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Pan CX, He ZF, Lin SZ, Yue JQ, Chen ZM, Guan WJ. Clinical Characteristics and Outcomes of the Phenotypes of COPD-Bronchiectasis Association. Arch Bronconeumol 2024; 60:356-363. [PMID: 38714385 DOI: 10.1016/j.arbres.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/16/2024] [Accepted: 04/03/2024] [Indexed: 05/09/2024]
Abstract
INTRODUCTION Although COPD may frequently co-exist with bronchiectasis [COPD-bronchiectasis associated (CBA)], little is known regarding the clinical heterogeneity. We aimed to identify the phenotypes and compare the clinical characteristics and prognosis of CBA. METHODS We conducted a retrospective cohort study involving 2928 bronchiectasis patients, 5158 COPD patients, and 1219 patients with CBA hospitalized between July 2017 and December 2020. We phenotyped CBA with a two-step clustering approach and validated in an independent retrospective cohort with decision-tree algorithms. RESULTS Compared with patients with COPD or bronchiectasis alone, patients with CBA had significantly longer disease duration, greater lung function impairment, and increased use of intravenous antibiotics during hospitalization. We identified five clusters of CBA. Cluster 1 (N=120, CBA-MS) had predominantly moderate-severe bronchiectasis, Cluster 2 (N=108, CBA-FH) was characterized by frequent hospitalization within the previous year, Cluster 3 (N=163, CBA-BI) had bacterial infection, Cluster 4 (N=143, CBA-NB) had infrequent hospitalization but no bacterial infection, and Cluster 5 (N=113, CBA-NHB) had no hospitalization or bacterial infection in the past year. The decision-tree model predicted the cluster assignment in the validation cohort with 91.8% accuracy. CBA-MS, CBA-BI, and CBA-FH exhibited higher risks of hospital re-admission and intensive care unit admission compared with CBA-NHB during follow-up (all P<0.05). Of the five clusters, CBA-FH conferred the worst clinical prognosis. CONCLUSION Bronchiectasis severity, recent hospitalizations and sputum culture findings are three defining variables accounting for most heterogeneity of CBA, the characterization of which will help refine personalized clinical management.
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Affiliation(s)
- Cui-Xia Pan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory and Critical Care Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhen-Feng He
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory and Critical Care Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Sheng-Zhu Lin
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory and Critical Care Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jun-Qing Yue
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory and Critical Care Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhao-Ming Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory and Critical Care Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wei-Jie Guan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory and Critical Care Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China; Guangzhou National Laboratory, Guangzhou, Guangdong, China.
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Guida G, Bertolini F, Carriero V, Levra S, Sprio AE, Sciolla M, Orpheu G, Arrigo E, Pizzimenti S, Ciprandi G, Ricciardolo FLM. Reliability of Total Serum IgE Levels to Define Type 2 High and Low Asthma Phenotypes. J Clin Med 2023; 12:5447. [PMID: 37685515 PMCID: PMC10488214 DOI: 10.3390/jcm12175447] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023] Open
Abstract
Background: High total IgE levels are weak predictors of T2High and have been reported in nonallergic asthma. Therefore, the role of total serum IgE (IgE) in the T2High phenotype is still debated. Objective: This study investigated the reliability of stratifying asthmatics into IgEHigh and IgELow within the T2High and T2Low phenotypes. Methods: This cross-sectional single-center study investigated the association of clinical, functional, and bio-humoral parameters in a large asthmatic population stratified by IgE ≥ 100 kU/L, allergen sensitization, B-EOS ≥ 300/µL, and FENO ≥ 30 ppb. Results: Combining T2 biomarkers and IgE identifies (1) T2Low-IgELow (15.5%); (2) T2Low-IgEHigh (5.1%); (3) T2High-IgELow (33.6%); and T2High-IgEHigh (45.7%). T2Low-IgELow patients have more frequent cardiovascular and metabolic comorbidities, a higher prevalence of emphysema, and higher LAMA use than the two T2High subgroups. Higher exacerbation rates, rhinitis, and anxiety/depression syndrome characterize the T2Low-IgEHigh phenotype vs. the T2Low-IgELow phenotype. Within the T2High, low IgE was associated with female sex, obesity, and anxiety/depression. Conclusions: High IgE in T2Low patients is associated with a peculiar clinical phenotype, similar to T2High in terms of disease severity and nasal comorbidities, while retaining the T2Low features. IgE may represent an additional biomarker for clustering asthma in both T2High and T2Low phenotypes rather than a predictor of T2High asthma "per se".
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Affiliation(s)
- Giuseppe Guida
- Severe Asthma and Rare Lung Disease Unit, San Luigi Gonzaga University Hospital, Orbassano, 10043 Turin, Italy; (S.P.); (F.L.M.R.)
