–55.0) 0.760 Comorbidities HTN 9 (45.0%) 2 (33.3%) 0.897 DM 7 (35.0%) 2 (33.3%) IHD 3 (15.0%) 1 (16.7%) CKD 1 (5.0%) 1 (16.7%) Baseline COPD criteria COPD grade Moderate 17 (43.6%) 0 (0.0%) 0.009* Severe 22 (56.4%) 11 (100.0%) mMRC dyspnea scale 3.0 (2.0–3.0) 3.0 (2.0–3.0) 0.663 Rate of exacerbation in last year 3.0 (2.0–4.0) 3.0 (2.0–4.0) 0.932 Number of COPD related hospitalization in last year 2.0 (1.0–3.0) 2.0 (1.0–3.0) 0.668 Data were presented as mean ± SD or frequency (%) or median (IQR). *Significant p value < 0.05, FEV1: forced expiratory volume in first second, BMI: Body mass index, HTN: hypertension, DM: diabetes mellitus, IHD: ischemic heart disease, CKD: chronic kidney disease, COPD: Chronic obstructive pulmonary disease .mMRC: modified medical research council In relation to baseline spirometric data, the responder group exhibited substantially lower values for FEV1%, FEV1 (L), and the FEV1/FVC ratio in comparison to the non-responder group (P = 0.001). However, no significant differences were observed in FVC% and FVC (L) among the two groups under investigation. The ABG parameters (pH, PaCO2, PaO2, HCO3) exhibited negligible variation among the two groups under investigation. The responder group exhibited significantly higher levels of plasma periostin and baseline blood eosinophil count compared to the non-responder group (p < 0.001). There was statistical significance in this difference. Table 2 Table 2 Comparison between FEV1 non- responder and FEV1 responder groups regarding baseline spirometry, ABG, WBC count, blood eosinophil count and plasma periostin concentration FEV1-non-responder (n = 39) FEV1 responder (n = 11) P Baseline Spirometry FVC % of predicted 79.0 (78.0–81.0) 78.0 (78.0–80.0) 0.328 FVC actual value (L) 3.5 (3.3–3.6) 3.5 (3.5–3.6) 0.300 FEV1% of predicted 48.0 (43.0–52.0) 34.0 (31.0–39.0) < 0.001* FEV1 actual value (L) 1.7 (1.5–1.8) 1.2 (1.1–1.4) < 0.001* FEV1/FVC ratio (%) 48.0 (43.0–51.0) 33.0 (31.0–38.0) < 0.001* Baseline ABG pH 7.4 (7.39–7.43) 7.44 (7.39–7.45) 0.376 PaCO 2 (mmHg) 41.0 (38.0–45.0) 40.0 (36.0–44.0) 0.359 PaO 2 (mmHg) 66.0 (60.0–72.0) 65.0 (60.0–70.0) 0.596 HCO 3 (mmol/ L) 37.0 (31.0–40.0) 37.0 (29.0–45.0) 0.907 White blood cell count (cell /µL) 7000.0 (6500.0–8000.0) 7500.0 (7300.0–8200.0) 0.360 Neutrophil count (cell/µL) 4000.0 (3600.0–4553.0) 3900.0 (3289.0–4230.0) 0.227 Eosinophil count (cell/µL) 200.0 (180.0–220.0) 300.0 (290.0–320.0) < 0.001* Plasma periostin concentration (ng/mL) 8.732 (7.968–9.87) 23.88 (19.43–35.31) < 0.001* Data were presented as mean ± SD or median (IQR). *Significant p value < 0.05,, FVC: Forced vital capacity, FEV1: forced expiratory volume in first second, ABG: arterial blood gases, WBC: white blood cell count Regarding post treatment spirometric data, blood eosinophil and plasma Periostin concentration : FVC% of predicted, FVC actual value(L), FEV1/FVC ratio, change in FEV1% of predicted, change in FEV1 actual value (L), blood eosinophil count, plasma Periostin concentration and plasma Periostin concentration change showed statistically significant difference among two groups (p;<0.001) but FEV1 actual value (L), FEV1% of predicted, blood eosinophil count change showed non statistically significant difference among two groups. Table 3 Table 3 Comparison between FEV1 non-responder and FEV1 responder groups regarding post treatment spirometric data, blood eosinophil count and plasma Periostin concentration FEV1-non-responder (n = 39) FEV1 responder (n = 11) P FVC % of predicted 81.0 (78.0–82.0) 89.0 (84.0–91.0) < 0.001* FVC actual value (L) 3.6 (3.5–3.7) 4.0 (3.8–4.1) < 0.001* FEV1%of predicted 53.0 (48.0–54.0) 48.0 (46.0–54.0) 0.338 FEV1 actual value (L) 1.8 (1.7–1.9) 1.7 (1.6–1.9) 0.574 FEV1/FVC ratio 50.0 (47.0–53.0) 45.0 (42.0–46.0) 0.001* Change in FEV1% of predicted 3.0 (0.0–6.0) 15.0 (14.0–17.0) < 0.001* Change in FEV1 actual value (L) 0.1 (0.01 − 0. 2) 0.5 (0.5–0.6) < 0.001* Blood eosinophil count ( cell/µL) 190.0 (170.0–208.0) 290.0 (279.0–308.0) < 0.001* Blood eosinophil count change (cell/µL) 10.0 (5.0–15.0) 11.0 (8.0–20.0) 0.236 Plasma Periostin concentration (ng/mL) 7.23 (6.166–8.008) 12.676 (8.208–18.818) < 0.001* Plasma Periostin concentration change ( ng/mL) 1.713 (1.028–3.18) 14.562 (6.749–19.532) < 0.001* Data were presented as median (IQR). *Significant p value < 0.05, FVC: Forced vital capacity, FEV1: forced expiratory volume in first second. Regarding correlation between blood eosinophil count, plasma Periostin concentration and other variables at baseline At baseline, blood eosinophil count had a significant positive correlation with and plasma periostin concentration and smoking index (pack/year) (rs = 0.454, P = 0.001), (r s ; 0,299 P = 0.035) respectively but had significant negative correlations with FEV1 actual value (L), FEV1% of predicted and FEV1/FVC ratio (r s =-0.503, P = < 0.001), (r s ;-0.435, P = 0.002) and (r s =-.434, P = 0.002) respectively. Also, plasma periostin concentration had significant positive correlations with COPD grade and FVC actual value (L) (r s =.410; P = .003) (r s =.290, P = 0.041) respectively but had significant negative correlations with FEV1/FVC ratio, FEV1% of predicted and FEV1 actual value (L) (r s = − .648; P = < 0.001), (r s =- 0.630; P = < 0.001) and (r s = − .579; P = < 0.001) respectively. Table 4 Table 4 Correlation between baseline blood eosinophil count, plasma Periostin concentration and other variables at baseline: Variables Blood eosinophil count Plasma Periostin concentration r s P r s P Age 0.013 0.928 0.057 0.693 Sex 0.008 0.954 0.008 0.954 BMI -0.122 0.400 0.139 0.335 Smoking index (pack/ year) 0.299 0.035* 0.059 0.682 Comorbidities 0.220 0.281 0.189 0.356 No. of COPD related hospitalization in last year -0.054 0.711 0.072 0.619 Rate of exacerbation in last year -0.265 0.063 0.084 0.563 mMRC dyspnea scale -0.061 0.676 0.074 0.610 COPD grade 0.179 0.214 0.410 0.003* Neutrophil count 0.078 0.589 -0.236 0.099 Plasma Periostin concentration 0.454 0.001* -- --- pH 0.152 0.293 0.063 0.666 PaCO 2 0.085 0.557 -0.049 0.734 PaO 2 -0.159 0.269 -0.094 0.516 HCO 3 0.072 0.617 -0.057 0.692 FVC % of predicted -0.023 0.874 -0.067 0.646 FVC actual value (L) -0.051 0.723 0.290 0.041* FEV1% of predicted -0.435 0.002* -0.630 < 0.001* FEV1 actual value (L) -0.503 < 0.001* -0.579 < 0.001* FEV1/FVC ratio -0.434 0.002* -0.648 < 0.001* rs: Spearman correlation, *significant p value < 0.05, BMI: Body mass index, HTN: hypertension, DM: diabetes mellitus, IHD: ischemic heart disease, CKD: chronic kidney disease, COPD: Chronic obstructive pulmonary disease, MRC: Medical Research Council, FVC: Forced vital capacity, FEV1: forced expiratory volume in first second, ABG: arterial blood gases, WBC: white blood cell count In order to ascertain cutoff values for plasma periostin concentration, blood eosinophil count, and FEV1% as predictors of post-treatment FEV1 response, a Roc curve was constructed. It was found, baseline blood eosinophil count, cut off value count > 265cell/ µL, plasma periostin concentration > 15.747 ng/ml and FEV1% predicted < 40.5% (sensitivity, specificity, PPV, NPV and accuracy were 100% for all) were associated with post treatment FEV1 response with (AUC = 1, 95% C. I = 1.00–1.00),P < 0.001 for all .Table 5 , Fig. 1 Table 5 ROC curve for sensitivity and specificity of baseline blood eosinophil count, plasma periostin concentration and FEV1% of predicted for prediction of post treatment FEV1 response: AUC P 95% C.I Cut off Sensitivity Specificity PPV NPV Accuracy Baseline blood eosinophil count (cells/µL) 1.00 265.0 100.0 100.0 100.0 100.0 100.0 Baseline Plasma periostin concentration (ng/mL) 1.00 15.747 100.0 100.0 100.0 100.0 100.0 Baseline FEV1% of predicted 1.00 < 0.001* 1.00–1.00 < 40.5 100.0 100.0 100.0 100.0 100.0 *p ≤ 0.05 statistically significant AUC = 1.00 (Excellent predictor) Discussion Although eosinophilic inflammation is frequently observed in individuals with asthma, it is also present in a subset of patients with COPD. Although serum periostin is commonly administered to patients with asthma, it is seldom studied in patients with COPD [ 14 ] . Periostin, an extracellular matrix protein, has been identified in the airway subepithelial layer of patients with COPD IL-4 and IL-13 induce an upregulation of periostin expression in airway epithelial cells [ 15 ] . Produced by bronchial epithelial cells and pulmonary fibroblasts, this biomarker is linked to type 2 eosinophilic inflammation [ 16 , 17 ] . Plasma periostin and Blood eosinophils were examined in this trail as biomarkers of COPD patients' response to ICS/LABA therapy. In relation to demographic characteristics, there were no statistically significant difference observed among the two groups under investigation, including age, gender, BMI, smoking status, smoking index (pack/year), and comorbidities, when comparing FEV1-non-responder and FEV1 responder groups. In agreement with this study, Park et al., [ 18 ] who conducted a trial on 130 COPD patients, demonstrated that 42 subjects (32 percent) were classified as FEV1 responders after three months of treatment with ICS and LABA. Sexual orientation, smoking history, and body mass