Surface Expression of CD63 and HLA-DR in circulating eosinophils correlates with Improved Clinical Control After Treatment Optimization in asthma

Surface Expression of CD63 and HLA-DR in circulating eosinophils correlates with Improved Clinical Control After Treatment Optimization in asthma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Surface Expression of CD63 and HLA-DR in circulating eosinophils correlates with Improved Clinical Control After Treatment Optimization in asthma Simone Scarlata, Carmen Mazzuca, Laura Vitiello, Panaiotis Finamore, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7923148/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background Eosinophils are key effectors in asthma, especially within the T2-high phenotype. Conventional biomarkers such as blood eosinophils, FeNO, and IgE incompletely reflect disease activity. Surface markers like CD63 and HLA-DR might provide additional insight into eosinophil activation and treatment response. Objective To evaluate whether CD63 and HLA-DR expression on circulating eosinophils correlates with clinical improvement after therapy optimization in severe asthma. Methods In this pre-randomization analysis of 105 adults enrolled in an anti-IL5 trial (NCT05001529), patients underwent a 3-month run-in with optimized therapy per GINA guidelines. Clinical data, lung function, FeNO, IgE, and eosinophil counts were collected. Flow cytometry assessed CD63 and HLA-DR expression. Associations with clinical improvement, defined by ACT score and exacerbation frequency, were analysed. Results ACT scores improved from 21 to 24 and uncontrolled asthma prevalence dropped from 50% to 16%. HLA-DR expression declined significantly and correlated with improved asthma control (β = 0.03, p = 0.05). CD63 expression did not change overall but remained elevated in patients with persistent symptoms. Neither marker correlated with eosinophil count, FeNO, or IgE. Conclusions HLA-DR and CD63 may serve as functional biomarkers of asthma activity. Eosinophil immunophenotyping could complement traditional biomarkers and guide personalized therapy. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Severe asthma Eosinophil activation CD63 HLA-DR Flow cytometry biomarkers Figures Figure 1 Figure 2 Introduction Asthma is a chronic inflammatory disease of the airways characterized by variable airflow obstruction, airway hyperresponsiveness, and a complex immune profile involving various leukocyte subsets. Eosinophils play a central role in the pathophysiology of asthma, particularly in the T2-high endotype, where elevated eosinophil counts in blood and sputum are commonly observed [ 1 ]. Beyond simple enumeration, the functional state and activation profile of eosinophils have emerged as more informative indicators of disease activity and therapeutic response [ 2 ]. In this context, surface markers associated with eosinophil activation and antigen presentation, such as CD63 and HLA-DR, might offer valuable insights into disease monitoring and the evaluation of treatment efficacy. CD63 is a tetraspanin commonly associated with the activation and degranulation of eosinophils, particularly in response to IgE-mediated stimuli. Upon degranulation, CD63 translocates to the cell surface, making it a reliable surrogate marker for eosinophil activation in various allergic and inflammatory settings [ 3 ]. Its expression has been linked to disease severity and exacerbation risk in asthma, yet its utility in tracking treatment response remains underexplored [ 4 ]. Similarly, HLA-DR, a major histocompatibility complex (MHC) class II molecule, is not typically expressed on resting eosinophils, but may be upregulated in response to pro-inflammatory cytokines, such as interferon-γ and IL-5 [ 5 ]. HLA-DR expression on eosinophils has been proposed as an indicator of their capacity to engage in antigen presentation and interact with T cells, thus reflecting a more immunologically active and disease-relevant phenotype [ 6 ]. While most research has focused on HLA-DR expression in monocytes and dendritic cells, its expression in eosinophils, particularly in the context of asthma, has garnered increasing attention [ 7 ]. Advances in flow cytometry have facilitated the precise identification and functional characterization of eosinophils in peripheral blood. Multicolor immunophenotyping allows simultaneous evaluation of activation markers, maturation status, and immune engagement, offering a dynamic view of eosinophil behavior during disease progression and under therapeutic intervention [ 8 ]. In contrast to more static parameters such as blood eosinophil count, flow cytometric assessment of surface markers can provide early indications of biological response to treatment, even before clinical improvement becomes apparent [ 9 ]. The optimization of asthma therapy, whether through inhaled corticosteroids (ICS), long-acting bronchodilators, or biologic agents targeting eosinophilic inflammation, has been shown to improve symptom control and reduce exacerbations [ 10 ]. However, objective markers that reflect biological response to these interventions are limited. Identifying peripheral biomarkers that correlate with improved clinical outcomes would be valuable for both individualized treatment plans and real-time monitoring of disease control [ 11 ]. In this study, we investigated the surface expression of CD63 and HLA-DR in circulating eosinophils of patients with asthma undergoing treatment optimization. Our objective was to determine whether changes in these activation markers, as measured by flow cytometry, correlate with improvements in clinical control, as assessed by standardized asthma control questionnaires and pulmonary function testing. We hypothesized that effective therapeutic adjustment would lead to a reduction in eosinophil activation and immunologic engagement, reflected by decreased CD63 and HLA-DR surface expression. Methods Study Design, Setting, and Participants This is a preliminary analysis of an ongoing randomized interventional clinical trial investigating the effects of anti-IL5 antibodies in patients with severe asthma. The study commenced in March 2021 at the tertiary-care asthma outpatient clinic of Fondazione Policlinico Campus Bio-Medico in Rome, Italy. The protocol adheres to the Declaration of Helsinki and was approved by the Campus Bio Medico institutional Ethics Committee (ComEt CBM 94/20 OSS). The study protocol was also submitted and registered at Clinicaltrial.gov (Identifier: NCT05001529). All participants provided written informed consent. Eligible participants were adults (≥ 18 years) with a diagnosis of severe asthma, either controlled or uncontrolled, according to the latest Global Initiative for Asthma (GINA) guidelines [ 10 ]. Patients were excluded if they had a diagnosis of COPD/asthma overlap, other acute or chronic conditions affecting eosinophil levels, or were undergoing chronic treatment with immunosuppressants, systemic corticosteroids (for diseases other than asthma), or other biologic medications. This analysis focuses on data collected during the three-month run-in period preceding randomization. This period was designed to confirm the diagnosis of severe asthma and optimize treatment according to current guidelines. Clinical Assessment and Therapy Optimization Upon recruitment, a detailed clinical history was recorded, including allergic comorbidities. Physical examination and comprehensive pulmonary function tests (PFTs) were conducted, including spirometry, residual volume via helium dilution, and single-breath diffusing capacity for carbon monoxide (DLCO), referenced to Global Lung Initiative (GLI) standards [ 13 ]. When asthma diagnosis had been made externally, existing documentation (e.g., bronchodilator reversibility or methacholine challenge testing) was reviewed. Symptom control was assessed using the Italian version of the Asthma Control Test (ACT). The number of asthma exacerbations and hospitalizations in the previous year was also recorded. Asthma exacerbation was defined as worsening of asthma symptoms above baseline, marked by increasing shortness of breath, wheezing, cough, or chest tightness, which often requires a change in treatment and/or asthma that necessitates a step up of treatment or the use of systemic corticosteroids to achieve control. Inhaler technique and adherence were evaluated, and rescue medication use was documented. Fractional exhaled nitric oxide (FeNO) levels were measured using the Vivatmo™ pro device (COSMED, Rome, Italy), following standardized guidelines [ 14 ]. Morning blood samples were collected for total immunoglobulin E (IgE), eosinophil count, and eosinophil surface marker analysis. Asthma therapy was optimized according to the GINA step-up approach, which includes increasing the dose of inhaled corticosteroids and adding long-acting β2 agonists (LABAs), leukotriene receptor antagonists (LTRAs), or tiotropium. After three months, patients were reassessed. Those with persistent symptoms or exacerbations were considered for biologic therapy. Eosinophil Immunophenotyping by Flow Cytometry Multicolor flow cytometry was used to evaluate surface marker expression on peripheral blood eosinophils. Blood samples collected in EDTA tubes were processed within two hours at room temperature (~ 20°C). A 100 µL aliquot of each sample was stained with a panel of fluorochrome-conjugated antibodies. Eosinophils were identified using markers CD45, CD66b, CD15, CD16, and Siglec-8. Surface expression of CD63 (a marker of degranulation and activation), HLA-DR (MHC class II molecule upregulated during activation), CD193 and CD294 were assessed in order to provide a comprehensive characterization [ 3 , 12 , 15 – 17 ]. Collectively, CD193 and CD294 characterize eosinophil migratory and Th2-driven activation pathways, whereas