Prognostic value of cluster analysis of severe asthma phenotypes

Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data

BACKGROUND

Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes.

OBJECTIVES

To further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data.

METHODS

We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We used k-prototype unsupervised ML to develop a clustering model where predictors were age, sex, race, body mass index, prebronchodilator and postbronchodilator spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated through LightGBM's supervised ML approach on their cross-validated F1 score to support their distinctiveness.

RESULTS

Data from 13,498 patients with asthma with available postbronchodilator spirometry measurements were extracted to identify 5 stable clusters. Cluster 1 included a young nonsevere asthma population with normal lung function and higher frequency of acute exacerbation (0.8 /patient-year). Cluster 2 had the highest body mass index (mean ± SD, 44.44 ± 7.83 kg/m2), and the highest proportion of females (77.5%) and Blacks (28.9%). Cluster 3 comprised patients with normal lung function. Cluster 4 included patients with lower percent of predicted FEV1 of 77.03 (12.79) and poor response to bronchodilators. Cluster 5 had the lowest percent of predicted FEV1 of 68.08 (15.02), the highest postbronchodilator reversibility, and the highest proportion of severe asthma (44.9%) and blood eosinophilia (>300 cells/μL) (34.8%).

CONCLUSIONS

Using real-world data and unsupervised ML, we classified asthma into 5 clinically important subphenotypes where group-specific asthma treatment and management strategies can be designed and deployed.

Deep learning prediction of hospital readmissions for asthma and COPD

QUESTION

Severe asthma and COPD exacerbations requiring hospitalization are linked to increased disease morbidity and healthcare costs. We sought to identify Electronic Health Record (EHR) features of severe asthma and COPD exacerbations and evaluate the performance of four machine learning (ML) and one deep learning (DL) model in predicting readmissions using EHR data.

STUDY DESIGN AND METHODS

Observational study between September 30, 2012, and December 31, 2017, of patients hospitalized with asthma and COPD exacerbations.

RESULTS

This study included 5,794 patients, 1,893 with asthma and 3,901 with COPD. Patients with asthma were predominantly female (n = 1288 [68%]), 35% were Black (n = 669), and 25% (n = 479) were Hispanic. Black (44 vs. 33%, p = 0.01) and Hispanic patients (30 vs. 24%, p = 0.02) were more likely to be readmitted for asthma. Similarly, patients with COPD readmissions included a large percentage of Blacks (18 vs. 10%, p < 0.01) and Hispanics (8 vs. 5%, p < 0.01). To identify patients at high risk of readmission index hospitalization data of a subset of 2,682 patients, 777 with asthma and 1,905 with COPD, was analyzed with four ML models, and one DL model. We found that multilayer perceptron, the DL method, had the best sensitivity and specificity compared to the four ML methods implemented in the same dataset.

INTERPRETATION

Multilayer perceptron, a deep learning method, had the best performance in predicting asthma and COPD readmissions, demonstrating that EHR and deep learning integration can improve high-risk patient detection.

Blood eosinophil percentage as a predictor of response to inhaled corticosteroid in bronchiectasis

INTRODUCTION

The role of inhaled corticosteroid (ICS) among patients with bronchiectasis remains controversial. There is limited evidence of using baseline eosinophil count (absolute and percentage) as a marker to predict the role of ICS among patients with bronchiectasis.

METHODS

A retrospective case-control study was conducted in a major regional hospital and tertiary respiratory referral centre in Hong Kong, including 140 Chinese patients with noncystic fibrosis (CF) bronchiectasis, to investigate the exacerbation risks of bronchiectasis among ICS users and nonusers with different baseline eosinophil counts.

RESULTS

ICS user had significantly lower risk to develop bronchiectasis exacerbation with adjusted odds ratio (OR) of 0.461 (95% confidence interval [CI] 0.225-0.945, p-value 0.035). Univariate logistic regression was performed for different cut-offs of blood eosinophil count (by percentage) from 2% to 4% (with a 0.5% grid each time). Baseline eosinophil 3.5% was found to be the best cut-off among all with adjusted OR of 0.138 (95% CI = 0.023-0.822, p-value = 0.030).

CONCLUSION

Baseline eosinophil count of 3.5% might serve as a marker to predict the benefits of ICS on exacerbation risk among patients with non-CF bronchiectasis.

Influence of Genetics on the Response to Omalizumab in Patients with Severe Uncontrolled Asthma with an Allergic Phenotype

Omalizumab is a monoclonal antibody indicated for the treatment of severe uncontrolled asthma with an allergic phenotype. Its effectiveness could be influenced by clinical variables and single nucleotide polymorphisms (SNPs) in one or more of the genes involved in the mechanism of action and process of response to omalizumab, and these could be used as predictive biomarkers of response. We conducted an observational retrospective cohort study that included patients with severe uncontrolled allergic asthma treated with omalizumab in a tertiary hospital. Satisfactory response after 12 months of treatment was defined as (1) Reduction ≥ 50% of exacerbations or no exacerbations, (2) Improvement of lung function ≥ 10% FEV1, and (3) Reduction ≥ 50% of OCS courses or no OCS. Polymorphisms in the FCER1A (rs2251746, rs2427837), FCER1B (rs1441586, rs573790, rs1054485, rs569108), C3 (rs2230199), FCGR2A (rs1801274), FCGR2B (rs3219018, rs1050501), FCGR3A (rs10127939, rs396991), IL1RL1 (rs1420101, rs17026974, rs1921622), and GATA2 (rs4857855) genes were analyzed by real-time polymerase chain reaction (PCR) using TaqMan probes. A total of 110 patients under treatment with omalizumab were recruited. After 12 months of treatment, the variables associated with a reduction in exacerbations were the absence of polyposis (odds ratio [OR] = 4.22; 95% confidence interval [CI] = 0.95-19.63), IL1RL1 rs17026974-AG (OR = 19.07; 95% CI = 1.27-547), and IL1RL1 rs17026974-GG (OR = 16.76; 95% CI = 1.22-438.76). Reduction in oral corticosteroids (OCS) was associated with age of starting omalizumab treatment (OR = 0.95; 95% CI = 0.91-0.99) and blood eosinophil levels > 300 cells/µL (OR = 2.93; 95% CI = 1.01-9.29). Improved lung function showed a relationship to the absence of chronic obstructive pulmonary disease (COPD) (OR = 12.16; 95% CI = 2.45-79.49), FCGR2B rs3219018-C (OR = 8.6; 95% CI = 1.12-117.15), GATA2 rs4857855-T (OR = 15.98; 95% CI = 1.52-519.57) and FCGR2A rs1801274-G (OR = 13.75; 95% CI = 2.14-142.68; AG vs. AA and OR = 7.46; 95% CI = 0.94-89.12; GG vs. AA). Meeting one response criterion was related to FCER1A rs2251746-TT (OR = 24; 95% CI = 0.77-804.57), meeting two to age of asthma diagnosis (OR = 0.93; 95% CI = 0.88-0.99), and meeting all three to body mass index (BMI) < 25 (OR = 14.23; 95% CI = 3.31-100.77) and C3 rs2230199-C (OR = 3; 95% CI = 1.01-9.92). The results of this study show the possible influence of the polymorphisms studied on the response to omalizumab and the clinical benefit that could be obtained by defining predictive biomarkers of treatment response.

The association of asthma duration with body mass index and Weight-Adjusted-Waist index in a nationwide study of the U.S. adults

BACKGROUNDS

The association between obesity and asthma has been of interest, but whether the duration of asthma has an effect on obesity is still limitedly studied.

AIM

The purpose of this study was to investigate the association between asthma duration and obesity-related indexes, where obesity-related indexes include Body mass index (BMI) and Weight-adjusted-waist index (WWI).

METHODS

Data from National Health and Nutrition Examination Survey (NHANES) 2009-2018 were obtained to conduct this cross-sectional study. Duration of asthma was used as the independent variable and obesity-related indexes as the response variables. Multiple linear regression was used to assess the association between the independent variable and the response variables, and subsequently smoothed curve fitting and threshold effect analysis were performed to clarify whether there was a nonlinear correlation between the independent variable and the response variables. Finally, subgroup analysis was conducted to find sensitive populations.