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy; (F.B.); (V.C.); (S.L.); (M.S.); (G.O.); (E.A.)
| | - Francesca Bertolini
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy; (F.B.); (V.C.); (S.L.); (M.S.); (G.O.); (E.A.)
| | - Vitina Carriero
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy; (F.B.); (V.C.); (S.L.); (M.S.); (G.O.); (E.A.)
| | - Stefano Levra
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy; (F.B.); (V.C.); (S.L.); (M.S.); (G.O.); (E.A.)
| | - Andrea Elio Sprio
- Department of Research, ASOMI College of Sciences, 19112 Marsa, Malta;
| | - Martina Sciolla
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy; (F.B.); (V.C.); (S.L.); (M.S.); (G.O.); (E.A.)
| | - Giulia Orpheu
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy; (F.B.); (V.C.); (S.L.); (M.S.); (G.O.); (E.A.)
| | - Elisa Arrigo
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy; (F.B.); (V.C.); (S.L.); (M.S.); (G.O.); (E.A.)
| | - Stefano Pizzimenti
- Severe Asthma and Rare Lung Disease Unit, San Luigi Gonzaga University Hospital, Orbassano, 10043 Turin, Italy; (S.P.); (F.L.M.R.)
| | - Giorgio Ciprandi
- Allergy Clinic, Casa di Cura Villa Montallegro, 16145 Genoa, Italy;
| | - Fabio Luigi Massimo Ricciardolo
- Severe Asthma and Rare Lung Disease Unit, San Luigi Gonzaga University Hospital, Orbassano, 10043 Turin, Italy; (S.P.); (F.L.M.R.)
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy; (F.B.); (V.C.); (S.L.); (M.S.); (G.O.); (E.A.)
- Institute of Translational Pharmacology, National Research Council (IFT-CNR), Section of Palermo, 90146 Palermo, Italy
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Matabuena M, Salgado FJ, Nieto-Fontarigo JJ, Álvarez-Puebla MJ, Arismendi E, Barranco P, Bobolea I, Caballero ML, Cañas JA, Cárdaba B, Cruz MJ, Curto E, Domínguez-Ortega J, Luna JA, Martínez-Rivera C, Mullol J, Muñoz X, Rodriguez-Garcia J, Olaguibel JM, Picado C, Plaza V, Quirce S, Rial MJ, Romero-Mesones C, Sastre B, Soto-Retes L, Valero A, Valverde-Monge M, Del Pozo V, Sastre J, González-Barcala FJ. Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis. Arch Bronconeumol 2023; 59:223-231. [PMID: 36732158 DOI: 10.1016/j.arbres.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/05/2023] [Accepted: 01/05/2023] [Indexed: 01/20/2023]
Abstract
INTRODUCTION The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. METHODS We performed a multicentre prospective cohort study, including adult patients with asthma (N=512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. RESULTS Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities. CONCLUSION We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps.
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Affiliation(s)
- Marcos Matabuena
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), University of Santiago of Compostela, Santiago de Compostela, Spain
| | - Francisco Javier Salgado
- Department of Biochemistry and Molecular Biology, School of Biology-Biological Research Centre (CIBUS), University of Santiago de Compostela, Santiago de Compostela, Spain; Translational Research in Airway Diseases Group (TRIAD) - Health Research Institute of Santiago de Compostela (IDIS), Spain
| | - Juan José Nieto-Fontarigo
- Department of Biochemistry and Molecular Biology, School of Biology-Biological Research Centre (CIBUS), University of Santiago de Compostela, Santiago de Compostela, Spain; Translational Research in Airway Diseases Group (TRIAD) - Health Research Institute of Santiago de Compostela (IDIS), Spain; Department of Experimental Medical Science, Lund University, Sweden.
| | | | - Ebymar Arismendi
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Pneumology and Allergy Department, Hospital Clínic de Barcelona, Barcelona, Spain; Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain; University of Barcelona, Barcelona, Spain
| | - Pilar Barranco
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Allergy, La Paz University Hospital, IdiPAZ (Research Institute), Madrid, Spain
| | - Irina Bobolea
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain; University of Barcelona, Barcelona, Spain; Allergy Unit & Severe Asthma Unit, Pneumonology and Allergy Department, Hospital Clínic de Barcelona, Barcelona, Spain
| | - María L Caballero
- Department of Allergy, La Paz University Hospital, IdiPAZ (Research Institute), Madrid, Spain
| | - José Antonio Cañas
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Allergology Service, Jiménez Díaz Foundation University Hospital, Madrid, Spain; Department of Immunology, Health Research Institute Jiménez Díaz Foundation, Madrid, Spain
| | - Blanca Cárdaba
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Immunology, Health Research Institute Jiménez Díaz Foundation, Madrid, Spain
| | - María Jesus Cruz
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Cellular Biology, Physiology and Immunology, Autonomous University of Barcelona, Barcelona, Spain; Pneumology Department, Vall d'Hebron Hospital, Barcelona, Spain
| | - Elena Curto
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Respiratory Medicine, Santa Creu i Sant Pau Hospital, Barcelona, Spain; Sant Pau Biomedical Research Institute, Sant Pau, Barcelona, Spain; Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Javier Domínguez-Ortega
- Department of Allergy, La Paz University Hospital, IdiPAZ (Research Institute), Madrid, Spain
| | - Juan Alberto Luna
- Department of Allergy, La Paz University Hospital, IdiPAZ (Research Institute), Madrid, Spain
| | - Carlos Martínez-Rivera
