The Role of Triple CFTR Modulator Therapy in Reducing Systemic Inflammation in Cystic Fibrosis

The Role of Triple CFTR Modulator Therapy in Reducing Systemic Inflammation in Cystic Fibrosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Role of Triple CFTR Modulator Therapy in Reducing Systemic Inflammation in Cystic Fibrosis Marta Solís García, Claudia Janeth Madrid-Carbajal, Adrián Peláez Laderas, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5874180/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2025 Read the published version in Lung → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose: Cystic fibrosis (CF) is a genetic disease caused by mutations in the CFTR gene, leading to multisystemic complications, particularly in the lungs. CFTR dysfunction results in altered ion transport, chronic inflammation, and progressive lung damage. The triple therapy elexacaftor/tezacaftor/ivacaftor (ETI) has demonstrated significant improvements in pulmonary function and quality of life. This study aimed to evaluate the anti-inflammatory effects of ETI by analysing systemic cytokine profiles over 12 months. Methods: A prospective study included 32 CF patients ≥18 years with at least one F508del mutation, undergoing ETI therapy. Clinical stability was ensured prior to therapy initiation. Demographic data, BMI (Body Mass Index), FEV1% (Forced expiratory Volume in the first second), VR/TLC (residual volume/total lung capacity) and sweat chloride concentrations were recorded at baseline, 6 months and 12 months. Inflammatory markers, including fibrinogen, C-reactive protein (CRP), and a panel of 8 cytokines, were measured using multiplex bead-based immunoassays and electrochemiluminescence. Longitudinal changes were analysed using mixed-effects models and statistical tests, with significance set at p < 0.05. Results: During a 12-month follow-up, the neutrophils number and proinflammatory biomarkers analyzed, fibrinogen, CRP, TNF- α, IFN-gamma, IL-1 alpha, IL-1 beta, IL-6, IL-8, IL-12, IL-17, significantly decreased, while eosinophils remained stable. Mixed-effects models confirmed the significant association of inflammatory biomarkers with FEV1, BMI, sweat chloride levels, and VR/TLC highlighting the role of inflammation in the progression of CF. Conclusions: ETI demonstrated marked anti-inflammatory effects in CF patients, reducing systemic inflammation and improving clinical parameters. Cystic Fibrosis inflammation CFTR Modulators Inflammation Figures Figure 1 1. Introduction Cystic fibrosis (CF) is a genetic disease caused by mutation, in different forms, of the gene encoding the transmembrane conductance regulator protein CFTR (Cystic Fibrosis Transmembrane Conductance Regulator), which consists of a chloride and bicarbonate transport ion channel which also regulates sodium transport ( 1 ). In CF patients, the genetic defect produces an aberration or absence of this protein, which leads to a multisystemic disease that particularly affects the organs in whose epithelium this channel is most relevant, especially the lung, which determines the prognosis and evolution of the disease. In the respiratory tract, dysfunction in the transport of sodium and chloride across cell membranes causes an imbalance in the production and composition of mucus that promotes the accumulation of thick secretions and their progressive infection, which obstructs the airways and leads to chronic inflammation characterized by the release of cytokines, chemokines and other inflammatory mediators that contribute to tissue damage and fibrosis ( 2 ). In this proinflammatory state to which we refer, cytokines such as TNF-α, IL-1β, IL-6, IL-8, IL-17, IL-33 and GM-CSF.7 are involved, mediators of a persistent neutrophilic inflammatory response that contributes to bronchial destruction, infectious predisposition ( 3 ) and disease progression ( 4 , 5 , 6 ). It is believed that this systemic inflammation may be due to dysregulation of the immune system secondary to chronic bronchial infection, being part of a vortex that progressively destroys the airway and increases bronchial secretions, leading to a loss of lung function. Adequate management of this inflammation is therefore crucial to delay disease progression, with anti-inflammatory agents often being used as part of the therapeutic arsenal for CF patients. In the last decade, we have witnessed the rise of CFTR protein modulators, small molecules that include CFTR “correcting” and “potentiating” drugs that allow to improve its channel function. In 2020, the Food and Drug Administration (FDA) approved the use of the combination of elexacaftor/tezacaftor/ivacaftor (ETI) for those patients with reduced lung function and at least one copy of delF508 variant in the CFTR gene, showing improvement in lung function, an increase in body mass index (BMI) and a reduction in bronchial infections by different pathogens ( 7 , 8 ). As a result of these findings, major questions are raised about the effect that this drug could have on systemic inflammation. The aim of our study was monitoring the evolution of the inflammatory response in CF patients after receiving one year of treatment with ETI, analysing different cytokines and inflammatory markers. 2. Methods This was a prospective study that included 32 patients from the Hospital Universitario Central de Asturias (Oviedo). As selection criteria, we included patients over 18 years of age with at least one copy of delF508, who had started treatment with triple modulator therapy in 2022 and therefore had been on the drug for at least 1 year. In all cases, patients started treatment in a period of clinical stability, delaying the start of treatment in the event of exacerbation. The study was approved by the hospital's ethics committee (CEIM Ref. No. 067/2020) and all patients included in the study signed their consent to participate in the study, in which they also allowed their data to be used for research purposes. Regarding the variables collected, demographic and nutritional data (BMI), lung function and air trapping data (Forced expiratory volume in 1 second, FEV1%, and Residual volume/ total lung capacity, RV/TLC) and sweat test results were collected in all cases. For all of them, these parameters were recorded at baseline (T0) and subsequently at 6 (T1) and 12 months (T2) after initiation of treatment with ETI. With respect to inflammation, an analytical extraction was performed before (T0), 6 months (T1) and 12 months (T2) after the start of treatment. A panel of 8 cytokines was performed, covering the complete profile of the inflammatory situation of the patients and including GM-CSF, IFN gamma, IL-1 alpha, IL-1 beta, IL-8 (CXCL8), IL-12p70, IL-17A (CTLA-8) and TNF- α. The method used in the analysis was a multiplex bead-based immunoassay (ProcartaPlex, Thermo Fisher Scientific) for protein quantitation based on the principles of a sandwich ELISA using Luminex technology. Additionally, as the main systemic interleukin, IL-6 was determined by a sandwich immunoassay with electrochemiluminescence detection (ECLIA) on a Cobas e601 autoanalyzer (Roche Diagnostics). To complete the study, other blood count and biochemistry data such as total neutrophil and eosinophil counts, and fibrinogen or C-reactive protein (CRP) were collected. 2.1 Statistical Analysis A preliminary descriptive analysis of patient characteristics was performed to summarize the data. For quantitative variables, measures of central tendency and dispersion were calculated, while counts and percentages were used for qualitative variables. Group differences were assessed using normality tests (Shapiro-Wilk, Kolmogorov-Smirnov) and homogeneity tests (Levene). Depending on the results, parametric tests (ANOVA, Student's t-test) or non-parametric tests (Kruskal-Wallis, Wilcoxon rank-sum) were applied. For qualitative variables, group comparisons were made using χ² or Fisher's exact tests. To identify and quantify the impact of interleukins on FEV1%, VR/TLC, BMI and sweat test, several mixed-effects models were developed. These included longitudinal mixed-effects models with random intercepts (patient ID), random slopes (patient age), and combinations of both random intercepts and slopes (patient ID and age). Model comparisons were conducted using ANOVA, as well as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) metrics, to select the best-fitting model. After selecting the best fit model for FEV1%, BMI, and the sweat test, the complexity of the models was reduced using stepwise selection (both directions). Statistical significance was set at p < 0.05 for all analyses. Data analysis and visualization were performed using RStudio. 