index did not differ significantly among FEV1 responders and non-responders.In the context of baseline COPD criteria, this trail compared FEV1-non-responder and FEV1 responder groups as follows: of the non-responder group, 17 (43.6%) patients had moderate COPD (GOLD II), while 22 (56.4%) patients had severe COPD (GOLD III). In contrast, the responder group comprised 11 (100%) patients with severe COPD (GOLD III). Although a statistically significant difference was observed among the two groups (p;0.009), the former group utilized the mMRC dyspnea scale, rate of exacerbation in the previous year, and number of patients with severe COPD (GOLD III). In agreement with present study, Park et al. [ 18 ] found comparable results and there were non significant difference in mMRC dyspnea scale, exacerbation rate among FEV1 responders and FEV1 non-responders. In relation to the comparison of baseline spirometric data among the two groups, the findings of this research indicated that the responder group exhibited significantly lower values for FEV1% of predicted, FEV1 actual value (L), and FEV1/FVC ratio (P = 0.001). On the contrary, there was no statistically significant difference observed among the two groups with regard to the FVC% of predicted value and the FVC actual value (L). In agreement with present study, Park et al. [ 18 ] distinguished among FEV1 responders and non-responders by demonstrating that the former had a higher likelihood of having a baseline FEV1 less than 50% pred, while the latter had higher FVC (L) and (% pred) values than the former. The FEV1 responder group had significantly higher baseline blood eosinophil count and plasma Periostin concentration than the FEV1 non-responder group (p0.001), whereas the differences among the two groups in terms of white blood cell count and neutrophil count were not statistically significant. Pascoe et al. [ 19 ] , Siddiqui et al. [ 20 ] and Barnes et al. [ 21 ] reported that elevated eosinophil concentrations have been found to predict response to ICS in COPD patients. FVC% of predicted, FVC actual value (L), change in FEV1% of predicted, and change in FEV1 actual value (L) were all significantly higher in the FEV1 responder group compared to the FEV1 non-responder group in this trail (p0.001). In contrast to the FEV1 non-responder group, the FEV1 actual value (L), FEV1% of predicted value, and FEV1/FVC ratio were all significantly lower in the FEV1 responder group. These findings were anticipated given that responders had reduced FEV1%, FEV1 (L), and FEV1/FVC ratios at baseline than non-responders. Brightling et al. [ 7 ] and Brightling et al. [ 22 ] demonstrated that COPD patients with high eosinophil count had a greater forced expiratory volume in first second (FEV1) improvement after corticosteroid treatment. In this study, post treatment blood eosinophil count, plasma periostin concentration and change in plasma periostin concentration were significantly higher in responder group compared to non-responder group (p < 0.001) while change in blood eosinophil count was not statistically significant different among two groups. Compatible with these findings, Park et al. [ 18 ] observed that the blood eosinophil count and plasma Periostin concentration were most significantly elevated in the FEV1 responders compared to the FEV1 non-responders among COPD patients undergoing ICS/LABA treatment. Fingleton et al., [ 23 ] , recorded in their trail on 386 patients with obstructive airway diseases, among them (17 patients were diagnosed as COPD) that ICS responsiveness patients, showed reduction in serum periostin after 12 weeks of ICS treatment. Regarding correlation among baseline blood eosinophil count, plasma periostin concentration and other variables: blood eosinophil count had significant positive correlations with plasma periostin concentration and smoking index (pack/year) but had significant negative correlations with FEV1 actual value (L), FEV1% of predicted, FEV1/FVC ratio. Other variables; age, sex, BMI, comorbidities, number of COPD related hospitalization, rate of exacerbation, mMRC dyspnea scale, neutrophil count, pH, PaCO 2 , PaO 2 , HCO3, FVC%, FVC (L) were insignificantly correlated with blood eosinophil count. Consistent with the findings of this study, Jensen et al. [ 24 ] examined a relation among lung functions and blood eosinophil and monocyte counts. They discovered that individuals with a history of heavy smoking had a higher blood eosinophil count compared to light smokers, but a lower monocyte count. These findings suggested that smoking does indeed impact blood eosinophil and monocyte counts, which may have a marginally detrimental effect on lung functions. Additionally, the relation among eosinophil blood count and lung function differed among smokers and nonsmokers.. Plasma periostin concentration had significant positive correlations with COPD grade and FVC actual value (L) but had significant negative correlations with FEV1/FVC ratio, FEV1% predicted and FEV1 actual value (L) .Other variables: age, sex, BMI, smoking index (pack/ year), comorbidities, number of COPD related hospitalization, rate of exacerbation, mMRC dyspnea scale, neutrophil count, pH, PaCO 2 , PaO 2 , HCO 3 and FVC % were insignificantly correlated with plasma periostin concentration. In agreement with these findings, Fingleton et al., [ 23 ] provided evidence of a statistically significant association among the logarithm of serum periostin and blood eosinophil count in individuals diagnosed with obstructive airway diseases. A minor negative association was observed among serum periostin and predicted FEV1%, while a weak positive correlation was observed among serum periostin and functional residual capacity%. However, no statistically significant associations were found among serum periostin and FEV1/FVC ratio. Additionally, a small negative association was observed among body mass index and serum periostin. Also, Clarenbach et al., [ 25 ] who investigated relation among blood periostin levels and exacerbation rates on 26 COPD patients demonstrated that there is no significant correlation among exacerbation rate and periostin levels in blood. Conversely, Shirai et al. [ 26 ] demonstrated a mild positive correlation (p = 0.24) among serum periostin and FEV1/FVC in patients with COPD, as well as a moderate correlation (p = 0.41) among serum periostin and FEV1. Eosinophil counts were greater in COPD patients with elevated serum periostin concentrations; however, no correlation was observed among periostin and body mass index (BMI) in this subset of patients. In this study, regarding cut off values, baseline blood eosinophil count > 265cell/ µL, plasma periostin concentration > 15.747 ng/ml and FEV1% predicted < 40.5% (sensitivity, specificity, PPV, NPV and accuracy were 100% for all) were associated with post treatment FEV1 response with (AUC = 1, 95% C. I = 1.00–1.00), P < 0.001 for all. In agreement with these findings, Park et al., [ 18 ] conducted the initial investigation into the relationship among plasma periostin and blood eosinophils and alterations in lung function associated with the combination therapy of ICS and LABA in stable COPD patients. They discovered that elevated levels of plasma periostin (> 23 ng/mL) and blood eosinophils (> 260/µL) were significantly related with a 50% improvement in FEV1 following 12 weeks of treatment with ICS and LABA in patients with COPD who had a baseline FEV1 below 50% predilection. Also, Tashkin et al., [ 27 ] reported that elevated blood eosinophil counts could potentially serve as a suitable substitute for airway eosinophilia and function as a readily available biomarker to assess the response of COPD patients to ICS treatment. Limitations: The sample size was relatively small. The short follow up period didn't allow detection of long-term outcomes of treatment on COPD exacerbation and related hospitalization and decline in lung functions. The trail did not assess the correlations among blood eosinophils, bronchoalveolar lavage fluid cells, airway inflammatory markers (e.g., sputum eosinophil), or bronchoalveolar lavage fluid cells; consequently, the relationship among airway and blood inflammatory markers was not investigated. COPD Assessment Test (CAT) was not included in analysis of these findings as it was not obtained at initial evaluation. Conclusions COPD patients with poor lung functions regarding FEV1/FVC ratio, FEV1 actual value (L) and FEV1% of predicted as well as high blood eosinophil count and high plasma periostin concentration are predicted to have FEV1 response with fixed dose ICS/LABA combination treatment. Baseline cut off values of blood eosinophil count > 265cell/ µL, plasma periostin concentration > 15.747 ng/ml and FEV1% < 40.5% may be used for selection of COPD patients who will show benefit from ICS/LABA treatment. Abbreviations COPD: Chronic Obstructive Pulmonary Disease Th2: T Helper Type 2 GOLD 2020: Global Initiatives for Chronic Obstructive Lung Disease ABG: Arterial Blood Gases CBC: Complete Blood Count FEV1: Forced Expiratory Volume in First Second FVC: Forced Vital Capacity MRC: Medical Research Council IHD: Ischemic Heart