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Pneumology Service, Germans Trias i Pujol Hospital, Badalona, Barcelona, Spain
| | - Joaquim Mullol
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain; University of Barcelona, Barcelona, Spain; Ear, Nose and Throat (ENT) Department, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Xavier Muñoz
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Cellular Biology, Physiology and Immunology, Autonomous University of Barcelona, Barcelona, Spain; Pneumology Department, Vall d'Hebron Hospital, Barcelona, Spain
| | - Javier Rodriguez-Garcia
- Department of Biochemistry and Molecular Biology, School of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - José María Olaguibel
- Allergology Department, Navarre University Hospital, Pamplona, Navarra, Spain; Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain
| | - César Picado
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Pneumology and Allergy Department, Hospital Clínic de Barcelona, Barcelona, Spain; Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain; University of Barcelona, Barcelona, Spain
| | - Vicente Plaza
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Respiratory Medicine, Santa Creu i Sant Pau Hospital, Barcelona, Spain; Sant Pau Biomedical Research Institute, Sant Pau, Barcelona, Spain; Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Santiago Quirce
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Allergy, La Paz University Hospital, IdiPAZ (Research Institute), Madrid, Spain
| | - Manuel J Rial
- Allergology Department, A Coruña University Hospital, A Coruña, Spain
| | | | - Beatriz Sastre
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Allergology Service, Jiménez Díaz Foundation University Hospital, Madrid, Spain; Department of Immunology, Health Research Institute Jiménez Díaz Foundation, Madrid, Spain
| | - Lorena Soto-Retes
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Respiratory Medicine, Santa Creu i Sant Pau Hospital, Barcelona, Spain; Sant Pau Biomedical Research Institute, Sant Pau, Barcelona, Spain; Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Antonio Valero
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Pneumology and Allergy Department, Hospital Clínic de Barcelona, Barcelona, Spain; Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain; University of Barcelona, Barcelona, Spain
| | - Marcela Valverde-Monge
- Allergology Service, Jiménez Díaz Foundation University Hospital, Madrid, Spain; Department of Immunology, Health Research Institute Jiménez Díaz Foundation, Madrid, Spain
| | - Victoria Del Pozo
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Allergology Service, Jiménez Díaz Foundation University Hospital, Madrid, Spain; Department of Immunology, Health Research Institute Jiménez Díaz Foundation, Madrid, Spain
| | - Joaquín Sastre
- Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Allergology Service, Jiménez Díaz Foundation University Hospital, Madrid, Spain; Department of Immunology, Health Research Institute Jiménez Díaz Foundation, Madrid, Spain
| | - Francisco Javier González-Barcala
- Translational Research in Airway Diseases Group (TRIAD) - Health Research Institute of Santiago de Compostela (IDIS), Spain; Biomedical Research Centre Network - Respiratory Diseases (CIBERES), Madrid, Spain; Department of Respiratory Medicine, University Hospital of Santiago de Compostela, Spain; Department of Medicine, University of Santiago de Compostela, Spain
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Trivedi AP, Hall C, Goss CW, Lew D, Krings JG, McGregor MC, Samant M, Sieren JP, Li H, Schechtman KB, Schirm J, McEleney S, Peterson S, Moore WC, Bleecker ER, Meyers DA, Israel E, Washko GR, Levy BD, Leader JK, Wenzel SE, Fahy JV, Schiebler ML, Fain SB, Jarjour NN, Mauger DT, Reinhardt JM, Newell JD, Hoffman EA, Castro M, Sheshadri A, Levy B, Cernadas M, Washko GR, Haley K, Cardet JC, Duvall M, Forth V, Le M, Fandozzi E, O'Neill A, Gentile K, Cinelli M, Tulchinsky A, Lawrance G, Czajkowski R, Lemole P, Antunes W, McGinnis A, Klokeid K, Phipatanakul W, Sheehan W, Bartnikas L, Baxi S, Crestani E, Etsy B, Gaffin J, Hauptman M, Kantor D, Lai P, Louisias M, Nelson K, Permaul P, Schneider L, Wright L, Minnicozzi S, Maciag M, Haktanir-Abul M, Gunnlaugsson S, Burke-Roberts E, Cunningham A, Ansel-Kelly E, Waskosky S, Ramsey A, Feloney L, Wenzel S, Fajt M, Celedon J, Larkin A, Di P, Chu HW, Gauthier M, Wu W, Jain S, Camiolo M, Rauscher C, Luyster F, Rebovich P, Demas J, Wunderley R, Vitari C, Ilnicki M, Srollo D, Takosky C, Lanzo R, Leader J, Lapic DM, Etling E, Rhodes D, Burger J, Glover E, Peters A, Smith C, Bonfiglio N, Trudeau J, Bang SJ, Lin Q, Liu CH, Kupul S, Jarjour N, Denlinger L, Lemanske R, Fain S, Viswanathan R, Moss M, Jackson D, Sorkness R, Ramratnam S, Tattersall M, Crisafi G, Klaus D, Wollet L, Bach J, Johansson M, Schiebler M, Esnault S, Mathur S, Yakey J, Floerke H, Guadarrama A, Maddox A, Peters B, Beaman K, Sumino K, Castro M, Bacharier L, Gierada D, Woods J, Schechtman K, Patterson B, Sheshadri A, Coverstone A, Shifren A, Quirk J, Byers D, Krings J, McGregor MC, Samant M, Tarsi J, Koch T, Curtis V, Yin-Declue H, Boomer J, Saylor M, Frei S, Rowe L, Sajol G, Kozlowski J, Hoffman E, Allard E, Atha J, Ching-Long L, Fahy J, Woodruff P, Ly N, Bhakta N, Peters M, Moreno C, Baum A, Liu D, Kalra A, Orain X, Charbit A, Njoku N, Dunican E, Teague WG, Greenwald R, DeBoer M, Wavell K, deRonde K, Erzurum S, Carl J, Khatri S, Dweik R, Comhair S, Sharp J, Lempel J, Farha S, Taliercio R, Aronica M, Zein J, Koo M, Painter TA, Hopkins K, Lawrence J, Abi-Saleh