3. Results 3.1. Descriptive analysis of the sample. The study included a total of 32 participants, as shown in Table 1 . Regarding sex distribution, the sample was balanced, with 15 participants (46.9%) being males and 17 (53.1%) females. Genetic analysis revealed that delF508 mutations were equally prevalent among the cohort. Specifically, 15 participants (46.9%) were homozygous for the mutation, while 17 participants (53.1%) were heterozygous. Pancreatic dysfunction was a common feature in the study population: exocrine insufficiency was observed in 27 participants (84.4%), and 7 participants (21.9%) presented with diabetes mellitus (DM) associated with CF. Bronchial chronic infection with Pseudomonas aeruginosa was identified in 13 participants (40.6%). The mean age of the participants was 27.62 years (± 10.34). Lung function, assessed through FEV1%, showed a mean value of 65.87% (± 21.70). Air trapping, measured by VR/TLC had a mean value of 39, 2 (± 9, 4).The mean BMI was 21.07 (± 2.42 kg/m2). Additionally, the mean sweat chloride concentration was 93.50 mmol/L (± 10.80). Table 1 Baseline characteristics of the sample Characteristic Baseline Total sample ( n = 32) Sex - Male - Female 15 (46,9%) 17 (53,1%) delF508 - Homozygous - Heterozygous 15 (46,9%) 17 (53,1%) Páncreas - Exocrine insufficiency - DM associated with CF 27 (84,4%) 7 (21,9%) BCI - Pseudomonas aeruginosa 13 (40,6%) Age 27,62 (± 10,34) FEV1% 65,87 (± 21,70) RV/TLC 39,2 (± 9,4) BMI 21,07 (± 2,42) Sweat test 93,50 (± 10,80) DM: Diabetes mellitus; CF: Cystic Fibrosis; FEV1: Forced Expiratory Volume in 1 second; RV/TLC: residual volume (Total lung capacity; BMI: Body mass index; BCI: Bronchial chronic infection. 3.2. Longitudinal changes in pro-inflammatory markers over 12 months The study analysed changes in various biomarkers at T0 (baseline), T1 (6 months), and T2 (12 months) among 32 participants. As we can see in Table 2 . and Fig. 1 ., significant reductions were observed of all inflammatory and immunological parameters over the study period. In contrast, eosinophil counts remained stable over time, with no significant differences observed across the three time points ( p = 0.477). Table 2 Evolution of the inflammatory profile during 1 year of follow-up. Item 0 months (N = 108) 6 months (N = 108) 12 months (N = 108) P-value Fibrinogen 535.0 (± 140.0) 384.0 (± 64.4) 411.0 (± 78.7) < 0.001 C reactive protein 1.7 (± 2.2) 0.1 (± 0.1) 0.1 (± 0.1) < 0.001 CSF 22.1 (± 10.2) 16.9 (± 1.7) 13.9 (± 4.5) < 0.001 TNF 21.6 (± 21.6) 11.0 (10.2) 9.4 (± 4.7) 0.014 IFN-gamma 45.6 (± 2.8) 39.9 (± 7.7) 33.2 (± 10.8) < 0.001 IL-1alfa 2140.0 (± 1960.0) 868.0 (± 490.0) 496 (± 457.0) < 0.001 IL-1gamma 59.5 (± 8.9) 52.0 (± 7.2) 42.7 (± 13.3) < 0.001 IL-4 24.6 (± 10.4) 18.7 (± 3.9) 17.1 (± 18.2) < 0.001 IL-6 9.3 (± 8.5) 3.3 (± 3.5) 3.1 (± 4.3) < 0.001 IL-8 674.0 (± 489.0) 358.0 (± 122.0) 247.0 (± 107.0) < 0.001 IL-10 282.0 (± 124.0) 166.0 (± 77.2) 117.0 (± 72.0) < 0.001 IL-12 206.0 (± 21.7) 177.0 (± 32.5) 144.0 (± 68.1) < 0.001 IL-13 139.0 (± 62.5) 107.0 (± 9.7) 114.0 (± 152.0) < 0.001 IL-17 35.6 (± 66.6) 20.5 (± 8.0) 17.3 (± 8.0) 0.008 CSF: Colony Stimulating Factor; TNF: tumor necrosis factor; IFN: Interferons; IL: interleukins. Significant associations (p < 0.05) are in bold. When analyzing pairwise differences (Fig. 1 ), we observed that fibrinogen, CRP, TNF- α, neutrophils and IL-6 showed a significant decrease at both 6 and 12 months compared to baseline levels. Additionally, some biomarkers, including CSF, IFN gamma, IL-1 beta, IL-8 and IL-12 exhibited significant reductions not only at 6 and 12 months compared to baseline but also at 12 months relative to 6 months. Lastly, for IL-17, a significant decrease was observed only at 12 months compared to baseline leve. 3.3 Relationsip between Inflammatory Biomarkers and FEV1, BMI, and Sweat Test Results Mixed-effects models were employed to identify and quantify the impact of inflammatory biomarkers on FEV1%, BMI, and sweat test outcomes. These models incorporated random intercepts (patient ID), random slopes (patient age), and combinations of both. After creating the models, their fit to the response variables was assessed as shown in Table 3 , revealing that the random intercept model provided the best fit for predicting FEV1%, BMI and sweat test. Table 3 Assessing models for predicting FEV1%, BMI and Sweat Test. Random Intercept Random Slope Random Intercept and Slope P-value AIC BIC AIC BIC AIC BIC FEV1% 830.4 868.9 837.3 875.8 834.4 877.8 0.326 BMI 483.6 522.1 494.3 532.8 524.1 564.7 0.265 Sweat Test 916.0 954.5 917.6 956.1 920 963.3 0.456 RV/TLC 540.7 572.8 545.8 577.9 544.2 580.3 0.154 FEV1: Forced Expiratory Volume in 1 second; RV/TLC: residual volume (Total lung capacity; BMI: Body mass index. After evaluating the models and selecting the random intercept model for FEV1, BMI, sweat test, and VR/TLC, model complexity was reduced using stepwise regression in both directions. This process optimized the models and facilitated data interpretation. The results of the mixed-effects models after stepwise selection revealed significant relationships between various biomarkers and interleukins with FEV1, BMI, the sweat test, and VR/TLC, as we can see in Table 4 . Table 4 Mixed-Effects Models for the Association between Inflammatory Biomarkers and FEV1, BMI, and Sweat Test Outcomes Response variable Predictor variables Beta-Coefficient [CI95%] p-value Model 1 FEV1 Intercept 97.393 [84.099–110.687] < 0.001 Fibrinogen -0.022 [-0.042 – -0.002] 0.038 C-reactive protein -1.869 [-3.57 – -0.169] 0.040 Colony stimulating factor -0.482 [-0.700 – -0.264] < 0.001 IL–6 0.269 [-0.039–0.576] 0.099 IL–12 -0.053 [-0.094 – -0.011] 0.018 Model 2 BMI Intercept 25.173 [24.056–26.291] < 0.001 Fibrinogen -0.004 [-0.006 – -0.002] < 0.001 Il–8 -0.001 [-0.002–0.000] 0.064 Neutrophils 0.000 [0.000–0.000] 0.052 Model 3 Sweat test Intercept -36.582 [-58.873 – -14.292] 0.002 Fibrinogen 0.109 [0.074–0.145] < 0.001 IL-8 0.055 [0.033–0.078] < 0.001 IL-12 0.126 [0.017–0.234] 0.028 Model 4 RV/TLC Intercept 16.898 [10.492–23.303] < 0.001 Fibrinogen 0.020 [0.010–0.029] < 0.001 IL–1 beta 0.217 [0.107–0.327] < 0.001 IL-17 0.041 [0.016–0.066] 0.003 Neutrophils 0.000 [0.000–0.001] 0.070 Significant associations (p < 0.05) are in bold. FEV1: Forced Expiratory Volume in 1 second; RV/TLC: residual volume (Total lung capacity; BMI: Body mass index. For FEV1, significant associations were found with fibrinogen (Beta-Coefficient = -0.022, 95% CI: -0.042 to -0.002, p = 0.038), C-reactive protein (Beta-Coefficient = -1.869, 95% CI: -3.57 to -0.169, p = 0.040), colony-stimulating factor (Beta-Coefficient = -0.482, 95% CI: -0.700 to -0.264, p < 0.001), and IL-12 (Beta-Coefficient = -0.053, 95% CI: -0.094 to -0.011, p = 0.018). For BMI, significant relationships were identified with fibrinogen (Beta-Coefficient = -0.004, 95% CI: -0.006 to -0.002, p < 0.001). Although not statistically significant, trends were observed for IL -8 (Beta-Coefficient = -0.001, 95% CI: -0.002 to 0.000, p = 0.064) and neutrophils (Beta-Coefficient = 0.000, 95% CI: 0.000 to 0.000, p = 0.052). In the sweat test model, significant associations were observed with fibrinogen (Beta-Coefficient = 0.109, 95% CI: 0.074 to 0.145, p < 0.001), IL -8 (Beta-Coefficient = 0.055, 95% CI: 0.033 to 0.078, p < 0.001), and IL -12 (Beta-Coefficient = 0.126, 95% CI: 0.017 to 0.234, p = 0.028). Finally, in the VR/TLC model, significant relationships were detected with fibrinogen (Beta-Coefficient = 0.020, 95% CI: 0.010 to 0.029, p < 0.001), IL -1 beta (Beta-Coefficient = 0.217, 95% CI: 0.107 to 0.327, p < 0.001), and IL -17 (Beta-Coefficient = 0.041, 95% CI: 0.016 to 0.066, p = 0.003). A non-significant trend was also noted for neutrophils (Beta-Coefficient = 0.000, 95% CI: 0.000 to 0.001, p = 0.070). 4. Discusion Inflammation plays a crucial role in CF, a genetic disease characterized by excessive mucus accumulation and fibrous tissue formation in various organs, especially in the lung. Inflammation, as an immune response to cell damage and the accumulation of thick secretions, triggers a series of events that contribute to tissue degradation and fibrosis. This inflammatory process accelerates disease progression, reducing patients' quality of life and life expectancy. Our study aims to evaluat the clinical and anti-inflammatory effect of ETI treatment in CF patients with at least one copy of delF508 after one year of follow-up. The results confirm and extend previous observations from pivotal clinical trials ( 7 , 9 , 10 ), demonstrating not only significant improvements in nutritional status, lung function, air trapping and sweat test values over the one-year follow-up, but also a significant decrease in systemic inflammatory markers, such as cytokines, CRP, fibrinogen and neutrophil count. Although the efficacy and safety of ETI therapy has been widely studied, its anti-inflammatory activity has been less considered. We know that CF is a disease with a large inflammatory component ( 11 ) and that the use of anti-inflammatory agents (ibuprofen, azithromycin, inhaled corticosteroids...) is widespread in its therapeutic arsenal. Previous modulators, such as Ivacaftor, had already demonstrated a significant reduction in systemic proinflammatory markers ( 12 , 13 ) so our study hypothesizes a decrease in inflammatory parameters and cytokines, especially those related to neutrophilic inflammation, associated with the initiation of treatment with ETI. Although the efficacy and safety of ETI therapy has been widely studied, its anti-inflammatory activity has been less considered. We know that CF is a disease with a large In our sample, the decrease in inflammatory biomarkers was significant for Fibrinogen, C reactive protein, CSF, TNF- α, IFN-gamma, IL-1 alpha, IL-1 beta, IL-6, IL-8, IL-12 and IL-17. Of note was the activity of IL-8, key in the attraction of neutrophils to lung tissue, which showed a direct relationship with the improvement in FEV1 that was also significantly