Cluster Analysis Identifies 3 Phenotypes within Allergic Asthma

Clinical Characteristics and Outcomes of the Phenotypes of COPD-Bronchiectasis Association

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.

Reliability of Total Serum IgE Levels to Define Type 2 High and Low Asthma Phenotypes

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".

Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis

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.

Quantitative CT Characteristics of Cluster Phenotypes in the Severe Asthma Research Program Cohorts

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.

Trajectories of asthma and allergy symptoms from childhood to adulthood

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.

Classification of subtypes of patients with eating disorders by correspondence analysis

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.

Asthma clustering methods: a literature-informed application to the children's health study data

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.

A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

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.

Molecular Analysis of IL-5 Receptor Subunit Alpha as a Possible Pharmacogenetic Biomarker in Asthma

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.

Clinical Phenotypes of Patients Hospitalized for an Asthma Exacerbation: Prognostic Implications

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 [RR_adj], 18.10 and 19.17, respectively) and mechanical ventilation (RR_adj, 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 (RR_adj, 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.

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

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.

Severe asthma phenotypes in patients controlled with omalizumab: A real-world study

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.

Phenotypes of allergic diseases in children and their application in clinical situations

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.

Comparison of hypothesis- and data-driven asthma phenotypes in NHANES 2007-2012: the importance of comprehensive data availability

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.

The Journal of Allergy and Clinical Immunology: In Practice 2018 Highlights

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.