S, Labadia M, Qirjaz E, Wehrmann R, Arbruster D, Markle T, Matuska B, Baicker-McKee S, Wyszynski P, Fitzgerald K, Ross K, Gaston B, Myers R, Craven D, Roesch E, Thomas R, Logan L, Veri L, Gluvna A, Wallace J, Pryor M, Smith S, Allerton P, Emrich T, Hilliard J, Krenicky J, Smith L, Ferrebee M, Moore W, Bleecker E, Meyers D, Peters S, Li X, Hastie A, Ortega V, Hawkins G, Krings J, Ampleford E, Pippins A, Field P, Rector B, Sprissler R, Fransway B, Fitzpatrick A, Stephenson S, Mauger DT, Phillips B. Quantitative CT Characteristics of Cluster Phenotypes in the Severe Asthma Research Program Cohorts. Radiology 2022; 304:450-459. [PMID: 35471111 PMCID: PMC9340243 DOI: 10.1148/radiol.210363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Clustering key clinical characteristics of participants in the Severe Asthma Research Program (SARP), a large, multicenter prospective observational study of patients with asthma and healthy controls, has led to the identification of novel asthma phenotypes. Purpose To determine whether quantitative CT (qCT) could help distinguish between clinical asthma phenotypes. Materials and Methods A retrospective cross-sectional analysis was conducted with the use of qCT images (maximal bronchodilation at total lung capacity [TLC], or inspiration, and functional residual capacity [FRC], or expiration) from the cluster phenotypes of SARP participants (cluster 1: minimal disease; cluster 2: mild, reversible; cluster 3: obese asthma; cluster 4: severe, reversible; cluster 5: severe, irreversible) enrolled between September 2001 and December 2015. Airway morphometry was performed along standard paths (RB1, RB4, RB10, LB1, and LB10). Corresponding voxels from TLC and FRC images were mapped with use of deformable image registration to characterize disease probability maps (DPMs) of functional small airway disease (fSAD), voxel-level volume changes (Jacobian), and isotropy (anisotropic deformation index [ADI]). The association between cluster assignment and qCT measures was evaluated using linear mixed models. Results A total of 455 participants were evaluated with cluster assignments and CT (mean age ± SD, 42.1 years ± 14.7; 270 women). Airway morphometry had limited ability to help discern between clusters. DPM fSAD was highest in cluster 5 (cluster 1 in SARP III: 19.0% ± 20.6; cluster 2: 18.9% ± 13.3; cluster 3: 24.9% ± 13.1; cluster 4: 24.1% ± 8.4; cluster 5: 38.8% ± 14.4; P < .001). Lower whole-lung Jacobian and ADI values were associated with greater cluster severity. Compared to cluster 1, cluster 5 lung expansion was 31% smaller (Jacobian in SARP III cohort: 2.31 ± 0.6 vs 1.61 ± 0.3, respectively, P < .001) and 34% more isotropic (ADI in SARP III cohort: 0.40 ± 0.1 vs 0.61 ± 0.2, P < .001). Within-lung Jacobian and ADI SDs decreased as severity worsened (Jacobian SD in SARP III cohort: 0.90 ± 0.4 for cluster 1; 0.79 ± 0.3 for cluster 2; 0.62 ± 0.2 for cluster 3; 0.63 ± 0.2 for cluster 4; and 0.41 ± 0.2 for cluster 5; P < .001). Conclusion Quantitative CT assessments of the degree and intraindividual regional variability of lung expansion distinguished between well-established clinical phenotypes among participants with asthma from the Severe Asthma Research Program study. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.
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Forster F, Ege MJ, Gerlich J, Weinmann T, Kreißl S, Weinmayr G, Genuneit J, Nowak D, von Mutius E, Vogelberg C, Radon K. Trajectories of asthma and allergy symptoms from childhood to adulthood. Allergy 2022; 77:1192-1203. [PMID: 34473346 DOI: 10.1111/all.15075] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/14/2021] [Accepted: 07/27/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND Phenotypes of asthma and allergic diseases are mainly studied separately for children and adults. To explore the role of adolescence and young adulthood, we investigated symptom trajectories at the transition from childhood into adulthood. METHODS Latent class analysis (LCA) was conducted in a population initially recruited for the German arm of Phase II of the International Study of Asthma and Allergies in Childhood and followed-up three times until their early 30s (N=2267). Indicators included in LCA were 12-month prevalences of symptoms of wheeze, rhinoconjunctivitis, and eczema. Latent classes were further characterised regarding important traits such as skin prick tests. Logistic regression models were used to investigate associations with environmental determinants such as smoking and occupational exposures. RESULTS Six latent classes were identified: an asymptomatic one as well as three with single and two with co-occurring symptoms. All trajectories essentially established between baseline assessment at around 10 years and the first follow-up at around 17 years. Probabilities for symptoms increased from childhood to adolescence, especially for wheeze-related latent classes, while they remained constant in adulthood. Wheeze-related latent classes were also positively associated with exposures during adolescence (e.g. active smoking). CONCLUSION Distinct trajectories of asthma and allergy symptoms establish from childhood through adolescence and stabilize during early adulthood. This pattern was most notable in wheeze-related latent classes which also showed the strongest positive associations with environmental exposures in adolescence/young adulthood. Therefore, not only childhood but also adolescence is relevant for disease development and offers considerable potential for prevention and health promotion.