CD63 and HLA-DR reflect, respectively, their effector degranulation status and potential involvement in antigen presentation and immune regulation. Following a 30-minute incubation at 4°C, erythrocytes were lysed using BD FACS™ lysing solution, and cells were resuspended in phosphate-buffered saline (PBS). Acquisition was performed on a BD LSRFortessa™ X-20 cytometer. Marker expression was quantified using median fluorescence intensity (MFI) or geometric mean for skewed distributions. Additionally, the proportion of eosinophils expressing each marker was reported as a percentage of the total eosinophil population (Fig. 1 ). Outcomes The primary outcomes were the association of eosinophil surface expression of CD63 and HLA-DR with clinical asthma control and exacerbation frequency after therapy optimization. Poor control was defined as an ACT score < 20 and/or ≥ 1 exacerbation in the previous year. Secondary, exploratory outcomes included associations with blood eosinophil count, FeNO, and total IgE. Statistical Analysis Descriptive statistics were reported as medians and interquartile ranges (IQR) for continuous variables and frequencies (%) for categorical variables. Analyses were limited to the 73 patients (91% of enrolled) who completed the run-in period prior to randomization due to previously non-optimized therapy. The expected increase in the CD63 level is estimated to reach the level of 20% in severe asthma patients. The required number of subjects, assuming a statistical power of 80% and a I type error of 5% (p value = 0.05) is 67 individuals, with a total amount of patients to be recruited of about 100, considering the possibility of drop out at follow up. Comparisons between recruitment and post-optimization values were made using the Wilcoxon signed-rank test for continuous variables and McNemar's test for paired categorical data. Correlations between eosinophil marker expression and baseline blood eosinophils, FeNO, and IgE were evaluated using Spearman's ρ. Marker expression, FeNO, IgE, and eosinophil counts were visualized in boxplots, stratified by exacerbation frequency and ACT score. Between-group differences were tested using the Wilcoxon rank-sum test. To assess the association between changes in eosinophil markers and clinical improvement, mixed linear models with random intercepts were employed. Results are reported as β estimates with corresponding p-values. All statistical analyses were performed using R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria), with the “lme4” and “multicomp” packages. Results Baseline Characteristics of Study Population A total of 105 adult patients with severe asthma were included in the baseline analysis. Clinical, functional, and laboratory data were collected prior to therapy optimization. Median age was 57 years (IQR: 13), with a mild male predominance (51%). The median blood eosinophil count was 265 cells/µL (IQR:342.8), total IgE were 170 kU/L (IQR: 261), and FeNO measured 29 ppb (IQR: 46). ACT mean score was 20 (SD = 7), with 50% of participants classified as having uncontrolled asthma (ACT < 20). These details are summarized in Table 1 . Table 1 Clinical, functional, and laboratory data at baseline and after therapy optimization (n = 105). Recruitment (n = 105) After therapy optimization (n = 72) P-value Age (years), median (IQR) 57 (13) - - Sex (M), n (%) 53(51) - - BMI, mean (SD) 26.6 (8.2) - - Exacerbation previous year (≥ 1), n (%) 26 (28) 17 (31) 0.19 ACT, median (IQR) 21 (7) 24 (3.8) 50 ppb, n (%) 23 (31) 15 (33) 0.72 Eos (abs_value), median (IQR) 265 (342) 180 (275) 0.02 Eos (%), median (IQR) 4 (4) 2.3 (2.2) 0.03 IgE total, median (IQR) 170 (261) 142 (182.7) 0.30 IgE total > 420, n (%) 10(9) 5(7) 0.52 OCS 69 (65.7) 43 (41) 0.01 Inhaled therapy Low-dose ICS/LABA, n (%) 22 (21) 19 (18) 0.12 High-dose ICS/LABA, n (%) 68 (65) 86 (82) < 0.01 LTRA, n (%) 38 (36) 69 (66) 0.03 Tiotropium, n (%) 10 (10) 26 (25) < 0.01 % of Eos CD294, median (IQR) 98 (5.8) 99 (3) 0.39 CD63, median (IQR) 98 (4.4) 98 (6) 0.97 HLA DR, median (IQR) 41 (67.8) 13.3 (42) < 0.01 CD193, median (IQR) 99 (5) 96 (9.5) < 0.01 MIF (gated on Eos) CD294, median (IQR) 1751 (527.8) 1604 (589) 0.16 CD63, median (IQR) 4544.5 (3553.8) 4313 (3816) 0.30 HLA DR, median (IQR) 731 (1000.5) 441 (644) < 0.001 CD193, median (IQR) 12469 (6841) 9938.5 (5587.5) 0.03 Continuous variables are reported as median (IQR) while categorical variables as frequency (%). Legend: BMI: Body mass index; ACT: Asthma Control Test; FEV1: Forced expiratory volume in the 1st second; FeNO: Fractional Exhaled Nitric Oxide; Eos: Eosinophils; IgE: Immunoglobulin E; OCS: oral corticosteroids; ICS: inhaled corticosteroids; LABA: long-acting beta-2 agonists; LTRA: leukotriene receptor antagonists; MFI: mean fluorescence intensity; HLA-DR: Human leukocyte antigen-D receptor. Eosinophil Surface Marker Expression at Baseline Flow cytometry exploratory analysis showed variable expression of surface markers across the eosinophil population. CD63 and HLA-DR demonstrated a wider range of surface expression intensities compared to other markers, including CD193 and CD294. Specifically, HLA-DR and CD193 showed higher degrees of surface heterogeneity, suggestive of polyclonal activation states or variable immunological roles among eosinophils. In contrast, CD63 and CD294 displayed more consistent, though still elevated, surface patterns (Fig. 2 ). Effect of Therapy Optimization After the three-month period of therapy optimization, significant improvements in asthma control were observed. ACT scores increased from a median of 21 to 24 (p < 0.01), and the proportion of uncontrolled patients (ACT < 20) decreased from 50% to 16%. This was paralleled by a reduction in the number of exacerbators (patients with ≥ 1 exacerbation/year), in oral corticosteroids use and in total Ig E level. Interestingly, no significant change in circulating eosinophils was observed (Table 1 ). The improved control was associated with the overall increase in ICS/LABA, anti-leukotrienes, and tiotropium consumption at the end of the optimization period (Table 1 ). Immunophenotypically, HLA-DR expression showed a significant decrease after therapy optimization, both in terms of percentage of positive eosinophils and mean fluorescence intensity (MFI) (p < 0.01 and p < 0.001, respectively; CD193 also showed a significant decrease in MFI (p = 0.03), though the percentage of positive eosinophils remained high, despite a modest reduction (p 0.05) (Table 1 ). Correlations with Eosinophil Count, FeNO, and IgE Spearman’s rank correlation analysis revealed a weak, but statistically significant positive correlation between the percentage of CD193-positive eosinophils and blood eosinophil count (ρ = 0.30, p = 0.04) (Table 2 ). No significant correlations were found between CD63 or HLA-DR expression (either as % positive cells or MFI) and blood eosinophils, FeNO, or total IgE (all p-values > 0.05). Similarly, CD294 expression did not correlate significantly with any of the examined biomarkers. Table 2 Sperman’s rank correlation rho between eosinophils’ surface markers and eosinophil count, FeNO and total IgE Eos. Count (cell/ul) P-value FeNO (ppb) P-value Total IgE (UI) P-value % Eos CD294 0.18 0.22 0.14 0.34 0.22 0.16 CD63 -0.19 0.20 -0.21 0.13 -0.22 0.17 HLA-DR -0.08 0.61 0.05 0.70 0.02 0.89 CD193 0.30 0.04 0.09 0.51 0.13 0.43 MFI (Eos) CD294 0.05 0.74 -0.07 0.64 -0.15 0.33 CD63 -0.14 0.35 -0.16 0.24 -0.09 0.59 HLA-DR -0.09 0.55 -0.25 0.08 -0.02 0.89 CD193 -0.01 0.95 -0.18 0.21 -0.19 0.23 Association Between Surface Marker Changes and Clinical Improvement To determine whether changes in eosinophil surface marker expression were associated with improved asthma control, we performed mixed linear model analyses adjusting for intra-individual variability. Among the markers analyzed, only HLA-DR expression (measured as % of positive eosinophils) showed a statistically significant, negative, association with ACT improvement (β = 0.03, p = 0.05), suggesting that decreased HLA-DR expression may be linked to poorer clinical control. No significant associations were observed for CD63, CD294, or CD193 expression, either in terms of percentage or MFI (all p-values > 0.05; Table 3 ). Table 3 Eosinophils’ surface receptors change and association with disease control after therapy adjustment. % Eos Beta P-value CD294 -0.11 0.08 CD193 -0.08 0.14 CD63 -0.03 0.77 HLA-DR 0.03 0.05 MFI (gated on Eos) CD294 0.01 0.57 CD193 -0.01 0.96 CD63 -0.01 0.98 HLA-DR 0.01 0.15 Nonetheless, patients with persistent uncontrolled asthma (ACT < 20 after the run-in phase) exhibited significantly higher CD63 and HLA-DR expression compared to those who achieved control, supporting their potential role as peripheral markers of therapeutic response. Discussion The findings from this study provide novel insights into the functional immunophenotyping of circulating eosinophils in severe asthma and highlight the potential of flow cytometric surface marker analysis, particularly HLA-DR and CD63 expression, as adjunctive tools for treatment monitoring and personalization of therapy. These results underscore the value of going beyond simple eosinophil enumeration to capture activation and immunologic engagement, offering a more dynamic understanding of disease biology and therapeutic response. While blood eosinophil count is a widely used biomarker to identify patients eligible for anti-IL5 or anti-IL5R biologics, its limitations are increasingly recognized. Many patients with high eosinophil counts remain symptomatic despite adequate ICS/LABA therapy, while others exhibit poor clinical control with relatively low eosinophilia [ 18 , 19 ]. Our study confirms that eosinophil activation and immune engagement, reflected by surface expression of CD63 and HLA-DR, may be discordant with eosinophil counts, FeNO, and IgE levels. These