Disease CKD: Chronic Kidney Disease CAT: COPD Assessment Test Declarations Ethics approval and consent to participate It was approved by the ethics committee of Faculty of medicine, Tanta University Hospitals, Tanta Chest Hospital and Mansoura Chest Hospital from February 2020 to February 2022. An informed written consent was obtained from the participants. approval No. 33652/1/20. Consent for publication : All authors give their consent for publication in the journal. Availability of data and material: Data and material are available on a reasonable request from the author. Competing interests : The authors declare no conflict of interest. Funding: Nil. Authors' contributions: SAA and MSH conceived and supervised the study; HAH and AAE were responsible for data collection. GAE and SAA analysed and interpreted the data. All authors provided comments on the manuscript at various stages of development. All authors read and approved the final manuscript. Acknowledgements: Nil References Agustí A, Celli BR, Criner GJ, et al. (2023) Global initiative for chronic obstructive lung disease 2023 report: Gold executive summary. Am J Respir Crit Care Med 207:819–37. https://doi.org/10.1164/rccm.202301-0106PP Hogg JC, Timens W (2009) The pathology of chronic obstructive pulmonary disease. Annu Rev Pathol 4:435–59. https://doi.org/10.1146/annurev.pathol.4.110807.092145 Christenson SA, Steiling K, van den Berge M, et al. (2015) Asthma-COPD overlap. Clinical relevance of genomic signatures of type 2 inflammation in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 191:758–66. https://doi.org/10.1164/rccm.201408-1458OC Matheson MC, Benke G, Raven J, et al. (2005) Biological dust exposure in the workplace is a risk factor for chronic obstructive pulmonary disease. Thorax 60:645–51. https://doi.org/10.1136/thx.2004.035170 Pelkonen M, Notkola IL, Nissinen A, et al. (2006) Thirty-year cumulative incidence of chronic bronchitis and COPD in relation to 30-year pulmonary function and 40-year mortality: a follow-up in middle-aged rural men. Chest 130:1129–37. https://doi.org/10.1378/chest.130.4.1129 Papi A, Romagnoli M, Baraldo S, et al. (2000) Partial reversibility of airflow limitation and increased exhaled NO and sputum eosinophilia in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 162:1773–7. https://doi.org/10.1164/ajrccm.162.5.9910112 Brightling CE, McKenna S, Hargadon B, et al. (2005) Sputum eosinophilia and the short term response to inhaled mometasone in chronic obstructive pulmonary disease. Thorax 60:193–8. https://doi.org/10.1136/thx.2004.032516 Miravitlles M, Soler-Cataluña JJ, Calle M, et al. (2013) Treatment of COPD by clinical phenotypes: putting old evidence into clinical practice. Eur Respir J 41:1252–6. https://doi.org/10.1183/09031936.00118912 Jia G, Erickson RW, Choy DF, et al. (2012) Periostin is a systemic biomarker of eosinophilic airway inflammation in asthmatic patients. J Allergy Clin Immunol 130:647 – 54.e10. https://doi.org/10.1016/j.jaci.2012.06.025 KF R (2007) Global initiative for chronic obstructive lung disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. GOLD executive summary. Am J Respir Crit Care Med 176:532–55. Gillan L, Matei D, Fishman DA, et al. (2002) Periostin secreted by epithelial ovarian carcinoma is a ligand for αVβ3 and αVβ5 integrins and promotes cell motility. Cancer Res 62:5358–64. Graham BL, Steenbruggen I, Miller MR, et al. (2019) Standardization of spirometry 2019 update. An official american thoracic society and european respiratory society technical statement. Am J Respir Crit Care Med 200:70–88. https://doi.org/10.1164/rccm.201908-1590ST Han MK, Agusti A, Celli BR, et al. (2021) From GOLD 0 to Pre-COPD. Am J Respir Crit Care Med 203:414 – 23. https://doi.org/10.1164/rccm.202008-3328PP Loutsios C, Farahi N, Porter L, et al. (2014) Biomarkers of eosinophilic inflammation in asthma. Expert Rev Respir Med 8:143–50. https://doi.org/10.1586/17476348.2014.880052 Lee JH, Kim SH, Choi Y, et al. (2018) Serum periostin levels: A potential serologic marker for toluene diisocyanate-induced occupational asthma. Yonsei Med J 59:1214–21. https://doi.org/10.3349/ymj.2018.59.10.1214 Li W, Gao P, Zhi Y, et al. (2015) Periostin: its role in asthma and its potential as a diagnostic or therapeutic target. Respir Res 16:57–9. https://doi.org/10.1186/s12931-015-0218-2 Matsusaka M, Kabata H, Fukunaga K, et al. (2015) Phenotype of asthma related with high serum periostin levels. Allergol Int 64:175 – 80. https://doi.org/10.1016/j.alit.2014.07.003 Park HY, Lee H, Koh WJ, et al. (2016) Association of blood eosinophils and plasma periostin with FEV1 response after 3-month inhaled corticosteroid and long-acting beta2-agonist treatment in stable COPD patients. Int J Chron Obstruct Pulmon Dis 11:23–30. https://doi.org/10.2147/copd.S94797 Pascoe S, Locantore N, Dransfield MT, et al. (2015) Blood eosinophil counts, exacerbations, and response to the addition of inhaled fluticasone furoate to vilanterol in patients with chronic obstructive pulmonary disease: a secondary analysis of data from two parallel randomised controlled trials. Lancet Respir Med 3:435–42. https://doi.org/10.1016/s2213-2600(15)00106-x Siddiqui SH, Guasconi A, Vestbo J, et al. (2015) Blood eosinophils: A biomarker of response to extrafine beclomethasone/formoterol in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 192:523–5. https://doi.org/10.1164/rccm.201502-0235LE Barnes NC, Sharma R, Lettis S, et al. (2016) Blood eosinophils as a marker of response to inhaled corticosteroids in COPD. Eur Respir J 47:1374–82. https://doi.org/10.1183/13993003.01370-2015 Brightling CE, Monteiro W, Ward R, et al. (2000) Sputum eosinophilia and short-term response to prednisolone in chronic obstructive pulmonary disease: a randomised controlled trial. Lancet 356:1480–5. https://doi.org/10.1016/s0140-6736(00)02872-5 Fingleton J, Braithwaite I, Travers J, et al. (2016) Serum periostin in obstructive airways disease. Eur Respir J 47:1383–91. https://doi.org/10.1183/13993003.01384-2015 Jensen EJ, Pedersen B, Narvestadt E, et al. (1998) Blood eosinophil and monocyte counts are related to smoking and lung function. Respir Med 92:63–9. https://doi.org/10.1016/s0954-6111(98)90034-8 Clarenbach CF, Sievi NA, Brock M, et al. (2016) Periostin levels do not distinguish chronic obstructive pulmonary disease patients with frequent and infrequent exacerbations. Pulmonary research and respiratory medicine 3:33–6. Shirai T, Hirai K, Gon Y, et al. (2019) Combined assessment of serum periostin and YKL-40 may identify asthma-COPD overlap. J Allergy Clin Immunol Pract 7:134–45. https://doi.org/10.1016/j.jaip.2018.06.015 Tashkin DP, Wechsler ME (2018) Role of eosinophils in airway inflammation of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 13:335–49. https://doi.org/10.2147/copd.S152291 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Sep, 2024 Reviews received at journal 30 Aug, 2024 Reviews received at journal 21 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers agreed at journal 18 Aug, 2024 Reviewers invited by journal 16 Aug, 2024 Editor assigned by journal 14 Aug, 2024 Submission checks completed at journal 13 Aug, 2024 First submitted to journal 10 Aug, 2024 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. 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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-4892123","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348775144,"identity":"6d3852e0-c260-4236-a573-acb1aac88300","order_by":0,"name":"Sara Abdulbadie Abdulsattar","email":"data:image/png;base64,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","orcid":"","institution":"Chest Diseases Department, Mansoura Chest Hospital, Ministry of Health, Mansoura","correspondingAuthor":true,"prefix":"","firstName":"Sara","middleName":"Abdulbadie","lastName":"Abdulsattar","suffix":""},{"id":348775145,"identity":"529701fc-3484-430b-b337-475172d1d394","order_by":1,"name":"Mohamed Sayed Hantera","email":"","orcid":"","institution":"Chest Diseases Department, Faculty of Medicine, Tanta University, Tanta","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Sayed","lastName":"Hantera","suffix":""},{"id":348775146,"identity":"0efe6d1a-2e3c-4eb8-904e-2ca1872545cb","order_by":2,"name":"Hossam Abdel Mohsen Hodeb","email":"","orcid":"","institution":"Clinical Pathology Department, Faculty of Medicine, Tanta University, Tanta","correspondingAuthor":false,"prefix":"","firstName":"Hossam","middleName":"Abdel Mohsen","lastName":"Hodeb","suffix":""},{"id":348775147,"identity":"ef0b0394-6b95-4426-a6d8-ae3fca4ce6e8","order_by":3,"name":"Amira Abdelgalil El kholy","email":"","orcid":"","institution":"Chest Diseases Department, Faculty of Medicine, Tanta University, Tanta","correspondingAuthor":false,"prefix":"","firstName":"Amira","middleName":"Abdelgalil El","lastName":"kholy","suffix":""},{"id":348775148,"identity":"a7aeb6ae-f51e-48f4-962e-ece1e10a5f12","order_by":4,"name":"Gamal Amer El Kholy","email":"","orcid":"","institution":"Chest Diseases Department, Faculty of Medicine, Tanta University, Tanta","correspondingAuthor":false,"prefix":"","firstName":"Gamal","middleName":"Amer El","lastName":"Kholy","suffix":""}],"badges":[],"createdAt":"2024-08-10 14:26:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4892123/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4892123/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66125797,"identity":"e3541861-55a2-461f-8f1b-6f643faaa779","added_by":"auto","created_at":"2024-10-08 02:42:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17480,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for sensitivity and specificity of (A) baseline blood eosinophil count and plasma Periostin concentration, (B) baseline forced expiratory volume in first second (FEV1%) of predicted as predictors of post treatment FEV1 response\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4892123/v1/76e1b4ffd299ff8ed5275f83.jpg"},{"id":66126231,"identity":"cd3e821e-1e88-499b-832c-d3f9e914ecb4","added_by":"auto","created_at":"2024-10-08 02:50:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1086446,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4892123/v1/0c602f6b-17d3-4ec9-975b-bd1892c3f069.