RESULTS

A total of 9170 participants were included in the analysis. Asthma duration was statistically different between the two groups when all participants were grouped by median WWI (Q1 < 11.65, Q2 ≥ 11.65) (P < 0.001), but not by median BMI (Q1 < 31.8, Q2 ≥ 31.8) (P = 0.130). There was a positive association between asthma duration and WWI [β = 0.016, 95% CI (0.016, 0.017)], but a negative one with BMI [β = - 0.098, 95% CI (- 0.112, - 0.085)], and the correlations between the independent and response variables became more pronounced with increasing asthma duration (P for trend < 0.01). In addition, there were nonlinear relationships between asthma duration with BMI and WWI (log likelihood ratio < 0.001), with the best valid inflection points for asthma duration being 2 years (with WWI as the response variable) and 3 years (with BMI as the response variable), respectively. In the subgroup analysis, the positive association between asthma duration and WWI was more pronounced in the participants who were male, aged less than 40 years, and had asthma onset before 12 years of age. In contrast, when BMI was used as the response variable, the negative association between it and asthma duration was more pronounced among participants of female, aged 60 years or older, and with asthma onset less than 12 years of age.

CONCLUSIONS

In US adults, asthma duration might cause changes in obesity-related indexes. Longer asthma duration might cause weight loss, but might increase the risk of abdominal obesity.

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.

Definition, Phenotyping of Severe Asthma, Including Cluster Analysis

Asthma is defined as severe when it is uncontrolled despite the high intensity of treatment, or that loses control when a therapeutic step down is tried.These patients, for years, have been "uniformly" treated with massive doses of inhaled and oral corticosteroids regardless of their inflammatory state.Initially, asthma was considered of genesis "exclusively allergic." Subsequently, thanks to the development of noninvasive tools and of human monoclonal antibodies targeting interleukin 5, a pathogenetic role has been given to eosinophils. Management of steroids based on sputum eosinophil counts has been suggested according to clinical phenotypes identified through cluster analysis.The algorithms obtained from the cluster analysis have proved later to be poorly predictive of the inflammatory phenotype and difficult to apply in clinical practice.In the new era of precision medicine, the greatest challenge is finding clinical or biological elements predictive of response to therapies such as biologics. Cluster analyses performed on omics data or on cohorts of patients treated with biologics are more promising in this sense.In this article, starting from the current definition of severe asthma, we review the phenotypes proposed over time to date, showing the difficulty underlying the process of "phenotyping" due to the scarcity of available biomarkers.

Body Composition-Specific Asthma Phenotypes: Clinical Implications

Background: Previous studies have indicated the limitations of body mass index for defining disease phenotypes. The description of asthma phenotypes based on body composition (BC) has not been largely reported. Objective: To identify and characterize phenotypes based on BC parameters in patients with asthma. Methods: A study with two prospective observational cohorts analyzing adult patients with stable asthma (n = 541 for training and n = 179 for validation) was conducted. A body composition analysis was performed for the included patients. A cluster analysis was conducted by applying a 2-step process with stepwise discriminant analysis. Logistic regression models were used to evaluate the association between identified phenotypes and asthma exacerbations (AEs). The same algorithm for cluster analysis in the independent validation set was used to perform an external validation. Results: Three clusters had significantly different characteristics associated with asthma outcomes. An external validation identified the similarity of the participants in training and the validation set. In the training set, cluster Training (T) 1 (29.4%) was “patients with undernutrition”, cluster T2 (18.9%) was “intermediate level of nutrition with psychological dysfunction”, and cluster T3 (51.8%) was “patients with good nutrition”. Cluster T3 had a decreased risk of moderate-to-severe and severe AEs in the following year compared with the other two clusters. The most important BC-specific factors contributing to being accurately assigned to one of these three clusters were skeletal muscle mass and visceral fat area. Conclusion: We defined three distinct clusters of asthma patients, which had distinct clinical features and asthma outcomes. Our data reinforced the importance of evaluating BC to determining nutritional status in clinical practice.

Asthma phenotypes and endotypes: a systematic review

INTRODUCTION

Asthma is a complex disorder characterized by expiratory airflow limitation, wheeze, shortness of breath, chest tightness and cough, which can vary over time and in intensity. Being highly heterogeneous, asthma was characterized and classified in several asthma phenotypes and endotypes from 1947 until today. The present systematic review aims to summarize and describe evidence that was published in the last ten years in the field of asthma phenotyping and endotyping.

EVIDENCE ACQUISITION

The systematic review resumed high-quality evidence (clinical trials and randomized control trials) retrieved on MEDLINE and EMBASE databanks and involving adult asthmatic populations. Analyses of literature were conducted according to PRISMA and CASP guidelines.

EVIDENCE SYNTHESIS

Querying MEDLINE and EMBASE databanks, 5019 and 12261 entries were retrieved, respectively. Applying limitations for year of publication, age of participants, and type of publication, the search results were reduced to 98 and 132 articles, respectively. After data abstraction and resolution of duplications, only 50 articles were further evaluated. The research products were then classified first in macro-areas of interest (phenotypes or endotypes) and then in detailed micro-areas.

CONCLUSIONS

This systematic review overviews the principal findings available from high-quality literature in the last decade concerning asthma phenotypes and endotypes. Asthma has been described from different points of view, characterizing symptoms, microbiota composition, comorbidities, viral infections, and airway and/or systemic inflammatory status. The comprehension of precise mechanisms underlying asthma pathogenesis is thereby the basis for the development of novel therapeutic strategies, likely essential to the development of precision medicine.

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.

Phenotypes Determined by Cluster Analysis and Their Survival in the Prospective European Scleroderma Trials and Research Cohort of Patients With Systemic Sclerosis

Objective

Systemic sclerosis (SSc) is a heterogeneous connective tissue disease that is typically subdivided into limited cutaneous SSc (lcSSc) and diffuse cutaneous SSc (dcSSc) depending on the extent of skin involvement. This subclassification may not capture the entire variability of clinical phenotypes. The European Scleroderma Trials and Research (EUSTAR) database includes data on a prospective cohort of SSc patients from 122 European referral centers. This study was undertaken to perform a cluster analysis of EUSTAR data to distinguish and characterize homogeneous phenotypes without any a priori assumptions, and to examine survival among the clusters obtained.

Methods

A total of 11,318 patients were registered in the EUSTAR database, and 6,927 were included in the study. Twenty‐four clinical and serologic variables were used for clustering.

Results

Clustering analyses provided a first delineation of 2 clusters showing moderate stability. In an exploratory attempt, we further characterized 6 homogeneous groups that differed with regard to their clinical features, autoantibody profile, and mortality. Some groups resembled usual dcSSc or lcSSc prototypes, but others exhibited unique features, such as a majority of lcSSc patients with a high rate of visceral damage and antitopoisomerase antibodies. Prognosis varied among groups and the presence of organ damage markedly impacted survival regardless of cutaneous involvement.

Conclusion

Our findings suggest that restricting subsets of SSc patients to only those based on cutaneous involvement may not capture the complete heterogeneity of the disease. Organ damage and antibody profile should be taken into consideration when individuating homogeneous groups of patients with a distinct prognosis.

[Update in severe asthma physiopathology and treatments]

Asthma is an inflammatory airway disease which presentation is highly heterogeneous. Last two decades provided new clinical and basic data concerning asthma physiopathology that make global understanding much complex. Phenotypes based on clinical settings and paraclinical investigations from large cohorts confirm old paradigm (eosinophilic vs. non-eosinophilic asthma) but also introduce new concepts (obesity-related asthma, late onset asthma, etc.). Conversely, improvement of big data analytics allows to initiate new cohorts aiming at better understanding the pathophysiology underlying those phenotypes and unraveling new ones. However, clinical and therapeutic impacts of those big data need to be further detailed. In parallel, biotherapies and innovative techniques as bronchial thermoplasty become available for severe asthmatic patients who did not respond to specific treatment in the past. Development of a personalized medicine in severe asthma becomes an important challenge for tomorrow. This review will focus on new pathophysiological concepts arisen from large cohorts and new therapeutic strategies available and in progress for severe asthma.