findings align with emerging evidence suggesting that qualitative features of eosinophils, such as degranulation status or antigen-presenting capacity, may better correlate with disease activity than absolute cell counts [ 20 ]. The observation that HLA-DR expression significantly declined following treatment optimization, despite no change in blood eosinophil counts, suggests that standard therapies can modulate eosinophil functionality without necessarily altering their count. Moreover, the association between persistent HLA-DR expression and poor ACT improvement implies that eosinophils retaining an immunologically active phenotype may drive continued symptoms or inflammation. Thus, surface HLA-DR may serve as a peripheral marker of insufficient response to conventional therapy, identifying candidates who may benefit from escalation to biologics. Biologic agents targeting type 2 inflammation have transformed the management of severe asthma. However, not all patients respond equally, and current eligibility criteria remain suboptimal [ 21 , 22 ]. The integration of flow cytometric profiling of eosinophil surface markers could refine patient selection in several ways: first, persistent elevation of CD63 or HLA-DR after ICS/LABA/LAMA optimization could identify patients with ongoing eosinophil activation despite appropriate standard therapy, suggesting the need for biologic intervention; secondly, reduced HLA-DR following therapy may serve as an early surrogate of treatment efficacy, preceding improvements in ACT or PFTs, and could support continued use of current regimens without premature escalation. Third, patients with discordantly high HLA-DR despite low eosinophil counts might benefit from non-IL5-targeted biologics aimed at broader type 2 pathways or upstream cytokines (e.g., TSLP, IL-4/IL-13), given the possible T-cell interaction implied by HLA-DR expression [ 23 , 24 ]. Thus, functional immunophenotyping could support tailored therapy decisions, not only determining when biologics are needed but also helping to select the most appropriate mechanism of action based on eosinophil behavior [ 25 ]. In a recent pooled analysis by Shackleford and colleagues, several clinical factors accounting for the uncomplete clinical remission rate (38% only) in severe asthma were identified. The predictors of outcome identified in the cited study do not fully account for the reduced rate of clinical remission observed in severe asthma treated with biologics, suggesting that additional determinants are likely to be involved and might be uncovered through the characterization of eosinophil surface receptor expression [ 26 ]. In clinical practice, tracking asthma control typically relies on symptoms, exacerbation history, and surrogate biomarkers such as FeNO or eosinophil counts. However, these measures may lag behind immunological changes or be influenced by external factors (e.g., infections, allergen exposure). The stable and reproducible expression of surface markers like CD63 and HLA-DR offers a complementary approach for early detection of subclinical loss of control, enabling therapy adjustments, allowing objective measurement of biologic therapy impact, especially in patients with overlapping symptoms or poor symptom perception and facilitating the differentiation between eosinophil persistence versus activation, which may have therapeutic implications [ 27 , 28 ]. This seems to be particularly true when we look at the clinical differences between the early and late onset asthma phenotypes where eosinophil’s count alone is unable to address the therapeutic orienting and predict the response to biologics, suggesting that a qualitative, instead of quantitative characterization in eosinophils differentiation could provide adjunctive information [ 29 ] Although our data did not demonstrate strong correlations between CD63 or HLA-DR and traditional biomarkers (FeNO, IgE), this dissociation reinforces the added value of surface marker profiling as an independent dimension of asthma biology [ 30 ]. This study presents several limitations. This preliminary analysis focuses on a run-in phase of therapy optimization prior to biologic initiation, limiting our ability to directly assess response to targeted biologics. Nonetheless, the findings lay the groundwork for longitudinal studies assessing CD63 and HLA-DR dynamics during biologic therapy, where their predictive or monitoring utility could be better validated. Furthermore, the lack of significant changes in CD63, despite improved clinical control, suggests it may reflect a distinct activation pathway or subset of eosinophil responses. Future studies should explore whether CD63 is more responsive to IgE- or allergen-mediated triggers and whether its modulation differs between anti-IL5 and anti-IgE therapies [ 31 ]. Lastly, given the technical complexity of flow cytometry, its integration into routine care would require simplified, standardized protocols, potentially using point-of-care cytometry or selected marker panels [ 32 ]. In summary, this study supports the clinical relevance of eosinophil surface marker profiling, particularly HLA-DR, as a biomarker of functional activity in severe asthma. Its modulation with therapy and association with clinical improvement suggest potential roles in personalized treatment planning, biologic selection, and early response monitoring. As asthma care continues to evolve toward precision medicine, integrating immunophenotypic data could enhance our ability to match the right treatment to the right patient at the right time. Declarations Competing Interest Statement The authors declare no competing interests Funding declaration This study was granted and funded by GSK Supported Studies Programme (study id 213719) and sponsored by Fondazione Policlinico Universitario Campus Bio Medico di Roma. Author Contribution SS: Project administration, conceptualization, methodology, formal analysis, investigation, data curation, supervision, drafting, reviewing and editing of the final manuscript; CM: investigation, data curation; LV: methodology, formal analysis, investigation; PF: investigation, methodology, formal analysis, data curation; AC: investigation; FM: data curation; ST: investigation; RAI: supervision. Acknowledgement This study was granted and funded by GSK Supported Studies Programme (study id 213719) and sponsored by Fondazione Policlinico Universitario Campus Bio Medico di Roma. Data Availability The datasets generated and analyzed during the current study are not publicly available due to ethical and privacy considerations but are available from the corresponding author on reasonable and specific request. Requests for access to de-identified data will be considered in accordance with institutional policies and applicable data sharing agreements. References Pavord, I. D. et al. Eosinophilic inflammation in asthma. Lancet 393 (10173), 1591–1601 (2019). Fitzpatrick, A. M. et al. Heterogeneity of severe asthma in childhood: confirmation by cluster analysis of children in the NIH/NHLBI Severe Asthma Research Program. J. Allergy Clin. Immunol. 127 (2), 382–389e13 (2011). Ohnishi, H. et al. Surface CD63: a sensitive marker for basophil activation by IL-3 and other stimuli. J. Allergy Clin. Immunol. 125 (2), 341–348 (2010). Kita, H. Eosinophils: multifaceted biological properties and roles in health and disease. Immunol. Rev. 242 (1), 161–177 (2011). Simon, D. et al. Eosinophils and allergic skin diseases. Chem. Immunol. Allergy . 100 , 154–169 (2014). Legrand, F. et al. Activated eosinophils in association with macrophages can participate in antigen presentation in vivo. PLoS One . 6 (3), e17540 (2011). Weller, P. F. & Spencer, L. A. Functions of tissue-resident eosinophils. Nat. Rev. Immunol. 17 (12), 746–760 (2017). Hartl, D. et al. Quantitative and functional impairment of pulmonary CD4 + CD25hi regulatory T cells in pediatric asthma. J. Allergy Clin. Immunol. 119 (5), 1258–1266 (2007). Gibson, P. G. et al. Biomarkers in asthma: clinical relevance and future directions. Respirology 14 (6), 780–789 (2009). Global Initiative for Asthma (GINA). Global Strategy for Asthma Management and Prevention – Update. (2024). Bousquet, J. et al. Precision medicine in allergic rhinitis and asthma. J. Allergy Clin. Immunol. 141 (2), 344–359 (2018). Cepelak, I. et al. Eosinophil biological properties and their role in asthma. Period Biol. 119 (3), 111–119 (2017). Quanjer, P. H. et al. Multi-ethnic reference values for spirometry for the 3–95-yr age range: the Global Lung Function 2012 equations. Eur. Respir J. 40 (6), 1324–1343 (2012). Dweik, R. A. et al. An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FeNO) for clinical applications. Am. J. Respir Crit. Care Med. 184 (5), 602–615 (2011). Gounni, A. S. et al. Human eosinophils express and release IL-13 in response to IL-5 and GM-CSF. J. Immunol. 157 (12), 5521–5528 (1996). Nagase, H. et al. Expression and function of CRTH2, an endogenous prostaglandin D2 receptor, on human eosinophils and basophils. J. Immunol. 170 (10), 5483–5489 (2003). Sabroe, I. et al. The role of chemokines and their receptors in asthma. Clin. Exp. Allergy . 32 (4), 460–470 (2002). Bafadhel, M., Pavord, I. D. & Russell, R. E. K. Eosinophils in COPD: just another biomarker? Lancet Respir Med. 5 (9), 747–759 (2017). Wenzel, S. E. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat. Med. 18 (5), 716–725 (2012). Wagener, A. H. et al. External validation of blood eosinophils, FENO and serum periostin as surrogates for sputum eosinophils in asthma. Thorax 70 (2), 115–120 (2015). Bhargava, A. et al. Anti–IL-5 therapy reduces eosinophil activation in severe asthma: analysis of a multicenter randomized controlled trial. J. Allergy Clin. Immunol. 147 (5), 1584–1591e2 (2021). Fulkerson, P. C. & Rothenberg, M. E. Targeting eosinophils in allergy, inflammation and beyond. Nat. Rev. Drug Discov . 