biomedicines Article Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study Xuejie Wang 1,2 , Carmen Villa 3 , Yadira Dobarganes 3 , Casilda Olveira 4,5 , Rosa Girón 6 , Marta García-Clemente 7 , Luis Máiz 8 , Oriol Sibila 9,10 , Rafael Golpe 11 , Rosario Menéndez 12 , Juan Rodríguez-López 13 , Concepción Prados 14 , Miguel Angel Martinez-García 10,12 , Juan Luis Rodriguez 15,16,17 , David de la Rosa 18 , Xavier Duran 19 , Jordi Garcia-Ojalvo 20 and Esther Barreiro 1,10,20, * 1 Lung Cancer and Muscle Research Group, Pulmonology Department, Hospital del Mar-IMIM, Parc de Salut Mar, PRBB, C/Dr. Aiguader, 88, 08003 Barcelona, Spain; Xuejie.Wang@e-campus.uab.cat 2 Department of Medicine, Universitat Autònoma de Barcelona (UAB), 08035 Barcelona, Spain 3 Respiratory Department, Clínica Fuensanta, 28015 Madrid, Spain; carmenvilla@neumo.es (C.V.); Dogar_yad@clinicmadrid.org (Y.D.) 4 Respiratory Department, Hospital Regional Universitario de Málaga, 29003 Málaga, Spain; casi1547@separ.es 5 Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga, 29003 Málaga, Spain 6 Respiratory Department, Instituto de Investigación Sanitaria, Hospital Universitario de la Princesa, 28015 Madrid, Spain; rosamaria.giron@salud.madrid.org 7 Respiratory Department, Hospital Universitario Central de Asturias, 33071 Oviedo, Spain; marta.garciac@sespa.es 8 Respiratory Department, Hospital Ramon y Cajal, 28015 Madrid, Spain; luis.maiz@salud.madrid.org 9 Respiratory Department, Hospital Clínic, 08035 Barcelona, Spain; OSIBILA@clinic.cat 10 Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos  III (ISCIII), 28015 Madrid, Spain; martinez_miggar@gva.es  11 Respiratory Department, Hospital Lucus Augusti, 27080 Lugo, Spain; rafa898@separ.es 12 Respiratory Department, Hospital Universitario y Politécnico La Fe, 46003 Valencia, Spain; Citation: Wang, X.; Villa, C.; Dobarganes, Y.; Olveira, C.; Girón, R.; Menendez_ros@gva.es 13 Respiratory Department, Hospital San Agustin, 33401 Avilés, Spain; juan.rodriguez@sespa.es García-Clemente, M.; Máiz, L.; Sibila, 14 Respiratory Department, Hospital la Paz, 28015 Madrid, Spain; mconcepcion.prados@salud.Madrid.org O.; Golpe, R.; Menéndez, R.; et al. 15 Respiratory Department, Hospital Clínico San Carlos, 28015 Madrid, Spain; jlrodr01@ucm.es Systemic Inflammatory Biomarkers 16 Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), 28015 Madrid, Spain Define Specific Clusters in Patients 17 Departamento de Medicina, Universidad Complutense de Madrid, 28015 Madrid, Spain with Bronchiectasis: A Large-Cohort 18 Respiratory Department, Hospital Santa Creu I Sant Pau, 08035 Barcelona, Spain; drosa@santpau.cat Study. Biomedicines 2022, 10, 225. 19 Scientific and Technical Department, Hospital del Mar-IMIM, 08035 Barcelona, Spain; xduran@imim.es https://doi.org/10.3390/ 20 Department of Health and Experimental Sciences (CEXS), Universitat Pompeu Fabra (UPF), biomedicines10020225 08035 Barcelona, Spain; jordi.g.ojalvo@upf.edu * Correspondence: ebarreiro@imim.es; Tel.: +34-93-316-0385; Fax: +34-93-316-0410 Academic Editor: Alice M Turner Received: 10 December 2021 Abstract: Differential phenotypic characteristics using data mining approaches were defined in Accepted: 17 January 2022 a large cohort of patients from the Spanish Online Bronchiectasis Registry (RIBRON). Three differ- Published: 21 January 2022 ential phenotypic clusters (hierarchical clustering, scikit-learn library for Python, and agglomerative Publisher’s Note: MDPI stays neutral methods) according to systemic biomarkers: neutrophil, eosinophil, and lymphocyte counts, C re- with regard to jurisdictional claims in active protein, and hemoglobin were obtained in a patient large-cohort (n = 1092). Clusters #1–3 published maps and institutional affil- were named as mild, moderate, and severe on the basis of disease severity scores. Patients in iations. cluster #3 were significantly more severe (FEV1 , age, colonization, extension, dyspnea (FACED), exacerbation (EFACED), and bronchiectasis severity index (BSI) scores) than patients in clusters #1 and #2. Exacerbation and hospitalization numbers, Charlson index, and blood inflammatory markers were significantly greater in cluster #3 than in clusters #1 and #2. Chronic colonization by Copyright: © 2022 by the authors. Pseudomonas aeruginosa and COPD prevalence were higher in cluster # 3 than in cluster #1. Airflow lim- Licensee MDPI, Basel, Switzerland. itation and diffusion capacity were reduced in cluster #3 compared to clusters #1 and #2. Multivariate This article is an open access article ordinal logistic regression analysis further confirmed these results. Similar results were obtained after distributed under the terms and conditions of the Creative Commons excluding COPD patients. Clustering analysis offers a powerful tool to better characterize patients Attribution (CC BY) license (https:// with bronchiectasis. These results have clinical implications in the management of the complexity creativecommons.org/licenses/by/ and heterogeneity of bronchiectasis patients. 4.0/). Biomedicines 2022, 10, 225. https://doi.org/10.3390/biomedicines10020225 https://www.mdpi.com/journal/biomedicines Biomedicines 2022, 10, 225 2 of 18 Keywords: non-cystic fibrosis bronchiectasis; blood neutrophil; eosinophil; lymphocyte counts; C reactive protein; hemoglobin; hierarchical clustering; phenotypic clusters; multivariate analyses; clinical outcomes; disease severity scores 1. Introduction Bronchiectasis is a chronic respiratory disease characterized by a permanent dilatation of the airways of different etiology [1–3]. Bronchiectasis is a very heterogeneous and complex disease, in which patients experience recurrent exacerbations mainly due to bronchial infection. Exacerbations negatively impact on the patients’ quality of life and disease prognosis [4]. The immune response against infections is crucial in bronchiectasis patients [5]. Neu- trophilic inflammation is the predominant phenotype in these patients. In response to bacterial loads, neutrophils are recruited to the lungs, where they secrete antimicrobial peptides to fight against infection [6]. However, other inflammatory cell types such as eosinophils are also involved in the pathobiology of bronchiectasis, particularly in the response to different biological agents [7–10] as well as to inhaled corticosteroids [11,12]. Lymphocytic infiltration was also demonstrated to take place just beneath the basement membrane of the epithelium in bronchiectasis patients [13]. Nutritional abnormalities and systemic inflammation are common in patients with bronchiectasis, particularly in male patients compared to females, as demonstrated in a recent study [14]. Moreover, body composition and systemic inflammation have a prog- nosis value in other chronic respiratory conditions such as chronic obstructive pulmonary disease (COPD) [15–17]. Disease severity scores usually take into account variables that reflect the status of the lung disease, but not those involving the systemic components. This imposes a challenge in the overall