Data-driven adult asthma phenotypes based on clinical characteristics are associated with asthma outcomes twenty years later

BACKGROUND

Research based on cluster analyses led to the identification of particular phenotypes confirming phenotypic heterogeneity of asthma. The long-term clinical course of asthma phenotypes defined by clustering analysis remains unknown, although it is a key aspect to underpin their clinical relevance. We aimed to estimate risk of poor asthma events between asthma clusters identified 20 years earlier.

METHODS

The study relied on two cohorts of adults with asthma with 20-year follow-up, ECRHS (European Community Respiratory Health Survey) and EGEA (Epidemiological study on Genetics and Environment of Asthma). Regression models were used to compare asthma characteristics (current asthma, asthma exacerbations, asthma control, quality of life, and FEV₁ ) at follow-up and the course of FEV₁  between seven cluster-based asthma phenotypes identified 20 years earlier.

RESULTS

The analysis included 1325 adults with ever asthma. For each asthma characteristic assessed at follow-up, the risk for adverse outcomes differed significantly between the seven asthma clusters identified at baseline. As compared with the mildest asthma phenotype, ORs (95% CI) for asthma exacerbations varied from 0.9 (0.4 to 2.0) to 4.0 (2.0 to 7.8) and the regression estimates (95% CI) for FEV₁ % predicted varied from 0.6 (-3.5 to 4.6) to -9.9 (-14.2 to -5.5) between clusters. Change in FEV₁ over time did not differ significantly across clusters.

CONCLUSION

Our findings show that the long-term risk for poor asthma outcomes differed between comprehensive adult asthma phenotypes identified 20 years earlier, and suggest a strong tracking of asthma activity and impaired lung function over time.

Topographic Phenotypes of Alopecia Areata and Development of a Prognostic Prediction Model and Grading System: A Cluster Analysis

Importance

Diverse assessment tools and classification have been used for alopecia areata; however, their prognostic values are limited.

Objective

To identify the topographic phenotypes of alopecia areata using cluster analysis and to establish a prediction model and grading system for stratifying prognoses.

Design, Setting, and Participants

A retrospective cohort study of 321 patients with alopecia areata who visited a single tertiary referral center between October 2012 and February 2017 and underwent 4-view photographic assessment.

Exposures

Clinical photographs were reviewed to evaluate hair loss using the Severity of Alopecia Tool 2. Topographic phenotypes of alopecia areata were identified using hierarchical clustering with Ward's method. Differences in clinical characteristics and prognosis were compared across the clusters. The model was evaluated for its performance, accuracy, and interobserver reliability by comparison to conventional methods.

Main Outcomes and Measures

Topographic phenotypes of alopecia areata and their major (60%-89%) and complete regrowth probabilities (90%-100%) within 12 months.

Results

A total of 321 patients were clustered into 5 subgroups. Grade 1 (n = 200; major regrowth, 93.4%; complete regrowth, 65.2%) indicated limited hair loss, whereas grades 2A (n = 66; major regrowth, 87.8%; complete regrowth, 64.2%) and 2B (n = 20; major regrowth, 73.3%; complete regrowth, 45.5%) exhibited greater hair loss than grade 1. The temporal area was predominantly involved in grade 2B, but not in grade 2A, despite being comparable in total extent of hair loss. Grade 3 (n = 20; major regrowth, 45.5%; complete regrowth, 25.5%) included diffuse or extensive alopecia areata, and grade 4 (n = 15; major regrowth, 28.2%; complete regrowth, 16.7%) corresponded to alopecia (sub)totalis. No significant differences in prognosis (hazard ratio [HR] for major regrowth, 0.79; 95% CI, 0.56-1.12) were found between grades 2A and 1, whereas grades 2B (HR, 0.41; 95% CI, 0.21-0.81), 3 (HR, 0.24; 95% CI, 0.12-0.50), and 4 (HR, 0.16; 95% CI, 0.06-0.39) had significantly poorer response. Among multiple models, the cluster solution had the greatest prognostic performance and accuracy. The tree model of the cluster solution was converted into the Topography-based Alopecia Areata Severity Tool (TOAST), which revealed an excellent interobserver reliability among 4 dermatologists (median quadratic-weighted κ, 0.89).

Conclusions and Relevance

Temporal area involvement should be independently measured for better prognostic stratification. The TOAST is an effective tool for describing the topographical characteristics and prognosis of hair loss and may enable clinicians to establish better treatment plans.

Distinct Phenotypes of Smokers with Fixed Airflow Limitation Identified by Cluster Analysis of Severe Asthma

RATIONALE

Smoking may have multifactorial effects on asthma phenotypes, particularly in severe asthma. Cluster analysis has been applied to explore novel phenotypes, which are not based on any a priori hypotheses.

OBJECTIVES

To explore novel severe asthma phenotypes by cluster analysis when including smoking patients with asthma.

METHODS

We recruited a total of 127 subjects with severe asthma, including 59 current or ex-smokers, from our university hospital and its 29 affiliated hospitals/pulmonary clinics. Clinical variables obtained during a 2-day hospital stay were used for cluster analysis. After clustering using clinical variables, the sputum levels of 14 molecules were measured to biologically characterize the clinical clusters.

RESULTS

Five clinical clusters, including two characterized by low forced expiratory volume in 1 second/forced vital capacity, were identified. When characteristics of smoking subjects in these two clusters were compared, there were marked differences between the two groups: one had high levels of circulating eosinophils, high immunoglobulin E levels, and a high sinus score, and the other was characterized by low levels of the same parameters. Sputum analysis revealed intriguing differences of cytokine/chemokine pattern in these two groups. The other three clusters were similar to those previously reported: young onset/atopic, nonsmoker/less eosinophilic, and female/obese. Key clinical variables were confirmed to be stable and consistent 3 years later.

CONCLUSIONS

This study reveals two distinct phenotypes with potentially different biological pathways contributing to fixed airflow limitation in cigarette smokers with severe asthma.

Novel Trajectories for Identifying Asthma Phenotypes: A Longitudinal Study in Korean Asthma Cohort, COREA

BACKGROUND

Unbiased cluster analysis has identified several asthma phenotypes. However, these phenotypes did not consistently predict disease prognosis and reflect temporal variability in airway inflammation.

OBJECTIVE

We aimed to identify longitudinal trajectories in terms of pulmonary function parameters and investigated whether the trajectories are associated with prognosis.

METHODS

Data were extracted from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA). Three-year pulmonary function test results were used to apply finite mixture models for group-based trajectory in 486 patients with eligible data set.

RESULTS

Two main sets of longitudinal trajectories were identified in terms of FEV₁% predicted, and FEV₁ variability. In the 4 trajectories determined with FEV₁% predicted, the pulmonary function showed a consistent course in 4 stratified levels during 3 years of follow-up, which was associated with unexpected hospital visits and the use of steroid bursts due to exacerbation. The variability in pulmonary function showed 3 different patterns, and we found that higher blood and sputum eosinophil levels were associated with the higher variability in pulmonary function and more exacerbations.

CONCLUSIONS

Trajectory analysis is a novel method that provides longitudinal asthma phenotypes and aids in prediction of future risk of exacerbation. Further analysis is needed to validate the usefulness of these trajectories in an independent population.

Prospective observational study in patients with obstructive lung disease: NOVELTY design

Asthma and chronic obstructive pulmonary disease (COPD) have overlapping clinical features and share pathobiological mechanisms but are often considered distinct disorders. Prospective, observational studies across asthma, COPD and asthma–COPD overlap are limited. NOVELTY is a global, prospective observational 3-year study enrolling ∼12 000 patients ≥12 years of age from primary and specialist clinical practices in 19 countries (ClinicalTrials.gov identifier: NCT02760329).

NOVELTY's primary objectives are to describe patient characteristics, treatment patterns and disease burden over time, and to identify phenotypes and molecular endotypes associated with differential outcomes over time in patients with a diagnosis/suspected diagnosis of asthma and/or COPD. NOVELTY aims to recruit real-world patients, unlike clinical studies with restrictive inclusion/exclusion criteria.