12 (2), 117–129 (2013). Pelaia, C., Crimi, C. & Pelaia, G. Biologic therapies for severe asthma: current and emerging options. Respir Med. 197 , 106825 (2022). Heaney, L. G. et al. Eosinophilic and non-eosinophilic asthma: an expert consensus framework to optimize diagnosis and treatment. Clin. Transl Allergy . 11 (6), e12088 (2021). Menzella, F., Lusuardi, M., Galeone, C., Taddei, S. & Zucchi, L. Tailored therapy for severe asthma. Multidiscip Respir Med. 16 (1), 792 (2021). Shackleford, A., Heaney, L. G., Redmond, C., McDowell, P. J. & Busby, J. Clinical remission attainment, definitions, and correlates among patients with severe asthma treated with biologics: a systematic review and meta-analysis. Lancet Respir Med. 13 (1), 23–34 (2025). Jackson, D. J. et al. Biological therapies in asthma: current and future perspectives. Lancet 400 (10351), 2031–2046 (2022). Adcock, I. M., Caramori, G. & Chung, K. F. New targets for drug development in asthma. Lancet 372 (9643), 1073–1087 (2008). Lommatzsch, M. et al. Eosinophils in asthma phenotypes: perpetrators or guilty by association? Lancet Respir Med. Jul 8 , S2213 (2025). 2600(25)00174-2. Kupczyk, M. & Kuna, P. Flow cytometry in asthma: a powerful tool for phenotyping and monitoring. Curr. Allergy Asthma Rep. 20 (10), 49 (2020). Varricchi, G. et al. The role of eosinophils in asthma: from pathogenesis to new biologic therapies. Front. Med. (Lausanne) . 4 , 75 (2017). Glaenzel, A. et al. Flow cytometry in severe asthma: applications for biologic decision-making. Respir Res. 24 (1), 215 (2023). Additional Declarations No competing interests reported. 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Angelo","middleName":"","lastName":"Coppola","suffix":""},{"id":549157683,"identity":"53caa1e3-dfcc-4689-b2fb-d874f0176c64","order_by":5,"name":"Fatima Maurizi","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Campus Bio-Medico","correspondingAuthor":false,"prefix":"","firstName":"Fatima","middleName":"","lastName":"Maurizi","suffix":""},{"id":549157684,"identity":"78cbba34-82df-4cc0-bb94-7f6990bdfd83","order_by":6,"name":"Silvia Travaglini","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Campus Bio-Medico","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Travaglini","suffix":""},{"id":549157685,"identity":"04bc34c2-a51d-4df5-8268-42f261ed073f","order_by":7,"name":"Anna Zito","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Campus 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06:54:21","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106330,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7923148/v1/8ff76cb5c5d6880b15c83a94.html"},{"id":96967310,"identity":"7e749954-95de-4cac-bb3b-940c43091e6f","added_by":"auto","created_at":"2025-11-28 06:54:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97526,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGating strategy and representative histograms for eosinophil surface marker expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–C) Sequential gating strategy used for the identification of circulating eosinophils in peripheral blood by multicolor flow cytometry. (A) Leukocytes were first gated on CD45⁺/SSC-A properties. (B) Granulocytes were then identified as CD15⁺/CD16⁻/low and (C) eosinophils were further distinguished based on Siglec-8 positivity.\u003cbr\u003e\n(D) Representative histograms of surface marker expression on gated eosinophils from a single subject. Median fluorescence intensity (MFI) values are shown for: HLA-DR (BV605), CD193 (BV650), CD294 (PE-CF594), and CD63 (PE-Cy7). Markers were stained using fluorochrome-conjugated antibodies as described in the Methods section. Data were acquired on a BD LSRFortessa™ X-20 cytometer and analyzed using standard flow cytometry software.\u003c/p\u003e","description":"","filename":"Figure1new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7923148/v1/b75247a3b66d86f88969de50.jpg"},{"id":96967307,"identity":"1bc3df83-43f7-458f-b66d-224430cc2ba4","added_by":"auto","created_at":"2025-11-28 06:54:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of clinical and immunophenotypic variables in patients with severe asthma at baseline.\u003c/strong\u003e\u003cbr\u003e\nHistograms represent the frequency distribution of blood eosinophil count (absolute and %), total IgE, and eosinophil identification (CD45⁺CD66b⁺CD15⁺CD16⁻) by flow cytometry. Surface marker expression (% positive eosinophils and mean fluorescence intensity [MFI]) is shown for CD294, CD63, HLA-DR, and CD193. Highlighted plots indicate broader inter-individual variability in HLA-DR and CD193 expression, suggesting heterogeneous eosinophil activation or immune engagement.\u003c/p\u003e","description":"","filename":"Figure2new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7923148/v1/7b91bca2d6b7568e28bfed13.jpg"},{"id":102785181,"identity":"5de2e2b4-7d30-4425-86e0-1eac86b1b3e1","added_by":"auto","created_at":"2026-02-16 16:01:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1215248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7923148/v1/ee3cfe0c-a306-4b66-9869-8efb30c5741b.pdf"},{"id":96967346,"identity":"98745313-75c8-4a4a-92c6-efe85603c9cc","added_by":"auto","created_at":"2025-11-28 06:54:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33471,"visible":true,"origin":"","legend":"","description":"","filename":"CONSORT2025filledwithpagesSurfaceExpression.docx","url":"https://assets-eu.researchsquare.com/files/rs-7923148/v1/d46e7233545db8b40b8c40b9.docx"},{"id":96967308,"identity":"399ca477-687b-4ae4-9b76-406950e5c4a0","added_by":"auto","created_at":"2025-11-28 06:54:18","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":83949,"visible":true,"origin":"","legend":"","description":"","filename":"ClinTrial.gov2ndreceipt.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7923148/v1/869fa5a55e121b03ab9c21e3.pdf"},{"id":96967330,"identity":"5efa9d7f-81b4-4e0b-80d8-838fd5dbbcfc","added_by":"auto","created_at":"2025-11-28 06:54:21","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2928531,"visible":true,"origin":"","legend":"","description":"","filename":"ProtocollostudioEoAGvers.3.0.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7923148/v1/dfc9c8466abe4960db902c4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Surface Expression of CD63 and HLA-DR in circulating eosinophils correlates with Improved Clinical Control After Treatment Optimization in asthma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAsthma is a chronic inflammatory disease of the airways characterized by variable airflow obstruction, airway hyperresponsiveness, and a complex immune profile involving various leukocyte subsets. Eosinophils play a central role in the pathophysiology of asthma, particularly in the T2-high endotype, where elevated eosinophil counts in blood and sputum are commonly observed [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Beyond simple enumeration, the functional state and activation profile of eosinophils have emerged as more informative indicators of disease activity and therapeutic response [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In this context, surface markers associated with eosinophil activation and antigen presentation, such as CD63 and HLA-DR, might offer valuable insights into disease monitoring and the evaluation of treatment efficacy.\u003c/p\u003e\u003cp\u003eCD63 is a tetraspanin commonly associated with the activation and degranulation of eosinophils, particularly in response to IgE-mediated stimuli. Upon degranulation, CD63 translocates to the cell surface, making it a reliable surrogate marker for eosinophil activation in various allergic and inflammatory settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Its expression has been linked to disease severity and exacerbation risk in asthma, yet its utility in tracking treatment response remains underexplored [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilarly, HLA-DR, a major histocompatibility complex (MHC) class II molecule, is not typically expressed on resting eosinophils, but may be upregulated in response to pro-inflammatory cytokines, such as interferon-γ and IL-5 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. HLA-DR expression on eosinophils has been proposed as an indicator of their capacity to engage in antigen presentation and interact with T cells, thus reflecting a more immunologically active and disease-relevant phenotype [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While most research has focused on HLA-DR expression in monocytes and dendritic cells, its expression in eosinophils, particularly in the context of asthma, has garnered increasing attention [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdvances in flow cytometry have facilitated the precise identification and functional characterization of eosinophils in peripheral blood. Multicolor immunophenotyping allows simultaneous evaluation of activation markers, maturation status, and immune engagement, offering a dynamic view of eosinophil behavior during disease progression and under therapeutic intervention [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In contrast to more static parameters such as blood eosinophil count, flow cytometric assessment of surface markers can provide early indications of biological response to treatment, even before clinical improvement becomes apparent [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe optimization of asthma therapy, whether through inhaled corticosteroids (ICS), long-acting bronchodilators, or biologic agents targeting eosinophilic inflammation, has been shown to improve symptom control and reduce exacerbations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, objective markers that reflect biological response to these interventions are