monitored by an improvement in the sweat test. This IL-8 modulation observed in our study is consistent with the data of Sheikh et al., Schaupp et al. and Whesthölter et al ( 14 , 15 , 17 ). Although our study evaluated blood samples, other studies have hypothesized similar hypotheses by evaluating inflammation at the airway level using bronchial aspirates in CF patients, with similar results ( 16 ). Since neutrophilic inflammation, driven by neutrophil elastase, is strongly associated with lung disease progression in CF, our study also evaluated the evolution of the number of circulating neutrophils after initiating treatment with ETI, observing a significant decrease in them at 6 and 12 months of treatment, results that seem logical and are in agreement with previous studies ( 17 ). Our findings reinforce the hypothesis that reduction of neutrophils and proinflammatory cytokines by ETI may mitigate neutrophilic inflammation-mediated lung damage. Mixed-model analysis revealed significant relationships between inflammatory biomarkers and clinical parameters. For example, elevated fibrinogen levels were significantly associated with worse FEV1 and lower BMI, while the sweat test showed significant correlation also with IL-8 and IL-12. Finally, VR/TLC showed a positive association with fibrinogen, IL − 1 Beta and IL − 17. The improvement in pulmonary function and weight of the patients could be a consequence of this general reduction in inflammation, which would facilitate the decrease and fluidization of secretions and the decrease in caloric expenditure that breathing previously entailed for these patients. As for the sweat test, it is used as a way of monitoring CFTR channel function, so it also seems appropriate to think that this decrease in inflammatory parameters may be related to the improvement of the test. These findings underline the concordance of the inflammatory parameters with the physiological data that we usually monitor in CF patients, highlighting the improvement of all of them after initiating treatment with ETI and opening new questions about the possibility of withdrawing or decreasing the anti-inflammatory treatments that these patients had been receiving until now. This study is characterized by a comprehensive analysis of inflammatory markers and clinical parameters, with a detailed evaluation of their evolution over time. Longitudinal monitoring at T0, T1 and T2 time points reinforces the temporal validity of the findings, evidencing sustained changes that prevent misinterpretation. On the other hand, these patients were well characterized from the beginning, and in addition to classic pulmonary function parameters such as FEV1%, they also had air trapping data, data that provide a great deal of information in these patients and which are less frequently seen in other studies. Despite these strengths, there are several important limitations that should be considered when interpreting the results. First, the small sample size may reduce the statistical power and generalizability of the findings to a larger population. In addition, the absence of a control group precludes definitive attribution of the observed effects to treatment with ETI, without being able to rule out other confounding variables. Finally, the use of systemic inflammatory biomarkers rather than airway-specific biomarkers limits the accurate interpretation of the results in the pulmonary setting. In conclusion, treatment with ETI not only improves clinical parameters in CF patients, but also reduces systemic inflammatory markers. These findings open a future door to consider reducing or withdrawing the usual anti-inflammatory treatments in these patients. The results of our study, although promising, require further studies that delve deeper into the inflammatory pathways modulated by ETI and their relationship to the long-term prognosis of CF. Declarations Author Contribution MSG wrote the main manuscript. APL did all the statistics, tables and figures.MMGC and RMGM were the coordinators of the workEFA and BPG did the biomarkers analysis. CMJC is the reference doctor of the patients. RMGP, JAB and JMEB corrected and revised the manuscript. References Hanssens LS, Duchateau J, Casimir GJ. CFTR Protein: Not Just a Chloride Channel? Cells. 2021; 10 (11). Epub 2021/11/28. https://doi.org/10.3390/cells10112844 PMID: 34831067. Bergeron C, Cantin AM. Cystic Fibrosis: Pathophysiology of Lung Disease. Semin Respir Crit Care Med. 2019 Dec;40(6):715-726. doi: 10.1055/s-0039-1694021. Epub 2019 Oct 28. PMID: 31659725. Witko-Sarsat V, Burgel PR. Cystic fibrosis in the era of CFTR modulators: did the neutrophil slip through the cracks? J Leukocyte Biol. 2024; 115(3):417–9. https://doi.org/10.1093/jleuko/qiad164 PMID: 38193848. D.T. Groves, Y. Jiang, Chemokines, a family of chemotactic cytokines, Crit. Rev. Oral Biol. Med. 6 (2) (1995) 109–118, https://doi.org/10.1177/ 10454411950060020101. S.D. Sagel, B.D. Wagner, M.M. Anthony, P. Emmett, E.T. Zemanick, Sputum biomarkers of inflammation and lung function decline in children with cystic fibrosis, Am. J. Respir. Crit. Care Med. 186 (9) (2012) 857–865, https://doi.org/10.1164/rccm.201203-0507OC. J. Laval, A. Ralhan, D. Hartl, Neutrophils in cystic fibrosis, Biol. Chem. 397 (6) (2016) 485–496, https://doi.org/10.1515/hsz-2015-0271, K. Jundi, C.M. Greene, Transcription of interleukin-8: how altered regulation can affect cystic fibrosis lung disease, Biomolecules 5 (3) (2015) 1386–1398, https://doi.org/10.3390/biom5031386.) Heijerman HGM, McKone EF, Downey DG, Van Braeckel E, Rowe SM, Tullis E, Mall MA, Welter JJ, Ramsey BW, McKee CM, Marigowda G, Moskowitz SM, Waltz D, Sosnay PR, Simard C, Ahluwalia N, Xuan F, Zhang Y, Taylor-Cousar JL, McCoy KS; VX17-445-103 Trial Group. Efficacy and safety of the elexacaftor plus tezacaftor plus ivacaftor combination regimen in people with cystic fibrosis homozygous for the F508del mutation: a double-blind, randomised, phase 3 trial. Lancet. 2019 Nov 23;394(10212):1940-1948. doi: 10.1016/S0140-6736(19)32597-8. Epub 2019 Oct 31. Erratum in: Lancet. 2020 May 30;395(10238):1694. doi: 10.1016/S0140-6736(20)31021-7. PMID: 31679946; PMCID: PMC7571408. Sutharsan S, McKone EF, Downey DG, Duckers J, MacGregor G, Tullis E, Van Braeckel E, Wainwright CE, Watson D, Ahluwalia N, Bruinsma BG, Harris C, Lam AP, Lou Y, Moskowitz SM, Tian S, Yuan J, Waltz D, Mall MA; VX18-445-109 study group. Efficacy and safety of elexacaftor plus tezacaftor plus ivacaftor versus tezacaftor plus ivacaftor in people with cystic fibrosis homozygous for F508del-CFTR: a 24-week, multicentre, randomised, double-blind, active-controlled, phase 3b trial. Lancet Respir Med. 2022 Mar;10(3):267-277. doi: 10.1016/S2213-2600(21)00454-9. Epub 2021 Dec 20. PMID: 34942085. Middleton PG, Mall MA, Dřevínek P, Lands LC, McKone EF, Polineni D, Ramsey BW, Taylor-Cousar JL, Tullis E, Vermeulen F, Marigowda G, McKee CM, Moskowitz SM, Nair N, Savage J, Simard C, Tian S, Waltz D, Xuan F, Rowe SM, Jain R; VX17-445-102 Study Group. Elexacaftor-Tezacaftor-Ivacaftor for Cystic Fibrosis with a Single Phe508del Allele. N Engl J Med. 2019 Nov 7;381(19):1809-1819. doi: 10.1056/NEJMoa1908639. Epub 2019 Oct 31. PMID: 31697873; PMCID: PMC7282384. Taylor-Cousar JL, Mall MA, Ramsey BW, et al. Clinical development of triple-combination CFTR modulators for cystic fibrosis patients with one or two F508del alleles. ERJ Open Res 2019; 5: 00082-2019. doi: 10.1183/23120541.00082-2019. Shoki AH , Mayer-Hamblett N , Wilcox PG , et al . Systematic review of blood biomarkers in cystic fibrosis pulmonary exacerbations. Chest 2013;144:1659–70. doi:10.1378/chest.13-0693. Hisert KB , Heltshe SL , Pope C , et al . Restoring cystic fibrosis transmembrane conductance regulator function reduces airway bacteria and inflammation in people with cystic fibrosis and chronic lung infections. Am J Respir Crit Care Med 2017;195:1617–28. doi:10.1164/rccm.201609-1954OC. Mainz JG , Arnold C , Wittstock K , et al . Ivacaftor reduces inflammatory mediators in upper airway lining fluid from cystic fibrosis patients with a G551D mutation: serial non-invasive home-based collection of upper airway lining fluid. Front Immunol 2021;12:642180. doi:10.3389/fimmu.2021.642180. Schaupp L, Addante A, Völler M, et al. Longitudinal effects of elexacaftor/tezacaftor/ivacaftor on sputum viscoelastic properties, airway infection and inflammation in patients with cystic fibrosis. Eur Respir J 2023: 62: 2202153. doi: 10.1183/13993003.02153-2022, Westhölter D, Pipping J, Raspe J, Schmitz M, Sutharsan S, Straßburg S, Welsner M, Taube C, Reuter S. Plasma levels of chemokines decrease during elexacaftor/tezacaftor/ivacaftor therapy in adults with cystic fibrosis. Heliyon. 2023 Dec 12;10(1):e23428. doi: 10.1016/j.heliyon.2023.e23428. PMID: 38173511; PMCID: PMC10761561. Atteih SE, Armbruster CR, Hilliam Y, Rapsinski GJ, Bhusal JK, Krainz LL, Gaston JR, DuPont M, Zemke AC, Alcorn JF, Moore JA, Cooper VS, Lee SE, Forno E, Bomberger JM. Effects of highly effective modulator therapy on the dynamics of the respiratory mucosal environment and inflammatory response in cystic fibrosis. Pediatr Pulmonol. 2024 May;59(5):1266-1273. doi: 10.1002/ppul.26898. Epub 2024 Feb 14. PMID: 38353361; PMCID: PMC11058019. Sheikh S, Britt RD Jr, Ryan-Wenger NA, Khan AQ, Lewis BW, Gushue C, Ozuna H, Jaganathan D, McCoy K, Kopp BT. Impact of elexacaftor-tezacaftor-ivacaftor on bacterial colonization and inflammatory responses in cystic fibrosis. Pediatr Pulmonol. 