Data collected at yearly intervals include clinical assessments, spirometry, biospecimens, patient-reported outcomes (PROs) and healthcare utilisation (HCU). PROs and HCU will also be collected 3-monthly via internet/telephone. Data will be used to identify phenotypes and endotypes associated with different trajectories for symptom burden, clinical progression or remission and HCU. Results may allow patient classification across obstructive lung disease by clinical outcomes and biomarker profile, rather than by conventional diagnostic labels and severity categories.

NOVELTY will provide a rich data source on obstructive lung disease, to help improve patient outcomes and aid novel drug development.

NOVELTY is a global study to characterise patients with asthma and/or COPD and identify novel phenotypes and endotypeshttp://ow.ly/QFiH30n3IBF

Pathophysiological Mechanisms of Asthma

The recent Lancet commission has highlighted that "asthma" should be used to describe a clinical syndrome of wheeze, breathlessness, chest tightness, and sometimes cough. The next step is to deconstruct the airway into components of fixed and variable airflow obstruction, inflammation, infection and altered cough reflex, setting the airway disease in the context of extra-pulmonary co-morbidities and social and environmental factors. The emphasis is always on delineating treatable traits, including variable airflow obstruction caused by airway smooth muscle constriction (treated with short- and long-acting β-2 agonists), eosinophilic airway inflammation (treated with inhaled corticosteroids) and chronic bacterial infection (treated with antibiotics with benefit if it is driving the disease). It is also important not to over-treat the untreatable, such as fixed airflow obstruction. These can all be determined using simple, non-invasive tests such as spirometry before and after acute administration of a bronchodilator (reversible airflow obstruction); peripheral blood eosinophil count, induced sputum, exhaled nitric oxide (airway eosinophilia); and sputum or cough swab culture (bacterial infection). Additionally, the pathophysiology of risk domains must be considered: these are risk of an asthma attack, risk of poor airway growth, and in pre-school children, risk of progression to eosinophilic school age asthma. Phenotyping the airway will allow more precise diagnosis and targeted treatment, but it is important to move to endotypes, especially in the era of increasing numbers of biologicals. Advances in -omics technology allow delineation of pathways, which will be particularly important in TH2 low eosinophilic asthma, and also pauci-inflammatory disease. It is very important to appreciate the difficulties of cluster analysis; a patient may have eosinophilic airway disease because of a steroid resistant endotype, because of non-adherence to basic treatment, and a surge in environmental allergen burden. Sophisticated -omics approaches will be reviewed in this manuscript, but currently they are not being used in clinical practice. However, even while they are being evaluated, management of the asthmas can and should be improved by considering the pathophysiologies of the different airway diseases lumped under that umbrella term, using simple, non-invasive tests which are readily available, and treating accordingly.

Identification and prospective stability of electronic nose (eNose)-derived inflammatory phenotypes in patients with severe asthma

BACKGROUND

Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using "omics" technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes.

OBJECTIVES

We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to within-patient clinical and inflammatory changes.

METHODS

In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability.

RESULTS

Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNose-driven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045).

CONCLUSIONS

We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma.

Translating Asthma: Dissecting the Role of Metabolomics, Genomics and Personalized Medicine

The management of asthma has largely stagnated over the last 25 years, but we are at the dawning of a new age wherein -omics technology can help us manage the disease objectively and rationally. Even in this new scientific age, getting the basics of asthma management right remains essential. The new technologies which can be applied to multiple biological samples include genomics (study of the genome), transcriptomics (gene transcription), lipidomics, proteomics and metabolomics (lipids, proteins and metabolites, respectively) and breathomics, using exhaled breath as a source of biomarkers, which is of particular interest in view of its non-invasive nature in pediatrics. Important applications will include the diagnosis of airways disease, including its components; the pathways driving airway pathology; monitoring the response to treatment; and measuring future risk (asthma attacks, poor lung growth trajectory). With the advent of a wide range of novel biologicals to treat asthma, -omics technology to personalize therapy will be especially important. The U-BIOPRED (Europe) and SARP (USA) groups have been most active in this field, especially using bronchoscopically obtained samples to perform cluster analyses to define new asthma endotypes. However, stability over time and consistency between investigators is imperfect. This is perhaps unsurprising; results of biomarker studies in asthma will be a composite of the underlying disease, the (variable) effects of adverse drivers such as allergen exposure and pollution, the effects of treatment, and the effects of adherence or otherwise to treatment. Ultimately, the aim should be an exhaled breath based tool with a rapid result that can be used as a routine in the clinic. However, at the moment, there are as yet no clinical applications in children of -omics technology.

Multidimensional endotypes of chronic rhinosinusitis and their association with treatment outcomes

BACKGROUND

The expression of chronic rhinosinusitis (CRS) is multidimensional. Disease heterogeneity in patients with CRS remains poorly understood. This study aimed to identify endotypes of CRS using cluster analysis by integrating multidimensional characteristics and to explore their association with treatment outcomes.

METHODS

A total of 28 clinical variables and 39 mucosal cellular and molecular variables were analyzed using principal component analysis. Cluster analysis was performed on 246 prospectively recruited Chinese CRS patients with at least 1-year postoperative follow-up. Difficult-to-treat CRS was characterized in each generated cluster.

RESULTS

Seven subject clusters were identified. Cluster 1 (13.01%) was comparable to the classic well-defined eosinophilic CRS with polyps, having severe disease and the highest proportion of difficult-to-treat CRS. Patients in cluster 2 (16.26%) and cluster 4 (13.82%) had relatively lower proportions of presence of polyps and presented mild inflammation with moderate proportions of difficult-to-treat cases. Subjects in cluster 2 were highly atopic. Cluster 3 (7.31%) and cluster 6 (21.14%) were characterized by severe or moderate neutrophilic inflammation, respectively, and with elevated levels of IL-8 and high proportions of difficult-to-treat CRS. Cluster 5 (4.07%) was a unique group characterized by the highest levels of IL-10 and lacked difficult-to-treat cases. Cluster 7 (24.39%) demonstrated the lowest symptom severity, a low proportion of difficult-to-treat CRS, and low inflammation load. Finally, we found that difficult-to-treat CRS was associated with distinct clinical features and biomarkers in the different clusters.

CONCLUSIONS

Distinct clinicopathobiologic clusters of CRS display differences in clinical response to treatments and characteristics of difficult-to-treat CRS.

Omalizumab for severe asthma: toward personalized treatment based on biomarker profile and clinical history

Asthma is a heterogeneous syndrome with numerous underlining molecular and inflammatory mechanisms contributing to the wide spectrum of clinical phenotypes. Multiple therapies targeting severe asthma with type 2 (T2) high inflammation are or soon will be available. T2 high inflammation is defined as inflammation associated with atopy or eosinophilia or an increase in cytokines associated with T-helper 2 lymphocytes. Omalizumab is a humanized anti-IgE monoclonal antibody and the first biologic therapy approved for moderate-severe allergic asthma. Despite the specificity of biologic therapies like omalizumab, clinical response is variable, with approximately 50% of treated patients achieving the primary outcome. A prior identification of the ideal candidate for therapy would improve patient outcomes and optimize the use of health care resources. As the number of biologic therapies for asthma increases, the goal is identification of biomarkers or clinical phenotypes likely to respond to a specific therapy. This review focuses on potential biomarkers and clinical history that may identify responders to omalizumab therapy for asthma.

Severe Asthma Phenotypes - How Should They Guide Evaluation and Treatment?

Although patients with "severe" asthma tend to be characterized by ongoing symptoms and airway inflammation despite treatment with high doses of inhaled and systemic corticosteroids, there is increasing recognition of marked phenotypic heterogeneity within affected patients. Although "precision medicine" approaches for patients with severe asthma are needed, there are many hurdles that must be overcome in daily practice. The National Heart, Lung and Blood Institute's Severe Asthma Research Program (SARP) has been at the forefront of phenotype discovery in severe asthma for the past decade. SARP, along with other international groups, has described clinical severe asthma phenotypes in both adults and children that can be evaluated in the clinical setting. Although these clinical phenotypes provide a good "starting point" for addressing disease heterogeneity in severe asthma in everyday practice, more efforts are needed to understand how these phenotypes relate to underlying disease mechanisms and pharmacological treatment responses. This review highlights the clinical asthma phenotypes identified to date, their associations with underlying endotypes and potential biomarkers, and remaining knowledge gaps that must be addressed before precision medicine can become a reality for patients with severe asthma.