limited. Identifying peripheral biomarkers that correlate with improved clinical outcomes would be valuable for both individualized treatment plans and real-time monitoring of disease control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we investigated the surface expression of CD63 and HLA-DR in circulating eosinophils of patients with asthma undergoing treatment optimization. Our objective was to determine whether changes in these activation markers, as measured by flow cytometry, correlate with improvements in clinical control, as assessed by standardized asthma control questionnaires and pulmonary function testing. We hypothesized that effective therapeutic adjustment would lead to a reduction in eosinophil activation and immunologic engagement, reflected by decreased CD63 and HLA-DR surface expression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design, Setting, and Participants\u003c/h2\u003e\u003cp\u003eThis is a preliminary analysis of an ongoing randomized interventional clinical trial investigating the effects of anti-IL5 antibodies in patients with severe asthma. The study commenced in March 2021 at the tertiary-care asthma outpatient clinic of Fondazione Policlinico Campus Bio-Medico in Rome, Italy. The protocol adheres to the Declaration of Helsinki and was approved by the Campus Bio Medico institutional Ethics Committee (ComEt CBM 94/20 OSS). The study protocol was also submitted and registered at Clinicaltrial.gov (Identifier: NCT05001529). All participants provided written informed consent.\u003c/p\u003e\u003cp\u003eEligible participants were adults (\u0026ge;\u0026thinsp;18 years) with a diagnosis of severe asthma, either controlled or uncontrolled, according to the latest Global Initiative for Asthma (GINA) guidelines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Patients were excluded if they had a diagnosis of COPD/asthma overlap, other acute or chronic conditions affecting eosinophil levels, or were undergoing chronic treatment with immunosuppressants, systemic corticosteroids (for diseases other than asthma), or other biologic medications.\u003c/p\u003e\u003cp\u003eThis analysis focuses on data collected during the three-month run-in period preceding randomization. This period was designed to confirm the diagnosis of severe asthma and optimize treatment according to current guidelines.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical Assessment and Therapy Optimization\u003c/h3\u003e\n\u003cp\u003eUpon recruitment, a detailed clinical history was recorded, including allergic comorbidities. Physical examination and comprehensive pulmonary function tests (PFTs) were conducted, including spirometry, residual volume via helium dilution, and single-breath diffusing capacity for carbon monoxide (DLCO), referenced to Global Lung Initiative (GLI) standards [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhen asthma diagnosis had been made externally, existing documentation (e.g., bronchodilator reversibility or methacholine challenge testing) was reviewed. Symptom control was assessed using the Italian version of the Asthma Control Test (ACT). The number of asthma exacerbations and hospitalizations in the previous year was also recorded. Asthma exacerbation was defined as worsening of asthma symptoms above baseline, marked by increasing shortness of breath, wheezing, cough, or chest tightness, which often requires a change in treatment and/or asthma that necessitates a step up of treatment or the use of systemic corticosteroids to achieve control.\u003c/p\u003e\u003cp\u003eInhaler technique and adherence were evaluated, and rescue medication use was documented.\u003c/p\u003e\u003cp\u003eFractional exhaled nitric oxide (FeNO) levels were measured using the Vivatmo\u0026trade; pro device (COSMED, Rome, Italy), following standardized guidelines [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Morning blood samples were collected for total immunoglobulin E (IgE), eosinophil count, and eosinophil surface marker analysis.\u003c/p\u003e\u003cp\u003eAsthma therapy was optimized according to the GINA step-up approach, which includes increasing the dose of inhaled corticosteroids and adding long-acting β2 agonists (LABAs), leukotriene receptor antagonists (LTRAs), or tiotropium. After three months, patients were reassessed. Those with persistent symptoms or exacerbations were considered for biologic therapy.\u003c/p\u003e\n\u003ch3\u003eEosinophil Immunophenotyping by Flow Cytometry\u003c/h3\u003e\n\u003cp\u003eMulticolor flow cytometry was used to evaluate surface marker expression on peripheral blood eosinophils. Blood samples collected in EDTA tubes were processed within two hours at room temperature (~\u0026thinsp;20\u0026deg;C). A 100 \u0026micro;L aliquot of each sample was stained with a panel of fluorochrome-conjugated antibodies.\u003c/p\u003e\u003cp\u003eEosinophils were identified using markers CD45, CD66b, CD15, CD16, and Siglec-8. Surface expression of CD63 (a marker of degranulation and activation), HLA-DR (MHC class II molecule upregulated during activation), CD193 and CD294 were assessed in order to provide a comprehensive characterization [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Collectively, CD193 and CD294 characterize eosinophil migratory and Th2-driven activation pathways, whereas CD63 and HLA-DR reflect, respectively, their effector degranulation status and potential involvement in antigen presentation and immune regulation.\u003c/p\u003e\u003cp\u003eFollowing a 30-minute incubation at 4\u0026deg;C, erythrocytes were lysed using BD FACS\u0026trade; lysing solution, and cells were resuspended in phosphate-buffered saline (PBS). Acquisition was performed on a BD LSRFortessa\u0026trade; X-20 cytometer. Marker expression was quantified using median fluorescence intensity (MFI) or geometric mean for skewed distributions. Additionally, the proportion of eosinophils expressing each marker was reported as a percentage of the total eosinophil population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes were the association of eosinophil surface expression of CD63 and HLA-DR with clinical asthma control and exacerbation frequency after therapy optimization. Poor control was defined as an ACT score\u0026thinsp;\u0026lt;\u0026thinsp;20 and/or \u0026ge;\u0026thinsp;1 exacerbation in the previous year. Secondary, exploratory outcomes included associations with blood eosinophil count, FeNO, and total IgE.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were reported as medians and interquartile ranges (IQR) for continuous variables and frequencies (%) for categorical variables. Analyses were limited to the 73 patients (91% of enrolled) who completed the run-in period prior to randomization due to previously non-optimized therapy.\u003c/p\u003e\u003cp\u003eThe expected increase in the CD63 level is estimated to reach the level of 20% in severe asthma patients. The required number of subjects, assuming a statistical power of 80% and a I type error of 5% (p value\u0026thinsp;=\u0026thinsp;0.05) is 67 individuals, with a total amount of patients to be recruited of about 100, considering the possibility of drop out at follow up.\u003c/p\u003e\u003cp\u003eComparisons between recruitment and post-optimization values were made using the Wilcoxon signed-rank test for continuous variables and McNemar's test for paired categorical data. Correlations between eosinophil marker expression and baseline blood eosinophils, FeNO, and IgE were evaluated using Spearman's ρ.\u003c/p\u003e\u003cp\u003eMarker expression, FeNO, IgE, and eosinophil counts were visualized in boxplots, stratified by exacerbation frequency and ACT score. Between-group differences were tested using the Wilcoxon rank-sum test. To assess the association between changes in eosinophil markers and clinical improvement, mixed linear models with random intercepts were employed. Results are reported as β estimates with corresponding p-values. All statistical analyses were performed using R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria), with the \u0026ldquo;lme4\u0026rdquo; and \u0026ldquo;multicomp\u0026rdquo; packages.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of Study Population\u003c/h2\u003e\u003cp\u003eA total of 105 adult patients with severe asthma were included in the baseline analysis. Clinical, functional, and laboratory data were collected prior to therapy optimization. Median age was 57 years (IQR: 13), with a mild male predominance (51%). The median blood eosinophil count was 265 cells/\u0026micro;L (IQR:342.8), total IgE were 170 kU/L (IQR: 261), and FeNO measured 29 ppb (IQR: 46). ACT mean score was 20 (SD\u0026thinsp;=\u0026thinsp;7), with 50% of participants classified as having uncontrolled asthma (ACT\u0026thinsp;\u0026lt;\u0026thinsp;20). These details are summarized in 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\u003eClinical, functional, and laboratory data at baseline and after therapy optimization (n\u0026thinsp;=\u0026thinsp;105).\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\u003eRecruitment\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAfter therapy optimization\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (M), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53(51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.6 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExacerbation previous year (\u0026ge;\u0026thinsp;1), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACT, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1% of predicted (post-bronch), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90(36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1/FVC % (post bronch)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeNO (ppb), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.5 (46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeNO\u0026thinsp;\u0026gt;\u0026thinsp;50 ppb, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEos (abs_value), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e265 (342)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180 (275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEos (%), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgE total, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170 (261)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 (182.