2023 Mar;58(3):825-833. doi: 10.1002/ppul.26261. Epub 2022 Dec 9. PMID: 36444736; PMCID: PMC9957929 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Mar, 2025 Read the published version in Lung → Version 1 posted Editorial decision: Revision requested 18 Feb, 2025 Reviews received at journal 18 Feb, 2025 Reviews received at journal 31 Jan, 2025 Reviewers agreed at journal 30 Jan, 2025 Reviewers agreed at journal 27 Jan, 2025 Reviewers invited by journal 26 Jan, 2025 Editor assigned by journal 24 Jan, 2025 Submission checks completed at journal 24 Jan, 2025 First submitted to journal 21 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5874180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407840467,"identity":"be1c1374-5582-4cbb-b403-b2607f1c16ac","order_by":0,"name":"Marta Solís García","email":"","orcid":"","institution":"Hospital Universitario de la Princesa","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"Solís","lastName":"García","suffix":""},{"id":407840468,"identity":"d77f630a-b5a8-4659-9c93-1fca395b1f1f","order_by":1,"name":"Claudia Janeth Madrid-Carbajal","email":"","orcid":"","institution":"Hospital Universitario Central de Asturias","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"Janeth","lastName":"Madrid-Carbajal","suffix":""},{"id":407840469,"identity":"5391aa99-147b-4677-9300-b13ccff09e3c","order_by":2,"name":"Adrián Peláez Laderas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACNgbmhgMJBjZy/CBeQgERWvgZGBsffKhIM5ZsAGkxIEKLZANjs+GMM4cTNxwAcYnRYnC7sU2at43Z2Pj86sQPDwwY5PnFDhDQcucgSAubnNmNt5slgA4znDk7gYCWG4kgLTzGZjfObgBpSTC4TUCLPUSLROLmGWc3/yBKC9AWkPcNEjfw924jzhagX0CBnGAscYN3m0WCgQQRfrndfAAYlf/l+PvPbr75o8JGnl+agBYGCTgjAYVLjBb+A0SoHgWjYBSMghEJAICqTCeHyO6iAAAAAElFTkSuQmCC","orcid":"","institution":"Spanish National Centre for Cardiovascular Research","correspondingAuthor":true,"prefix":"","firstName":"Adrián","middleName":"Peláez","lastName":"Laderas","suffix":""},{"id":407840471,"identity":"aa506413-3fa4-4a50-a54f-c43d9971425f","order_by":3,"name":"Rosa María Girón Moreno","email":"","orcid":"","institution":"Hospital Universitario de la Princesa","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"María Girón","lastName":"Moreno","suffix":""},{"id":407840473,"identity":"80565366-0216-4772-9ab0-b565252e27df","order_by":4,"name":"Esther Ferreira Alonso","email":"","orcid":"","institution":"Hospital Universitario Central de Asturias","correspondingAuthor":false,"prefix":"","firstName":"Esther","middleName":"Ferreira","lastName":"Alonso","suffix":""},{"id":407840475,"identity":"6ed644f6-a2b3-4d55-ab5c-22004a60eda1","order_by":5,"name":"Belén Prieto García","email":"","orcid":"","institution":"Hospital Universitario Central de Asturias","correspondingAuthor":false,"prefix":"","firstName":"Belén","middleName":"Prieto","lastName":"García","suffix":""},{"id":407840477,"identity":"c17c1c0a-2904-4187-b5e9-65be06785306","order_by":6,"name":"Rosa Mar Gómez Punter","email":"","orcid":"","institution":"Hospital Universitario de la Princesa","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"Mar Gómez","lastName":"Punter","suffix":""},{"id":407840478,"identity":"7d9d20ed-9fc0-4a57-80e4-b42a76452dbf","order_by":7,"name":"Julio Ancochea Bermúdez","email":"","orcid":"","institution":"Hospital Universitario de la Princesa","correspondingAuthor":false,"prefix":"","firstName":"Julio","middleName":"Ancochea","lastName":"Bermúdez","suffix":""},{"id":407840479,"identity":"3c22a8da-36e1-4c87-885a-9bef8fb07dda","order_by":8,"name":"Jose María Eiros Bachiller","email":"","orcid":"","institution":"Hospital Universitario de la Princesa","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"María Eiros","lastName":"Bachiller","suffix":""},{"id":407840480,"identity":"284cdba9-e694-4bcf-bdd3-a2699090d69a","order_by":9,"name":"Marta María García Clemente","email":"","orcid":"","institution":"Hospital Universitario Central de Asturias","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"María García","lastName":"Clemente","suffix":""}],"badges":[],"createdAt":"2025-01-21 14:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5874180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5874180/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00408-025-00806-6","type":"published","date":"2025-03-28T15:57:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74948265,"identity":"a12ddb1e-74ca-4a68-8b15-0ef519beae83","added_by":"auto","created_at":"2025-01-28 15:52:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":180148,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of the distribution of biomarker concentrations at baseline (0 months), 6 months, and 12 months. Significant differences between time points are marked with asterisks (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5874180/v1/048240d672735bdf075f3380.png"},{"id":79605398,"identity":"04534377-023e-4769-892d-ca63da5f239c","added_by":"auto","created_at":"2025-03-31 16:11:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1047934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5874180/v1/be2a4a7e-285e-444e-ba81-451a90398361.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Triple CFTR Modulator Therapy in Reducing Systemic Inflammation in Cystic Fibrosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCystic fibrosis (CF) is a genetic disease caused by mutation, in different forms, of the gene encoding the transmembrane conductance regulator protein CFTR (Cystic Fibrosis Transmembrane Conductance Regulator), which consists of a chloride and bicarbonate transport ion channel which also regulates sodium transport (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In CF patients, the genetic defect produces an aberration or absence of this protein, which leads to a multisystemic disease that particularly affects the organs in whose epithelium this channel is most relevant, especially the lung, which determines the prognosis and evolution of the disease.\u003c/p\u003e \u003cp\u003eIn the respiratory tract, dysfunction in the transport of sodium and chloride across cell membranes causes an imbalance in the production and composition of mucus that promotes the accumulation of thick secretions and their progressive infection, which obstructs the airways and leads to chronic inflammation characterized by the release of cytokines, chemokines and other inflammatory mediators that contribute to tissue damage and fibrosis (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In this proinflammatory state to which we refer, cytokines such as TNF-α, IL-1β, IL-6, IL-8, IL-17, IL-33 and GM-CSF.7 are involved, mediators of a persistent neutrophilic inflammatory response that contributes to bronchial destruction, infectious predisposition (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and disease progression (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is believed that this systemic inflammation may be due to dysregulation of the immune system secondary to chronic bronchial infection, being part of a vortex that progressively destroys the airway and increases bronchial secretions, leading to a loss of lung function.\u003c/p\u003e \u003cp\u003eAdequate management of this inflammation is therefore crucial to delay disease progression, with anti-inflammatory agents often being used as part of the therapeutic arsenal for CF patients. In the last decade, we have witnessed the rise of CFTR protein modulators, small molecules that include CFTR \u0026ldquo;correcting\u0026rdquo; and \u0026ldquo;potentiating\u0026rdquo; drugs that allow to improve its channel function. In 2020, the Food and Drug Administration (FDA) approved the use of the combination of elexacaftor/tezacaftor/ivacaftor (ETI) for those patients with reduced lung function and at least one copy of delF508 variant in the CFTR gene, showing improvement in lung function, an increase in body mass index (BMI) and a reduction in bronchial infections by different pathogens (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). As a result of these findings, major questions are raised about the effect that this drug could have on systemic inflammation.\u003c/p\u003e \u003cp\u003eThe aim of our study was monitoring the evolution of the inflammatory response in CF patients after receiving one year of treatment with ETI, analysing different cytokines and inflammatory markers.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis was a prospective study that included 32 patients from the Hospital Universitario Central de Asturias (Oviedo). As selection criteria, we included patients over 18 years of age with at least one copy of delF508, who had started treatment with triple modulator therapy in 2022 and therefore had been on the drug for at least 1 year. In all cases, patients started treatment in a period of clinical stability, delaying the start of treatment in the event of exacerbation.\u003c/p\u003e \u003cp\u003e The study was approved by the hospital's ethics committee (CEIM Ref. No. 067/2020) and all patients included in the study signed their consent to participate in the study, in which they also allowed their data to be used for research purposes.