Phenotypes of symptomatic airways disease in China and New Zealand

It is uncertain whether phenotypes of asthma and chronic obstructive pulmonary disease (COPD) vary between populations with different genetic and environmental characteristics. Here, our objective was to compare the phenotypes of airways disease in two separate populations.This was a cross-sectional observational study in adult populations from New Zealand and China. Participants aged 40-75 years who reported wheeze and breathlessness in the last 12 months were randomly selected from the general population and underwent detailed characterisation. Complete data for cluster analysis were available for 345 participants. Hierarchical cluster analysis was undertaken, based on 12 variables: forced expiratory volume in 1 s (FEV₁), FEV₁/forced vital capacity ratio, bronchodilator reversibility, peak expiratory flow variability, transfer coefficient of the lung for carbon monoxide, exhaled nitric oxide fraction, total IgE, C-reactive protein, age of symptom onset, body mass index, health status and cigarette smoke exposure.Cluster analysis of the combined dataset described five phenotypes: "severe late-onset asthma/COPD overlap group", "moderately severe early-onset asthma/COPD overlap group", "moderate to severe asthma group with type 2 predominant disease", and two groups with minimal airflow obstruction, differentiated by age of onset. Separate analyses by country showed similar patterns; however, a distinct obese/comorbid group was observed in the New Zealand population.Cluster analysis of adults with symptomatic airways disease suggests the presence of similar asthma/COPD overlap phenotypes within populations with different genetic and environmental characteristics, and an obese/comorbid phenotype in a Western population.

Phenotypes and Emerging Endotypes of Chronic Rhinosinusitis

Chronic rhinosinusitis can be differentiated into several phenotypes based on clinical criteria; however, these phenotypes do not teach us much about the underlying inflammatory mechanisms. Thus, the use of nasal endoscopy and CT scanning, and eventually taking a swab or a biopsy, may not be sufficient to fully appreciate the individual patient's pathology. Endotyping of chronic rhinosinusitis on the basis of pathomechanisms, functionally and pathologically different from others by the involvement of specific molecules or cells, may in contrast provide us with information on the risk of disease progression or recurrence and on the best available treatment, and also helps us identifying innovative therapeutic targets for treatment. Endotyping may best be structured around T helper cells and their downstream events, such as tissue eosinophilia or neutrophilia; this approach involves the cytokines and chemokines related to specific T helper cell populations, and related markers such as IgE. Endotyping is of specific interest at the time of the arrival of new biologicals, confronting us with the challenge of the selection of eligible patients for treatment and predicting their therapeutic response; defining suitable biomarkers is therefore an urgent task. Failure to appreciate the underlying mechanisms and endotypes of chronic rhinosinusitis may limit progress in the management of the disease at present.

Characterisation of asthma subgroups associated with circulating YKL-40 levels

The chitinase-like protein YKL-40 mediates airway inflammation and serum levels are associated with asthma severity. However, asthma phenotypes associated with YKL-40 levels have not been precisely defined.We conducted an unsupervised cluster analysis of asthma patients treated at the Yale Center for Asthma and Airways Disease (n=156) to identify subgroups according to YKL-40 level. The resulting YKL-40 clusters were cross-validated in cohorts from the Severe Asthma Research Programme (n=167) and the New York University/Bellevue Asthma Repository (n=341). A sputum transcriptome analysis revealed molecular pathways associated with YKL-40 subgroups.Four YKL-40 clusters (C1-C4) were identified. C3 and C4 had high serum YKL-40 levels compared with C1 and C2. C3 was associated with earlier onset and longer duration of disease, severe airflow obstruction, and near-fatal asthma exacerbations. C4 had the highest serum YKL-40 levels, adult onset and less airflow obstruction, but frequent exacerbations. An airway transcriptome analysis in C3 and C4 showed activation of non-type 2 inflammatory pathways.Elevated serum YKL-40 levels were associated with two distinct clinical asthma phenotypes: one with irreversible airway obstruction and another with severe exacerbations. The YKL-40 clusters are potentially useful for identification of individuals with severe or exacerbation-prone asthma.

Long-term future risk of severe exacerbations: Distinct 5-year trajectories of problematic asthma

Background

Assessing future risk of exacerbations is an important component of asthma management. Existing studies have investigated short‐ but not long‐term risk. Problematic asthma patients with unfavorable long‐term disease trajectory and persistently frequent severe exacerbations need to be identified early to guide treatment.

Aim

To identify distinct trajectories of severe exacerbation rates among “problematic asthma” patients and develop a risk score to predict the most unfavorable trajectory.

Methods

Severe exacerbation rates over five years for 177 “problematic asthma” patients presenting to a specialist asthma clinic were tracked. Distinct trajectories of severe exacerbation rates were identified using group‐based trajectory modeling. Baseline predictors of trajectory were identified and used to develop a clinical risk score for predicting the most unfavorable trajectory.

Results

Three distinct trajectories were found: 58.5% had rare intermittent severe exacerbations (“infrequent”), 32.0% had frequent severe exacerbations at baseline but improved subsequently (“nonpersistently frequent”), and 9.5% exhibited persistently frequent severe exacerbations, with the highest incidence of near‐fatal asthma (“persistently frequent”). A clinical risk score composed of ≥2 severe exacerbations in the past year (+2 points), history of near‐fatal asthma (+1 point), body mass index ≥25kg/m² (+1 point), obstructive sleep apnea (+1 point), gastroesophageal reflux (+1 point), and depression (+1 point) was predictive of the “persistently frequent” trajectory (area under the receiver operating characteristic curve: 0.84, sensitivity 72.2%, specificity 81.1% using cutoff ≥3 points). The trajectories and clinical risk score had excellent performance in an independent validation cohort.

Conclusions

Patients with problematic asthma follow distinct illness trajectories over a period of five years. We have derived and validated a clinical risk score that accurately identifies patients who will have persistently frequent severe exacerbations in the future.

Identification and validation of asthma phenotypes in Chinese population using cluster analysis

BACKGROUND

Asthma is a heterogeneous airway disease, so it is crucial to clearly identify clinical phenotypes to achieve better asthma management.

OBJECTIVE

To identify and prospectively validate asthma clusters in a Chinese population.

METHODS

Two hundred eighty-four patients were consecutively recruited and 18 sociodemographic and clinical variables were collected. Hierarchical cluster analysis was performed by the Ward method followed by k-means cluster analysis. Then, a prospective 12-month cohort study was used to validate the identified clusters.

RESULTS

Five clusters were successfully identified. Clusters 1 (n = 71) and 3 (n = 81) were mild asthma phenotypes with slight airway obstruction and low exacerbation risk, but with a sex differential. Cluster 2 (n = 65) described an "allergic" phenotype, cluster 4 (n = 33) featured a "fixed airflow limitation" phenotype with smoking, and cluster 5 (n = 34) was a "low socioeconomic status" phenotype. Patients in clusters 2, 4, and 5 had distinctly lower socioeconomic status and more psychological symptoms. Cluster 2 had a significantly increased risk of exacerbations (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.03-1.25), unplanned visits for asthma (RR 1.98, 95% CI 1.07-3.66), and emergency visits for asthma (RR 7.17, 95% CI 1.26-40.80). Cluster 4 had an increased risk of unplanned visits (RR 2.22, 95% CI 1.02-4.81), and cluster 5 had increased emergency visits (RR 12.72, 95% CI 1.95-69.78). Kaplan-Meier analysis confirmed that cluster grouping was predictive of time to the first asthma exacerbation, unplanned visit, emergency visit, and hospital admission (P < .0001 for all comparisons).

