FASE-CPHG Study: identification of asthma phenotypes in the French Severe Asthma Study using cluster analysis

Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review

Introduction

Computational sciences have significantly contributed to characterizing airway disease phenotypes, complementing medical expertise. However, comparing studies that derive phenotypes is challenging due to varying decisions made during phenotyping. We conducted a systematic review to describe studies that utilized unsupervised computational approaches for phenotyping obstructive airway diseases in children and adults.

Methods

We searched for relevant papers published between 2010 and 2020 in PubMed, EMBASE, Scopus, Web of Science, and Google Scholar. Additional sources included conference proceedings, reference lists, and expert recommendations. Two reviewers independently screened studies for eligibility, extracted data, and assessed study quality. Disagreements were resolved by a third reviewer. An in-house quality appraisal tool was used. Evidence was synthesized, focusing on populations, variables, and computational approaches used for deriving phenotypes.

Results

Of 120 studies included in the review, 60 focused on asthma, 19 on severe asthma, 28 on COPD, 4 on asthma-COPD overlap (ACO), and 9 on rhinitis. Among asthma studies, 31 focused on adults and 9 on children, with phenotypes related to atopy, age at onset, and disease severity. Severe asthma phenotypes were characterized by symptomatology, atopy, and age at onset. COPD phenotypes involved lung function, emphysematous changes, smoking, comorbidities, and daily life impairment. ACO and rhinitis phenotypes were mostly defined by symptoms, lung function, and sensitization, respectively. Most studies used hierarchical clustering, with some employing latent class modeling, mixture models, and factor analysis. The comprehensiveness of variable reporting was the best quality indicator, while reproducibility measures were often lacking.

Conclusion

Variations in phenotyping methods, study settings, participant profiles, and variables contribute to significant differences in characterizing asthma, severe asthma, COPD, ACO, and rhinitis phenotypes across studies. Lack of reproducibility measures limits the evaluation of computational phenotyping in airway diseases, underscoring the need for consistent approaches to defining outcomes and selecting variables to ensure reliable phenotyping.

An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma

Asthma is a disease characterized by chronic airway hyperresponsiveness and inflammation, with signs of variable airflow limitation and impaired lung function leading to respiratory symptoms such as shortness of breath, chest tightness and cough. Eosinophilic asthma is a distinct phenotype that affects more than half of patients diagnosed with severe asthma. It can be effectively treated with monoclonal antibodies targeting specific immunological signaling pathways that fuel the inflammation underlying the disease, particularly Interleukin-5 (IL-5), a cytokine that plays a crucial role in asthma. In this study, we propose a data analysis pipeline aimed at identifying subphenotypes of severe eosinophilic asthma in relation to response to therapy at follow-up, which could have great potential for use in routine clinical practice. Once an optimal partition of patients into subphenotypes has been determined, the labels indicating the group to which each patient has been assigned are used in a novel way. For each input variable in a specialized logistic regression model, a clusterwise effect on response to therapy is determined by an appropriate interaction term between the input variable under consideration and the cluster label. We show that the clusterwise odds ratios can be meaningfully interpreted conditional on the cluster label. In this way, we can define an effect measure for the response variable for each input variable in each of the groups identified by the clustering algorithm, which is not possible in standard logistic regression because the effect of the reference class is aliased with the overall intercept. The interpretability of the model is enforced by promoting sparsity, a goal achieved by learning interactions in a hierarchical manner using a special group-Lasso technique. In addition, valid expressions are provided for computing odds ratios in the unusual parameterization used by the sparsity-promoting algorithm. We show how to apply the proposed data analysis pipeline to the problem of sub-phenotyping asthma patients also in terms of quality of response to therapy with monoclonal antibodies.

Delineating asthma according to inflammation phenotypes with a focus on paucigranulocytic asthma

ABSTRACT

Asthma is characterized by chronic airway inflammation and airway hyper-responsiveness. However, the differences in pathophysiology and phenotypic symptomology make a diagnosis of "asthma" too broad hindering individualized treatment. Four asthmatic inflammatory phenotypes have been identified based on inflammatory cell profiles in sputum: eosinophilic, neutrophilic, paucigranulocytic, and mixed-granulocytic. Paucigranulocytic asthma may be one of the most common phenotypes in stable asthmatic patients, yet it remains much less studied than the other inflammatory phenotypes. Understanding of paucigranulocytic asthma in terms of phenotypic discrimination, distribution, stability, surrogate biomarkers, underlying pathophysiology, clinical characteristics, and current therapies is fragmented, which impedes clinical management of patients. This review brings together existing knowledge and ongoing research about asthma phenotypes, with a focus on paucigranulocytic asthma, in order to present a comprehensive picture that may clarify specific inflammatory phenotypes and thus improve clinical diagnoses and disease management.

Characteristics of Severe Non-Eosinophilic Asthma: Analysis of Data from 1075 Patients Included in the FASE-CPHG Study

Purpose

Data on severe non-eosinophilic asthma are scarce. Moreover, as compared with eosinophilic asthma, non-eosinophilic asthma less frequently benefits from the latest therapeutic advances. This study aimed to highlight differences between non-eosinophilic and eosinophilic asthma as they may help the development of new therapeutic agents.

Patients and Methods

Data from 1075 adult patients with severe asthma (GINA treatment: 4/5) collected during the cross-sectional non-interventional FASE-CPHG study were analyzed. Two groups of patients (EOS-/EOS+) were constituted based on blood eosinophil counts (cutoff value: 300 G/l). Characteristics of EOS- (N = 500) and EOS+ (N = 575) patients were described; EOS- patients were also described according to their allergic profile based on skin allergy or allergen-specific immunoglobulin E (IgE) assays (cutoff value: 150 IU/mL).

Results

Percentages of patients with obesity (29%), allergen sensitization (57%), or ≥2 annual exacerbations in the last 12 months (68%) were similar in both groups. As compared with EOS+ patients, EOS- patients less frequently reported chronic rhinitis (41.1% vs 50.5%, p < 0.01) or nasal polyposis (13.6% vs 27.5%, p < 0.01), and more frequently reported GERD (45.2% vs 37.1%, p < 0.01), anxiety (45.5% vs 38.1%, p = 0.01), or depression (18.3% vs 13.3%, p = 0.02). EOS- patients had lower serum total IgE levels (median: 158 vs 319 IU/mL, p < 0.01) and were less frequently treated with long-term oral corticosteroid therapy (16.0% vs 23.7%; p < 0.01). Their asthma was more frequently uncontrolled (48% vs 40%, p < 0.01). Similar results were found with a cutoff value for blood eosinophil counts at 150 G/l. EOS- patients with allergic profile less frequently reported high serum IgE levels (35.6% vs 57.9%, p < 0.01). EOS- and EOS+ patients treated with long-term oral corticosteroids had similar profiles.

Conclusion

In our patients with severe asthma, EOS- asthma was approximately as frequent as EOS+ asthma; EOS- asthma was frequently poorly controlled or uncontrolled, confirming the need for a better management. Allergy did not appear to worsen clinical profile.