Heterogeneity Of Asthma Research Articles

Epidemiology of Asthma: Prevalence and Burden of Disease.

Asthma, a common airway disease, results in a significant burden to both patients and society worldwide. Yet, despite global political commitment backed by the United Nations, progress to reduce the burden of asthma remains inadequate. This is particularly true in low-income countries. To date, progress has been delayed by the lack of uniform data collection, imperfect surveillance methods, inadequate resources, poor access to effective therapies, substandard asthma education, ineffective governmental policies, rapid urbanization, progressive increase in asthma prevalence, increased life expectancy and obesity rates worldwide, asthma heterogeneity and disease complexity, smoking, and environmental exposures to allergens and pollution. A thorough understanding of the challenges facing the international community is essential to define future strategies to improve the burden of asthma.

Heterogeneity of asthma-chronic obstructive pulmonary disease (COPD) overlap from a cohort of patients with severe asthma and COPD.

A considerable proportion of patients have features of both asthma and chronic obstructive pulmonary disease (COPD) simultaneously, called asthma-COPD overlap (ACO). The aim of this study was to identify heterogeneity of ACO from a cohort of patients with severe asthma and COPD using the same diagnostic criteria. We used the International Severe Asthma Registry (ISAR) and the Korean COPD Subgroup Study (KOCOSS) to evaluate clinical characteristics of ACO from each cohort. We classified subjects into four groups: (1) pure severe asthma, (2) ACO from the severe asthma cohort, (3) ACO from the COPD cohort, and (4) pure COPD. ACO was defined by satisfying extreme bronchodilator response (BDR) >15% and 400 ml and/or blood eosinophil count ⩾300 /µL in patients aged 40 years or older and post-BD forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio <0.7. The ACO group had 25 (23%) of 111 in the ISAR cohort and 403 (23%) of 1781 in the KOCOSS cohort. The ACO from the COPD cohort was older with more males and more smokers, but had similar degree of airflow limitation compared with the ACO from the severe asthma cohort. ICS-containing inhaler treatment was prescribed for all severe asthma subjects, but only for 43.9% of ACO subjects from the COPD cohort. Compared with patients having pure severe asthma, the risk for exacerbation was comparable in ACO either from severe asthma or COPD cohort [adjusted odds ratio (aOR): 1.54, 95% CI: 0.22-10.95 or aOR: 2.15, 95% CI: 0.59-7.85]. The prevalence of ACO was similar in severe asthma and COPD cohorts applying identical diagnostic criteria. ACO from the severe asthma cohort was similar to ACO from the COPD cohort in terms of lung function and exacerbation risk.

Imaging Regional Airway Involvement of Asthma: Heterogeneity in Ventilation, Mucus Plugs and Remodeling.

The imaging of asthma using chest computed tomography (CT) is well-established (Jarjour et al., Am J Respir Crit Care Med 185(4):356-62, 2012; Castro et al., J Allergy Clin Immunol 128:467-78, 2011). Moreover, recent advances in functional imaging of the lungs with advanced computer analysis of both CT and magnetic resonance images (MRI) of the lungs have begun to play a role in quantifying regional obstruction. Specifically, quantitative measurements of the airways for bronchial wall thickening, luminal narrowing and distortion, the amount of mucus plugging, parenchymal density, and ventilation defects that could contribute to the patient's disease course are instructive for the entire care team. In this chapter, we will review common imaging methods and findings that relate to the heterogeneity of asthma. This information can help to guide treatment decisions. We will discuss mucous plugging, quantitative assessment of bronchial wall thickening, delta lumen phenomenon, parenchymal low-density lung on CT, and ventilation defect percentage on MRI as metrics for assessing regional ventilatory dysfunction.

Search for new therapeutic targets in asthma. Review.