CONCLUSION

We identified 3 clinical clusters as "allergic asthma," "fixed airflow limitation," and "low socioeconomic status" phenotypes that are at high risk of severe asthma exacerbations and that have management implications for clinical practice in developing countries.

Inflammatory and Comorbid Features of Patients with Severe Asthma and Frequent Exacerbations

RATIONALE

Reducing asthma exacerbation frequency is an important criterion for approval of asthma therapies, but the clinical features of exacerbation-prone asthma (EPA) remain incompletely defined.

OBJECTIVES

To describe the clinical, physiologic, inflammatory, and comorbidity factors associated with EPA.

METHODS

Baseline data from the NHLBI Severe Asthma Research Program (SARP)-3 were analyzed. An exacerbation was defined as a burst of systemic corticosteroids lasting 3 days or more. Patients were classified by their number of exacerbations in the past year: none, few (one to two), or exacerbation prone (≥3). Replication of a multivariable model was performed with data from the SARP-1 + 2 cohort.

MEASUREMENTS AND MAIN RESULTS

Of 709 subjects in the SARP-3 cohort, 294 (41%) had no exacerbations and 173 (24%) were exacerbation prone in the prior year. Several factors normally associated with severity (asthma duration, age, sex, race, and socioeconomic status) did not associate with exacerbation frequency in SARP-3; bronchodilator responsiveness also discriminated exacerbation proneness from asthma severity. In the SARP-3 multivariable model, blood eosinophils, body mass index, and bronchodilator responsiveness were positively associated with exacerbation frequency (rate ratios [95% confidence interval], 1.6 [1.2-2.1] for every log unit of eosinophils, 1.3 [1.1-1.4] for every 10 body mass index units, and 1.2 [1.1-1.4] for every 10% increase in bronchodilatory responsiveness). Chronic sinusitis and gastroesophageal reflux were also associated with exacerbation frequency (1.7 [1.4-2.1] and 1.6 [1.3-2.0]), even after adjustment for multiple factors. These effects were replicated in the SARP-1 + 2 multivariable model.

CONCLUSIONS

EPA may be a distinct susceptibility phenotype with implications for the targeting of exacerbation prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT 01760915).

Endotypes of difficult-to-control asthma in inner-city African American children

African Americans have higher rates of asthma prevalence, morbidity, and mortality in comparison with other racial groups. We sought to characterize endotypes of childhood asthma severity in African American patients in an inner-city pediatric asthma population. Baseline blood neutrophils, blood eosinophils, and 38 serum cytokine levels were measured in a sample of 235 asthmatic children (6-17 years) enrolled in the NIAID (National Institute of Allergy and Infectious Diseases)-sponsored Asthma Phenotypes in the Inner City (APIC) study (ICAC (Inner City Asthma Consortium)-19). Cytokines were quantified using a MILLIPLEX panel and analyzed on a Luminex analyzer. Patients were classified as Easy-to-Control or Difficult-to-Control based on the required dose of controller medications over one year of prospective management. A multivariate variable selection procedure was used to select cytokines associated with Difficult-to-Control versus Easy-to-Control asthma, adjusting for age, sex, blood eosinophils, and blood neutrophils. In inner-city African American children, 12 cytokines were significant predictors of Difficult-to-Control asthma (n = 235). CXCL-1, IL-5, IL-8, and IL-17A were positively associated with Difficult-to-Control asthma, while IL-4 and IL-13 were positively associated with Easy-to-Control asthma. Using likelihood ratio testing, it was observed that in addition to blood eosinophils and neutrophils, serum cytokines improved the fit of the model. In an inner-city pediatric population, serum cytokines significantly contributed to the definition of Difficult-to-Control asthma endotypes in African American children. Mixed responses characterized by TH2 (IL-5) and TH17-associated cytokines were associated with Difficult-to-Control asthma. Collectively, these data may contribute to risk stratification of Difficult-to-Control asthma in the African American population.

Cluster Analysis on Longitudinal Data of Patients with Adult-Onset Asthma

BACKGROUND

Previous cluster analyses on asthma are based on cross-sectional data.

OBJECTIVE

To identify phenotypes of adult-onset asthma by using data from baseline (diagnostic) and 12-year follow-up visits.

METHODS

The Seinäjoki Adult Asthma Study is a 12-year follow-up study of patients with new-onset adult asthma. K-means cluster analysis was performed by using variables from baseline and follow-up visits on 171 patients to identify phenotypes.

RESULTS

Five clusters were identified. Patients in cluster 1 (n = 38) were predominantly nonatopic males with moderate smoking history at baseline. At follow-up, 40% of these patients had developed persistent obstruction but the number of patients with uncontrolled asthma (5%) and rhinitis (10%) was the lowest. Cluster 2 (n = 19) was characterized by older men with heavy smoking history, poor lung function, and persistent obstruction at baseline. At follow-up, these patients were mostly uncontrolled (84%) despite daily use of inhaled corticosteroid (ICS) with add-on therapy. Cluster 3 (n = 50) consisted mostly of nonsmoking females with good lung function at diagnosis/follow-up and well-controlled/partially controlled asthma at follow-up. Cluster 4 (n = 25) had obese and symptomatic patients at baseline/follow-up. At follow-up, these patients had several comorbidities (40% psychiatric disease) and were treated daily with ICS and add-on therapy. Patients in cluster 5 (n = 39) were mostly atopic and had the earliest onset of asthma, the highest blood eosinophils, and FEV₁ reversibility at diagnosis. At follow-up, these patients used the lowest ICS dose but 56% were well controlled.

CONCLUSIONS

Results can be used to predict outcomes of patients with adult-onset asthma and to aid in development of personalized therapy (NCT02733016 at ClinicalTrials.gov).

Patient Characteristics and Individualization of Biologic Therapy

Progress in the understanding of disease processes has provided additional therapeutic targets, best exemplified by the increasing role of biologics in the clinical armamentarium. This article provides a focused review of current treatment paradigms and pathophysiology for asthma, atopic dermatitis, urticaria, as well as C1 inhibitor deficiency. It elucidates the populations in which biologics were studied for the aforementioned disease states, emphasizing characteristics to consider when selecting therapy. It is important to correctly estimate patient outcome before starting therapy based on cost analysis. Treatment decisions need to be guided by appropriate patient stratification based on each individual's underlying phenotype.

Targeting patients with asthma for omalizumab therapy: choosing the right patient to get the best value for money

The asthma syndrome has many manifestations, termed phenotypes, that arise by specific cellular and molecular mechanisms, termed endotypes. Understanding an individual's asthma phenotype helps clinicians make rational therapeutic decisions while the understanding of endotypes has led to the development of specific precision medications. Allergic asthma is an example of an asthma phenotype and omalizumab, a monoclonal antibody that neutralizes serum immunoglobulin (Ig)E, is a specific targeted treatment which was developed as a result of an understanding of the endotype of allergic asthma. Omalizumab has been widely used in clinical practice in Europe for over a decade as an add-on therapy to treat patients who have severe refractory allergic asthma. Over this period, many centres have reported their experience with omalizumab as an add-on therapy in patients with severe asthma. These 'real world' clinical effectiveness studies have confirmed the benefits, cost-effectiveness and clinical utility of this medication. Combining the outcomes of both sources of research has yielded important insights that may benefit patients with severe asthma, clinicians who treat them, as well as the funding agencies that reimburse the cost of this medication. The purpose of this review is to describe how to identify and evaluate a patient with asthma for whom treatment with omalizumab may be of clinical and cost-effective benefit. The assessment and investigations used to confirm allergic asthma, the objective assessment of adherence to asthma therapy and the expected benefits of add-on omalizumab treatment are described.

Syk Regulates Neutrophilic Airway Hyper-Responsiveness in a Chronic Mouse Model of Allergic Airways Inflammation

Background

Asthma is a chronic inflammatory disease characterized by airways hyper-responsiveness (AHR), reversible airway obstruction, and airway inflammation and remodeling. We previously showed that Syk modulates methacholine-induced airways contractility in naïve mice and in mice with allergic airways inflammation. We hypothesize that Syk plays a role in the pathogenesis of AHR; this was evaluated in a chronic 8-week mouse model of house dust mite (HDM)-induced allergic airways inflammation.