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgE total\u0026thinsp;\u0026gt;\u0026thinsp;420, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10(9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (65.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhaled therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-dose ICS/LABA, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-dose ICS/LABA, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLTRA, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTiotropium, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% of Eos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD294,\u0026nbsp;median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD63,\u0026nbsp;median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHLA DR,\u0026nbsp;median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (67.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.3 (42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD193,\u0026nbsp;median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIF (gated on Eos)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD294,\u0026nbsp;median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1751 (527.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1604 (589)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD63,\u0026nbsp;median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4544.5 (3553.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4313 (3816)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHLA DR,\u0026nbsp;median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e731 (1000.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e441 (644)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD193, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12469 (6841)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9938.5 (5587.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous variables are reported as median (IQR) while categorical variables as frequency (%).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eLegend: BMI: Body mass index; ACT: Asthma Control Test; FEV1: Forced expiratory volume in the 1st second; FeNO: Fractional Exhaled Nitric Oxide; Eos: Eosinophils; IgE: Immunoglobulin E; OCS: oral corticosteroids; ICS: inhaled corticosteroids; LABA: long-acting beta-2 agonists; LTRA: leukotriene receptor antagonists; MFI: mean fluorescence intensity; HLA-DR: Human leukocyte antigen-D receptor.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEosinophil Surface Marker Expression at Baseline\u003c/h3\u003e\n\u003cp\u003eFlow cytometry exploratory analysis showed variable expression of surface markers across the eosinophil population. CD63 and HLA-DR demonstrated a wider range of surface expression intensities compared to other markers, including CD193 and CD294. Specifically, HLA-DR and CD193 showed higher degrees of surface heterogeneity, suggestive of polyclonal activation states or variable immunological roles among eosinophils. In contrast, CD63 and CD294 displayed more consistent, though still elevated, surface patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Therapy Optimization\u003c/h2\u003e\u003cp\u003eAfter the three-month period of therapy optimization, significant improvements in asthma control were observed. ACT scores increased from a median of 21 to 24 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the proportion of uncontrolled patients (ACT\u0026thinsp;\u0026lt;\u0026thinsp;20) decreased from 50% to 16%. This was paralleled by a reduction in the number of exacerbators (patients with \u0026ge;\u0026thinsp;1 exacerbation/year), in oral corticosteroids use and in total Ig E level. Interestingly, no significant change in circulating eosinophils was observed (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The improved control was associated with the overall increase in ICS/LABA, anti-leukotrienes, and tiotropium consumption at the end of the optimization period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eImmunophenotypically, HLA-DR expression showed a significant decrease after therapy optimization, both in terms of percentage of positive eosinophils and mean fluorescence intensity (MFI) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively; CD193 also showed a significant decrease in MFI (p\u0026thinsp;=\u0026thinsp;0.03), though the percentage of positive eosinophils remained high, despite a modest reduction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, CD63 and CD294 expression did not change significantly in either percentage of positive eosinophils or MFI (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCorrelations with Eosinophil Count, FeNO, and IgE\u003c/h2\u003e\u003cp\u003eSpearman\u0026rsquo;s rank correlation analysis revealed a weak, but statistically significant positive correlation between the percentage of CD193-positive eosinophils and blood eosinophil count (ρ\u0026thinsp;=\u0026thinsp;0.30, p\u0026thinsp;=\u0026thinsp;0.04) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No significant correlations were found between CD63 or HLA-DR expression (either as % positive cells or MFI) and blood eosinophils, FeNO, or total IgE (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, CD294 expression did not correlate significantly with any of the examined biomarkers.\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\u003eSperman\u0026rsquo;s rank correlation rho between eosinophils\u0026rsquo; surface markers and eosinophil count, FeNO and total IgE\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eEos. Count\u003c/p\u003e\u003cp\u003e(cell/ul)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFeNO\u003c/p\u003e\u003cp\u003e(ppb)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal IgE\u003c/p\u003e\u003cp\u003e(UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Eos\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD294\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHLA-DR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD193\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMFI (Eos)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD294\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHLA-DR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD193\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAssociation Between Surface Marker Changes and Clinical Improvement\u003c/h2\u003e\u003cp\u003eTo determine whether changes in eosinophil surface marker expression were associated with improved asthma control, we performed mixed linear model analyses adjusting for intra-individual variability. Among the markers analyzed, only HLA-DR expression (measured as % of positive eosinophils) showed a statistically significant, negative, association with ACT improvement (β\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.05), suggesting that decreased HLA-DR expression may be linked to poorer clinical control. No significant associations were observed for CD63, CD294, or CD193 expression, either in terms of percentage or MFI (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05; 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\u003eEosinophils\u0026rsquo; surface receptors change and association with disease control after therapy adjustment.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Eos\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\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\u003eCD294\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD193\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHLA-DR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMFI (gated on Eos)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD294\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD193\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHLA-DR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\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\u003eNonetheless, patients with persistent uncontrolled asthma (ACT\u0026thinsp;\u0026lt;\u0026thinsp;20 after the run-in phase) exhibited significantly higher CD63 and HLA-DR expression compared to those who achieved control, supporting their potential role as peripheral markers of therapeutic response.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from this study provide novel insights into the functional immunophenotyping of circulating eosinophils in severe asthma and highlight the potential of flow cytometric surface marker analysis, particularly HLA-DR and CD63 expression, as adjunctive tools for treatment monitoring and personalization of therapy. These results underscore the value of going beyond simple eosinophil enumeration to capture activation and immunologic engagement, offering a more dynamic understanding of disease biology and therapeutic response.