assessment and phenotyping of the patients. Thus, phenotypic clustering in which variables related to systemic manifestations and inflammatory parameters are analyzed jointly may be of interest in clinical decision processes of patients with bronchiectasis. In this regard, results obtained from clinical tests can be analyzed using different approaches, in which software tools are applied to com- plex clinical and biological data sets obtained from large cohorts of patients. Phenotypic classification of bronchiectasis patients according to several clinical and biological markers may help predict disease severity, the risk of exacerbations, and disease prognosis. Recent investigations showed that a cut-off value greater than 5 reliably predicted hospitalizations and all-cause mortality according to the FACED (FEV1 , age, chronic colonization, radiolog- ical extension, and dyspnea) and bronchiectasis severity index (BSI) scores [18] and that eosinophil levels defined differential clinical clusters of bronchiectasis patients [19]. The current investigation sought to tease out differential phenotypic characteristics of patients with bronchiectasis using data mining approaches on the basis of the fol- lowing systemic inflammatory and nutritional parameters: blood neutrophil, eosinophil, and lymphocyte counts, C reactive protein, and hemoglobin in a large-cohort of patients from the Spanish Online Bronchiectasis Registry (RIBRON) [19,20]. Three clusters of pa- tients with differential clinical phenotypes were obtained. Thus, the study objectives were: (1) to identify different clusters of patients included in this registry that could discriminate differential phenotypes on the basis of blood neutrophil, eosinophil, and lymphocyte counts along with C reactive protein and hemoglobin levels, (2) to analyze potential differences be- tween the clusters in several clinical parameters involving the assessment of lung function, nutritional status, and a general clinical evaluation, and (3) to stratify the clusters according to disease severity following the exacerbation FACED (EFACED), FACED, and BSI indices. 2. Methods 2.1. Study Design This was a multicenter, prospective, and observational study, in which 43 centers from Spain participated within the frame of the RIBRON database between February 2015 Biomedicines 2022, 10, 225 and October 2019 [21–23]. Strengthening the Reporting of Observational studies in Epide- 3 of 18 miology (STROBE) reporting guidelines were used to design the current investigation [24]. The quality of the data introduced in the registry was always monitored and ensured by an external contract research organization (CRO). 2. Methods 2.1. Study Design 2.2. Study Population This was a multicenter, prospective, and observational study, in which 43 centers from SpainThe patient recruitment participated within theflow-chart frame of the is depicted RIBRONin Figure 1. database Inclusion between criteria2015 February wereand as follows: Octoberadult 2019 patients [21–23]. who had been diagnosed Strengthening the Reporting withofnon-CF bronchiectasis Observational studiesasina Epidemi- result of aology high-resolution (STROBE) computerized tomography reporting guidelines were used(HRCT) [21,23,25–28]. to design The participants the current investigationwere[24]. stable patients and did not report any acute exacerbation at least in the The quality of the data introduced in the registry was always monitored and ensured by an last four weeks prior to study external entry. contract A totalorganization research of 1092 patients (CRO). were analyzed from the registry. Study vari- ables were as follows: anthropometry, smoking history, lung function, hemogram, inflam- 2.2. Study matory Population blood cells, and nutritional parameters were analyzed using custom data-analysis software The tools. patientThe exclusion flow-chart recruitment criteria were the following: is depicted traction in Figure bronchiectasis 1. Inclusion and/or criteria were as cystic fibrosis follows: adult(sweat patientschloride who hadtestbeen and/or genetic with diagnosed confirmation), and age younger non-CF bronchiectasis than 18 as a result of years old. a high-resolution computerized tomography (HRCT) [21,23,25–28]. The participants were The stable Worldand patients Medical Association did not report anyfor Research acute in Humans exacerbation (Seventh at least revision in the last of the Dec- four weeks prior laration to studyofentry. Helsinki, A totalFortaleza, Brazil, 2013) of 1092 patients wereguidelines werethe analyzed from followed in Study registry. the study [29]. variables Ethics were as approval follows:was obtained fromsmoking anthropometry, the Ethics Committee history, at the Hospital lung function, Josep Trueta hemogram, Gi- inflamma- rona tory (# 001-2012, blood cells, Hospital Universitari and nutritional Dr. Josep parameters Trueta, were Girona, analyzed using Spain) from custom all the par- data-analysis software All ticipants. tools. theThe exclusion patients signedcriteria were thewritten the informed following: traction consent bronchiectasis to participate in theand/or regis- cystic try. Thefibrosis (sweatremained information chloride test and/or genetic confidential confirmation), at all times and age and no personal younger than information re- 18 years old. lated to any of the participants was introduced in the registry. Flow-chartof Figure1.1.Flow-chart Figure ofthe thestudy. study. The World Medical Association for Research in Humans (Seventh revision of the Declaration of Helsinki, Fortaleza, Brazil, 2013) guidelines were followed in the study [29]. Ethics approval was obtained from the Ethics Committee at the Hospital Josep Trueta Girona (# 001-2012, Hospital Universitari Dr. Josep Trueta, Girona, Spain) from all the participants. All the patients signed the informed written consent to participate in the registry. The information remained confidential at all times and no personal information related to any of the participants was introduced in the registry. Biomedicines 2022, 10, 225 4 of 18 2.3. Study Variables and Scores Etiology of the non-CF bronchiectasis, anthropometry (age, sex, and body mass index), lung function, exercise capacity, chronic colonization by Pseudomonas aeruginosa, chronic colonization with other microorganisms, radiologic extension, dyspnea, the number of exacerbations and hospitalizations for exacerbations in the previous year, the Charlson index, smoking history, nutritional status, and systemic inflammatory cells and markers were obtained from all the patients. FACED [30], EFACED [31], and bronchiectasis severity index (BSI) [32] were calculated on the basis of the clinical study variables. 