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study of Blood Eosinophils and Plasma Periostin as Biomarkers for Response of COPD Patients to ICS/LABA Treatment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic Obstructive Pulmonary Disease (COPD) is a complex pulmonary disorder characterized by persistent and often progressive obstruction of airflow due to abnormalities in the alveoli (emphysema) and/or airway (bronchitis, bronchiolitis). These abnormalities cause respiratory symptoms including dyspnea, cough, and sputum production, as well as exacerbations \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePathological changes in the airways, lung parenchyma, and pulmonary vasculature are observed in patients with COPD. These changes encompass both structural and inflammatory modifications, which worsen in severity as airflow obstruction persists despite smoking cessation \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Eosinophilia and T helper type 2 (Th2) inflammation may also play a significant role in the development of COPD in a subset of patients, despite the widespread belief that neutrophils are the primary factor. Furthermore, a Th2-high signature has been observed in approximately 20% of smokers with COPD \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eElevated levels of eosinophils in the bloodstream are correlated with heightened eosinophil counts in the lungs and the prevalence of more pronounced indicators of type-2 inflammation in the airways among patients with COPD. These variations in airway inflammation may account for the differential response to ICS based on eosinophil count in the blood \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePatients with frequent or severe exacerbations (two or more moderate exacerbations annually, or at least one exacerbation requiring hospitalization) and a blood eosinophil count of 300 cells/µl4 should receive initial treatment with ICS (as LAMA/LABA/ICS; triple therapy), according to the 2023 GOLD strategy. Furthermore, patients who exhibit persistent exacerbations despite receiving LAMA/LABA therapy and have blood eosinophil counts below 100 cells/µl may undergo triple therapy subsequent to a thorough evaluation of the anticipated benefits and associated risks. Although the recommendation has shifted away from LABA/ICS usage in patients with COPD and concurrent asthma, the administration of ICS remains obligatory \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Additionally, a number of studies have demonstrated a correlation among elevated sputum eosinophil counts and ameliorated lung function in patients with COPD who received ICS in conjunction with LABA \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn recent trail, a significant correlation has been established not only among eosinophils in the sputum and those in the blood (8), but also among eosinophils and serum periostin, which can serve as an additional blood surrogate marker for eosinophilic airway inflammation \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eAim of the work:\u003c/h2\u003e \u003cp\u003eThe purpose of this trail was to examine the correlation among plasma periostin and blood eosinophils and alterations in lung function associated with LABA and ICS combination therapy in stable COPD patients over a three-month period.\u003c/p\u003e \u003c/div\u003e "},{"header":"Patients and Methods","content":"\u003cp\u003eFifty patients who were clinically stable COPD as defined by the Global Initiatives for Chronic Obstructive Lung Disease (GOLD 2020) \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e and had a post-bronchodilator FEV1/FVC ratio of less than 70% at the time of follow-up in the outpatient clinics of the Chest Department Tanta University Hospitals, Tanta Chest Hospital, and Mansoura Chest Hospital participated in this prospective experimental study. The trail was conducted from February 2020 to February 2022 with the authorization of the Ethical Committee of the Faculty of Medicine, Tanta University in Tanta, Egypt (approval code: 33652/1/20). Written informed assent was obtained from each patient. Patients who meet the following inclusion criteria: age exceeding 40 years, a history of cigarette consumption exceeding 10 pack years, and a post-bronchodilator FEV1/FVC ratio below 70%. Pregnant women, patients with a recent history of COPD exacerbation within one month, and those with concurrent lung diseases including interstitial lung disease, pneumonia, active pulmonary tuberculosis, pulmonary embolism, or lung cancer were excluded from the study. Every individual underwent the following procedures: comprehensive history taking, clinical examination encompassing general and local chest examinations, radiological assessment (chest x-ray), and laboratory investigations (including eosinophilic count assessment via arterial blood gases (ABG), complete blood count (CBC) and plasma periostin). Regarding eosinophilic count and plasma periostin concentration, blood samples about (3cm,2cm for doing CBC and assessing eosinophilic count and 1cm to detect plasma Periostin concentration) were collected after 2 weeks of washout period ( After cessation of ICS for 2 weeks, an inhaled LABA, or LAMA for 2 days, an inhaled short acting B2 for 12 hours) and recollected again after 3 months of treatment with a fixed dose of combined inhalers of ICS and LABA (50mcg salmetrol/500mcg fluticasone or 9mcg formotrol/320mcg budesonide, twice daily) when they separated into their components and transferred to the Clinical Pathology Department; Tanta University Hospitals on dry ice, and kept in -80°c freezers until use. plasma samples were thawed for periostin and measured using a commercially available ELISA kit according to the manufactors instructions. The Human Periostin ELISA (Enzyme-Linked Immunosorbent Assay) kit is an in vitro enzyme-linked immunosorbent assay for the quantitative measurement of Human Periostin in Cell Culture Supernatants, Serum, Plasma. from Biokit for Scientific Research \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eProcedure of Spirometry\u003c/h2\u003e\u003cp\u003eSpirometry was done according to ATS guidelines 2019 \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The procedure was explained to the patients who were informed about the activities that should be avoided before the technique. Without shoes on, the subjects' age and gender were documented, their weight was estimated to the nearest kilogram, and their height was measured to the nearest centimeter. The hands of the physician were cleansed. The patient was situated in a seated position, maintaining a modest elevation of the head. A nose clip was utilized to close the patient's nose, and a mouthpiece was inserted into his oral cavity. He was instructed to firmly clasp his lips around the mouthpiece. At total lung capacity (TLC), the patient was instructed to inhale maximally with a pause of less than one second, followed by an exhalation of maximal force in a single continuous sequence until no further air was emitted for a minimum of six seconds. The procedure step was repeated as necessary for a minimum of three satisfied maneuvers and rest among every trial for 10 minutes. The patient was prepared 10–15 minutes before the procedure by inhalation of 400 mcg of salbutamol. The presence of a post-bronchodilator FEV1/FVC ratio less than 0.70 confirm non fully reversible airflow obstruction \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThen patients were subdivided into 4 subgroups according to post bronchodilator FEV1%. FEV1% ≥ 80% predicted; GOLD 1 – mild airflow obstruction, 50% ≤ FEV1 \u0026lt; 80% predicted; GOLD 2 – moderate airflow obstruction, 30% ≤ FEV1 \u0026lt; 50% predicted GOLD 3 – severe airflow obstruction, FEV1% \u0026lt; 30% predicted; GOLD 4 - very severe airflow obstruction). After three months of treatment with a fixed dose of ICS/LABA, spirometry was repeated again, and patients were classified into two groups according to improvement in FEV1 of at least 12% and 200ml from baseline into: FEV1 non- responders who did not record this criterion and FEV1 responders who recorded this criterion.