A review of recent publications devoted to the study of risk factors for the onset of the disease, mechanisms, diagnosis and treatment of asthma from the point of view of molecular allergology is presented. New concepts and problems in the implementation of the exposome paradigm and its practical application are considered, including genetic and epigenetic factors, environmental impact. The most relevant experimental studies are noted that contribute to further understanding of molecular and immune mechanisms with potential new targets for the development of therapeutic agents. Reliable diagnosis of asthma, endotyping of the disease and monitoring of its severity are of great importance in the treatment of asthma. The heterogeneity of asthma is due to individual genetic and epigenetic variability, exposure to individual environmental factors (depending on regional characteristics, changing climatic conditions and population distribution), which explains the occurrence of asthma is not associated only with allergies. The modern assessment and treatment of comorbid/multimorbid asthma is described, including interaction with asthma phenotypes, which is important for the formation of a new therapeutic personalized approach to precision medicine and testing of prognostic biomarkers. The results of clinical trials, multicenter international studies on the use of new approaches in the diagnosis of asthma (candidate biomarkers) based on molecular allergology and treatment in adults and children using biological preparations are given.

Treatment by Biomarker-informed Endotype Versus Guideline Care in Children With Difficult-to-treat Asthma

To develop a biomarker-informed treatment model for difficult-to-treat (DTT) asthma and conduct a pilot feasibility study of its use.The study included 20 children aged 6 to 17 years with DTT asthma defined as severe persistent asthma or moderate persistent asthma in addition to at least 1 of the following in the last year: 2 asthma control tests (ACT) less than 20, an emergency department visit or hospitalization, or 2 courses of steroids because of asthma.This was a feasibility pilot study with a case-crossover design comparing asthma outcomes before and after the implementation of an individualized biomarker-informed treatment algorithm in addition to guideline-based care. This was developed a priori and included 3 phenotypes defined based on total IgE, specific IgE, skin prick testing, and serum IL-17 level: allergic, nonallergic, and mixed. The algorithm also incorporated IL-6, 25(OH)-vitamin D, an indicator of environmental tobacco smoke (urinary cotinine), tests for DNA methylation in the gene promotors of proteins related to poor asthma phenotypes (OTX2 and LDHC), and a genetic marker of rhinovirus susceptibility (CDHR3 variant rs6967330). Transepidermal water loss and the pediatric sleep questionnaire (to assess for sleep disordered breathing) were also assessed.There was no difference in ACT scores after guideline care only. There was a statistically significant, but not clinically relevant increase in ACT scores compared with baseline by the end of the study and after the personalized, biomarker-informed treatment plans were implemented (22.5 vs 23.0; P = .01). Emergency department visits decreased compared with baseline (1 vs 0; P = .04). The was no difference in the rate of systemic steroids. Pulmonary function tests did not significantly improve over the course of the study.This pilot study demonstrated feasibility of delivering personalized, biomarker-informed care to children with DTT asthma, with all children in the study receiving additional interventions to guideline-based care. Outcomes suggest this may result in superior control. Many molecular endotypes were identified within the broader clinical phenotypes, as well as a minimal number of nonredundant biomarkers that could be used in a larger, future study.This article highlights the heterogeneity of asthma and a novel approach to treating patients with DTT asthma by assimilating multiple biomarkers into a personalized treatment algorithm and studying its clinical impact. The study is limited in its size and generalizability, and some improvements from baseline to the end of the study could have been related to the structure and attention of the program rather than the biomarker-informed interventions. However, this study was innovative and provides a compelling foundation for larger studies in the future to improve asthma management beyond standard care.URL: www.pediatrics.org/cgi/doi/10.1542/peds.10.1016/j.anai.2022.01.030

Memory-like innate lymphoid cells in the pathogenesis of asthma.

Innate lymphoid cells (ILCs) are recently discovered innate immune cells that reside and self-renew in mucosal tissues and serve as the first line of defense against various external insults. They include natural killer (NK) cells, ILC1s, ILC2s, ILC3s, and lymphoid tissue inducer cells. The development and functions of ILC1-3 reflect those of their adaptive immunity TH1, TH2, and TH17 T-cell counterparts. Asthma is a heterogeneous disease caused by repeated exposure to specific allergens or host/environmental factors (e.g., obesity) that stimulate pathogenic pulmonary immune cells, including ILCs. Memory used to be a hallmark of adaptive immune cells until recent studies of monocytes, macrophages, and NK cells showed that innate immune cells can also exhibit greater responses to re-stimulation and that these more responsive cells can be long-lived. Besides, a series of studies suggest that the tissue-resident innate lymphoid cells have memory-like phenotypes, such as increased cytokine productions or epigenetic modifications following repetitive exposure to allergens. Notably, both clinical and mouse studies of asthma show that various allergens can generate memory-like features in ILC2s. Here, we discuss the biology of ILCs, their roles in asthma pathogenesis, and the evidence supporting ILC memory. We also show evidence suggesting memory ILCs could help drive the phenotypic heterogeneity in asthma. Thus, further research on memory ILCs may be fruitful in terms of developing new therapies for asthma.