\u003c/p\u003e \u003cp\u003eRegarding the variables collected, demographic and nutritional data (BMI), lung function and air trapping data (Forced expiratory volume in 1 second, FEV1%, and Residual volume/ total lung capacity, RV/TLC) and sweat test results were collected in all cases. For all of them, these parameters were recorded at baseline (T0) and subsequently at 6 (T1) and 12 months (T2) after initiation of treatment with ETI.\u003c/p\u003e \u003cp\u003eWith respect to inflammation, an analytical extraction was performed before (T0), 6 months (T1) and 12 months (T2) after the start of treatment. A panel of 8 cytokines was performed, covering the complete profile of the inflammatory situation of the patients and including GM-CSF, IFN gamma, IL-1 alpha, IL-1 beta, IL-8 (CXCL8), IL-12p70, IL-17A (CTLA-8) and TNF- α. The method used in the analysis was a multiplex bead-based immunoassay (ProcartaPlex, Thermo Fisher Scientific) for protein quantitation based on the principles of a sandwich ELISA using Luminex technology. Additionally, as the main systemic interleukin, IL-6 was determined by a sandwich immunoassay with electrochemiluminescence detection (ECLIA) on a Cobas e601 autoanalyzer (Roche Diagnostics). To complete the study, other blood count and biochemistry data such as total neutrophil and eosinophil counts, and fibrinogen or C-reactive protein (CRP) were collected.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Statistical Analysis\u003c/h2\u003e \u003cp\u003eA preliminary descriptive analysis of patient characteristics was performed to summarize the data. For quantitative variables, measures of central tendency and dispersion were calculated, while counts and percentages were used for qualitative variables. Group differences were assessed using normality tests (Shapiro-Wilk, Kolmogorov-Smirnov) and homogeneity tests (Levene). Depending on the results, parametric tests (ANOVA, Student's t-test) or non-parametric tests (Kruskal-Wallis, Wilcoxon rank-sum) were applied. For qualitative variables, group comparisons were made using χ\u0026sup2; or Fisher's exact tests.\u003c/p\u003e \u003cp\u003eTo identify and quantify the impact of interleukins on FEV1%, VR/TLC, BMI and sweat test, several mixed-effects models were developed. These included longitudinal mixed-effects models with random intercepts (patient ID), random slopes (patient age), and combinations of both random intercepts and slopes (patient ID and age). Model comparisons were conducted using ANOVA, as well as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) metrics, to select the best-fitting model. After selecting the best fit model for FEV1%, BMI, and the sweat test, the complexity of the models was reduced using stepwise selection (both directions). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses. Data analysis and visualization were performed using RStudio.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive analysis of the sample.\u003c/h2\u003e \u003cp\u003eThe study included a total of 32 participants, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Regarding sex distribution, the sample was balanced, with 15 participants (46.9%) being males and 17 (53.1%) females. Genetic analysis revealed that delF508 mutations were equally prevalent among the cohort. Specifically, 15 participants (46.9%) were homozygous for the mutation, while 17 participants (53.1%) were heterozygous. Pancreatic dysfunction was a common feature in the study population: exocrine insufficiency was observed in 27 participants (84.4%), and 7 participants (21.9%) presented with diabetes mellitus (DM) associated with CF. Bronchial chronic infection with \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e was identified in 13 participants (40.6%).\u003c/p\u003e \u003cp\u003eThe mean age of the participants was 27.62 years (\u0026plusmn;\u0026thinsp;10.34). Lung function, assessed through FEV1%, showed a mean value of 65.87% (\u0026plusmn;\u0026thinsp;21.70). Air trapping, measured by VR/TLC had a mean value of 39, 2 (\u0026plusmn;\u0026thinsp;9, 4).The mean BMI was 21.07 (\u0026plusmn;\u0026thinsp;2.42 kg/m2). Additionally, the mean sweat chloride concentration was 93.50 mmol/L (\u0026plusmn;\u0026thinsp;10.80).\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\u003eBaseline characteristics of the sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic Baseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal sample\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003e- Male\u003c/p\u003e \u003cp\u003e- Female\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (46,9%)\u003c/p\u003e \u003cp\u003e17 (53,1%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003edelF508\u003c/p\u003e \u003cp\u003e- Homozygous\u003c/p\u003e \u003cp\u003e- Heterozygous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (46,9%)\u003c/p\u003e \u003cp\u003e17 (53,1%)\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\u003eP\u0026aacute;ncreas\u003c/b\u003e\u003c/p\u003e \u003cp\u003e- Exocrine insufficiency\u003c/p\u003e \u003cp\u003e- DM associated with CF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (84,4%)\u003c/p\u003e \u003cp\u003e7 (21,9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e- \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (40,6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27,62 (\u0026plusmn;\u0026thinsp;10,34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65,87 (\u0026plusmn;\u0026thinsp;21,70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRV/TLC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e39,2 (\u0026plusmn;\u0026thinsp;9,4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21,07 (\u0026plusmn;\u0026thinsp;2,42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSweat test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e93,50 (\u0026plusmn;\u0026thinsp;10,80)\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\u003eDM: Diabetes mellitus; CF: Cystic Fibrosis; FEV1: Forced Expiratory Volume in 1 second; RV/TLC: residual volume (Total lung capacity; BMI: Body mass index; BCI: Bronchial chronic infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Longitudinal changes in pro-inflammatory markers over 12 months\u003c/h2\u003e \u003cp\u003eThe study analysed changes in various biomarkers at T0 (baseline), T1 (6 months), and T2 (12 months) among 32 participants. As we can see in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e., significant reductions were observed of all inflammatory and immunological parameters over the study period. In contrast, eosinophil counts remained stable over time, with no significant differences observed across the three time points (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.477).\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\u003eEvolution of the inflammatory profile during 1 year of follow-up.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 months\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 months\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 months\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eFibrinogen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e535.0 (\u0026plusmn;\u0026thinsp;140.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e384.0 (\u0026plusmn;\u0026thinsp;64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e411.0 (\u0026plusmn;\u0026thinsp;78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC reactive protein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.7 (\u0026plusmn;\u0026thinsp;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 (\u0026plusmn;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.1 (\u0026plusmn;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.1 (\u0026plusmn;\u0026thinsp;10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.9 (\u0026plusmn;\u0026thinsp;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e13.9 (\u0026plusmn;\u0026thinsp;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.6 (\u0026plusmn;\u0026thinsp;21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.4 (\u0026plusmn;\u0026thinsp;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIFN-gamma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e45.6 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.9 (\u0026plusmn;\u0026thinsp;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e33.2 (\u0026plusmn;\u0026thinsp;10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-1alfa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2140.0 (\u0026plusmn;\u0026thinsp;1960.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e868.0 (\u0026plusmn;\u0026thinsp;490.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e496 (\u0026plusmn;\u0026thinsp;457.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-1gamma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e59.5 (\u0026plusmn;\u0026thinsp;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.0 (\u0026plusmn;\u0026thinsp;7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e42.7 (\u0026plusmn;\u0026thinsp;13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.6 (\u0026plusmn;\u0026thinsp;10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7 (\u0026plusmn;\u0026thinsp;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e17.1 (\u0026plusmn;\u0026thinsp;18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.3 (\u0026plusmn;\u0026thinsp;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3 (\u0026plusmn;\u0026thinsp;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.1 (\u0026plusmn;\u0026thinsp;4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e674.0 (\u0026plusmn;\u0026thinsp;489.