2.4. Patient Clustering The study population was clustered into three major groups of patients on the basis of the following analytical parameters: eosinophils, neutrophils, lymphocytes, C-reactive protein (CRP), and hemoglobin that correlated with EFACED score. A total of 1092 patients met the criteria for these analyses (all these patients had valid values for all five parameters). A hierarchical clustering was performed on the basis of the five biomark- ers in the 1092 patients using the scikit-learn library for Python [33]. Agglomerative methods, in which clusters start by a single patient and are subsequently merged and fused from the previous steps into bigger clusters, were used in this study. The criterion of such fusion is the minimal distance between the two clusters to be fused, as specified by the cho- sen linkage function. The algorithm stopped when the number of clusters fell to 5, which is the level that produced an optimal classification of the patients according to the EFACED score (Table 1). As seen in Table 1, cluster # 3 was very small and was indistinguishable from cluster # 2 (EFACED score 3.57 in both clusters). In a similar fashion, clusters # 1 and # 4 were similar as no statistically significant difference was observed in EFACED score (2.60 versus 2.88, respectively). Hence, the 1092 patients were finally subdivided into three major clusters 1–3 that were labeled as mild (n = 242 patients), moderate (n = 515 patients), and severe (n = 335 patients) clusters of patients (Table 1). Table 1. Characteristics of the five clusters resulting from the hierarchical clustering approach. Cluster Size Average EFACED Disease Severity Summary of the Size 1 367 2.60 moderate N = 242 Cluster # 1: mild 2 311 3.57 severe N = 515 Cluster # 2: moderate 3 24 3.67 severe N = 335 Cluster # 3: severe 4 148 2.88 moderate 5 242 2.13 mild As described in methods, the five clusters were transformed into three, which were the ones analyzed in the study. Definition of abbreviations: N, number. The five-dimensional space containing the patient data was reduced to a two-dimensional representation (Figure 2). Uniform Manifold Approximation and Projection (UMAP) was applied to transform the data into two linear combinations (UMAP1 and UMAP2, respec- tively) of the five target markers (blood parameters: neutrophils, lymphocytes, eosinophils, CRP, and hemoglobin) in which all the patients were represented in three colors (mild, moderate, and severe represented in blue, light brown, and green, respectively) [34] (Figure 2). Moreover, the distribution of the five target markers across the three patient clusters was also analyzed in the study as shown in Figure 3. The diagonal plots repre- sented the distribution of the target variable across the three patient clusters. In addition, the distribution of the individual values in pairs of variables for all three clusters (blue, light brown, and green colors) were represented in scattered plots (Figure 3). Biomedicines 2022, 10, x FOR PEER REVIEW 5 of 18 Biomedicines 2022, 10, x FOR PEER REVIEW 5 of 18 Biomedicines 2022, 10, 225 5 of 18 Figure 2. Uniform Manifold Approximation and Projection (UMAP) representation of the three clus- Uniform Figure2.2.Uniform Figure Manifold Manifold Approximation Approximation andand Projection Projection (UMAP) (UMAP) representation representation of theof the clus- three three ters of patients. Three clusters of patients: blue, light brown, and green represent mild, moderate, clusters of ters severe patients. of patients. Three clusters Three clusters of patients: of patients: blue, light brown, and green represent mild, moderate, blue, light brown, and green represent mild, moderate, and bronchiectasis patients, respectively. andsevere and severebronchiectasis bronchiectasispatients, patients,respectively. respectively. Diagonalplots Figure3.3.Diagonal Figure plotsrepresent representthe thedistribution distributionofofthethetarget targetvariable variableacross acrossthe thethree threepatient patient Figure 3. clusters. Diagonal The plots represent distribution of the the distribution individual values of in the target pairs of variable variables across for the the threethree patient clusters clusters. The distribution of the individual values in pairs of variables for the three clusters are rep- are clusters. The represented distribution in scattered of the plots. individual Three values clusters of in pairs patients: of variables blue, light for brown, the andthree greenclusters are represent resented in scattered plots. Three clusters of patients: blue, light brown, and green represent mild, rep- mild, resented moderate, moderate, inand scattered and severeplots. severe Three clusters bronchiectasis bronchiectasis of patients: patients, patients, blue, light brown, and green represent mild, respectively. respectively. moderate, and severe bronchiectasis patients, respectively. 2.5.Statistical 2.5. StatisticalAnalysis Analysis 2.5. Statistical The study Analysis variables are presented as mean (standard deviation) in tables. A subanalysis The study variables are presented as mean (standard deviation) in tables. A subanal- in which The patients study with COPD variables are were excluded presented meanin(standard all three clusters wasinalso conducted. Po- ysis in which patients with COPD wereas excluded in all threedeviation) clusters was tables. A subanal- also conducted. tential ysis in differences which among patients with the COPDthreewere clusters of patients excluded in all (cluster three # 1, cluster clusters was # 2,conducted. also cluster # 3), Potential differences among the three clusters of patients (cluster # 1, cluster # 2, cluster # includingdifferences Potential those in which COPD patients were excluded, were assessed using one-way 3), including those in amongwhich COPD the three clusters patients of patients were excluded, (cluster # 1, cluster were assessed # 2,one-way using cluster # analysis of 3), including variance those in (ANOVA) which COPD and Tukey’s post patientspost hoc werehoc for excluded,the quantitative were assessed variables and using one-way the analysis of variance (ANOVA) and Tukey’s for the quantitative variables and the Chi-square analysis of test for the variance categorical (ANOVA) and variables. Tukey’s Correlations post hoc for the between clinical quantitative and biological variables and the Chi-square test for the categorical variables. Correlations between clinical and biological variables were Chi-square test explored for the using thevariables. categorical Pearson’s correlationbetween Correlations coefficient. A Bonferroni-type clinical and biological variables were explored using the Pearson’s correlation coefficient. A Bonferroni-type ad- adjustment variables were was performed explored usingto the considering Pearson’s the effect of having correlation multiple coefficient. correlations. ad- justment was performed to considering the effect of having multipleAcorrelations. Bonferroni-type justmentCorrelations are displayed was performed in graphical to considering correlation the effect of having matrixes, multipleobtained from R pack- correlations. age corrplot (https://cran.r-project.org/web/packages/corrplot/index.html, accessed on Biomedicines 2022, 10, 225 6 of 18 2 December 2021), in different colors: blue for positive correlations and red for negative ones (Penn State University, World