Heterogeneity of asthma with nasal polyposis phenotypes: Acluster analysis.

Chronic rhinosinusitis with nasal polyposis (CRSwNP) affects a significant number of asthmatic patients and is notably associated with a more difficult-to-control asthma and marked inflammation. We need more studies on this specific asthma phenotype and its possible subphenotypes, in order to better individualize treatments. The aim of this study is to identify and characterize subphenotypes of asthma patients with CRSwNP using clinical, physiological and inflammatory variables. K-means cluster analysis was performed on 17 clinical, physiological, and inflammatory variables from 1263 patients of all asthma severity and on a subpopulation of patients with asthma and CRSwNP. Study was registered on ClinicalTrials.gov (NCT03694847). On the overall population, three groups were identified. Cluster T1 (n=708) are young, have a short asthma duration and a low prevalence of CRSwNP. Cluster T2 (n=263) have the longest asthma duration and Cluster T3 (n=292) are older with the shortest asthma duration. Patients in Clusters T2 and T3 have similar prevalences of CRSwNP. On the subpopulation of asthma with CRSwNP, three clusters were also identified. Cluster S1 (n=83) have mild-to-moderate asthma with normal lung function. Clusters S2 (N=53) and S3 (N=42) include patients with severe asthma and decreased lung function, but those in Cluster S2 have a longer asthma duration, whereas those Cluster S3 have late-onset asthma. Despite coexistence of asthma and CRSwNP, not all patients have the same evolution of their asthma. Different phenotypes of asthma with CRSwNP can be identified and exploration of the characteristics of these subgroups could lead to a better individualized, targeted management.

CD4+ T effector memory cell responses in Chlamydia pneumoniae-stimulated peripheral blood mononuclear cells in nonasthmatic subjects.

Chlamydia pneumoniae (C. pneumoniae) is a gram‐negative intracellular bacterium that causes respiratory infection in humans, including subjects with or without asthma. C. pneumoniae activates cells (e.g., monocytes/macrophages) in vitro, and produces cytokines that may contribute to inflammatory responses observed in asthma. Immunological differences exist between subjects with or without asthma, with regard to host responses to C. pneumoniae. The heterogeneity and subsequent diverse pathophysiology of asthma can be better understood by analyzing the repertoire of T‐cell subpopulations; the most common distinction between different asthma endotypes includes cytokines produced by CD4+ cells (T helper (Th)2 high vs. Th2 low).

The Role of Systems Biology in Deciphering Asthma Heterogeneity.

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Asthma inflammatory phenotypes on four continents: most asthma is non-eosinophilic.

Most studies assessing pathophysiological heterogeneity in asthma have been conducted in high-income countries (HICs), with little known about the prevalence and characteristics of different asthma inflammatory phenotypes in low-and middle-income countries (LMICs). This study assessed sputum inflammatory phenotypes in five centres, in Brazil, Ecuador, Uganda, New Zealand (NZ) and the United Kingdom (UK). We conducted a cross-sectional study of 998 asthmatics and 356 non-asthmatics in 2016-20. All centres studied children and adolescents (age range 8-20 years), except the UK centre which involved 26-27 year-olds. Information was collected using questionnaires, clinical characterization, blood and induced sputum. Of 623 asthmatics with sputum results, 39% (243) were classified as eosinophilic or mixed granulocytic, i.e. eosinophilic asthma (EA). Adjusted for age and sex, with NZ as baseline, the UK showed similar odds of EA (odds ratio 1.04, 95% confidence interval 0.37-2.94) with lower odds in the LMICs: Brazil (0.73, 0.42-1.27), Ecuador (0.40, 0.24-0.66) and Uganda (0.62, 0.37-1.04). Despite the low prevalence of neutrophilic asthma in most centres, sputum neutrophilia was increased in asthmatics and non-asthmatics in Uganda. This is the first time that sputum induction has been used to compare asthma inflammatory phenotypes in HICs and LMICs. Most cases were non-eosinophilic, including in settings where corticosteroid use was low. A lower prevalence of EA was observed in the LMICs than in the HICs. This has major implications for asthma prevention and management, and suggests that novel prevention strategies and therapies specifically targeting non-eosinophilic asthma are required globally.