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358.0 (\u0026plusmn;\u0026thinsp;122.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e247.0 (\u0026plusmn;\u0026thinsp;107.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e282.0 (\u0026plusmn;\u0026thinsp;124.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166.0 (\u0026plusmn;\u0026thinsp;77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e117.0 (\u0026plusmn;\u0026thinsp;72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e206.0 (\u0026plusmn;\u0026thinsp;21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.0 (\u0026plusmn;\u0026thinsp;32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e144.0 (\u0026plusmn;\u0026thinsp;68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e139.0 (\u0026plusmn;\u0026thinsp;62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.0 (\u0026plusmn;\u0026thinsp;9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e114.0 (\u0026plusmn;\u0026thinsp;152.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e35.6 (\u0026plusmn;\u0026thinsp;66.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5 (\u0026plusmn;\u0026thinsp;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e17.3 (\u0026plusmn;\u0026thinsp;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCSF: Colony Stimulating Factor; TNF: tumor necrosis factor; IFN: Interferons; IL: interleukins. Significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are in bold.\u003c/p\u003e \u003cp\u003eWhen analyzing pairwise differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we observed that fibrinogen, CRP, TNF- α, neutrophils and IL-6 showed a significant decrease at both 6 and 12 months compared to baseline levels. Additionally, some biomarkers, including CSF, IFN gamma, IL-1 beta, IL-8 and IL-12 exhibited significant reductions not only at 6 and 12 months compared to baseline but also at 12 months relative to 6 months. Lastly, for IL-17, a significant decrease was observed only at 12 months compared to baseline leve.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Relationsip between Inflammatory Biomarkers and FEV1, BMI, and Sweat Test Results\u003c/h2\u003e \u003cp\u003eMixed-effects models were employed to identify and quantify the impact of inflammatory biomarkers on FEV1%, BMI, and sweat test outcomes. These models incorporated random intercepts (patient ID), random slopes (patient age), and combinations of both.\u003c/p\u003e \u003cp\u003eAfter creating the models, their fit to the response variables was assessed as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, revealing that the random intercept model provided the best fit for predicting FEV1%, BMI and sweat test.\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\u003eAssessing models for predicting FEV1%, BMI and Sweat Test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRandom Intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRandom Slope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRandom Intercept and Slope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e830.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e868.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e837.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e875.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e834.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e877.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e483.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e522.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e494.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e532.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e524.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e564.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSweat Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e916.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e954.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e917.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e956.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e963.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRV/TLC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e540.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e572.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e577.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e544.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e580.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.154\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\u003eFEV1: Forced Expiratory Volume in 1 second; RV/TLC: residual volume (Total lung capacity; BMI: Body mass index.\u003c/p\u003e \u003cp\u003eAfter evaluating the models and selecting the random intercept model for FEV1, BMI, sweat test, and VR/TLC, model complexity was reduced using stepwise regression in both directions. This process optimized the models and facilitated data interpretation.\u003c/p\u003e \u003cp\u003eThe results of the mixed-effects models after stepwise selection revealed significant relationships between various biomarkers and interleukins with FEV1, BMI, the sweat test, and VR/TLC, as we can see in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed-Effects Models for the Association between Inflammatory Biomarkers and FEV1, BMI, and Sweat Test Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredictor variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta-Coefficient [CI95%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFEV1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIntercept\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.393 [84.099\u0026ndash;110.687]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFibrinogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.022 [-0.042 \u0026ndash; -0.002]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.869 [-3.57 \u0026ndash; -0.169]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColony stimulating factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.482 [-0.700 \u0026ndash; -0.264]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.269 [-0.039\u0026ndash;0.576]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.053 [-0.094 \u0026ndash; -0.011]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIntercept\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.173 [24.056\u0026ndash;26.291]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFibrinogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.004 [-0.006 \u0026ndash; -0.002]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIl\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.001 [-0.002\u0026ndash;0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000 [0.000\u0026ndash;0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSweat test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIntercept\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-36.582 [-58.873 \u0026ndash; -14.292]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFibrinogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109 [0.074\u0026ndash;0.145]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055 [0.033\u0026ndash;0.078]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.126 [0.017\u0026ndash;0.234]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRV/TLC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIntercept\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.898 [10.492\u0026ndash;23.303]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFibrinogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020 [0.010\u0026ndash;0.029]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL\u0026ndash;1 beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.217 [0.107\u0026ndash;0.327]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041 [0.016\u0026ndash;0.066]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000 [0.000\u0026ndash;0.001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\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\u003eSignificant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are in bold.\u003c/p\u003e \n\u003cp\u003eFEV1: Forced Expiratory Volume in 1 second; RV/TLC: residual volume (Total lung capacity; BMI: Body mass index.\u003c/p\u003e\n\u003cp\u003eFor FEV1, significant associations were found with fibrinogen (Beta-Coefficient = -0.022, 95% CI: -0.042 to -0.002, p\u0026thinsp;=\u0026thinsp;0.038), C-reactive protein (Beta-Coefficient = -1.869, 95% CI: -3.57 to -0.169, p\u0026thinsp;=\u0026thinsp;0.040), colony-stimulating factor (Beta-Coefficient = -0.482, 95% CI: -0.700 to -0.264, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and IL-12 (Beta-Coefficient = -0.053, 95% CI: -0.094 to -0.011, p\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e \u003cp\u003eFor BMI, significant relationships were identified with fibrinogen (Beta-Coefficient = -0.004, 95% CI: -0.006 to -0.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although not statistically significant, trends were observed for IL -8 (Beta-Coefficient = -0.001, 95% CI: -0.002 to 0.000, p\u0026thinsp;=\u0026thinsp;0.064) and neutrophils (Beta-Coefficient\u0026thinsp;=\u0026thinsp;0.000, 95% CI: 0.000 to 0.000, p\u0026thinsp;=\u0026thinsp;0.052).