Campus, Pennsylvania, PA, USA). Comparisons among the three patient clusters were also made on the basis of the degree of the disease severity according to the different scores (FACED, EFACED and BSI), in which the percentages of patients in each category were depicted. Potential differences among the three clusters were explored using the Chi-square test. Multivariate ordinal logistic regression, in which the outcome variable was clus- ters (cluster # 1, cluster # 2, cluster # 3) was used to assess the potential associations of EFACED score with ordered clusters. The following clinically meaningful confounders were considered: Charlson index, COPD, platelets, ESR, fibrinogen, creatinine, total protein concentration, and albumin levels. The multivariate regression odds ratio (OR) is represented as a black dot in each of the confounders along with the corresponding confidence intervals, which were depicted in a forest plot. In the Y-axis, all the confounder variables are plotted, while in the X-axis, the width of the confidence intervals is represented. The one value is represented as a dotted vertical line. Statistical analyses were performed using SPSS 23.0 (SPSS Inc., Chicago, IL, USA) and Stata 15.1 (StataCorp LLC, College Station, TX, USA). Results were considered as statistically significant at a p-value < 0.05. 3. Results 3.1. General Clinical Characteristics of the Three Patient Clusters The number of female patients was greater than that of male patients in all three clusters (Table 2. Patients in cluster # 3 were significantly older than those represented in clusters # 1 and 2, respectively (Table 2). Disease severity scores FACED, EFACED, and BSI were significantly greater in cluster # 3 than in clusters # 1 and # 2 (Table 2). The proportions of cluster # 3 patients with mild and moderate degree of disease severity (BSI score) was significantly lower than in clusters # 1 and # 2 (mild, Figure 4A). Conversely, BSI score was greater in patients of clusters # 2 and # 3 than those in cluster # 1. Moreover, BSI score was also significantly higher in cluster # 3 than in cluster # 2 (Figure 4A). The proportions of patients with moderate and severe EFACED and FACED scores were significantly higher in cluster # 3 than in clusters # 1 and # 2 (EFACED), as illustrated in Figure 4B,C, respectively. Table 2. General characteristics of all the study patients according to cluster analyses. Cluster Cluster Cluster Group # 1 Group # 2 Group # 3 N = 242 N = 515 N = 335 Anthropometric variables, x(SD) Age, years 65.8 (14.9) 66.8 (15.1) 69.4 (14.7) *§ BMI, kg/m2 25.1 (4.6) 26.1 (5.0) * 25.8 (5.3) Female, N/male, N 184/58 330/185 213/122 Disease severity, x(SD) FACED score 1.64 (1.44) 2.05 (1.64) ** 2.61 (1.82) ***§§§ EFACED score 2.13 (1.91) 2.68 (2.08) ** 3.58 (2.33) ***§§§ BSI score 6.48 (4.32) 7.54 (4.54) * 9.31 (4.94) ***§§§ Exacerbations (mild/moderate) 1.33 (1.62) 1.57 (1.7) 1.72 (1.64) * Hospitalization for exacerbations 0.37 (0.79) 0.65 (1.53) * 0.99 (1.44) ***§§ Charlson Index 1.58 (1.27) 1.89 (1.63) * 2.12 (1.69) ***§ Chronic colonization by PA, N (%) 46 (19) 135 (26.2) * 109 (32.5) *** Radiological extension 2.8 (1.4) 2.9 (1.4) 2.9 (1.5) COPD 18 (7.4%) 54 (10.5%) 49 (14.6%) ** Asthma 29 (12%) 45 (8.7%) 31 (9.3%) Smoking history Never smokers, N (%) 149 (62) 313 (61) 193 (57.6) Current smokers, N (%) 19 (8) 46 (9) 26 (8) Ex-smokers, N (%) 74 (31) 156 (30) 116 (35) Packs-year, x(SD) 10.4 (20.7) 12.2 (22.3) 15.1 (25.5) § Radiological extension 2.8 (1.4) 2.9 (1.4) 3 (1.5) Smoking history Never smokers, N (%) 146 (65.2) 309 (67) 192 (67.1) Current smokers, N (%) 16 (7.1) 36 (7.8) 16 (5.6) Ex-smokers, Biomedicines 2022, 10, 225 N (%) 62 (27.7) 116 (25.2) 78 (27.3) 7 of 18 Packs-year,x (SD) 7.85 (16.27) 8.13 (17.61) 8.82 (17.34) Lung function,x (SD) FEV1, % predictedTable 2. Cont. 86 (20) 78 (21) ** 73 (23) ***§§ FVC, % predicted 92 (38) 88 (23) 85 (31) ***§ Cluster Cluster Cluster FEV1/FVC, % Group # 1 (30) 74 Group # 2(23) ** 71 Group (27) 69 # 3 ***§ DLCO, % predicted N = 242 90 (40) N = 51593 (30) N77 (20) **§§§ = 335 LungKCO , % predicted function, x(SD) 87 (1) 87 (1) 80 (1) FEV 1, % RV, %predicted predicted 81 (24) 143 (74) 75 (24)141 ** (41) 67 (26) 153 ***§§§ (36) FVC, % predicted 88 (20) 86 (21) 81 (24) ***§§ TLC, % predicted FEV1 /FVC, % 72 (12) 108 (58) 68 (12)103 ** (48) 65 (14)101 (48) * ***§§§ RV/TLC, DLCO , % predicted% 85 (17) 53 (31) 88 (25) (23) 50 69 (21) ***§§§ § 57 (29) KCO , % predicted Continuous variables79 are (31) presented as mean (standard 81 (40) deviation), while categorical 68 (37) § variables are RV, % predicted presented as the number 139 (43)of patients in each group 135 (49) 144for along with the percentage (54)the study group. TLC, % predicted 105 (18) 101 (19) 99 (22) Definition of abbreviations: x, mean; SD, standard deviation; N, number; kg, kilograms; m, meters; RV/TLC, % 50 (12) 49 (11) 53 (13) § BMI, body mass index; FACED: F, FEV1; A, Age; C, Chronic colonization by Pseudomonas aeruginosa; Continuous variables are presented as mean (standard deviation), while categorical variables are presented as E, radiologic extension; D, dyspnea; EFACED: FACED adding the exacerbation in the previous year; the number of patients in each group along with the percentage for the study group. Definition of abbreviations: BSI: bronchiectasis x, mean; SD, standardseverity index; deviation; FEV1,kg, N, number; forced expiratory kilograms; volume m, meters; in the BMI, body massfirst second; index; FACED: FVC, F, FEVforced 1; A, Age; vital C, Chronic capacity; RV, colonization by Pseudomonas residual volume; aeruginosa; TLC, total lungE,capacity; radiologic DLco, extension; D, dyspnea; carbon monoxideEFACED: FACEDKCO, transfer; adding the exacerbation in the previous year; BSI: bronchiectasis severity index; FEV1 , forced expiratory volume in Krogh transfer factor. Statistical analyses and significance: *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001: Com- the first second; FVC, forced vital capacity; RV, residual volume; TLC, total lung capacity; DLco, carbon monoxide parisons transfer;were assessed KCO , Krogh between transfer factor. either group Statistical # 3 and analyses or group # 2 and significance: *, pgroup ≤ 0.05;#**,1 p(less severe); ≤ 0.01; ***, p ≤§,0.001: p ≤ 0.05; §§,Comparisons p ≤ 0.01; §§§, werep assessed ≤ 0.001:between Comparisons between either group groups # 3 or group # group # 2 and (less severe); §, p ≤ 0.05; §§, p ≤ 0.01; 3 and##1 2. §§§, p ≤ 0.001: Comparisons between groups # 3 and # 2. Figure 4. Histograms of the proportions of patients who were classified as mild, moderate, or severe Figure 4. Histograms of the proportions of patients who were classified as mild, moderate, or severe according to BSI (A), EFACED (B) and FACED (C) among the three clusters. Score subdivisions were according to BSI (A), EFACED (B) and FACED (C) among the three clusters. Score subdivisions as follows: (1) BSI: mild: 0–4, moderate: 5–8, severe: ≥9, (2) EFACED: mild: 0–3, moderate: 4–6, were as follows: (1) BSI: mild: 0–4, moderate: 