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Affiliation(s)
- Felix Forster
- Institute and Clinic for Occupational, Social and Environmental Medicine University Hospital, LMU Munich Munich Germany
- Comprehensive Pneumology Center (CPC) Munich, member German Center for Lung Research (DZL) Munich Germany
| | - Markus Johannes Ege
- Comprehensive Pneumology Center (CPC) Munich, member German Center for Lung Research (DZL) Munich Germany
- Dr. v. Hauner Children’s Hospital University Hospital, LMU Munich Munich Germany
| | - Jessica Gerlich
- Institute and Clinic for Occupational, Social and Environmental Medicine University Hospital, LMU Munich Munich Germany
- Comprehensive Pneumology Center (CPC) Munich, member German Center for Lung Research (DZL) Munich Germany
| | - Tobias Weinmann
- Institute and Clinic for Occupational, Social and Environmental Medicine University Hospital, LMU Munich Munich Germany
- Comprehensive Pneumology Center (CPC) Munich, member German Center for Lung Research (DZL) Munich Germany
| | - Sylvia Kreißl
- Paediatric Department University Hospital Carl Gustav Carus Dresden, TU Dresden Dresden Germany
| | - Gudrun Weinmayr
- Institute of Epidemiology and Medical Biometry University of Ulm Ulm Germany
| | - Jon Genuneit
- Institute of Epidemiology and Medical Biometry University of Ulm Ulm Germany
- Pediatric Epidemiology, Department of Pediatrics, Medical Faculty Leipzig University Leipzig Germany
| | - Dennis Nowak
- Institute and Clinic for Occupational, Social and Environmental Medicine University Hospital, LMU Munich Munich Germany
- Comprehensive Pneumology Center (CPC) Munich, member German Center for Lung Research (DZL) Munich Germany
| | - Erika von Mutius
- Comprehensive Pneumology Center (CPC) Munich, member German Center for Lung Research (DZL) Munich Germany
- Dr. v. Hauner Children’s Hospital University Hospital, LMU Munich Munich Germany
| | - Christian Vogelberg
- Paediatric Department University Hospital Carl Gustav Carus Dresden, TU Dresden Dresden Germany
| | - Katja Radon
- Institute and Clinic for Occupational, Social and Environmental Medicine University Hospital, LMU Munich Munich Germany
- Comprehensive Pneumology Center (CPC) Munich, member German Center for Lung Research (DZL) Munich Germany
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Martín J, Anton-Ladislao A, Padierna Á, Berjano B, Quintana JM. Classification of subtypes of patients with eating disorders by correspondence analysis. World J Psychiatry 2021; 11:375-387. [PMID: 34327130 PMCID: PMC8311511 DOI: 10.5498/wjp.v11.i7.375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/09/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Grouping eating disorders (ED) patients into subtypes could help improve the establishment of more effective diagnostic and treatment strategies.
AIM To identify clinically meaningful subgroups among subjects with ED using multiple correspondence analysis (MCA).
METHODS A prospective cohort study was conducted of all outpatients diagnosed for an ED at an Eating Disorders Outpatient Clinic to characterize groups of patients with ED into subtypes according to sociodemographic and psychosocial impairment data, and to validate the results using several illustrative variables. In all, 176 (72.13%) patients completed five questionnaires (clinical impairment assessment, eating attitudes test-12, ED-short form health-related quality of life, metacognitions questionnaire, Penn State Worry Questionnaire) and sociodemographic data. ED patient groups were defined using MCA and cluster analysis. Results were validated using key outcomes of subtypes of ED.
RESULTS Four ED subgroups were identified based on the sociodemographic and psychosocial impairment data.
CONCLUSION ED patients were differentiated into well-defined outcome groups according to specific clusters of compensating behaviours.
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Affiliation(s)
- Josune Martín
- Department of Research, Galdakao-Usansolo Hospital, Galdakao 48960, Spain
- Kronikgune Institute for Health Services Research, Barakaldo 48902, Spain
- Health Services Research on Chronic Diseases Network – REDISSEC, Galdakao 48960, Spain
| | - Ane Anton-Ladislao
- Department of Research, Galdakao-Usansolo Hospital, Galdakao 48960, Spain
| | - Ángel Padierna
- Health Services Research on Chronic Diseases Network – REDISSEC, Galdakao 48960, Spain
- Department of Psychiatry, Galdakao-Usansolo Hospital, Galdakao 48960, Spain
| | - Belén Berjano
- Department of Psychiatry, Galdakao-Usansolo Hospital, Galdakao 48960, Spain
| | - José María Quintana
- Department of Research, Galdakao-Usansolo Hospital, Galdakao 48960, Spain
- Health Services Research on Chronic Diseases Network – REDISSEC, Galdakao 48960, Spain
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7
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Ross MK, Eckel SP, Bui AAT, Gilliland FD. Asthma clustering methods: a literature-informed application to the children's health study data. J Asthma 2021; 59:1305-1318. [PMID: 33926348 DOI: 10.1080/02770903.2021.1923738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
OBJECTIVE The heterogeneity of asthma has inspired widespread application of statistical clustering algorithms to a variety of datasets for identification of potentially clinically meaningful phenotypes. There has not been a standardized data analysis approach for asthma clustering, which can affect reproducibility and clinical translation of results. Our objective was to identify common and effective data analysis practices in the asthma clustering literature and apply them to data from a Southern California population-based cohort of schoolchildren with asthma. METHODS As of January 1, 2020, we reviewed key statistical elements of 77 asthma clustering studies. Guided by the literature, we used 12 input variables and three clustering methods (hierarchical clustering, k-medoids, and latent class analysis) to identify clusters in 598 schoolchildren with asthma from the Southern California Children's Health Study (CHS). RESULTS Clusters of children identified by latent class analysis were characterized by exhaled nitric oxide, FEV1/FVC, FEV1 percent predicted, asthma control and allergy score; and were predictive of control at two year follow up. Clusters from the other two methods were less clinically remarkable, primarily differentiated by sex and race/ethnicity and less predictive of asthma control over time. CONCLUSION Upon review of the asthma phenotyping literature, common approaches of data clustering emerged. When applying these elements to the Children's Health Study data, latent class analysis clusters-represented by exhaled nitric oxide and spirometry measures-had clinical relevance over time.