\u003c/p\u003e \u003cp\u003eIn the sweat test model, significant associations were observed with fibrinogen (Beta-Coefficient\u0026thinsp;=\u0026thinsp;0.109, 95% CI: 0.074 to 0.145, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IL -8 (Beta-Coefficient\u0026thinsp;=\u0026thinsp;0.055, 95% CI: 0.033 to 0.078, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and IL -12 (Beta-Coefficient\u0026thinsp;=\u0026thinsp;0.126, 95% CI: 0.017 to 0.234, p\u0026thinsp;=\u0026thinsp;0.028).\u003c/p\u003e \u003cp\u003eFinally, in the VR/TLC model, significant relationships were detected with fibrinogen (Beta-Coefficient\u0026thinsp;=\u0026thinsp;0.020, 95% CI: 0.010 to 0.029, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IL -1 beta (Beta-Coefficient\u0026thinsp;=\u0026thinsp;0.217, 95% CI: 0.107 to 0.327, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and IL -17 (Beta-Coefficient\u0026thinsp;=\u0026thinsp;0.041, 95% CI: 0.016 to 0.066, p\u0026thinsp;=\u0026thinsp;0.003). A non-significant trend was also noted for neutrophils (Beta-Coefficient\u0026thinsp;=\u0026thinsp;0.000, 95% CI: 0.000 to 0.001, p\u0026thinsp;=\u0026thinsp;0.070).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discusion","content":"\u003cp\u003eInflammation plays a crucial role in CF, a genetic disease characterized by excessive mucus accumulation and fibrous tissue formation in various organs, especially in the lung. Inflammation, as an immune response to cell damage and the accumulation of thick secretions, triggers a series of events that contribute to tissue degradation and fibrosis. This inflammatory process accelerates disease progression, reducing patients' quality of life and life expectancy. Our study aims to evaluat the clinical and anti-inflammatory effect of ETI treatment in CF patients with at least one copy of delF508 after one year of follow-up. The results confirm and extend previous observations from pivotal clinical trials (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), demonstrating not only significant improvements in nutritional status, lung function, air trapping and sweat test values over the one-year follow-up, but also a significant decrease in systemic inflammatory markers, such as cytokines, CRP, fibrinogen and neutrophil count.\u003c/p\u003e \u003cp\u003eAlthough the efficacy and safety of ETI therapy has been widely studied, its anti-inflammatory activity has been less considered. We know that CF is a disease with a large inflammatory component (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and that the use of anti-inflammatory agents (ibuprofen, azithromycin, inhaled corticosteroids...) is widespread in its therapeutic arsenal. Previous modulators, such as Ivacaftor, had already demonstrated a significant reduction in systemic proinflammatory markers (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) so our study hypothesizes a decrease in inflammatory parameters and cytokines, especially those related to neutrophilic inflammation, associated with the initiation of treatment with ETI.\u003c/p\u003e \u003cp\u003eAlthough the efficacy and safety of ETI therapy has been widely studied, its anti-inflammatory activity has been less considered. We know that CF is a disease with a large In our sample, the decrease in inflammatory biomarkers was significant for Fibrinogen, C reactive protein, CSF, TNF- α, IFN-gamma, IL-1 alpha, IL-1 beta, IL-6, IL-8, IL-12 and IL-17. Of note was the activity of IL-8, key in the attraction of neutrophils to lung tissue, which showed a direct relationship with the improvement in FEV1 that was also significantly monitored by an improvement in the sweat test. This IL-8 modulation observed in our study is consistent with the data of Sheikh et al., Schaupp et al. and Whesth\u0026ouml;lter et al (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Although our study evaluated blood samples, other studies have hypothesized similar hypotheses by evaluating inflammation at the airway level using bronchial aspirates in CF patients, with similar results (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince neutrophilic inflammation, driven by neutrophil elastase, is strongly associated with lung disease progression in CF, our study also evaluated the evolution of the number of circulating neutrophils after initiating treatment with ETI, observing a significant decrease in them at 6 and 12 months of treatment, results that seem logical and are in agreement with previous studies (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Our findings reinforce the hypothesis that reduction of neutrophils and proinflammatory cytokines by ETI may mitigate neutrophilic inflammation-mediated lung damage.\u003c/p\u003e \u003cp\u003eMixed-model analysis revealed significant relationships between inflammatory biomarkers and clinical parameters. For example, elevated fibrinogen levels were significantly associated with worse FEV1 and lower BMI, while the sweat test showed significant correlation also with IL-8 and IL-12. Finally, VR/TLC showed a positive association with fibrinogen, IL \u0026minus;\u0026thinsp;1 Beta and IL \u0026minus;\u0026thinsp;17. The improvement in pulmonary function and weight of the patients could be a consequence of this general reduction in inflammation, which would facilitate the decrease and fluidization of secretions and the decrease in caloric expenditure that breathing previously entailed for these patients. As for the sweat test, it is used as a way of monitoring CFTR channel function, so it also seems appropriate to think that this decrease in inflammatory parameters may be related to the improvement of the test. These findings underline the concordance of the inflammatory parameters with the physiological data that we usually monitor in CF patients, highlighting the improvement of all of them after initiating treatment with ETI and opening new questions about the possibility of withdrawing or decreasing the anti-inflammatory treatments that these patients had been receiving until now.\u003c/p\u003e \u003cp\u003eThis study is characterized by a comprehensive analysis of inflammatory markers and clinical parameters, with a detailed evaluation of their evolution over time. Longitudinal monitoring at T0, T1 and T2 time points reinforces the temporal validity of the findings, evidencing sustained changes that prevent misinterpretation. On the other hand, these patients were well characterized from the beginning, and in addition to classic pulmonary function parameters such as FEV1%, they also had air trapping data, data that provide a great deal of information in these patients and which are less frequently seen in other studies.\u003c/p\u003e \u003cp\u003eDespite these strengths, there are several important limitations that should be considered when interpreting the results. First, the small sample size may reduce the statistical power and generalizability of the findings to a larger population. In addition, the absence of a control group precludes definitive attribution of the observed effects to treatment with ETI, without being able to rule out other confounding variables. Finally, the use of systemic inflammatory biomarkers rather than airway-specific biomarkers limits the accurate interpretation of the results in the pulmonary setting.\u003c/p\u003e \u003cp\u003eIn conclusion, treatment with ETI not only improves clinical parameters in CF patients, but also reduces systemic inflammatory markers. These findings open a future door to consider reducing or withdrawing the usual anti-inflammatory treatments in these patients. The results of our study, although promising, require further studies that delve deeper into the inflammatory pathways modulated by ETI and their relationship to the long-term prognosis of CF.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMSG wrote the main manuscript. APL did all the statistics, tables and figures.MMGC and RMGM were the coordinators of the workEFA and BPG did the biomarkers analysis. CMJC is the reference doctor of the patients. RMGP, JAB and JMEB corrected and revised the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHanssens LS, Duchateau J, Casimir GJ. CFTR Protein: Not Just a Chloride Channel? Cells. 2021; 10 (11). Epub 2021/11/28. https://doi.org/10.3390/cells10112844 PMID: 34831067.\u003c/li\u003e\n\u003cli\u003eBergeron C, Cantin AM. Cystic Fibrosis: Pathophysiology of Lung Disease. Semin Respir Crit Care Med. 2019 Dec;40(6):715-726. doi: 10.1055/s-0039-1694021. Epub 2019 Oct 28. PMID: 31659725.\u003c/li\u003e\n\u003cli\u003eWitko-Sarsat V, Burgel PR. Cystic fibrosis in the era of CFTR modulators: did the neutrophil slip through the cracks? J Leukocyte Biol. 2024; 115(3):417\u0026ndash;9. https://doi.org/10.1093/jleuko/qiad164 PMID: 38193848. \u003c/li\u003e\n\u003cli\u003eD.T. Groves, Y. Jiang, Chemokines, a family of chemotactic cytokines, Crit. Rev. Oral Biol. Med. 6 (2) (1995) 109\u0026ndash;118, https://doi.org/10.1177/ 10454411950060020101. \u003c/li\u003e\n\u003cli\u003eS.D. Sagel, B.D. Wagner, M.M. Anthony, P. Emmett, E.T. Zemanick, Sputum biomarkers of inflammation and lung function decline in children with cystic fibrosis, Am. J. Respir. Crit. Care Med. 186 (9) (2012) 857\u0026ndash;865, https://doi.org/10.1164/rccm.201203-0507OC. \u003c/li\u003e\n\u003cli\u003eJ. Laval, A. Ralhan, D. Hartl, Neutrophils in cystic fibrosis, Biol. Chem. 397 (6) (2016) 485\u0026ndash;496, https://doi.org/10.1515/hsz-2015-0271, K. Jundi, C.M. Greene, Transcription of interleukin-8: how altered regulation can affect cystic fibrosis lung disease, Biomolecules 5 (3) (2015) 1386\u0026ndash;1398, https://doi.org/10.3390/biom5031386.)