\u003c/p\u003e\u003cp\u003eWhile blood eosinophil count is a widely used biomarker to identify patients eligible for anti-IL5 or anti-IL5R biologics, its limitations are increasingly recognized. Many patients with high eosinophil counts remain symptomatic despite adequate ICS/LABA therapy, while others exhibit poor clinical control with relatively low eosinophilia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study confirms that eosinophil activation and immune engagement, reflected by surface expression of CD63 and HLA-DR, may be discordant with eosinophil counts, FeNO, and IgE levels. These findings align with emerging evidence suggesting that qualitative features of eosinophils, such as degranulation status or antigen-presenting capacity, may better correlate with disease activity than absolute cell counts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe observation that HLA-DR expression significantly declined following treatment optimization, despite no change in blood eosinophil counts, suggests that standard therapies can modulate eosinophil functionality without necessarily altering their count. Moreover, the association between persistent HLA-DR expression and poor ACT improvement implies that eosinophils retaining an immunologically active phenotype may drive continued symptoms or inflammation. Thus, surface HLA-DR may serve as a peripheral marker of insufficient response to conventional therapy, identifying candidates who may benefit from escalation to biologics.\u003c/p\u003e\u003cp\u003eBiologic agents targeting type 2 inflammation have transformed the management of severe asthma. However, not all patients respond equally, and current eligibility criteria remain suboptimal [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The integration of flow cytometric profiling of eosinophil surface markers could refine patient selection in several ways: first, persistent elevation of CD63 or HLA-DR after ICS/LABA/LAMA optimization could identify patients with ongoing eosinophil activation despite appropriate standard therapy, suggesting the need for biologic intervention; secondly, reduced HLA-DR following therapy may serve as an early surrogate of treatment efficacy, preceding improvements in ACT or PFTs, and could support continued use of current regimens without premature escalation. Third, patients with discordantly high HLA-DR despite low eosinophil counts might benefit from non-IL5-targeted biologics aimed at broader type 2 pathways or upstream cytokines (e.g., TSLP, IL-4/IL-13), given the possible T-cell interaction implied by HLA-DR expression [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThus, functional immunophenotyping could support tailored therapy decisions, not only determining when biologics are needed but also helping to select the most appropriate mechanism of action based on eosinophil behavior [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In a recent pooled analysis by Shackleford and colleagues, several clinical factors accounting for the uncomplete clinical remission rate (38% only) in severe asthma were identified. The predictors of outcome identified in the cited study do not fully account for the reduced rate of clinical remission observed in severe asthma treated with biologics, suggesting that additional determinants are likely to be involved and might be uncovered through the characterization of eosinophil surface receptor expression [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In clinical practice, tracking asthma control typically relies on symptoms, exacerbation history, and surrogate biomarkers such as FeNO or eosinophil counts. However, these measures may lag behind immunological changes or be influenced by external factors (e.g., infections, allergen exposure). The stable and reproducible expression of surface markers like CD63 and HLA-DR offers a complementary approach for early detection of subclinical loss of control, enabling therapy adjustments, allowing objective measurement of biologic therapy impact, especially in patients with overlapping symptoms or poor symptom perception and facilitating the differentiation between eosinophil persistence versus activation, which may have therapeutic implications [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This seems to be particularly true when we look at the clinical differences between the early and late onset asthma phenotypes where eosinophil\u0026rsquo;s count alone is unable to address the therapeutic orienting and predict the response to biologics, suggesting that a qualitative, instead of quantitative characterization in eosinophils differentiation could provide adjunctive information [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAlthough our data did not demonstrate strong correlations between CD63 or HLA-DR and traditional biomarkers (FeNO, IgE), this dissociation reinforces the added value of surface marker profiling as an independent dimension of asthma biology [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study presents several limitations. This preliminary analysis focuses on a run-in phase of therapy optimization prior to biologic initiation, limiting our ability to directly assess response to targeted biologics. Nonetheless, the findings lay the groundwork for longitudinal studies assessing CD63 and HLA-DR dynamics during biologic therapy, where their predictive or monitoring utility could be better validated. Furthermore, the lack of significant changes in CD63, despite improved clinical control, suggests it may reflect a distinct activation pathway or subset of eosinophil responses. Future studies should explore whether CD63 is more responsive to IgE- or allergen-mediated triggers and whether its modulation differs between anti-IL5 and anti-IgE therapies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLastly, given the technical complexity of flow cytometry, its integration into routine care would require simplified, standardized protocols, potentially using point-of-care cytometry or selected marker panels [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, this study supports the clinical relevance of eosinophil surface marker profiling, particularly HLA-DR, as a biomarker of functional activity in severe asthma. Its modulation with therapy and association with clinical improvement suggest potential roles in personalized treatment planning, biologic selection, and early response monitoring. As asthma care continues to evolve toward precision medicine, integrating immunophenotypic data could enhance our ability to match the right treatment to the right patient at the right time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interest Statement\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\u003ch2\u003eFunding declaration\u003c/h2\u003e\u003cp\u003eThis study was granted and funded by GSK Supported Studies Programme (study id 213719) and sponsored by Fondazione Policlinico Universitario Campus Bio Medico di Roma.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSS: Project administration, conceptualization, methodology, formal analysis, investigation, data curation, supervision, drafting, reviewing and editing of the final manuscript; CM: investigation, data curation; LV: methodology, formal analysis, investigation; PF: investigation, methodology, formal analysis, data curation; AC: investigation; FM: data curation; ST: investigation; RAI: supervision.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was granted and funded by GSK Supported Studies Programme (study id 213719) and sponsored by Fondazione Policlinico Universitario Campus Bio Medico di Roma.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to ethical and privacy considerations but are available from the corresponding author on reasonable and specific request. Requests for access to de-identified data will be considered in accordance with institutional policies and applicable data sharing agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePavord, I. D. et al. Eosinophilic inflammation in asthma. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e393\u003c/b\u003e (10173), 1591\u0026ndash;1601 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFitzpatrick, A. M. et al. Heterogeneity of severe asthma in childhood: confirmation by cluster analysis of children in the NIH/NHLBI Severe Asthma Research Program. \u003cem\u003eJ. Allergy Clin. Immunol.\u003c/em\u003e \u003cb\u003e127\u003c/b\u003e (2), 382\u0026ndash;389e13 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhnishi, H. et al. Surface CD63: a sensitive marker for basophil activation by IL-3 and other stimuli. \u003cem\u003eJ. Allergy Clin. Immunol.\u003c/em\u003e \u003cb\u003e125\u003c/b\u003e (2), 341\u0026ndash;348 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKita, H. Eosinophils: multifaceted biological properties and roles in health and disease. \u003cem\u003eImmunol. Rev.\u003c/em\u003e \u003cb\u003e242\u003c/b\u003e (1), 161\u0026ndash;177 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimon, D. et al. Eosinophils and allergic skin diseases. \u003cem\u003eChem. Immunol. Allergy\u003c/em\u003e. \u003cb\u003e100\u003c/b\u003e, 154\u0026ndash;169 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLegrand, F. et al. Activated eosinophils in association with macrophages can participate in antigen presentation in vivo. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e (3), e17540 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeller, P. F. \u0026amp; Spencer, L. A. Functions of tissue-resident eosinophils. \u003cem\u003eNat. Rev. Immunol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (12), 746\u0026ndash;760 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartl, D. et al. Quantitative and functional impairment of pulmonary CD4\u0026thinsp;+\u0026thinsp;CD25hi regulatory T cells in pediatric asthma. \u003cem\u003eJ. Allergy Clin. Immunol.\u003c/em\u003e \u003cb\u003e119\u003c/b\u003e (5), 1258\u0026ndash;1266 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGibson, P. G. et al. Biomarkers in asthma: clinical relevance and future directions. \u003cem\u003eRespirology\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (6), 780\u0026ndash;789 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal Initiative for Asthma (GINA). Global Strategy for Asthma Management and Prevention \u0026ndash; Update. (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBousquet, J. et al. Precision medicine in allergic rhinitis and asthma. \u003cem\u003eJ. Allergy Clin. Immunol.