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFor statistical analysis, SPSS v26 (IBM Inc., Chicago, IL, USA) was utilized. Histograms and the Shapiro-Wilks test were utilized to assess the normality of the data distribution. The quantitative parametric variables were expressed as the mean and standard deviation (SD), and the unpaired Student's t-test was utilized to compare the two groups. Quantitative non-parametric data were analyzed using the Mann Whitney test and were presented as the median and interquartile range (IQR). Frequency and percentage (%) were used to present qualitative variables, which were analyzed utilizing the chi-square test or Fisher's exact test. The Spearman Correlation Test (rs) was employed to examine the direction and strength of the relationship among two nonparametric variables. The area under the curve (AUC) gauged the efficacy of the test as a whole. A p-value less than 0.05 was deemed to be statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe included 50 patients were subdivided at the end of the trail into two groups according to improvement in FEV1 of at least 12% and 200ml from baseline into: FEV1 non- responders included 39 (78%) patients and FEV1 responders included 11 (22%) patients.\u003c/p\u003e \u003cp\u003eRegarding demographic data, age, sex, BMI, smoking status, smoking index (pack/year) and co morbidities were insignificantly different among the two studied groups. COPD grade differed significantly among the two groups (p\u0026thinsp;=\u0026thinsp;0.009) with regard to baseline COPD criteria Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between FEV1 non- responder and FEV1 responder groups regarding demographic data and baseline COPD criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFEV1 non-responder\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFEV1 responder\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.0 (50.0\u0026ndash;67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.0 (53.0\u0026ndash;66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4 (20.8\u0026ndash;28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.0 (20.8\u0026ndash;26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePassive smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eActive smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEx- smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking index (pack/ year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0 (39.0\u0026ndash;54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.0 (39.0\u0026ndash;55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIHD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline COPD criteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCOPD grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (43.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.009*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSevere\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003emMRC dyspnea scale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRate of exacerbation in last year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of COPD related hospitalization in last year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.0\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (1.0\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or frequency (%) or median (IQR). *Significant p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FEV1: forced expiratory volume in first second, BMI: Body mass index, HTN: hypertension, DM: diabetes mellitus, IHD: ischemic heart disease, CKD: chronic kidney disease, COPD: Chronic obstructive pulmonary disease .mMRC: modified medical research council\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eIn relation to baseline spirometric data, the responder group exhibited substantially lower values for FEV1%, FEV1 (L), and the FEV1/FVC ratio in comparison to the non-responder group (P\u0026thinsp;=\u0026thinsp;0.001). However, no significant differences were observed in FVC% and FVC (L) among the two groups under investigation. The ABG parameters (pH, PaCO2, PaO2, HCO3) exhibited negligible variation among the two groups under investigation. The responder group exhibited significantly higher levels of plasma periostin and baseline blood eosinophil count compared to the non-responder group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was statistical significance in this difference. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between FEV1 non- responder and FEV1 responder groups regarding baseline spirometry, ABG, WBC count, blood eosinophil count and plasma periostin concentration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV1-non-responder\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFEV1 responder\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBaseline Spirometry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC % of predicted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.0 (78.0\u0026ndash;81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.0 (78.0\u0026ndash;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC actual value (L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5 (3.3\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (3.5\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1% of predicted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (43.0\u0026ndash;52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.0 (31.0\u0026ndash;39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1 actual value (L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7 (1.5\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (1.1\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1/FVC ratio (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (43.0\u0026ndash;51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.0 (31.0\u0026ndash;38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline ABG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4 (7.39\u0026ndash;7.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.44 (7.39\u0026ndash;7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.0 (38.0\u0026ndash;45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0 (36.0\u0026ndash;44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.0 (60.0\u0026ndash;72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0 (60.0\u0026ndash;70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(mmol/ L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0 (31.0\u0026ndash;40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0 (29.0\u0026ndash;45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite blood cell count (cell /\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7000.0 (6500.0\u0026ndash;8000.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7500.0 (7300.0\u0026ndash;8200.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil count (cell/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4000.0 (3600.0\u0026ndash;4553.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3900.0 (3289.0\u0026ndash;4230.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEosinophil count (cell/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.0 (180.0\u0026ndash;220.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300.0 (290.0\u0026ndash;320.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasma periostin concentration (ng/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.732 (7.968\u0026ndash;9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.88 (19.43\u0026ndash;35.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR). *Significant p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05,, FVC: Forced vital capacity, FEV1: forced expiratory volume in first second, ABG: arterial blood gases, WBC: white blood cell count\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eRegarding post treatment spirometric data, blood eosinophil and plasma Periostin concentration\u003c/b\u003e: FVC% of predicted, FVC actual value(L), FEV1/FVC ratio, change in FEV1% of predicted, change in FEV1 actual value (L), blood eosinophil count, plasma Periostin concentration and plasma Periostin concentration change showed statistically significant difference among two groups (p;\u0026lt;0.001) but FEV1 actual value (L), FEV1% of predicted, blood eosinophil count change showed non statistically significant difference among two groups. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between FEV1 non-responder and FEV1 responder groups regarding post treatment spirometric data, blood eosinophil count and plasma Periostin concentration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV1-non-responder\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFEV1 responder\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC % of predicted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.0 (78.0\u0026ndash;82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.0 (84.0\u0026ndash;91.