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Affiliation(s)
- Mindy K Ross
- Pediatrics, Pediatric Pulmonology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sandrah P Eckel
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alex A T Bui
- Radiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Frank D Gilliland
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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8
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A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods. Diagnostics (Basel) 2021; 11:diagnostics11040644. [PMID: 33918233 PMCID: PMC8066118 DOI: 10.3390/diagnostics11040644] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 12/13/2022] Open
Abstract
Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.
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9
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Elena-Pérez S, Heredero-Jung DH, García-Sánchez A, Estravís M, Martin MJ, Ramos-González J, Triviño JC, Isidoro-García M, Sanz C, Dávila I. Molecular Analysis of IL-5 Receptor Subunit Alpha as a Possible Pharmacogenetic Biomarker in Asthma. Front Med (Lausanne) 2021; 7:624576. [PMID: 33644088 PMCID: PMC7904892 DOI: 10.3389/fmed.2020.624576] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 12/30/2020] [Indexed: 01/17/2023] Open
Abstract
Background: Asthma is a heterogeneous syndrome with a broad clinical spectrum and high drug response variability. The inflammatory response in asthma involves multiple effector cells and mediator molecules. Based on asthma immunopathogenesis, precision medicine can be a promising strategy for identifying biomarkers. Biologic therapies acting on the IL-5/IL-5 receptor axis have been developed. IL-5 promotes proliferation, differentiation and activation of eosinophils by binding to the IL-5 receptor, located on the surface of eosinophils and basophils. This study aimed to investigate the expression of IL5RA in patients with several types of asthma and its expression after treatment with benralizumab, a biologic directed against IL-5 receptor subunit alpha. Methods: Sixty peripheral blood samples, 30 from healthy controls and 30 from asthmatic patients, were selected for a transcriptomic RNAseq study. Differential expression analysis was performed by statistical assessment of fold changes and P-values. A validation study of IL5RA expression was developed using qPCR in 100 controls and 187 asthmatic patients. The effect of benralizumab on IL5RA expression was evaluated in five patients by comparing expression levels between pretreatment and after 3 months of treatment. The IL5RA mRNA levels were normalized to GAPDH and TBP expression values for each sample. Calculations were made by the comparative ΔΔCt method. All procedures followed the MIQE guidelines. Results:IL5RA was one of the most differentially overexpressed coding transcripts in the peripheral blood of asthmatic patients (P = 8.63E-08 and fold change of 2.22). In the qPCR validation study, IL5RA expression levels were significantly higher in asthmatic patients than in controls (P < 0.001). Significant expression differences were present in different asthmatic types. In the biological drug study, patients treated with benralizumab showed a significant decrease in IL5RA expression and blood eosinophil counts. A notable improvement in ACT and lung function was also observed in these patients. Conclusions: These results indicate that IL5RA is overexpressed in patients with different types of asthma. It could help identify which asthmatic patients will respond more efficiently to benralizumab, moving toward a more personalized asthma management. Although further studies are required, IL5RA could play a role as a biomarker and pharmacogenetic factor in asthma.
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Affiliation(s)
- Sandra Elena-Pérez
- Department of Clinical Biochemistry, University Hospital of Salamanca, Salamanca, Spain
| | | | - Asunción García-Sánchez
- Allergic Disease Research Group IIMD-01, Institute for Biomedical Research of Salamanca, Salamanca, Spain.,Department of Biomedical Sciences and Diagnostics, University of Salamanca, Salamanca, Spain.,Network for Cooperative Research in Health - RETICS ARADyAL, Carlos III Health Institute, Madrid, Spain
| | - Miguel Estravís
- Allergic Disease Research Group IIMD-01, Institute for Biomedical Research of Salamanca, Salamanca, Spain.,Department of Biomedical Sciences and Diagnostics, University of Salamanca, Salamanca, Spain.,Network for Cooperative Research in Health - RETICS ARADyAL, Carlos III Health Institute, Madrid, Spain
| | - Maria J Martin
- Allergic Disease Research Group IIMD-01, Institute for Biomedical Research of Salamanca, Salamanca, Spain.,Network for Cooperative Research in Health - RETICS ARADyAL, Carlos III Health Institute, Madrid, Spain
| | | | | | - María Isidoro-García
- Department of Clinical Biochemistry, University Hospital of Salamanca, Salamanca, Spain.,Allergic Disease Research Group IIMD-01, Institute for Biomedical Research of Salamanca, Salamanca, Spain.,Network for Cooperative Research in Health - RETICS ARADyAL, Carlos III Health Institute, Madrid, Spain.,Department of Medicine, University of Salamanca, Salamanca, Spain
| | - Catalina Sanz
- Allergic Disease Research Group IIMD-01, Institute for Biomedical Research of Salamanca, Salamanca, Spain.,Network for Cooperative Research in Health - RETICS ARADyAL, Carlos III Health Institute, Madrid, Spain.,Department of Microbiology and Genetics, University of Salamanca, Salamanca, Spain
| | - Ignacio Dávila
- Allergic Disease Research Group IIMD-01, Institute for Biomedical Research of Salamanca, Salamanca, Spain.,Department of Biomedical Sciences and Diagnostics, University of Salamanca, Salamanca, Spain.,Network for Cooperative Research in Health - RETICS ARADyAL, Carlos III Health Institute, Madrid, Spain.,Department of Allergy, University Hospital of Salamanca, Salamanca, Spain