AsthmaKGxE: An asthma–environment interaction knowledge graph leveraging public databases and scientific literature

Motivation: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environmental and psychosocial aspects. Discovery of such interactions can provide insights into the pathophysiology and etiology of asthma. In this paper, we propose an asthma knowledge graph (KG) built using a hybrid methodology for graph-based modeling of asthma complexity with a focus on environmental interactions. Using a heterogeneous set of public sources, we construct a genetic and pharmacogenetic asthma knowledge graph. The construction of this KG allowed us to shed more light on the lack of curated resources focused on environmental influences related to asthma. To remedy the lack of environmental data in our KG, we exploit the biomedical literature using state-of-the-art natural language processing and construct the first Asthma–Environment interaction catalog incorporating a continuously updated ensemble of environmental, psychological, nutritional and socio-economic influences. The catalog’s most substantiated results are then integrated into the KG. Results:The resulting environmentally rich knowledge graph ”AsthmaKGxE” aims to provide a resource for several potential applications of artificial intelligence and allows for a multi-perspective study of asthma. Our insight extraction results indicate that stress is the most frequent asthma association in the corpus, followed by allergens and obesity. We contend that studying asthma–environment interactions in more depth holds the key to curbing the complexity and heterogeneity of asthma. Availability:A user interface to browse and download the extracted catalog as well as the KG are available at http://asthmakgxe.moreair.info/. The code and supplementary data are available on github (https://github.com/ChaiAsaad/MoreAIRAsthmaKGxE).

Microbiome, Metabolism, and Immunoregulation of Asthma: An American Thoracic Society and National Institute of Allergy and Infectious Diseases Workshop Report.

This report presents the proceedings from a workshop titled “Microbiome, Metabolism and Immunoregulation of Asthma” that was held virtually May 13 and 14, 2021. The workshop was jointly sponsored by the American Thoracic Society (Assembly on Allergy, Immunology, and Inflammation) and the National Institute of Allergy and Infectious Diseases. It convened an interdisciplinary group of experts with backgrounds in asthma immunology, microbiome science, metabolomics, computational biology, and translational pulmonary research. The main purpose was to identify key scientific gaps and needs to further advance research on microbial and metabolic mechanisms that may contribute to variable immune responses and disease heterogeneity in asthma. Discussions were structured around several topics, including 1) immune and microbial mechanisms of asthma pathogenesis in murine models, 2) the role of microbes in pediatric asthma exacerbations, 3) dysregulated metabolic pathways in asthma associated with obesity, 4) metabolism effects on macrophage function in adipose tissue and the lungs, 5) computational approaches to dissect microbiome–metabolite links, and 6) potential confounders of microbiome–disease associations in human studies. This report summarizes the major points of discussion, which included identification of specific knowledge gaps, challenges, and suggested directions for future research. These include questions surrounding mechanisms by which microbiota and metabolites shape host health versus an allergic or asthmatic state; direct and indirect influences of other biological factors, exposures, and comorbidities on these interactions; and ongoing technical and analytical gaps for clinical translation.

Advancement of natural language programming, machine learning and electronic health records for the digital health science field

The widespread use of “electronic health record systems (EHRs)” in health care provides a large amount of real-world data, opening up new opportunities for medical trials. “Deep learning, a subset of machine learning (ML)”, has experienced a meteoric rise in popularity over the last six years, owing to advances in computing power and the accessibility of enormous new datasets. “Natural language processing (NLP)” approaches have been used as an “artificial intelligence strategy” to obtain information from medical narratives in EHRs, as a huge quantity of useful clinical knowledge is contained in clinical stories. This NLP capacity may enable “automated chart review in clinical care to identify individuals with distinct clinical features” and decrease methodological heterogeneity in establishing “phenotypes, masking biological heterogeneity in allergy, asthma, and immunology research”. Aim of this research paper is to understand the advanced technologies such as “Machine Learning, Natural Language Programming” are helpful for digital health field. In this context, secondary data collection method is used to gather information related to this topic.

