Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study
Differential phenotypic characteristics using data mining approaches were defined in a large cohort of patients from the Spanish Online Bronchiectasis Registry (RIBRON). Three differential phenotypic clusters (hierarchical clustering, scikit-learn library for Python, and agglomerative methods) accor...
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| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/10/2/225 |
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| author | Xuejie Wang Carmen Villa Yadira Dobarganes Casilda Olveira Rosa Girón Marta García-Clemente Luis Máiz Oriol Sibila Rafael Golpe Rosario Menéndez Juan Rodríguez-López Concepción Prados Miguel Angel Martinez-García Juan Luis Rodriguez David de la Rosa Xavier Duran Jordi Garcia-Ojalvo Esther Barreiro |
| author_facet | Xuejie Wang Carmen Villa Yadira Dobarganes Casilda Olveira Rosa Girón Marta García-Clemente Luis Máiz Oriol Sibila Rafael Golpe Rosario Menéndez Juan Rodríguez-López Concepción Prados Miguel Angel Martinez-García Juan Luis Rodriguez David de la Rosa Xavier Duran Jordi Garcia-Ojalvo Esther Barreiro |
| author_sort | Xuejie Wang |
| collection | DOAJ |
| description | Differential phenotypic characteristics using data mining approaches were defined in a large cohort of patients from the Spanish Online Bronchiectasis Registry (RIBRON). Three differential phenotypic clusters (hierarchical clustering, scikit-learn library for Python, and agglomerative methods) according to systemic biomarkers: neutrophil, eosinophil, and lymphocyte counts, C reactive protein, and hemoglobin were obtained in a patient large-cohort (<i>n</i> = 1092). Clusters #1–3 were named as mild, moderate, and severe on the basis of disease severity scores. Patients in cluster #3 were significantly more severe (FEV<sub>1</sub>, 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 <i>Pseudomonas aeruginosa</i> and COPD prevalence were higher in cluster # 3 than in cluster #1. Airflow limitation and diffusion capacity were reduced in cluster #3 compared to clusters #1 and #2. Multivariate ordinal logistic regression analysis further confirmed these results. Similar results were obtained after excluding COPD patients. Clustering analysis offers a powerful tool to better characterize patients with bronchiectasis. These results have clinical implications in the management of the complexity and heterogeneity of bronchiectasis patients. |
| first_indexed | 2024-03-09T22:33:58Z |
| format | Article |
| id | doaj.art-8b50ad1ce57942c2b053300bf9f8487b |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T22:33:58Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-8b50ad1ce57942c2b053300bf9f8487b2023-11-23T18:52:16ZengMDPI AGBiomedicines2227-90592022-01-0110222510.3390/biomedicines10020225Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort StudyXuejie Wang0Carmen Villa1Yadira Dobarganes2Casilda Olveira3Rosa Girón4Marta García-Clemente5Luis Máiz6Oriol Sibila7Rafael Golpe8Rosario Menéndez9Juan Rodríguez-López10Concepción Prados11Miguel Angel Martinez-García12Juan Luis Rodriguez13David de la Rosa14Xavier Duran15Jordi Garcia-Ojalvo16Esther Barreiro17Lung Cancer and Muscle Research Group, Pulmonology Department, Hospital del Mar-IMIM, Parc de Salut Mar, PRBB, C/Dr. Aiguader, 88, 08003 Barcelona, SpainRespiratory Department, Clínica Fuensanta, 28015 Madrid, SpainRespiratory Department, Clínica Fuensanta, 28015 Madrid, SpainRespiratory Department, Hospital Regional Universitario de Málaga, 29003 Málaga, SpainRespiratory Department, Instituto de Investigación Sanitaria, Hospital Universitario de la Princesa, 28015 Madrid, SpainRespiratory Department, Hospital Universitario Central de Asturias, 33071 Oviedo, SpainRespiratory Department, Hospital Ramon y Cajal, 28015 Madrid, SpainRespiratory Department, Hospital Clínic, 08035 Barcelona, SpainRespiratory Department, Hospital Lucus Augusti, 27080 Lugo, SpainRespiratory Department, Hospital Universitario y Politécnico La Fe, 46003 Valencia, SpainRespiratory Department, Hospital San Agustin, 33401 Avilés, SpainRespiratory Department, Hospital la Paz, 28015 Madrid, SpainCentro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), 28015 Madrid, SpainRespiratory Department, Hospital Clínico San Carlos, 28015 Madrid, SpainRespiratory Department, Hospital Santa Creu I Sant Pau, 08035 Barcelona, SpainScientific and Technical Department, Hospital del Mar-IMIM, 08035 Barcelona, SpainDepartment of Health and Experimental Sciences (CEXS), Universitat Pompeu Fabra (UPF), 08035 Barcelona, SpainLung Cancer and Muscle Research Group, Pulmonology Department, Hospital del Mar-IMIM, Parc de Salut Mar, PRBB, C/Dr. Aiguader, 88, 08003 Barcelona, SpainDifferential phenotypic characteristics using data mining approaches were defined in a large cohort of patients from the Spanish Online Bronchiectasis Registry (RIBRON). Three differential phenotypic clusters (hierarchical clustering, scikit-learn library for Python, and agglomerative methods) according to systemic biomarkers: neutrophil, eosinophil, and lymphocyte counts, C reactive protein, and hemoglobin were obtained in a patient large-cohort (<i>n</i> = 1092). Clusters #1–3 were named as mild, moderate, and severe on the basis of disease severity scores. Patients in cluster #3 were significantly more severe (FEV<sub>1</sub>, 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 <i>Pseudomonas aeruginosa</i> and COPD prevalence were higher in cluster # 3 than in cluster #1. Airflow limitation and diffusion capacity were reduced in cluster #3 compared to clusters #1 and #2. Multivariate ordinal logistic regression analysis further confirmed these results. Similar results were obtained after excluding COPD patients. Clustering analysis offers a powerful tool to better characterize patients with bronchiectasis. These results have clinical implications in the management of the complexity and heterogeneity of bronchiectasis patients.https://www.mdpi.com/2227-9059/10/2/225non-cystic fibrosis bronchiectasisblood neutrophileosinophillymphocyte countsC reactive proteinhemoglobin |
| spellingShingle | Xuejie Wang Carmen Villa Yadira Dobarganes Casilda Olveira Rosa Girón Marta García-Clemente Luis Máiz Oriol Sibila Rafael Golpe Rosario Menéndez Juan Rodríguez-López Concepción Prados Miguel Angel Martinez-García Juan Luis Rodriguez David de la Rosa Xavier Duran Jordi Garcia-Ojalvo Esther Barreiro Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study Biomedicines non-cystic fibrosis bronchiectasis blood neutrophil eosinophil lymphocyte counts C reactive protein hemoglobin |
| title | Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study |
| title_full | Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study |
| title_fullStr | Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study |
| title_full_unstemmed | Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study |
| title_short | Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study |
| title_sort | systemic inflammatory biomarkers define specific clusters in patients with bronchiectasis a large cohort study |
| topic | non-cystic fibrosis bronchiectasis blood neutrophil eosinophil lymphocyte counts C reactive protein hemoglobin |
| url | https://www.mdpi.com/2227-9059/10/2/225 |
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