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10
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Clinical Phenotypes of Patients Hospitalized for an Asthma Exacerbation: Prognostic Implications. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE 2020; 9:830-841.e14. [PMID: 33011298 DOI: 10.1016/j.jaip.2020.09.031] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 09/09/2020] [Accepted: 09/15/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND Hospitalization due to acute asthma exacerbation (AE) is a highly detrimental situation requiring critical management to prevent further deterioration, including mechanical ventilation, intensive care unit (ICU) admission, and death. However, patients hospitalized for AEs are highly heterogeneous and remain largely unexplored. OBJECTIVE To identify clinical and inflammatory phenotypes of AE requiring hospitalization associated with in-hospital outcomes. METHODS We performed a hierarchical cluster analysis of 825 consecutively recruited patients hospitalized for AEs. Logistic regressions were conducted to quantify the independent associations of the identified phenotypes with in-hospital outcomes. Decision tree analysis was developed to predict cluster assignment. RESULTS We identified 3 clusters of patients, which had significantly different characteristics associated with in-hospital adverse outcomes. Cluster 1 (n = 526, 63.8%) was a late-onset phenotype, cluster 2 (n = 97, 11.8%) was an early-onset phenotype, and cluster 3 (n = 202, 24.5%) was a phenotype with fewer eosinophils and more comorbidities. Clusters 2 and 3 had an elevated risk of death (relative ratio [RRadj], 18.10 and 19.17, respectively) and mechanical ventilation (RRadj, 2.56 and 5.71, respectively) than did cluster 1. Individuals in cluster 3 had an extended length of hospital stay (11 days), increased hospitalization direct costs (13,481.57 Chinese Yuan), and a higher risk of ICU admission (RRadj, 2.14) than individuals in clusters 1 and 2. The decision tree assigned 90.8% of the participants correctly. CONCLUSIONS We identified 3 phenotypes with differential clinical and inflammatory characteristics associated with in-hospital adverse outcomes. These new phenotypes might have important and clinically relevant implications for the management of patients hospitalized for AEs.
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11
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Horne E, Tibble H, Sheikh A, Tsanas A. Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping. JMIR Med Inform 2020; 8:e16452. [PMID: 32463370 PMCID: PMC7290450 DOI: 10.2196/16452] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/10/2019] [Accepted: 02/10/2020] [Indexed: 12/27/2022] Open
Abstract
Background In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging. Objective This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies. Methods We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process. Results Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification. Conclusions This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.
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Affiliation(s)
- Elsie Horne
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Holly Tibble
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Aziz Sheikh
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Athanasios Tsanas
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
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12
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Campo P, Soto Campos G, Aparicio MB, Jorge AM, González Expósito HM, Quirce S, Dávila I. Severe asthma phenotypes in patients controlled with omalizumab: A real-world study. Respir Med 2019; 159:105804. [PMID: 31704593 DOI: 10.1016/j.rmed.2019.105804] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 10/17/2019] [Accepted: 10/18/2019] [Indexed: 01/16/2023]
Abstract
BACKGROUND The appropriate identification of asthma phenotypes of responders to omalizumab would optimize the selection of treatment. OBJECTIVE To describe the most frequent clinical phenotypes in patients with severe asthma responding to omalizumab and their clinical and pulmonary function improvement. METHODS This was an observational, retrospective, multicenter study. Adult patients with severe asthma, who achieved good control after the first year of treatment with omalizumab were included. Omalizumab was prescribed according to clinical routine practice. Responders were assigned to one pre-established phenotype based on the most predominant one before they had started treatment with omalizumab, all according to the physician's criteria. Data about asthma symptoms, number of non-severe asthma exacerbations, medication intake (inhaled and oral corticosteroids and rescue medication), lung function, high fractional exhaled nitric oxide (FeNO) and peripheral eosinophils counts were recorded. RESULTS Among the 345 patients included, the main phenotypes were severe asthma with frequent exacerbations (29.9%), early-onset allergic asthma (23.8%), severe steroid-dependent asthma (18.8%), and severe eosinophilic asthma (13.6%). Clinical and respiratory changes observed after first year of treatment with omalizumab included: reduction in asthma symptoms, reduction in the use and dose of corticosteroids and need for rescue therapy, improvement of pulmonary function, reduction in the number of episodes of non-severe asthma exacerbations regardless of the duration of severe disease since the diagnosis. Increased blood levels of peripheral eosinophils and high FeNO levels were found at baseline. CONCLUSION Several heterogeneous severe asthma phenotypes were observed as good responders to omalizumab.