Methods

We used the Syk^flox/flox//rosa26CreER^T2 conditional Syk knock-out mice to assess the role of Syk prior to HDM exposure, and treated HDM-sensitized mice with the Syk inhibitor, GSK143, to evaluate its role in established allergic airways inflammation. Respiratory mechanics and methacholine (MCh)-responsiveness were assessed using the flexiVent^® system. Lungs underwent bronchoalveolar lavage to isolate inflammatory cells or were frozen for determination of gene expression in tissues.

Results

MCh-induced AHR was observed following HDM sensitization in the Syk-intact (Syk^flox/flox) and vehicle-treated BALB/c mice. MCh responsiveness was reduced to control levels in HDM-sensitized Syk^del/del mice and in BALB/c and Syk^flox/flox mice treated with GSK143. Both Syk^del/del and GSK143-treated mice mounted appropriate immune responses to HDM, with HDM-specific IgE levels that were comparable to Syk^flox/flox and vehicle-treated BALB/c mice. HDM-induced increases in bronchoalveolar lavage cell counts were attenuated in both Syk^del/del and GSK143-treated mice, due primarily to decreased neutrophil recruitment. Gene expression analysis of lung tissues revealed that HDM-induced expression of IL-17 and CXCL-1 was significantly attenuated in both Syk^del/del and GSK143-treated mice.

Conclusion

Syk inhibitors may play a role in the management of neutrophilic asthma.

Personalised medicine in asthma: from curative to preventive medicine

The concept of asthma has changed substantially in recent years. Asthma is now recognised as a heterogeneous entity that is complex to treat. The subdivision of asthma, provided by "cluster" analyses, has revealed various groups of asthma patients who share phenotypic features. These phenotypes underlie the need for personalised asthma therapy because, in contrast to the previous approach, treatment must be tailored to the individual patient. Determination of the patient's asthma phenotype is therefore essential but sometimes challenging, particularly in elderly patients with a multitude of comorbidities and a complex exposure history. This review first describes the various asthma phenotypes, some of which were defined empirically and others through cluster analysis, and then discusses personalisation of the patient's diagnosis and therapy, addressing in particular biological therapies and patient education. This personalised approach to curative medicine should make way in the coming years for personalised preventive and predictive medicine, focused on subjects at risk who are not yet ill, with the aim of preventing asthma before it occurs. The concept of personalised preventive medicine may seem a long way off, but is it really?

Future treatment for asthma

The landscape of asthma has considerably changed after 40 years of inhaled corticosteroid development and nearly 20 years since the first monoclonal antibodies (mAbs) were approved. New members of pharmacological families and more effective drug-delivery devices have been designed but the proportion of uncontrolled patients, unfortunately, remains stable. The most promising treatments now rely on targeted therapies that encourage the improvement of the characterisation of our patients. These clinical (phenotype) or new biological (endotype) tools lead to palpable personalised medicine. This review examines not only the future of mAbs and other new ways of treating asthma but also describes futuristic views based on the paradigm shifts that are ready to occur.

Asthma heterogeneity and severity

Asthma is a common, chronic inflammatory airways disease characterized by a clinical syndrome of bronchial hyperresponsiveness, inflammation, and reversible airflow obstruction. Individuals with asthma can vary widely in clinical presentation, severity, and pathobiology. The incident factors, pathogenesis, prognosis, and treatment of asthma remain incompletely understood. Utilizing measurable characteristics of asthmatic patients, including demographic, physiologic, and biologic markers, can however identify meaningful phenotypic categories in asthma. Identification of these phenotypes may help improve precision therapeutics targeted toward an individual’s’ disease, and may identify strategies for preventing progression of disease severity.

Severe Asthma in Children: Lessons Learned and Future Directions

Severe asthma in children is a complicated and heterogeneous disorder that is extremely challenging to treat. Although most children with asthma derive clinical benefit from daily administration of low-to-medium-dose inhaled corticosteroid (ICS) therapy, a small subset of children with "severe" or "refractory" asthma require high doses of ICS and even systemic corticosteroids to maintain symptom control. These children with severe asthma are at increased risk for adverse outcomes including medication-related side effects and recurrent and life-threatening exacerbations that significantly impair quality of life. This review highlights findings on severe asthma in school-age children (age 6-17 years) from the National Heart, Lung and Blood Institute's Severe Asthma Research Program (SARP) over a 10-year period, between 2001 and 2011. Although SARP has advanced knowledge of the unique clinical, biological, and molecular attributes of severe asthma in children, considerable gaps remain for which additional studies are needed.

Asthma phenotypes in inner-city children

BACKGROUND

Children with asthma in low-income urban areas have high morbidity. Phenotypic analysis in these children is lacking, but may identify characteristics to inform successful tailored management approaches.

OBJECTIVE

We sought to identify distinct asthma phenotypes among inner-city children receiving guidelines-based management.

METHODS

Nine inner-city asthma consortium centers enrolled 717 children aged 6 to 17 years. Data were collected at baseline and prospectively every 2 months for 1 year. Participants' asthma and rhinitis were optimally managed by study physicians on the basis of guidelines. Cluster analysis using 50 baseline and 12 longitudinal variables was performed in 616 participants completing 4 or more follow-up visits.

RESULTS

Five clusters (designated A through E) were distinguished by indicators of asthma and rhinitis severity, pulmonary physiology, allergy (sensitization and total serum IgE), and allergic inflammation. In comparison to other clusters, cluster A was distinguished by lower allergy/inflammation, minimally symptomatic asthma and rhinitis, and normal pulmonary physiology. Cluster B had highly symptomatic asthma despite high step-level treatment, lower allergy and inflammation, and mildly altered pulmonary physiology. Cluster C had minimally symptomatic asthma and rhinitis, intermediate allergy and inflammation, and mildly impaired pulmonary physiology. Clusters D and E exhibited progressively higher asthma and rhinitis symptoms and allergy/inflammation. Cluster E had the most symptomatic asthma while receiving high step-level treatment and had the highest total serum IgE level (median, 733 kU/L), blood eosinophil count (median, 400 cells/mm3), and allergen sensitizations (15 of 22 tested).

CONCLUSIONS

Allergy distinguishes asthma phenotypes in urban children. Severe asthma often coclusters with highly allergic children. However, a symptomatic phenotype with little allergy or allergic inflammation was identified.

Distinct Phenotypes of Cigarette Smokers Identified by Cluster Analysis of Patients with Severe Asthma

RATIONALE

Smoking may have multifactorial effects on asthma phenotypes, particularly in severe asthma. Cluster analysis has been applied to explore novel phenotypes, which are not based on any a priori hypotheses.

OBJECTIVES

To explore novel severe asthma phenotypes by cluster analysis when including cigarette smokers.

METHODS

We recruited a total of 127 subjects with severe asthma, including 59 current or ex-smokers, from our university hospital and its 29 affiliated hospitals/pulmonary clinics. Twelve clinical variables obtained during a 2-day hospital stay were used for cluster analysis. After clustering using clinical variables, the sputum levels of 14 molecules were measured to biologically characterize the clinical clusters.

MEASUREMENTS AND MAIN RESULTS

Five clinical clusters were identified, including two characterized by high pack-year exposure to cigarette smoking and low FEV1/FVC. There were marked differences between the two clusters of cigarette smokers. One had high levels of circulating eosinophils, high IgE levels, and a high sinus disease score. The other was characterized by low levels of the same parameters. Sputum analysis revealed increased levels of IL-5 in the former cluster and increased levels of IL-6 and osteopontin in the latter. The other three clusters were similar to those previously reported: young onset/atopic, nonsmoker/less eosinophilic, and female/obese. Key clinical variables were confirmed to be stable and consistent 1 year later.

CONCLUSIONS

This study reveals two distinct phenotypes of severe asthma in current and former cigarette smokers with potentially different biological pathways contributing to fixed airflow limitation. Clinical trial registered with www.umin.ac.jp (000003254).