5–8, severe: ≥9, (2) EFACED: mild: 0–3, moderate: 4–6, severe: 7–9; and (3) FACED: mild: 0–2, moderate: 3–4, severe: 5–7. Statistical significance: *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001: Comparisons were assessed between either group # 3 or group # 2 and group # 1 (less severe); §§, p ≤ 0.01; §§§, p ≤ 0.001: Comparisons between groups # 3 and # 2. n.s.: no significance. Biomedicines 2022, 10, 225 8 of 18 The number of exacerbations was significantly higher in cluster # 3 patients than in cluster # 1 (Table 2). The number of hospitalizations and Charlson index were significantly higher in cluster # 3 than in clusters # 1 and # 2 (Table 2). The proportions of patients with colonization by Pseudomonas aeruginosa and with concomitant COPD were greater in cluster # 3 patients than in cluster # 1 (Table 2). Smoking history did not significantly differ among the three clusters (Table 2). The degree of airflow limitation and diffusion lung capacity was significantly impaired in cluster # 3 patients compared to clusters # 1 and # 2 (Table 2). When COPD patients were excluded from the analysis, similar results were obtained in the three analyzed clusters (Table 3). Table 3. General characteristics in the two clusters of bronchiectasis patients excluding those with COPD. Cluster Cluster Cluster Group # 1 Group # 2 Group # 3 N = 224 N = 461 N = 286 Anthropometric variables, x(SD) Age, years 64.9 (14.9) 65.7 (15.3) 68.1 (15) * BMI, kg/m2 25.2 (4.7) 25.9 (5.0) 25.8 (5.4) Female, N/male, N 178/46 313/148 203/83 Disease severity FACED score 1.54 (1.38) 1.94 (1.58) ** 2.38 (1.70) ***§§ EFACED score 2 (1.81) 2.54 (1.99) ** 3.24 (2.18) ***§§§ BSI score 6.17 (4.05) 7.25 (4.45) ** 8.58 (4.65) ***§§§ Exacerbations (mild/moderate) 1.3 (1.63) 1.57 (1.72) 1.61 (1.62) Hospitalization for exacerbations 0.32 (0.68) 0.6 (1.49) * 0.89 (1.47) ***§ Charlson Index 1.55 (1.26) 1.81 (1.56) 2.02 (1.69) ** Chronic colonization by PA, N (%) 42 (18.8) 127 (27.5) * 88 (30.8) ***§ Radiological extension 2.8 (1.4) 2.9 (1.4) 3 (1.5) Smoking history Never smokers, N (%) 146 (65.2) 309 (67) 192 (67.1) Current smokers, N (%) 16 (7.1) 36 (7.8) 16 (5.6) Ex-smokers, N (%) 62 (27.7) 116 (25.2) 78 (27.3) Packs-year, x(SD) 7.85 (16.27) 8.13 (17.61) 8.82 (17.34) Lung function, x(SD) FEV1 , % predicted 86 (20) 78 (21) ** 73 (23) ***§§ FVC, % predicted 92 (38) 88 (23) 85 (31) ***§ FEV1 /FVC, % 74 (30) 71 (23) ** 69 (27) ***§ DLCO , % predicted 90 (40) 93 (30) 77 (20) **§§§ KCO , % predicted 87 (1) 87 (1) 80 (1) RV, % predicted 143 (74) 141 (41) 153 (36) TLC, % predicted 108 (58) 103 (48) 101 (48) * RV/TLC, % 53 (31) 50 (23) 57 (29) § Continuous variables are presented as mean (standard deviation), while categorical variables are presented as the number of patients in each group along with the percentage for the study group. Definition of abbreviations: x, mean; SD, standard deviation; N, number; kg, kilograms; m, meters; BMI, body mass index; FACED: F, FEV1 ; A, Age; C, Chronic colonization by Pseudomonas aeruginosa; E, radiologic extension; D, dyspnea; EFACED: FACED adding the exacerbation in the previous year; BSI: bronchiectasis severity index; FEV1 , forced expiratory volume in the first second; FVC, forced vital capacity; RV, residual volume; TLC, total lung capacity; DLco, carbon monoxide transfer; KCO , Krogh transfer factor. Statistical analyses and significance: *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001: Comparisons were assessed between either group # 3 or group # 2 and group # 1 (less severe); §, p ≤ 0.05; §§, p ≤ 0.01; §§§, p ≤ 0.001: Comparisons between groups # 3 and # 2. 3.2. Systemic Inflammatory and Nutritional Parameters in the Three Cluster Patients Importantly, the levels of leukocytes and other inflammatory cells were significantly greater in cluster # 3 patients than in those represented in clusters # 1 and # 2 (Table 4 and Figure 5A,B and Figure 6A,B). Moreover, levels of other inflammatory parameters such as CRP, ESR, and fibrinogen were also significantly higher in cluster # 3 patients than in clusters # 1 and 2 (Table 4). CRP concentrations significantly and positively correlated with FACED, EFACED, BSI, exacerbations, hospitalizations, and neutrophil levels in all Biomedicines 2022, 10, 225 9 of 18 the patients as a whole (all three clusters, Figure 7A). Moreover, when patients from clusters # 1 and # 2, but not cluster # 3, were analyzed separately, significant associations were also detected between CRP levels and the variables FACED, EFACED, neutrophils, and eosinophils (cluster # 1) and FACED, EFACED, BSI, and eosinophils (cluster # 2), as depicted in Figure 7B,C, respectively. Protein levels inversely correlated with FACED, EFACED, BSI, exacerbations, hospitalizations, and neutrophil levels when patients were analyzed as a whole (clusters # 1–3) and when cluster # 3 was analyzed independently (Figure 7A,D, respectively). When cluster # 2 patients were analyzed independently, significant weaker correlations were observed between total protein levels and FACED, EFACED, BSI, and the number of exacerbations and hospitalizations (Figure 7C). The number of platelets along with hemoglobin and hematocrit levels was also greater in cluster # 3 patients than in clusters # 1 and # 2 (Table 4). Blood glucose levels were also higher in cluster # 3 patients than in clusters # 1 and # 2 (Table 4). Concentrations of total proteins and albumin, however, were significantly lower in cluster # 3 patients than in clusters # 1 and # 2 (Table 4). The analysis of the potential differences among the three clusters when COPD patients were excluded yielded similar results as shown in Table 5 and Figure 5A,B and Figure 6A,B. Table 4. Systemic inflammatory and nutritional parameters in the study patients according to clusters analyses. Cluster Cluster Cluster Group # 1 Group # 2 Group # 3 N = 242 N = 515 N = 335 Female, N/male, N 184/58 330/185 213/122 Blood parameters, x(SD) Total leukocytes, ×103 /µL 6.2 (1.8) 7.2 (2.1) *** 9.2 (3.5) ***§§§ Total neutrophils, ×103 /µL 2.9 (0.9) 4.2 (1.5) *** 6.9 (3.1) ***§§§ Neutrophils, % 46.6 (6.7) 58 (6.4) *** 73.3 (8.3) ***§§§ Total lymphocytes, ×103 /µL 2.5 (0.8) 2.1 (0.7) *** 1.5 (0.8) ***§§§ Lymphocytes, % 41 (6.2) 29.4 (5.8) *** 16.9 (6.7) ***§§§ Total eosinophils, ×103 /µL 0.2 (0.1) 0.3 (0.2) *** 0.1 (0.1) §§§ Eosinophils, % 2.6 (1.3) 3.5 (2.9) *** 1.6 (1.5) ***§§§ Platelets, ×103 /µL 245.8 (68) 250.7 (70.5) 264.1 (86.7) *§ CRP, mg/dL 2 (4.2) 1.8 (2.8) 7.3 (11.4) ***§§§ ESR, mm/h 15.9 (13.5) 15 (14.1) 25.9 (22.2) ***§§§ Fibrinogen, mg/dL 388.1 (109.7) 414 (125.3) 482 (153.8) ***§§§ Hemoglobin, g/dL 13.7 (1.1) 13.9 (1.6) 13.2 (1.6) **§§§ Hematocrit, % 41.6 (3.2) 42.3 (4.5) 40.4 (4.7) **§§§ Glucose, mg/dL 94.2 (16) 97.9 (27) 110.8 (46.9) ***§§§ Creatinine, mg/dL 0.8 (0.5) 0.8 (0.2) 0.9 (0.6) § Total proteins, g/dL 7.13 (0.54) 7.01 (0.62) 6.96 (0.66) * Albumin, g/dL 4.27 (0.36) 4.22 (0.43) 4.11 (0.49) **§ Continuous variables are presented as mean (standard deviation) for the study group. Definition of abbreviations: x, mean; SD, standard