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC actual value (L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6 (3.5\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.8\u0026ndash;4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1%of predicted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.0 (48.0\u0026ndash;54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.0 (46.0\u0026ndash;54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1 actual value (L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.7\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7 (1.6\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1/FVC ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0 (47.0\u0026ndash;53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0 (42.0\u0026ndash;46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChange in FEV1% of predicted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0 (0.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0 (14.0\u0026ndash;17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChange in FEV1 actual value (L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1 (0.01 \u0026minus;\u0026thinsp;0. 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.5\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood eosinophil count\u003c/b\u003e \u003csub\u003e\u003cb\u003e(\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ecell/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.0 (170.0\u0026ndash;208.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290.0 (279.0\u0026ndash;308.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood eosinophil count change (cell/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (5.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (8.0\u0026ndash;20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasma Periostin concentration (ng/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.23 (6.166\u0026ndash;8.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.676 (8.208\u0026ndash;18.818)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasma Periostin concentration change\u003c/b\u003e (\u003cb\u003eng/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.713 (1.028\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.562 (6.749\u0026ndash;19.532)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData were presented as median (IQR). *Significant p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FVC: Forced vital capacity, FEV1: forced expiratory volume in first second.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRegarding correlation between blood eosinophil count, plasma Periostin concentration and other variables at baseline\u003c/h2\u003e \u003cp\u003eAt baseline, blood eosinophil count had a significant positive correlation with and plasma periostin concentration and smoking index (pack/year) (rs\u0026thinsp;=\u0026thinsp;0.454, P\u0026thinsp;=\u0026thinsp;0.001), (r\u003csub\u003es\u003c/sub\u003e; 0,299 P\u0026thinsp;=\u0026thinsp;0.035) respectively but had significant negative correlations with FEV1 actual value (L), FEV1% of predicted and FEV1/FVC ratio (r\u003csub\u003es\u003c/sub\u003e=-0.503, P\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (r\u003csub\u003es\u003c/sub\u003e;-0.435, P\u0026thinsp;=\u0026thinsp;0.002) and (r\u003csub\u003es\u003c/sub\u003e=-.434, P\u0026thinsp;=\u0026thinsp;0.002) respectively. Also, plasma periostin concentration had significant positive correlations with COPD grade and FVC actual value (L) (r\u003csub\u003es\u003c/sub\u003e=.410; P\u0026thinsp;=\u0026thinsp;.003) (r\u003csub\u003es\u003c/sub\u003e=.290, P\u0026thinsp;=\u0026thinsp;0.041) respectively but had significant negative correlations with FEV1/FVC ratio, FEV1% of predicted and FEV1 actual value (L) (r\u003csub\u003es\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.648; P\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (r\u003csub\u003es\u003c/sub\u003e=- 0.630; P\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and (r\u003csub\u003es\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.579; P\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) respectively. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between baseline blood eosinophil count, plasma Periostin concentration and other variables at baseline:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBlood eosinophil count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePlasma Periostin concentration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking index (pack/ year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.035*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo. of COPD related hospitalization in last year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRate of exacerbation in last year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emMRC dyspnea scale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOPD grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil count\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlasma Periostin concentration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC % of predicted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC actual value (L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.041*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1% of predicted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1 actual value (L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1/FVC ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ers: Spearman correlation, *significant p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, BMI: Body mass index, HTN: hypertension, DM: diabetes mellitus, IHD: ischemic heart disease, CKD: chronic kidney disease, COPD: Chronic obstructive pulmonary disease, MRC: Medical Research Council, FVC: Forced vital capacity, FEV1: forced expiratory volume in first second, ABG: arterial blood gases, WBC: white blood cell count\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eIn order to ascertain cutoff values for plasma periostin concentration, blood eosinophil count, and FEV1% as predictors of post-treatment FEV1 response, a Roc curve was constructed. It was found, baseline blood eosinophil count, cut off value count\u0026thinsp;\u0026gt;\u0026thinsp;265cell/ \u0026micro;L, plasma periostin concentration\u0026thinsp;\u0026gt;\u0026thinsp;15.747 ng/ml and FEV1% predicted\u0026thinsp;\u0026lt;\u0026thinsp;40.5% (sensitivity, specificity, PPV, NPV and accuracy were 100% for all) were associated with post treatment FEV1 response with (AUC\u0026thinsp;=\u0026thinsp;1, 95% C. I\u0026thinsp;=\u0026thinsp;1.00\u0026ndash;1.00),P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all .Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC curve for sensitivity and specificity of baseline blood eosinophil count, plasma periostin concentration and FEV1% of predicted for prediction of post treatment FEV1 response:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% C.I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCut off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline blood eosinophil count (cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.00\u0026ndash;1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;265.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline Plasma periostin concentration (ng/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.00\u0026ndash;1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;15.747\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline FEV1% of predicted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.00\u0026ndash;1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;40.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*p\u0026thinsp;\u0026le;\u0026thinsp;0.05 statistically significant\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAUC\u0026thinsp;=\u0026thinsp;1.00 (Excellent predictor)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough eosinophilic inflammation is frequently observed in individuals with asthma, it is also present in a subset of patients with COPD. Although serum periostin is commonly administered to patients with asthma, it is seldom studied in patients with COPD \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePeriostin, an extracellular matrix protein, has been identified in the airway subepithelial layer of patients with COPD IL-4 and IL-13 induce an upregulation of periostin expression in airway epithelial cells \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Produced by bronchial epithelial cells and pulmonary fibroblasts, this biomarker is linked to type 2 eosinophilic inflammation \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePlasma periostin and Blood eosinophils were examined in this trail as biomarkers of COPD patients' response to ICS/LABA therapy.