\u003c/li\u003e\n\u003cli\u003eHeijerman HGM, McKone EF, Downey DG, Van Braeckel E, Rowe SM, Tullis E, Mall MA, Welter JJ, Ramsey BW, McKee CM, Marigowda G, Moskowitz SM, Waltz D, Sosnay PR, Simard C, Ahluwalia N, Xuan F, Zhang Y, Taylor-Cousar JL, McCoy KS; VX17-445-103 Trial Group. Efficacy and safety of the elexacaftor plus tezacaftor plus ivacaftor combination regimen in people with cystic fibrosis homozygous for the F508del mutation: a double-blind, randomised, phase 3 trial. Lancet. 2019 Nov 23;394(10212):1940-1948. doi: 10.1016/S0140-6736(19)32597-8. Epub 2019 Oct 31. Erratum in: Lancet. 2020 May 30;395(10238):1694. doi: 10.1016/S0140-6736(20)31021-7. PMID: 31679946; PMCID: PMC7571408.\u003c/li\u003e\n\u003cli\u003eSutharsan S, McKone EF, Downey DG, Duckers J, MacGregor G, Tullis E, Van Braeckel E, Wainwright CE, Watson D, Ahluwalia N, Bruinsma BG, Harris C, Lam AP, Lou Y, Moskowitz SM, Tian S, Yuan J, Waltz D, Mall MA; VX18-445-109 study group. Efficacy and safety of elexacaftor plus tezacaftor plus ivacaftor versus tezacaftor plus ivacaftor in people with cystic fibrosis homozygous for F508del-CFTR: a 24-week, multicentre, randomised, double-blind, active-controlled, phase 3b trial. Lancet Respir Med. 2022 Mar;10(3):267-277. doi: 10.1016/S2213-2600(21)00454-9. Epub 2021 Dec 20. PMID: 34942085.\u003c/li\u003e\n\u003cli\u003eMiddleton PG, Mall MA, Dřev\u0026iacute;nek P, Lands LC, McKone EF, Polineni D, Ramsey BW, Taylor-Cousar JL, Tullis E, Vermeulen F, Marigowda G, McKee CM, Moskowitz SM, Nair N, Savage J, Simard C, Tian S, Waltz D, Xuan F, Rowe SM, Jain R; VX17-445-102 Study Group. Elexacaftor-Tezacaftor-Ivacaftor for Cystic Fibrosis with a Single Phe508del Allele. N Engl J Med. 2019 Nov 7;381(19):1809-1819. doi: 10.1056/NEJMoa1908639. Epub 2019 Oct 31. PMID: 31697873; PMCID: PMC7282384. \u003c/li\u003e\n\u003cli\u003eTaylor-Cousar JL, Mall MA, Ramsey BW, et al. Clinical development of triple-combination CFTR modulators for cystic fibrosis patients with one or two F508del alleles. ERJ Open Res 2019; 5: 00082-2019. doi: 10.1183/23120541.00082-2019. \u003c/li\u003e\n\u003cli\u003eShoki AH , Mayer-Hamblett N , Wilcox PG , et al . Systematic review of blood biomarkers in cystic fibrosis pulmonary exacerbations. Chest 2013;144:1659\u0026ndash;70. doi:10.1378/chest.13-0693. \u003c/li\u003e\n\u003cli\u003eHisert KB , Heltshe SL , Pope C , et al . Restoring cystic fibrosis transmembrane conductance regulator function reduces airway bacteria and inflammation in people with cystic fibrosis and chronic lung infections. Am J Respir Crit Care Med 2017;195:1617\u0026ndash;28. doi:10.1164/rccm.201609-1954OC. \u003c/li\u003e\n\u003cli\u003eMainz JG , Arnold C , Wittstock K , et al . Ivacaftor reduces inflammatory mediators in upper airway lining fluid from cystic fibrosis patients with a G551D mutation: serial non-invasive home-based collection of upper airway lining fluid. Front Immunol 2021;12:642180. doi:10.3389/fimmu.2021.642180. \u003c/li\u003e\n\u003cli\u003eSchaupp L, Addante A, V\u0026ouml;ller M, et al. Longitudinal effects of elexacaftor/tezacaftor/ivacaftor on sputum viscoelastic properties, airway infection and inflammation in patients with cystic fibrosis. Eur Respir J 2023: 62: 2202153. doi: 10.1183/13993003.02153-2022, \u003c/li\u003e\n\u003cli\u003eWesth\u0026ouml;lter D, Pipping J, Raspe J, Schmitz M, Sutharsan S, Stra\u0026szlig;burg S, Welsner M, Taube C, Reuter S. Plasma levels of chemokines decrease during elexacaftor/tezacaftor/ivacaftor therapy in adults with cystic fibrosis. Heliyon. 2023 Dec 12;10(1):e23428. doi: 10.1016/j.heliyon.2023.e23428. PMID: 38173511; PMCID: PMC10761561. \u003c/li\u003e\n\u003cli\u003eAtteih SE, Armbruster CR, Hilliam Y, Rapsinski GJ, Bhusal JK, Krainz LL, Gaston JR, DuPont M, Zemke AC, Alcorn JF, Moore JA, Cooper VS, Lee SE, Forno E, Bomberger JM. Effects of highly effective modulator therapy on the dynamics of the respiratory mucosal environment and inflammatory response in cystic fibrosis. Pediatr Pulmonol. 2024 May;59(5):1266-1273. doi: 10.1002/ppul.26898. Epub 2024 Feb 14. PMID: 38353361; PMCID: PMC11058019. \u003c/li\u003e\n\u003cli\u003eSheikh S, Britt RD Jr, Ryan-Wenger NA, Khan AQ, Lewis BW, Gushue C, Ozuna H, Jaganathan D, McCoy K, Kopp BT. Impact of elexacaftor-tezacaftor-ivacaftor on bacterial colonization and inflammatory responses in cystic fibrosis. Pediatr Pulmonol. 2023 Mar;58(3):825-833. doi: 10.1002/ppul.26261. Epub 2022 Dec 9. PMID: 36444736; PMCID: PMC9957929\u003c/li\u003e\n\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":"lung","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lung","sideBox":"Learn more about [Lung](https://www.springer.com/journal/408)","snPcode":"408","submissionUrl":"https://submission.nature.com/new-submission/408/3","title":"Lung","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cystic Fibrosis, inflammation, CFTR, Modulators, Inflammation","lastPublishedDoi":"10.21203/rs.3.rs-5874180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5874180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eCystic fibrosis (CF) is a genetic disease caused by mutations in the CFTR gene, leading to multisystemic complications, particularly in the lungs. CFTR dysfunction results in altered ion transport, chronic inflammation, and progressive lung damage. The triple therapy elexacaftor/tezacaftor/ivacaftor (ETI) has demonstrated significant improvements in pulmonary function and quality of life. This study aimed to evaluate the anti-inflammatory effects of ETI by analysing systemic cytokine profiles over 12 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA prospective study included 32 CF patients ≥18 years with at least one F508del mutation, undergoing ETI therapy. Clinical stability was ensured prior to therapy initiation. Demographic data, BMI (Body Mass Index), FEV1% (Forced expiratory Volume in the first second), VR/TLC (residual volume/total lung capacity) and sweat chloride concentrations were recorded at baseline, 6 months and 12 months. Inflammatory markers, including fibrinogen, C-reactive protein (CRP), and a panel of 8 cytokines, were measured using multiplex bead-based immunoassays and electrochemiluminescence. Longitudinal changes were analysed using mixed-effects models and statistical tests, with significance set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDuring a 12-month follow-up, the neutrophils number and proinflammatory biomarkers analyzed, fibrinogen, CRP, TNF- α, IFN-gamma, IL-1 alpha, IL-1 beta, IL-6, IL-8, IL-12, IL-17, significantly decreased, while eosinophils remained stable. Mixed-effects models confirmed the significant association of inflammatory biomarkers with FEV1, BMI, sweat chloride levels, and VR/TLC highlighting the role of inflammation in the progression of CF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eETI demonstrated marked anti-inflammatory effects in CF patients, reducing systemic inflammation and improving clinical parameters.\u003c/p\u003e","manuscriptTitle":"The Role of Triple CFTR Modulator Therapy in Reducing Systemic Inflammation in Cystic Fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 15:52:49","doi":"10.21203/rs.3.rs-5874180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-18T20:09:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-18T17:55:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-31T17:49:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98083251731201975311884715902825936670","date":"2025-01-30T09:14:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154158865749720133993630719315395749747","date":"2025-01-27T18:31:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-26T15:52:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-25T03:04:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-25T03:03:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lung","date":"2025-01-21T14:03:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lung","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lung","sideBox":"Learn more about [Lung](https://www.springer.com/journal/408)","snPcode":"408","submissionUrl":"https://submission.nature.com/new-submission/408/3","title":"Lung","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0c0723fe-725e-4db1-9804-6f6214ddb44d","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T16:08:19+00:00","versionOfRecord":{"articleIdentity":"rs-5874180","link":"https://doi.org/10.1007/s00408-025-00806-6","journal":{"identity":"lung","isVorOnly":false,"title":"Lung"},"publishedOn":"2025-03-28 15:57:34","publishedOnDateReadable":"March 28th, 2025"},"versionCreatedAt":"2025-01-28 15:52:49","video":"","vorDoi":"10.1007/s00408-025-00806-6","vorDoiUrl":"https://doi.org/10.1007/s00408-025-00806-6","workflowStages":[]},"version":"v1","identity":"rs-5874180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5874180","identity":"rs-5874180","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}