deviation; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; µL, microliter; dL, deciliter; mg, milligrams; mm, millimeters; h, hour; ng, nanogram. Statistical analyses and significance: *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001: Comparisons were assessed between either group # 3 or group # 2 and group # 1 (less severe); §, p ≤ 0.05; §§§, p ≤ 0.001: Comparisons between groups # 3 and # 2. Table 5. Systemic inflammatory and nutritional parameters in the three clusters of patients exclud- ing those with COPD. Cluster Cluster Cluster Biomedicines 2022, 10, 225 10 of 18 Group # 1 Group # 2 Group # 3 N = 224 N = 461 N = 286 Female, N/male, N 178/46 313/148 203/83 Table 5. Systemic inflammatory and nutritional parameters in the three clusters of patients excluding Blood parameters, x (SD) those with COPD. Total leukocytes, ×103/μL 6.12 (1.73) 7.09 (2.16) *** 8.95 (3.34) ***§§§ Total neutrophils, ×103/μL 2.86 (0.93) Cluster 4.17 Cluster(1.51) *** 6.68 (2.99) ***§§§ Cluster Neutrophils, % Group # 1 46.5 (6.7) Group # 2 57.84 (6.45) *** Group 72.9 (8.1) # 3 ***§§§ N = 224 N = 461 N = 286 Total lymphocytes, ×103/μL 2.5 (0.81) 2.07 (0.66) *** 1.49 (0.77 )***§§§ Female, N/male, N 178/46 313/148 203/83 Lymphocytes, % 41.14 (6.17) 29.5 (5.84) *** 17.34 (6.64) ***§§§ Blood parameters, x(SD) Total Total eosinophils, leukocytes, ×10 /μL ×103 /µL 3 0.16 (0.08) 6.12 (1.73) 7.09 0.26 (2.16)(0.23) *** 0.13 ***§§§ 8.95 (3.34) (0.12) Total neutrophils, ×103 % Eosinophils, /µL 2.62 (1.35) 2.86 (0.93) 3.56 4.17 (2.92) (1.51) *** *** 1.65 6.68 (1.47) (2.99) ***§§§ ***§§§ Neutrophils, % Platelets, ×10 /μL 3 46.5 (6.7) 246 (68) 57.84 (6.45) 252 (70) *** 72.9 (8.1) ***§§§ 267 (88) **§ Total lymphocytes, ×103 /µL 2.5 (0.81) 2.07 (0.66) *** 1.49 (0.77 )***§§§ CRP, mg/dL 1.89 (3.95) 1.78 (2.75) 7.09 (10.91) ***§§§ Lymphocytes, % 41.14 (6.17) 29.5 (5.84) *** 17.34 (6.64) ***§§§ ESR, mm/h Total eosinophils, ×103 /µL 0.16 15.82 (0.08) (13.68) 15.31 0.26 (13.75) (0.23) 25.80.13(22.39) (0.12) ***§§§ Fibrinogen, Eosinophils, %mg/dL 388 (112) 2.62 (1.35) 412 (127) 3.56 (2.92) *** 1.65474 (154) (1.47) ***§§§ ***§§§ Platelets, × 103 /µL 246 (68) 252 (70) 267 (88) **§ Hemoglobin, g/dL 13.67 (1.04) 13.82 (1.55) 13.14 (1.54) ***§§§ CRP, mg/dL 1.89 (3.95) 1.78 (2.75) 7.09 (10.91) ***§§§ Hematocrit, ESR, mm/h % 41.54 15.82 (13.68) (3.1) 42.08 15.31 (13.75) (4.32) 40.17 25.8 (22.39)(4.48) **§§§ ***§§§ Glucose, Fibrinogen, mg/dL mg/dL 388 (112)95 (16) 412 97 (26) (127) 474108(154)(46) ***§§§ ***§§§ Hemoglobin, g/dL Creatinine, mg/dL 13.67 (1.04) 0.79 (0.56) 13.82 (1.55) 0.8 (0.22) 13.14 (1.54) ***§§§ 0.89 (0.61)§ Hematocrit, % 41.54 (3.1) 42.08 (4.32) 40.17 (4.48) **§§§ Total proteins, g/dL 7.13 (0.54) 7.03 (0.61) 7.03 (0.6) Glucose, mg/dL 95 (16) 97 (26) 108 (46) ***§§§ Albumin, Creatinine, mg/dLg/dL 4.27 (0.37) 0.79 (0.56) 0.84.24 (0.22)(0.42) 0.894.17 (0.44) (0.61)§ Total proteins, g/dL Continuous7.13 variables (0.54) are presented as mean7.03 (standard (0.61) deviation) for the study 7.03 group. (0.6) Definition of Albumin, g/dL abbreviations: 4.27x(0.37) , mean; SD, standard deviation; 4.24 (0.42) CRP, C-reactive protein; ESR, 4.17erythrocyte (0.44) sedimen- tation rate; variables Continuous μL, microliter; dL, as are presented deciliter; mg, milligrams; mean (standard deviation) formm, millimeters; the study h, hour; group. Definition ng, nanogram. of abbreviations: x, mean; SD, Statistical standardand analyses deviation; CRP, C-reactive significance: protein; *, p ≤ 0.05; **, p ESR, erythrocyte ≤ 0.01; sedimentation ***, p ≤ 0.001: rate; µL,were Comparisons microliter; assessed dL, deciliter; mg, milligrams; mm, millimeters; h, hour; ng, nanogram. Statistical analyses and significance: between **, p ≤ 0.01; ***, p ≤ 0.001: Comparisons were assessed between either group # 3 or group # 2 and group # 1 §§§, either group # 3 or group # 2 and group # 1 (less severe); §, p ≤ 0.05; §§, p ≤ 0.01; (less p ≤ 0.001: Comparisons between groups # 3 and # 2. severe); §, p ≤ 0.05; §§§, p ≤ 0.001: Comparisons between groups # 3 and # 2. (A) Mean Mean values 3 Figure 5. Figure 5. (A) values andandstandard standarddeviation deviation of of total number total numberof neutrophils of neutrophils(10 /microL) (103/microL) among cluster among cluster 1, cluster cluster 2, 2, and andcluster cluster3 3ininallallthe study the study patients. (B)(B) patients. MeanMeanvalues andand values standard standard deviation ofof total number 3 /microL) deviation numberof oflymphocytes lymphocytes(10 (10 3/microL)among amongcluster 1, 1, cluster cluster 2, and cluster cluster 2, and 3 in 3 in cluster all the study patients. Statistical significance: ***, p ≤ 0.001: Comparisons were assessed between either group # 3 or group # 2 and group # 1 (less severe §§§, p ≤ 0.001: Comparisons between groups # 3 and # 2. Biomedicines Biomedicines 2022, 2022, 10, 10, x FOR x FOR PEER PEER REVIEW REVIEW 11 of 18 11 of 18 all the all study patients. the study Statistical patients. significance: Statistical ***, p ≤***, significance: 0.001: p ≤ Comparisons were assessed 0.001: Comparisons betweenbetween were assessed Biomedicines 2022, 10, 225 either group either # 3 or# group group # 2 and 3 or group # 2group # 1 (less and group # 1severe §§§, p ≤§§§, (less severe 0.001: p ≤Comparisons betweenbetween 0.001: Comparisons groups 11 ofgroups 18 # 3 and # 2. # 2. # 3 and Figure 6. (A) Figure 6. Mean values (A) Mean and standard values deviation and standard of total deviation ofnumber of neutrophils total number (103/microL) of neutrophils in the in (103 /microL) three clusters Figure 6. of patients (A) Mean excluding values and those withdeviation standard COPD. (B)of Meannumber total values and of standard deviation neutrophils (10 of in the 3/microL) the three clusters of patients excluding those with COPD. (B) Mean values and standard deviation totalthree number of lymphocytes clusters (103/microL) of patients excluding in those the three with COPD.clusters (B) of patients Mean excluding values and those standard with deviation of of total number of lymphocytes (1033 /microL) in the three clusters of patients excluding those with COPD. totalStatistical number significance: of lymphocytes***, p (10 ≤ 0.001: Comparisons /microL) wereclusters in the three assessedofbetween either patients group #those excluding 3 with COPD. or group # 2Statistical and group significance: # 1 (less severe ≤ 0.001: ***, p §§§, Comparisons were between assessed between #either group # 3 or COPD. Statistical significance: ***, p ≤ p0.001: ≤ 0.001: Comparisons Comparisons groups were assessed 3 andeither between # 2. group #3 group # 2 and group # 1 (less severe §§§, p ≤ 0.001: Comparisons between groups # 3 and # 2. or group # 2 and group # 1 (less severe §§§, p ≤ 0.001: Comparisons between groups # 3 and # 2. Figure 7. Cont. Biomedicines 2022, 10, x FOR PEER REVIEW 12 of 18 Biomedicines 2022, 10, 225 12 of 18 Figure 7. Cont. Biomedicines 2022, 10, x FOR PEER REVIEW 13 of 18 Biomedicines 2022, 10, 225 13 of 18 Biomedicines 2022, 10, x FOR PEER REVIEW 13 