The Short Chain Free Fatty Acids and Their Receptors in the Microbiotic Concept for Asthma Development

Asthma is a chronic, heterogeneous inflammatory disease with a high variability of the flow caused by the trigger influence of genetic and environmental factors. The heterogeneity of asthma indicates the involvement of many components in the mechanism of the inflammation of the respiratory tract. Currently, the microbiotic concept of the pathogenesis of respiratory system diseases is studied, based on the existence of a bilateral relationship between the microflora lungs and the intestines. The key mechanism of these relationships is free fatty acids performing signal and regulatory functions in the body. The medium-chain and long-chain free fatty acids are synthesized by de novo or enter the body as a result of consumption of fats, while the short-chain free fatty acids are formed in the intestine as a result of partial digestion of soluble fibre. The mechanism connecting the free fatty acids and inflammatory reactions includes activation of their receptors (Free Fatty Acid Receptor, FFAR) expressed on the cells of the gastrointestinal and respiratory tract, as well as on immune cells. If the role of the medium-chain and long-chain fatty acids receptors (FFAR1, FFAR4) and their ligands in the pathogenesis of asthma is actively studied, the value of the short-chain receptors (FFAR2, FFAR3) only begins to attract the attention of researchers in connection with the emergence of numerous data on the interconnection of the respiratory microbiome paths and intestines and its possible role in the induction of broncho-pulmonary complications. This review includes modern knowledge of the microbiotic concept of asthma, the basic information about the nomenclature, metabolism and transport of the free fatty acids, the value of the individual classes of the free fatty acids in the regulation of immune processes is normal and at bronchopulmonary pathology. The latest data of the FFAR receptors are systematized, features of their activation and expression, the intended role of FFAR2 and FFAR3 in the development and asthma therapy. It is discussed that gastrointestinal microbiota correction can reduce the activity of chronic inflammation of the respiratory tract at asthma.

Modern Understanding of Severe Bronchial Asthma

The review provides data on severe bronchial asthma. Frequent exacerbations of asthma significantly reduce the quality of life of patients, cause disability, disability and death. The heterogeneity of severe bronchial asthma fits into the concepts of phenotype and endotype, the identification of which in clinical practice has limitations, but is necessary for personalized therapy. Analysis of the literature reflecting experience in patient data management is needed to form holistic perceptions of severe bronchial asthma and develop ways to optimize therapy.

The Korean Severe Asthma Registry (KoSAR): real world research in severe asthma.

Severe asthma constitutes a serious health burden with significant morbidity and socioeconomic costs. The development and introduction of new biologics targeting type 2 inflammation changed the paradigm for management of severe asthma and initiated a biological era. These changes impose a challenge to clinicians in managing difficult-to-treat and severe asthma. To understand the characteristics and heterogeneity of severe asthma and to develop a better strategy to manage it, the Korean Academy of Asthma, Allergy and Clinical Immunology, Working Group on Severe Asthma, has organized the Korean Severe Asthma Registry (KoSAR). In this review, we describe the challenges of severe asthma management regarding diagnosis, disease burden, heterogeneity, guidelines, and organization of severe asthma clinics. This review also examines the current global activities of national and regional registries and study groups. In addition, we present the KoSAR vision and organization and describe the findings of KoSAR in comparison with those of other countries.

Use of biologics for the treatment of moderate-to-severe asthma: the age of personalized medicine.

There are multiple FDA-approved biologics to treat poorly controlled moderate-to-severe asthma. Given the heterogeneity of asthma and the lack of head-to-head data between biologics, selecting the best biologic for a patient can be difficult. This review summarizes the key literature to date, in hopes of facilitating an evidence-based approach to selecting the most appropriate biologic for patients with asthma. In addition to unique mechanisms of action, there is increasing literature on predictors of response to each biologic, such as sensitizations to aeroallergens, peripheral eosinophil count, total serum IgE, and exhaled nitric oxide. Biologics available for asthma are also being increasingly studied in comorbid conditions with asthma, and this may facilitate selecting the most appropriate biologic for a patient. In the absence of head-to-head studies, there is literature of switching between biologics whenever necessary. The authors outline an approach to selecting a biologic based on various considerations, and hope this suggested approach facilitates selecting the biologic most suitable for each individual with poorly controlled moderate-to-severe asthma.