\u003c/em\u003e \u003cb\u003e141\u003c/b\u003e (2), 344\u0026ndash;359 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCepelak, I. et al. Eosinophil biological properties and their role in asthma. \u003cem\u003ePeriod Biol.\u003c/em\u003e \u003cb\u003e119\u003c/b\u003e (3), 111\u0026ndash;119 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuanjer, P. H. et al. Multi-ethnic reference values for spirometry for the 3\u0026ndash;95-yr age range: the Global Lung Function 2012 equations. \u003cem\u003eEur. Respir J.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e (6), 1324\u0026ndash;1343 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDweik, R. A. et al. An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FeNO) for clinical applications. \u003cem\u003eAm. J. Respir Crit. Care Med.\u003c/em\u003e \u003cb\u003e184\u003c/b\u003e (5), 602\u0026ndash;615 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGounni, A. S. et al. Human eosinophils express and release IL-13 in response to IL-5 and GM-CSF. \u003cem\u003eJ. Immunol.\u003c/em\u003e \u003cb\u003e157\u003c/b\u003e (12), 5521\u0026ndash;5528 (1996).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagase, H. et al. Expression and function of CRTH2, an endogenous prostaglandin D2 receptor, on human eosinophils and basophils. \u003cem\u003eJ. Immunol.\u003c/em\u003e \u003cb\u003e170\u003c/b\u003e (10), 5483\u0026ndash;5489 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSabroe, I. et al. The role of chemokines and their receptors in asthma. \u003cem\u003eClin. Exp. Allergy\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e (4), 460\u0026ndash;470 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBafadhel, M., Pavord, I. D. \u0026amp; Russell, R. E. K. Eosinophils in COPD: just another biomarker? \u003cem\u003eLancet Respir Med.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (9), 747\u0026ndash;759 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWenzel, S. E. Asthma phenotypes: the evolution from clinical to molecular approaches. \u003cem\u003eNat. Med.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (5), 716\u0026ndash;725 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWagener, A. H. et al. External validation of blood eosinophils, FENO and serum periostin as surrogates for sputum eosinophils in asthma. \u003cem\u003eThorax\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e (2), 115\u0026ndash;120 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhargava, A. et al. Anti\u0026ndash;IL-5 therapy reduces eosinophil activation in severe asthma: analysis of a multicenter randomized controlled trial. \u003cem\u003eJ. Allergy Clin. Immunol.\u003c/em\u003e \u003cb\u003e147\u003c/b\u003e (5), 1584\u0026ndash;1591e2 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFulkerson, P. C. \u0026amp; Rothenberg, M. E. Targeting eosinophils in allergy, inflammation and beyond. \u003cem\u003eNat. Rev. Drug Discov\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (2), 117\u0026ndash;129 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePelaia, C., Crimi, C. \u0026amp; Pelaia, G. Biologic therapies for severe asthma: current and emerging options. \u003cem\u003eRespir Med.\u003c/em\u003e \u003cb\u003e197\u003c/b\u003e, 106825 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeaney, L. G. et al. Eosinophilic and non-eosinophilic asthma: an expert consensus framework to optimize diagnosis and treatment. \u003cem\u003eClin. Transl Allergy\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (6), e12088 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMenzella, F., Lusuardi, M., Galeone, C., Taddei, S. \u0026amp; Zucchi, L. Tailored therapy for severe asthma. \u003cem\u003eMultidiscip Respir Med.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (1), 792 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShackleford, A., Heaney, L. G., Redmond, C., McDowell, P. J. \u0026amp; Busby, J. Clinical remission attainment, definitions, and correlates among patients with severe asthma treated with biologics: a systematic review and meta-analysis. \u003cem\u003eLancet Respir Med.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (1), 23\u0026ndash;34 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJackson, D. J. et al. Biological therapies in asthma: current and future perspectives. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e400\u003c/b\u003e (10351), 2031\u0026ndash;2046 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdcock, I. M., Caramori, G. \u0026amp; Chung, K. F. New targets for drug development in asthma. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e372\u003c/b\u003e (9643), 1073\u0026ndash;1087 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLommatzsch, M. et al. Eosinophils in asthma phenotypes: perpetrators or guilty by association? Lancet Respir Med. \u003cem\u003eJul\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, S2213 (2025). 2600(25)00174-2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKupczyk, M. \u0026amp; Kuna, P. Flow cytometry in asthma: a powerful tool for phenotyping and monitoring. \u003cem\u003eCurr. Allergy Asthma Rep.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (10), 49 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVarricchi, G. et al. The role of eosinophils in asthma: from pathogenesis to new biologic therapies. \u003cem\u003eFront. Med. (Lausanne)\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, 75 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlaenzel, A. et al. Flow cytometry in severe asthma: applications for biologic decision-making. \u003cem\u003eRespir Res.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (1), 215 (2023).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Severe asthma, Eosinophil activation, CD63, HLA-DR, Flow cytometry biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7923148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7923148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEosinophils are key effectors in asthma, especially within the T2-high phenotype. Conventional biomarkers such as blood eosinophils, FeNO, and IgE incompletely reflect disease activity. Surface markers like CD63 and HLA-DR might provide additional insight into eosinophil activation and treatment response.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo evaluate whether CD63 and HLA-DR expression on circulating eosinophils correlates with clinical improvement after therapy optimization in severe asthma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e In this pre-randomization analysis of 105 adults enrolled in an anti-IL5 trial (NCT05001529), patients underwent a 3-month run-in with optimized therapy per GINA guidelines. Clinical data, lung function, FeNO, IgE, and eosinophil counts were collected. Flow cytometry assessed CD63 and HLA-DR expression. Associations with clinical improvement, defined by ACT score and exacerbation frequency, were analysed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eACT scores improved from 21 to 24 and uncontrolled asthma prevalence dropped from 50% to 16%. HLA-DR expression declined significantly and correlated with improved asthma control (β\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.05). CD63 expression did not change overall but remained elevated in patients with persistent symptoms. Neither marker correlated with eosinophil count, FeNO, or IgE.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eHLA-DR and CD63 may serve as functional biomarkers of asthma activity. Eosinophil immunophenotyping could complement traditional biomarkers and guide personalized therapy.\u003c/p\u003e","manuscriptTitle":"Surface Expression of CD63 and HLA-DR in circulating eosinophils correlates with Improved Clinical Control After Treatment Optimization in asthma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 06:53:52","doi":"10.21203/rs.3.rs-7923148/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-05T15:19:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T16:15:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T14:39:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336476754534899252353085012837548723403","date":"2025-11-21T14:20:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9978328483756647670443814685469830731","date":"2025-11-19T12:20:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226737778243727780955451350182160070680","date":"2025-11-17T10:05:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-17T06:35:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-17T06:31:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-04T09:11:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T17:31:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-03T17:28:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95247cc2-7d97-4ea4-b16c-1418be26e022","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58435983,"name":"Health sciences/Biomarkers"},{"id":58435984,"name":"Health sciences/Diseases"},{"id":58435985,"name":"Biological sciences/Immunology"},{"id":58435986,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-02-16T16:00:10+00:00","versionOfRecord":{"articleIdentity":"rs-7923148","link":"https://doi.org/10.1038/s41598-026-37906-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-11 15:57:21","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2025-11-28 06:53:52","video":"","vorDoi":"10.1038/s41598-026-37906-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-37906-z","workflowStages":[]},"version":"v1","identity":"rs-7923148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7923148","identity":"rs-7923148","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}