of 18 Figure Figure 7. Correlation 7. Correlation matrix matrix of the of the disease disease severity severity andand analytical analytical variables, variables, in which in which the the positive positive Figure correlations 7. Correlation areare matrix represented of in in the blue, disease while severity the and analytical variables, in which the positive correlations represented blue, while thenegative negativecorrelations correlationsare arerepresented represented inin red: (A) all the correlations are represented in blue, while the negative correlations are represented in red: (A) all the study study patients, (B) patients in cluster cluster ## 1, 1, (C) (C)patients patientsinincluster cluster##2,2,and and(D) (D)patients patientsinincluster cluster# # 3. the study patients, (B) patients in cluster # 1, (C) patients in cluster # 2, and (D) patients in cluster # 3. The intersection within the circle represents a p-value > 0.05. The color intensity and the size of of The 3. Theintersection intersectionwithin withinthe thecircle representsaap-value circlerepresents p-value >> 0.05. The color 0.05. The colorintensity intensityand andthe thesize size of the the circle circle are are proportional proportionalto tothe the correlation correlationcoefficients, coefficients,as asindicated indicatedininthe theY-axis Y-axisononthe theright-hand right-handside the circle are proportional to the correlation coefficients, as indicated in the Y-axis on right-hand sideside of the graph. ofgraph. the graph. of the 3.3.3.3. Multivariate Analysis 3.3. Multivariate MultivariateAnalysis Analysis After adjusting After for potential confounders, an association ofof EFACEDscore score withclus- clus- After adjusting for adjusting for potential potential confounders, confounders, an anassociation association ofEFACED EFACED score with with clustered tered risk tered variable risk variable was demonstrated was demonstrated (OR = 1.136, CI 95%: 1.013–1.274; p-value = 0.029, risk variable was demonstrated (OR =(OR = 1.136, 1.136, CI 1.013–1.274; CI 95%: 95%: 1.013–1.274; p-value p-value = 0.029, = 0.029, Figure 6), Figure Figure6), 6), demonstrating demonstratingthat thatpatients patientswith withgreater greater EFACED scores were EFACED scores weremore morelikely likelytoto demonstrating that patients with greater EFACED scores were more likely to fall within fallfall within withinhigher higherclusters clusters(1–3). (1–3).Moreover, Moreover,associations associations of ESR ESR and and total totalprotein proteinlevels levels higher clusters (1–3). Moreover, associations of ESR and total protein levels with clustered with clustered with clusteredrisk variable risk variablewere werealso alsodemonstrated demonstrated (OR (OR = 1.025, 1.025, CI CI 95%: 95%:1.009–1.042; 1.009–1.042;p-p- risk variable were also demonstrated (OR = 1.025, CI 95%: 1.009–1.042; p-value = 0.003; value value = 0.003; = 0.003; OROR = 0.647, = 0.647, CICI95%: 95%:0.419–0.999; 0.419–0.999;p-value p-value == 0.049, respectively, respectively,Figure Figure8). 8). OR = 0.647, CI 95%: 0.419–0.999; p-value = 0.049, respectively, Figure 8). Figure Figure 8. 8. Multivariate Multivariateordinal ordinal logistic logistic regression, regression, in in which which the the outcome outcome variable variable was was clusters clusters (or- (ordered dered Figure 8. from 1, the Multivariatelowest level, ordinal to 3, logisticthe highest regression,level) in was which used the to assess outcome the potential variable was associations clusters from 1, the lowest level, to 3, the highest level) was used to assess the potential associations (or- of EFACED dered from 1, the lowest level, to 3, the highest level) was used to assess the potential associations Biomedicines 2022, 10, 225 14 of 18 score with each of the clusters. The following clinically meaningful confounders were considered: Charlson index, COPD, platelets, ESR, fibrinogen, creatinine, total protein concentration, and albumin levels. The multivariate regression odds ratio (OR) is represented as a black dot in each of the confounders along with the corresponding confidence intervals, which were depicted in a forest plot. In the Y-axis, all the confounder variables are plotted, while in the X-axis, the width of the confidence intervals is represented. The one value is represented as a dotted vertical line. 4. Discussion Chronic respiratory diseases are characterized by a complex interaction between the airways pathobiology and systemic manifestations, such as inflammation and nutritional and metabolic abnormalities. Specifically, in a great number of bronchiectasis patients, microbial colonization plays a relevant role in the perpetuation of inflammatory events in the airways and systemic compartment of the patients, further contributing to increasing the number of exacerbations, disease progression, and prognosis [5,35,36]. Identification of the bronchiectasis patients who are placed at a greater risk to progress more rapidly and/or to experience exacerbations is still a major challenge in clinical settings. Currently available tools do not always help identify these specific groups of patients. Thus, other instruments need to be developed with the goal to define patterns of behavior from large data sets, which can be translated into well-structured groups of patients that can be specifically targeted in clinical settings. In the present study, using a data mining approach, five different clusters of patients have been identified in an initial stage. A refined analysis allowed us to merge a few of the clusters into three clinically different ones, whose specific clinical features have been analyzed from different standpoints. We believe that these are novel results that will have clinical implications. Patients represented in cluster # 3 (n = 335) were older and had a significantly more severe disease as measured by all three disease scores (BSI, FACED, and EFACED) than patients in clusters # 1 and # 2. It is worth mentioning that in the three clusters, the behavior of patients with mild bronchiectasis was similar for all three indices (BSI, FACED, and EFACED). Nonetheless, in the group of patients with a moderate disease, the cluster distribution profile varied between the BSI score on the one hand and FACED and EFACED scores, on the other. The variables contributing to each specific score may account for the differences in disease severity distribution across the three identified clus- ters [30–32]. In addition, other important parameters such as Charlson index, the number of exacerbations and hospitalizations, the prevalence of COPD associated with bronchiec- tasis, and colonization by PA were also greater in cluster # 3 patients than in the other two clusters. These findings are in line with those recently reported in another investigation in which bronchiectasis patients were also subdivided into three major clusters on the basis of distinct airway phenotypic features [37,38]. The novelty in our investigation relies on the definition of three clinically relevant clusters