\u003c/p\u003e \u003cp\u003eIn relation to demographic characteristics, there were no statistically significant difference observed among the two groups under investigation, including age, gender, BMI, smoking status, smoking index (pack/year), and comorbidities, when comparing FEV1-non-responder and FEV1 responder groups. In agreement with this study, Park et al., \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e who conducted a trial on 130 COPD patients, demonstrated that 42 subjects (32 percent) were classified as FEV1 responders after three months of treatment with ICS and LABA. Sexual orientation, smoking history, and body mass index did not differ significantly among FEV1 responders and non-responders.In the context of baseline COPD criteria, this trail compared FEV1-non-responder and FEV1 responder groups as follows: of the non-responder group, 17 (43.6%) patients had moderate COPD (GOLD II), while 22 (56.4%) patients had severe COPD (GOLD III). In contrast, the responder group comprised 11 (100%) patients with severe COPD (GOLD III). Although a statistically significant difference was observed among the two groups (p;0.009), the former group utilized the mMRC dyspnea scale, rate of exacerbation in the previous year, and number of patients with severe COPD (GOLD III). In agreement with present study, Park et al. \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e found comparable results and there were non significant difference in mMRC dyspnea scale, exacerbation rate among FEV1 responders and FEV1 non-responders.\u003c/p\u003e \u003cp\u003eIn relation to the comparison of baseline spirometric data among the two groups, the findings of this research indicated that the responder group exhibited significantly lower values for FEV1% of predicted, FEV1 actual value (L), and FEV1/FVC ratio (P\u0026thinsp;=\u0026thinsp;0.001). On the contrary, there was no statistically significant difference observed among the two groups with regard to the FVC% of predicted value and the FVC actual value (L). In agreement with present study, Park et al. \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e distinguished among FEV1 responders and non-responders by demonstrating that the former had a higher likelihood of having a baseline FEV1 less than 50% pred, while the latter had higher FVC (L) and (% pred) values than the former.\u003c/p\u003e \u003cp\u003eThe FEV1 responder group had significantly higher baseline blood eosinophil count and plasma Periostin concentration than the FEV1 non-responder group (p0.001), whereas the differences among the two groups in terms of white blood cell count and neutrophil count were not statistically significant. Pascoe et al. \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, Siddiqui et al. \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e and Barnes et al. \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e reported that elevated eosinophil concentrations have been found to predict response to ICS in COPD patients.\u003c/p\u003e \u003cp\u003eFVC% of predicted, FVC actual value (L), change in FEV1% of predicted, and change in FEV1 actual value (L) were all significantly higher in the FEV1 responder group compared to the FEV1 non-responder group in this trail (p0.001). In contrast to the FEV1 non-responder group, the FEV1 actual value (L), FEV1% of predicted value, and FEV1/FVC ratio were all significantly lower in the FEV1 responder group. These findings were anticipated given that responders had reduced FEV1%, FEV1 (L), and FEV1/FVC ratios at baseline than non-responders. Brightling et al. \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e and Brightling et al. \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e demonstrated that COPD patients with high eosinophil count had a greater forced expiratory volume in first second (FEV1) improvement after corticosteroid treatment.\u003c/p\u003e \u003cp\u003eIn this study, post treatment blood eosinophil count, plasma periostin concentration and change in plasma periostin concentration were significantly higher in responder group compared to non-responder group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) while change in blood eosinophil count was not statistically significant different among two groups. Compatible with these findings, Park et al. \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] observed\u003c/sup\u003e that the blood eosinophil count and plasma Periostin concentration were most significantly elevated in the FEV1 responders compared to the FEV1 non-responders among COPD patients undergoing ICS/LABA treatment. Fingleton et al., \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, recorded in their trail on 386 patients with obstructive airway diseases, among them (17 patients were diagnosed as COPD) that ICS responsiveness patients, showed reduction in serum periostin after 12 weeks of ICS treatment.\u003c/p\u003e \u003cp\u003eRegarding correlation among baseline blood eosinophil count, plasma periostin concentration and other variables: blood eosinophil count had significant positive correlations with plasma periostin concentration and smoking index (pack/year) but had significant negative correlations with FEV1 actual value (L), FEV1% of predicted, FEV1/FVC ratio. Other variables; age, sex, BMI, comorbidities, number of COPD related hospitalization, rate of exacerbation, mMRC dyspnea scale, neutrophil count, pH, PaCO\u003csub\u003e2\u003c/sub\u003e, PaO\u003csub\u003e2\u003c/sub\u003e, HCO3, FVC%, FVC (L) were insignificantly correlated with blood eosinophil count. Consistent with the findings of this study, Jensen et al. \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e examined a relation among lung functions and blood eosinophil and monocyte counts. They discovered that individuals with a history of heavy smoking had a higher blood eosinophil count compared to light smokers, but a lower monocyte count. These findings suggested that smoking does indeed impact blood eosinophil and monocyte counts, which may have a marginally detrimental effect on lung functions. Additionally, the relation among eosinophil blood count and lung function differed among smokers and nonsmokers..\u003c/p\u003e \u003cp\u003ePlasma periostin concentration had significant positive correlations with COPD grade and FVC actual value (L) but had significant negative correlations with FEV1/FVC ratio, FEV1% predicted and FEV1 actual value (L) .Other variables: age, sex, BMI, smoking index (pack/ year), comorbidities, number of COPD related hospitalization, rate of exacerbation, mMRC dyspnea scale, neutrophil count, pH, PaCO\u003csub\u003e2\u003c/sub\u003e, PaO\u003csub\u003e2\u003c/sub\u003e, HCO\u003csub\u003e3\u003c/sub\u003e and FVC % were insignificantly correlated with plasma periostin concentration. In agreement with these findings, Fingleton et al., \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e provided evidence of a statistically significant association among the logarithm of serum periostin and blood eosinophil count in individuals diagnosed with obstructive airway diseases. A minor negative association was observed among serum periostin and predicted FEV1%, while a weak positive correlation was observed among serum periostin and functional residual capacity%. However, no statistically significant associations were found among serum periostin and FEV1/FVC ratio. Additionally, a small negative association was observed among body mass index and serum periostin. Also, Clarenbach et al., \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e who investigated relation among blood periostin levels and exacerbation rates on 26 COPD patients demonstrated that there is no significant correlation among exacerbation rate and periostin levels in blood. Conversely, Shirai et al. \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e demonstrated a mild positive correlation (p\u0026thinsp;=\u0026thinsp;0.24) among serum periostin and FEV1/FVC in patients with COPD, as well as a moderate correlation (p\u0026thinsp;=\u0026thinsp;0.41) among serum periostin and FEV1. Eosinophil counts were greater in COPD patients with elevated serum periostin concentrations; however, no correlation was observed among periostin and body mass index (BMI) in this subset of patients.\u003c/p\u003e \u003cp\u003eIn this study, regarding cut off values, baseline blood eosinophil count\u0026thinsp;\u0026gt;\u0026thinsp;265cell/ \u0026micro;L, plasma periostin concentration\u0026thinsp;\u0026gt;\u0026thinsp;15.747 ng/ml and FEV1% predicted\u0026thinsp;\u0026lt;\u0026thinsp;40.5% (sensitivity, specificity, PPV, NPV and accuracy were 100% for all) were associated with post treatment FEV1 response with (AUC\u0026thinsp;=\u0026thinsp;1, 95% C. I\u0026thinsp;=\u0026thinsp;1.00\u0026ndash;1.00), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all. In agreement with these findings, Park et al., \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e conducted the initial investigation into the relationship among plasma periostin and blood eosinophils and alterations in lung function associated with the combination therapy of ICS and LABA in stable COPD patients. They discovered that elevated levels of plasma periostin (\u0026gt;\u0026thinsp;23 ng/mL) and blood eosinophils (\u0026gt;\u0026thinsp;260/\u0026micro;L) were significantly related with a 50% improvement in FEV1 following 12 weeks of treatment with ICS and LABA in patients with COPD who had a baseline FEV1 below 50% predilection. Also, Tashkin et al., \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e reported that elevated blood eosinophil counts could potentially serve as a suitable substitute for airway eosinophilia and function as a readily available biomarker to assess the response of COPD patients to ICS treatment.