Targeting Downstream Type 2 Cytokines or Upstream Epithelial Alarmins for Severe Asthma

Biologics, including omalizumab, mepolizumab, benralizumab, and dupilumab, targeting downstream IgE, cytokines IL-5, and IL-4/13, respectively, have shown promising effects in terms of reduction in annualized asthma exacerbation rates (AER), oral corticosteroid-sparing effects, improvements in forced expiratory volume in 1 second, and improved Asthma Control Questionnaire scores. However, despite these welcome advances, approximately 30% of patients with severe asthma receiving biologics tailored to their specific downstream type 2 biomarkers, including total IgE, peripheral blood eosinophils, and fractional exhaled nitric oxide, do not experience meaningful improvements in their AER. Instead of blocking downstream cytokines, targeting upstream epithelial alarmins, including IL-33, thymic stromal lymphopoietin, and IL-25, has been proposed to tackle the immunologic heterogeneity of asthma. This review article aims to pragmatically summarize the latest key clinical data on antialarmin therapies in severe asthma and put these findings into context with regard to currently available downstream cytokine blockers.

Phenotypic clusters on computed tomography reflects asthma heterogeneity and severity.

BackgroundAsthma is a heterogeneous inflammatory airway disorder with various phenotypes. Quantitative computed tomography (QCT) methods can differentiate among lung diseases through accurate assessment of the location, extent, and severity of the disease. The purpose of this study was to identify asthma clusters using QCT metrics of airway and parenchymal structure, and to identify associations with visual analyses conducted by radiologists.MethodsThis prospective study used input from QCT-based metrics including hydraulic diameter (Dh), luminal wall thickness (WT), functional small airway disease (fSAD), and emphysematous lung (Emph) to perform a cluster analysis and made comparisons with the visual grouping analysis conducted by radiologists based on site of airway involvement and remodeling evaluated.ResultsA total of 61 asthmatics of varying severities were grouped into 4 clusters. From C1 to C4, more severe lung function deterioration, higher fixed obstruction rate, and more frequent asthma exacerbations were observed in the 5-year follow-up period. C1 presented non-severe asthma with increased WT, Dh of proximal airways, and fSAD. C2 was mixed with non-severe and severe asthmatics, and showed bronchodilator responses limited to the proximal airways. C3 and C4 included severe asthmatics that showed a reduced Dh of the proximal airway and diminished bronchodilator responses. While C3 was characterized by severe allergic asthma without fSAD, C4 included ex-smokers with high fSAD% and Emph%. These clusters correlated well with the grouping done by radiologists and clinical outcomes.ConclusionsFour QCT imaging-based clusters with distinct structural and functional changes in proximal and small airways can stratify heterogeneous asthmatics and can be a complementary tool to predict clinical outcomes.

Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications

BackgroundAsthma is a heterogeneous disease and different phenotypes based on clinical parameters have been identified. However, the molecular subgroups of asthma defined by gene expression profiles of induced sputum have been rarely reported.MethodsWe re-analyzed the asthma transcriptional profiles of the dataset of GSE45111. A deep bioinformatics analysis was performed. We classified 47 asthma cases into different subgroups using unsupervised consensus clustering analysis. Clinical features of the subgroups were characterized, and their biological function and immune status were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) network were performed to identify key gene modules and hub genes.ResultsUnsupervised consensus clustering of gene expression profiles in asthma identified two distinct subgroups (Cluster I/II), which were significantly associated with eosinophilic asthma (EA) and paucigranulocytic asthma (PGA). The differentially expressed genes (DEGs) between the two subgroups were primarily enriched in immune response regulation and signal transduction. The ssGSEA suggested the different immune infiltration and function scores between the two clusters. The WGCNA and PPI analysis identified three hub genes: THBS1, CCL22 and CCR7. ROC analysis further suggested that the three hub genes had a good ability to differentiate the Cluster I from the Cluster II.ConclusionsBased on the gene expression profiles of the induced sputum, we identified two asthma subgroups, which revealed different clinical characteristics, gene expression patterns, biological functions and immune status. The transcriptional classification confirms the molecular heterogeneity of asthma and provides a framework for more in-depth research on the mechanisms of asthma.