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Affiliation(s)
- Paloma Campo
- Allergy Unit, IBIMA-Regional University Hospital of Málaga, ARADyAL, Málaga, Spain.
| | | | | | | | | | - Santiago Quirce
- Department of Allergy, Hospital La Paz Institute for Health Research (IdiPAZ), Madrid, Spain; CIBER de Enfermedades Respiratorias, CIBERES, Madrid, Spain.
| | - Ignacio Dávila
- Allergy Service, University Hospital of Salamanca and Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain; Biomedical and Diagnosis Science Department, Salamanca University School of Medicine, Salamanca, Spain.
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13
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Lee E, Hong SJ. Phenotypes of allergic diseases in children and their application in clinical situations. KOREAN JOURNAL OF PEDIATRICS 2019; 62:325-333. [PMID: 31096745 PMCID: PMC6753312 DOI: 10.3345/kjp.2018.07395] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 04/18/2019] [Indexed: 12/25/2022]
Abstract
Allergic diseases, including allergic rhinitis, asthma, and atopic dermatitis, are common heterogeneous diseases that encompass diverse phenotypes and different pathogeneses. Phenotype studies of allergic diseases can facilitate the identification of risk factors and their underlying pathophysiology, resulting in the application of more effective treatment, selection of better treatment responses, and prediction of prognosis for each phenotype. In the early phase of phenotype studies in allergic diseases, artificial classifications were usually performed based on clinical features, such as triggering factors or the presence of atopy, which can result in the biased classification of phenotypes and limit the characterization of heterogeneous allergic diseases. Subsequent phenotype studies have suggested more diverse phenotypes for each allergic disease using relatively unbiased statistical methods, such as cluster analysis or latent class analysis. The classifications of phenotypes in allergic diseases may overlap or be unstable over time due to their complex interactions with genetic and encountered environmental factors during the illness, which may affect the disease course and pathophysiology. In this review, diverse phenotype classifications of allergic diseases, including atopic dermatitis, asthma, and wheezing in children, allergic rhinitis, and atopy, are described. The review also discusses the applications of the results obtained from phenotype studies performed in other countries to Korean children. Consideration of changes in the characteristics of each phenotype over time in an individual’s lifespan is needed in future studies.
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Affiliation(s)
- Eun Lee
- Department of Pediatrics, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Soo-Jong Hong
- Department of Pediatrics, Childhood Asthma Atopy Center, Environmental Health Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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14
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Comparison of hypothesis- and data-driven asthma phenotypes in NHANES 2007-2012: the importance of comprehensive data availability. Clin Transl Allergy 2019; 9:17. [PMID: 30918624 PMCID: PMC6419396 DOI: 10.1186/s13601-019-0258-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 03/01/2019] [Indexed: 12/28/2022] Open
Abstract
Background Half of the adults with current asthma among the US National Health and Nutrition Examination Survey (NHANES) participants could be classified in more than one hypothesis-driven phenotype. A data-driven approach applied to the same subjects may allow a more useful classification compared to the hypothesis-driven one. Aim To compare previously defined hypothesis-driven with newly derived data-driven asthma phenotypes, identified by latent class analysis (LCA), in adults with current asthma from NHANES 2007–2012. Methods Adults (≥ 18 years) with current asthma from the NHANES were included (n = 1059). LCA included variables commonly used to subdivide asthma. LCA models were derived independently according to age groups: < 40 and ≥ 40 years old. Results Two data-driven phenotypes were identified among adults with current asthma, for both age groups. The proportions of the hypothesis-driven phenotypes were similar among the two data-driven phenotypes (p > 0.05). Class A < 40 years (n = 285; 75%) and Class A ≥ 40 years (n = 462; 73%), respectively, were characterized by a predominance of highly symptomatic asthma subjects with poor lung function, compared to Class B < 40 years (n = 94; 25%) and Class B ≥ 40 years (n = 170; 27%). Inflammatory biomarkers, smoking status, presence of obesity and hay fever did not markedly differ between the phenotypes. Conclusion Both data- and hypothesis-driven approaches using clinical and physiological variables commonly used to characterize asthma are suboptimal to identify asthma phenotypes among adults from the general population. Further studies based on more comprehensive disease features are required to identify asthma phenotypes in population-based studies. Electronic supplementary material The online version of this article (10.1186/s13601-019-0258-7) contains supplementary material, which is available to authorized users.
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Schatz M, Sicherer SH, Khan D, Zeiger RS. The Journal of Allergy and Clinical Immunology: In Practice 2018 Highlights. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE 2018; 7:393-411. [PMID: 30557718 DOI: 10.1016/j.jaip.2018.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 12/07/2018] [Indexed: 12/12/2022]
Abstract
A large number of clinically impactful studies and reviews were published in this journal in 2018. This article provides highlights of the original research published in 2018 issues of The Journal of Allergy and Clinical Immunolgy: In Practice on the subjects of anaphylaxis, asthma, dermatitis, drug allergy, eosinophilic disorders, food allergy, immune deficiency, rhinitis, and urticaria/angioedema and mast cell disorders. Within each topic, practical aspects of diagnosis and management are emphasized. Treatments discussed include lifestyle modifications, allergen avoidance therapy, positive and negative effects of pharmacologic therapy, and various forms of immunologic and desensitization management. We hope this review will help readers consolidate and use this extensive and practical knowledge for the benefit of patients.
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Affiliation(s)
- Michael Schatz
- Department of Allergy, Kaiser Permanente Southern California, San Diego, Calif.
| | - Scott H Sicherer
- Jaffe Food Allergy Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - David Khan
- Department of Internal Medicine, Division of Allergy & Immunology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Robert S Zeiger
- Department of Allergy, Kaiser Permanente Southern California, San Diego, Calif; Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, Calif
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