\u003c/p\u003e \u003cp\u003eLimitations: The sample size was relatively small. The short follow up period didn't allow detection of long-term outcomes of treatment on COPD exacerbation and related hospitalization and decline in lung functions. The trail did not assess the correlations among blood eosinophils, bronchoalveolar lavage fluid cells, airway inflammatory markers (e.g., sputum eosinophil), or bronchoalveolar lavage fluid cells; consequently, the relationship among airway and blood inflammatory markers was not investigated. COPD Assessment Test (CAT) was not included in analysis of these findings as it was not obtained at initial evaluation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCOPD patients with poor lung functions regarding FEV1/FVC ratio, FEV1 actual value (L) and FEV1% of predicted as well as high blood eosinophil count and high plasma periostin concentration are predicted to have FEV1 response with fixed dose ICS/LABA combination treatment. Baseline cut off values of blood eosinophil count\u0026thinsp;\u0026gt;\u0026thinsp;265cell/ \u0026micro;L, plasma periostin concentration\u0026thinsp;\u0026gt;\u0026thinsp;15.747 ng/ml and FEV1% \u0026lt; 40.5% may be used for selection of COPD patients who will show benefit from ICS/LABA treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTh2:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eT Helper Type 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOLD 2020:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eGlobal Initiatives for Chronic Obstructive Lung Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eABG:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eArterial Blood Gases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBC:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eComplete Blood Count\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFEV1:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eForced Expiratory Volume in First Second\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFVC:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eForced Vital Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRC:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eMedical Research Council\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIHD:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eIschemic Heart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.72077922077922%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAT:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"83.27922077922078%\"\u003e\n \u003cp\u003eCOPD Assessment Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was approved by the ethics committee of Faculty of medicine,\u0026nbsp;Tanta University Hospitals, Tanta Chest Hospital and Mansoura Chest Hospital\u0026nbsp;from February 2020 to February 2022. An informed written consent was obtained from the participants. approval No. 33652/1/20.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: All authors give their consent for publication in the journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eData and material are available on a reasonable request from the author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e SAA and MSH conceived and supervised the study; HAH and AAE were responsible for data collection. GAE and SAA analysed and interpreted the data. All authors provided comments on the manuscript at various stages of development. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Nil\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgust\u0026iacute; A, Celli BR, Criner GJ, et al. (2023) Global initiative for chronic obstructive lung disease 2023 report: Gold executive summary. Am J Respir Crit Care Med 207:819\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1164/rccm.202301-0106PP\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202301-0106PP\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHogg JC, Timens W (2009) The pathology of chronic obstructive pulmonary disease. 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Int J Chron Obstruct Pulmon Dis 13:335\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/copd.S152291\u003c/span\u003e\u003cspan address=\"10.2147/copd.S152291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-bronchology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Bronchology](https://ejb.springeropen.com/)","snPcode":"43168","submissionUrl":"https://submission.nature.com/new-submission/43168/3","title":"The Egyptian Journal of Bronchology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Blood eosinophils, Plasma Periostin, COPD, ICS/LABA combination Treatment","lastPublishedDoi":"10.21203/rs.3.rs-4892123/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4892123/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Eosinophilic airway inflammation has been detected in up to 40% of chronic obstructive pulmonary disease (COPD) patients during stable periods of the disease and increased sputum eosinophil count was associated with better lung function, more future exacerbations, and symptoms that responded better to treatment with inhaled and oral corticosteroids.\u003c/p\u003e\n\u003cp\u003eThe aim of this work was to investigate the relationship of blood eosinophils and plasma Periostin with lung function changes related to ICS and long-acting beta2- agonist combination treatment in stable COPD patients for three months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This prospective experimental study was carried out on 50 COPD patients with post bronchodilator FEV1/ FVC ratio \u0026lt;70%. Patient collected from outpatient clinics of Chest Department Tanta University Hospitals, Tanta Chest Hospital and Mansoura Chest Hospital from February 2020 to February 2022.Patients subjected to complete history taking, clinical examinations including general and local examinations and laboratory investigations [complete blood count to assess eosinophilic count, arterial blood gases and plasma periostin] and spirometry were done for all patients pre and post treatment. All patients received three months of treatment with a fixed dose of combined inhalers of ICS and LABA and then they were classified into FEV1 responder and FEV1 non-responder according to improvement in FEV1 at least 12% and 200ml from base line of three months of combined treatment with ICS/LABA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eBlood eosinophil count had a significant positive correlation with smoking index (pack/year), negative correlations with FEV1% of predicted, FEV1 actual value (L), FEV1/FVC ratio. Plasma periostin concentration had significant positive correlation with blood eosinophil count, COPD grade, FVC actual value (L) and significant negative correlations with FEV1% of predicted, FEV1 actual value (L) and FEV1/FVC. Cut off values of blood eosinophil count \u0026gt;265cell/ µL, plasma Periostin concentration \u0026gt;15.747 ng/ml and FEV1% \u0026lt; 40.5% were associated with post treatment FEV1 response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eCOPD patients with poor lung functions regarding FEV1/FVC ratio, FEV1 actual value (L) and FEV1% of predicted as well as high blood eosinophil count and high plasma periostin concentration are predicted to have FEV1 response with fixed dose ICS/LABA combination treatment.\u003c/p\u003e","manuscriptTitle":"Study of Blood Eosinophils and Plasma Periostin as Biomarkers for Response of COPD Patients to ICS/LABA Treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 02:42:26","doi":"10.21203/rs.3.rs-4892123/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-03T07:19:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-30T07:12:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T09:46:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334207651862073275848100201428540474017","date":"2024-08-19T08:46:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294752023557909270336526199763517263448","date":"2024-08-18T14:35:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-16T15:47:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-14T06:45:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-13T09:33:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Bronchology","date":"2024-08-10T14:25:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-bronchology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Bronchology](https://ejb.springeropen.com/)","snPcode":"43168","submissionUrl":"https://submission.nature.com/new-submission/43168/3","title":"The Egyptian Journal of Bronchology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc79cfd7-15d1-417c-a364-a746d4f650e0","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-08T02:42:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 02:42:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4892123","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4892123","identity":"rs-4892123","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}