based on the analysis of systemic variables such as neutrophils, lymphocytes, eosinophils, CRP, and hemoglobin. Lung function parameters were also significantly impaired in cluster # 3 patients compared to patients classified in the other two clusters. Such an impairment in the degree of the airway obstruction and diffusion capacity was independent of the presence of COPD, as statistically significant differences were maintained among the three study clusters after excluding the COPD patients from the analysis in all three clusters. These results confirm that the differential phenotypes are associated with bronchiectasis per se rather than with COPD or the degree of the airflow limitation as also previously demonstrated [14,19]. A significant rise in the levels of cellular and molecular inflammatory parameters was observed in cluster # 3 patients compared to patients in clusters # 1 and # 2. These findings reveal that disease severity was probably associated with the degree of systemic inflammation in patients belonging to cluster # 3. In fact, significant positive associations were found between several inflammatory parameters (e.g., neutrophils and CRP) and disease severity scores as well as with the number of exacerbations and hospitalizations. Associations between disease severity and systemic inflammation have been previously Biomedicines 2022, 10, 225 15 of 18 reported [39–41]. The strong association between inflammation and disease progression and severity has also been confirmed in the present study. Importantly, the differential phenotypic characteristics of the three groups of patients identified using a clustering approach were further confirmed by the multivariate ordinal logistic regression analysis, in which the EFACED score was associated with the clus- tered risk variable (OR: 1.136), indicating that patients in cluster # 3 were more severe than patients in the other two clusters. These are clearly novel findings that validate the methodological approaches used in the present study. Study Critique A major strength in the investigation is the use of a large cohort of bronchiectasis patients from whom data have been obtained from 43 participating centers in Spain. Despite these advantages, several limitations also apply. The study of biological insights and disease pathogenesis would have required a different investigational approach, in which biological samples should be obtained from different compartments in the patients. The RIBRON, however, relies on clinical and analytical data obtained from the patients. Future investigations of different approaches based on the use of biological samples from the airways and lungs should be designed with the aim to elucidate specific mechanisms that are involved in the pathobiology of disease severity and progression in patients with bronchiectasis, especially in view of the profile of clinical and analytical parameters that have been analyzed in the current study. An external validation cohort aimed to further confirm the results reported herein would also be a matter of investigation in the near future. Differences between male and female patients were also analyzed previously in this registry [14]. Thus, these differences have not been analyzed in the present investigation. As abovementioned in the tables, the number of female patients was greater than that of male patients. Another limitation is related to the lack of information on the potential coloniza- tion by pathogens other than PA in the airways and lungs of the study participants. As that information was not obtained from all the patients, it has not been analyzed in the present study. Another limitation is related to the lack of additional analysis of biological parameters including cytokines (interleukin-6 and tumor necrosis factor-alpha) that could have been comprised in the cluster analysis. However, as those parameters were not collected in the RIBRON registry, they were not available for the cluster analysis. Future studies should aim to assess whether other systemic parameters may also contribute to differentiate several clinical phenotypes in patients with bronchiectasis. 5. Conclusions Clustering analyses of systemic blood parameters identified three differential clinical phenotypes of bronchiectasis patients that were further confirmed by a logistic regression. These analyses revealed that disease severity was associated with the clusters, particularly with cluster # 3. Patients included in this group had a more severe disease, worse lung function, and a rise in systemic inflammatory parameters compared to patients in clusters # 1 and # 2. These findings were independent of the presence of COPD. Clustering analysis of systemic parameters offers a powerful tool to better characterize patients with bronchiec- tasis. These results have clinical implications in the management of the complexity and heterogeneity of bronchiectasis patients. Author Contributions: Study conception and design: E.B., J.G.-O. and X.W.; Patient assessment and recruitment: X.W., C.V., Y.D., C.O., R.G. (Rosa Girón), M.G.-C., L.M., O.S., R.G. (Rafael Golpe), R.M., J.R.-L., C.P., M.A.M.-G., J.L.R., D.d.l.R. and E.B.; Statistical analyses and data interpretation: J.G.-O., X.D., X.W. and E.B.; manuscript drafting and intellectual input: E.B., J.G.-O., M.A.M.-G., X.D. and X.W.; manuscript writing final version: E.B. All authors have read and agreed to the published version of the manuscript. Biomedicines 2022, 10, 225 16 of 18 Funding: This study has been supported by FIS 21/00215 (FEDER, ISC-III) & CIBERES (ISC-III), SEPAR 2020, and ZAMBON-PHARMA (Spain). JGO was supported by the Spanish Ministry of Science and Innovation and FEDER (project PGC 2018-101251-B-I00 and Maria de Maeztu Programme for Units of Excellence in R&D, grant CEX2018-000792-M), and by the Generalitat de Catalunya (ICREA Academia programme). Institutional Review Board Statement: The research followed the guidelines of the World Medical Association for Research in Humans (Seventh revision of the Declaration of Helsinki, Fortaleza, Brazil, 2013). Ethics approval was obtained from the Ethics Committee at the Hospital Josep Trueta Girona (# 001-2012, Hospital Universitari Dr. Josep Trueta, Girona, Spain) in the coordinating center and in the local participating centers. Informed Consent Statement: All patients signed the informed written consent to participate in the registry. The information remained confidential at all times and no personal information related to any of the participants was introduced in the registry. Data Availability Statement: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Conflicts of Interest: The authors declare no conflict of interest. References 1. Martinez-Garcia, M.A.; Agustí, A. Heterogeneidad y complejidad del síndrome bronquiectasias: Un reto pendiente. Arch. Bronconeumol. 2019, 55, 187–188. [CrossRef] 2. Martinez-Garcia, M.A.; de la Rosa, D.; Cantón, R.; Olveira, C.; Máiz-Carro, L.; Girón, R.; Prados, C.; Blanco, M. 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