Severe or life-threatening asthma exacerbation: patient heterogeneity identified by cluster analysis

BACKGROUND

Severe or life-threatening asthma exacerbation is one of the worst outcomes of asthma because of the risk of death. To date, few studies have explored the potential heterogeneity of this condition.

OBJECTIVES

To examine the clinical characteristics and heterogeneity of patients with severe or life-threatening asthma exacerbation.

METHODS

This was a multicentre, prospective study of patients with severe or life-threatening asthma exacerbation and pulse oxygen saturation < 90% who were admitted to 17 institutions across Japan. Cluster analysis was performed using variables from patient- and physician-orientated structured questionnaires.

RESULTS

Analysis of data from 175 patients with severe or life-threatening asthma exacerbation revealed five distinct clusters. Cluster 1 (n = 27) was younger-onset asthma with severe symptoms at baseline, including limitation of activities, a higher frequency of treatment with oral corticosteroids and short-acting beta-agonists, and a higher frequency of asthma hospitalizations in the past year. Cluster 2 (n = 35) was predominantly composed of elderly females, with the highest frequency of comorbid, chronic hyperplastic rhinosinusitis/nasal polyposis, and a long disease duration. Cluster 3 (n = 40) was allergic asthma without inhaled corticosteroid use at baseline. Patients in this cluster had a higher frequency of atopy, including allergic rhinitis and furred pet hypersensitivity, and a better prognosis during hospitalization compared with the other clusters. Cluster 4 (n = 34) was characterized by elderly males with concomitant chronic obstructive pulmonary disease (COPD). Although cluster 5 (n = 39) had very mild symptoms at baseline according to the patient questionnaires, 41% had previously been hospitalized for asthma.

CONCLUSIONS & CLINICAL RELEVANCE

This study demonstrated that significant heterogeneity exists among patients with severe or life-threatening asthma exacerbation. Differences were observed in the severity of asthma symptoms and use of inhaled corticosteroids at baseline, and the presence of comorbid COPD. These findings may contribute to a deeper understanding and better management of this patient population.

Clinically relevant outcome measures for new therapies of asthma using pharmaceutical and biologic agents

PURPOSE OF REVIEW

To determine the benefits of new asthma drugs or therapies, they should be assessed with regard to their effects on relevant clinical outcomes.

RECENT FINDINGS

The most frequently used outcomes have been symptoms, rescue medication needs and pulmonary function tests, although others such as quality of life, exacerbations and impairment of activities have also been identified as important ones. Improvements in our understanding of basic mechanisms of asthma have led to the development of new sets of outcomes including inflammatory markers and a rapidly increasing number of biomarkers, which however require validation, and assessment of their clinical usefulness. Many studies have not only looked at induced sputum cell differentials or FENO to phenotype asthma but also as treatment efficacy markers. Periostin is considered a marker of TH2-induced airway inflammation and a predictor of response to drugs such as anti-IL13 and omalizumab, although at the individual level, such prediction remains imperfect.

SUMMARY

There is a need to develop new markers of activity of the disease, with a prognostic value with regard to the benefits of new treatments.

Asthma phenotypes and IgE responses

The discovery of IgE represented a major breakthrough in allergy and asthma research, whereas the clinical interest given to IgE in asthma has been blurred until the arrival of anti-IgE biotherapy. Novel facets of the complex link between IgE and asthma have been highlighted by the effect of this treatment and by basic research. In parallel, asthma phenotyping recently evolved to the concept of endotypes, relying on identified/suspected pathobiological mechanisms to phenotype patients, but has not yet clearly positioned IgE among biomarkers of asthma.In this review, we first summarise recent knowledge about the regulation of IgE production and its main receptor, FcεRI. In addition to allergens acting as classical IgE inducers, viral infections as well as air pollution may trigger the IgE pathway, notably resetting the threshold of IgE sensitivity by regulating FcεRI expression. We then analyse the place of IgE in different asthma endo/phenotypes and discuss the potential interest of IgE among biomarkers in asthma.

Chronic Comorbidities Contribute to the Burden and Costs of Persistent Asthma

BACKGROUND

We aimed to study the prevalence of chronic comorbidities in asthma patients and the costs of health care use associated with asthma with comorbidities.

MATERIAL AND METHODS

We analysed the prevalence of the four most common chronic diseases in asthma patients in 2008-2014 in Finland. Prevalence of coronary artery disease, diabetes and dyslipidaemia, hypertension, epilepsy, inflammatory bowel disease, rheumatic diseases, and severe psychiatric disease was studied by register of the Social Insurance Institution of Finland. The costs of health care services were collected from the registries maintained by the National Institute for Health and Welfare (THL).

RESULTS

Prevalence of asthma was 4.6% in 2014. Diabetes was among the four most common comorbidities in all the age groups. The other common comorbidities were hypertension (≥46 years; 12.9-37.6%), severe psychiatric disorders (age groups of 16-59 years; 1.4-3.5%), and ischaemic heart disease (≥60 years; 10-25%). In patients with both asthma and diabetes, the costs of hospitalization were approximately 169% compared with patients with asthma alone.

CONCLUSIONS

Prevalence of asthma increases by tenfold when aging. The comorbidity diversity and rate are age-dependent. Prevalence of diabetes as comorbidity in asthma has increased. Costs of hospitalizations in asthma approximately double with chronic comorbidities.

Unraveling the Pathophysiology of the Asthma-COPD Overlap Syndrome: Unsuspected Mild Centrilobular Emphysema Is Responsible for Loss of Lung Elastic Recoil in Never Smokers With Asthma With Persistent Expiratory Airflow Limitation

Investigators believe most patients with asthma have reversible airflow obstruction with treatment, despite airway remodeling and hyperresponsiveness. There are smokers with chronic expiratory airflow obstruction despite treatment who have features of both asthma and COPD. Some investigators refer to this conundrum as the asthma-COPD overlap syndrome (ACOS). Furthermore, a subset of treated nonsmokers with moderate to severe asthma have persistent expiratory airflow limitation, despite partial reversibility. This residuum has been assumed to be due to large and especially small airway remodeling. Alternatively, we and others have described reversible loss of lung elastic recoil in acute and persistent loss in patients with moderate to severe chronic asthma who never smoked and its adverse effect on maximal expiratory airflow. The mechanism(s) responsible for loss of lung elastic recoil and persistent expiratory airflow limitation in nonsmokers with chronic asthma consistent with ACOS remain unknown in the absence of structure-function studies. Recently we reported a new pathophysiologic observation in 10 treated never smokers with asthma with persistent expiratory airflow obstruction, despite partial reversibility: All 10 patients with asthma had a significant decrease in lung elastic recoil, and unsuspected, microscopic mild centrilobular emphysema was noted in all three autopsies obtained although it was not easily identified on lung CT scan. These sentinel pathophysiologic observations need to be confirmed to further unravel the epiphenomenon of ACOS. The proinflammatory and proteolytic mechanism(s) leading to lung tissue breakdown need to be further investigated.

Phenotypes, Risk Factors, and Mechanisms of Adult-Onset Asthma

Asthma is a heterogeneous disease with many phenotypes, and age at disease onset is an important factor in separating the phenotypes. Genetic factors, atopy, and early respiratory tract infections are well-recognized factors predisposing to childhood-onset asthma. Adult-onset asthma is more often associated with obesity, smoking, depression, or other life-style or environmental factors, even though genetic factors and respiratory tract infections may also play a role in adult-onset disease. Adult-onset asthma is characterized by absence of atopy and is often severe requiring treatment with high dose of inhaled and/or oral steroids. Variety of risk factors and nonatopic nature of adult-onset disease suggest that variety of mechanisms is involved in the disease pathogenesis and that these mechanisms differ from the pathobiology of childhood-onset asthma with prevailing Th2 airway inflammation. Recognition of the mechanisms and mediators that drive the adult-onset disease helps to develop novel strategies for the treatment. The aim of this review was to summarize the current knowledge on the pathogenesis of adult-onset asthma and to concentrate on the mechanisms and mediators involved in establishing adult-onset asthma in response to specific risk factors. We also discuss the involvement of these mechanisms in the currently recognized phenotypes of adult-onset asthma.