Multidimensional Assessment of Asthma Identifies Clinically Relevant Phenotype Overlap: A Cross-Sectional Study

Association of Small Airway Functional Indices With Respiratory Symptoms and Comorbidity in Asthmatics: A National Cross-Sectional Study

Background

Small airway dysfunction (SAD) and airway inflammation are vital in asthma exacerbations. Type 2 inflammation (T2), mediated by cytokines from T helper 2 cell (Th2) such as interleukin (IL)-4, IL-5, and IL-13, is a potential mechanism underlying SAD. Research on small airway function in asthma is limited. We aimed to explore the correlation between small airway function and respiratory symptoms and comorbidity in T2 and non-T2 asthma.

Methods

Derived from the National Health and Nutrition Examination Survey (NHANES), our study encompassed 2,420 asthma patients aged 6 - 79 years, including pulmonary function (PF) data such as forced expiratory flow between 25% and 75% of forced vital capacity (FEF25-75), forced expiratory volume in 1 second (FEV1), forced expiratory volume in 3 seconds (FEV3), forced expiratory volume in 6 seconds (FEV6), and forced vital capacity (FVC). To evaluate the small airway function, we calculated z-scores for FEF25-75, FEF25-75/FVC, FEV1/FEV6, and FEV3/FEV6. Logistic regression determined the adjusted odds ratios (aORs) for symptoms and comorbidity.

Results

FEF25-75, FEV1/FEV6, and FEV3/FEV6 correlated with asthmatic symptoms. FEF25-75 had the strongest association with wheezing or whistling attacks. An increase of 1 standard deviations (SD) in FEF25-75 reduced recurrent wheezing (aOR: 0.70; 95% confidence intervals (95% CIs): 0.65 - 0.76) and severe attacks (aOR: 0.67; 95% CI: 0.62 - 0.94). These indices were also linked to dry cough and hay fever, particularly FEV3/FEV6 reducing hay fever risk (aOR: 0.70; 95% CI: 0.55 - 0.91) in non-T2 asthma. FEF25-75/FVC related to persistent (aOR: 0.78; 95% CI: 0.72 - 0.84) and severe attacks (aOR: 1.14; 95% CI: 1.08 - 1.22) in non-T2 groups. Lower indices combined with T2 exposure raised severe attack risk.

Conclusions

In this nationwide study, small airway function correlated with symptom onset, especially in T2 asthma. Small airway injury differed between T2 and non-T2 asthma. Prospective research is needed to establish reference values.

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.

Genomics of Treatable Traits in Asthma

The astounding number of genetic variants revealed in the 15 years of genome-wide association studies of asthma has not kept pace with the goals of translational genomics. Moving asthma diagnosis from a nonspecific umbrella term to specific phenotypes/endotypes and related traits may provide insights into features that may be prevented or alleviated by therapeutical intervention. This review provides an overview of the different asthma endotypes and phenotypes and the genomic findings from asthma studies using patient stratification strategies and asthma-related traits. Asthma genomic research for treatable traits has uncovered novel and previously reported asthma loci, primarily through studies in Europeans. Novel genomic findings for asthma phenotypes and related traits may arise from multi-trait and specific phenotyping strategies in diverse populations.

Phenotype overlap in the natural history of asthma

The heterogeneity of asthma makes it challenging to unravel the pathophysiologic mechanisms of the disease. Despite the wealth of research identifying diverse phenotypes, many gaps still remain in our knowledge of the disease's complexity. A crucial aspect is the impact of airborne factors over a lifetime, which often results in a complex overlap of phenotypes associated with type 2 (T2), non-T2 and mixed inflammation. Evidence now shows overlaps between the phenotypes associated with T2, non-T2 and mixed T2/non-T2 inflammation. These interconnections could be induced by different determinants such as recurrent infections, environmental factors, T-helper plasticity and comorbidities, collectively resulting in a complex network of distinct pathways generally considered as mutually exclusive. In this scenario, we need to abandon the concept of asthma as a disease characterised by distinct traits grouped into static segregated categories. It is now evident that there are multiple interplays between the various physiologic, cellular and molecular features of asthma, and the overlap of phenotypes cannot be ignored.

Self-Reported Insufficient Sleep Is Associated With Clinical and Inflammatory Features of Asthma: A Prospective Cohort Study

BACKGROUND

A few studies have explored the association between short sleep duration and worse asthma outcomes in patients with self-reported asthma; however, all of them were cross-sectional.

OBJECTIVES

To investigate the association between self-reported sleep duration and asthma-related clinical and inflammatory characteristics and whether sleep duration is associated with asthma exacerbations (AEs) in the following year.

METHODS

A prospective cohort study consecutively recruited participants with asthma, who were classified into short (n = 58), normal (n = 380), and long (n = 84) sleep duration groups. We investigated the clinical and inflammatory characteristics and exacerbations within a 1-year follow-up.

RESULTS

Patients with short sleep duration were older and had significantly lower total IgE and FeNO levels and higher airway inflammation, characterized by increased levels of IL-6 and TNF-α in sputum than those of patients with normal sleep duration. Furthermore, they had a significantly increased risk for poorly controlled asthma (adjusted odds ratio = 2.741; 95% CI, 1.379-5.447; P = .004) and moderate to severe AEs (adjusted incidence rate ratio = 1.798; 95% CI, 1.098-2.942; P = .020).

CONCLUSIONS

Short sleep duration was associated with non-type 2 inflammation and is an independent risk factor for future AEs. Therefore, as a potentially treatable trait, sleep duration may have clinical implications for asthma management.

Dyslipidemia Is Associated With Worse Asthma Clinical Outcomes: A Prospective Cohort Study

BACKGROUND

Dyslipidemia has been widely documented to be associated with cardiovascular disease, and recent studies have found an association with asthma prevalence. However, longitudinal studies investigating the relationships between dyslipidemia, asthma phenotypes, and future asthma exacerbations (AEs) are lacking.

OBJECTIVE

To investigate the relationships between dyslipidemia, asthma phenotypes, and AEs.

METHODS

This study used an observational cohort study design with a 12-month follow-up. All subjects underwent serum lipid measurement, and they were then classified into 2 groups: the normal-lipidemia group and the dyslipidemia group. Demographic and clinical information and details regarding pulmonary function and asthma phenotypes at baseline were collected. All patients were followed up regularly to assess AEs. Associations of dyslipidemia with airway obstruction and asthma phenotypes were assessed at baseline, whereas dyslipidemia and AEs were assessed longitudinally.

RESULTS

A total of 477 patients with asthma were consecutively enrolled in this study. At baseline, the dyslipidemia group (n = 218) had a higher proportion of uncontrolled asthma, defined by the 6-item Asthma Control Questionnaire score (≥1.5). Furthermore, dyslipidemia was associated with severe asthma, nonallergic asthma, asthma with fixed airflow limitation, and older adult asthma phenotypes at baseline. In addition, dyslipidemia was associated with increased frequencies of severe AEs and moderate to severe AEs during the 12-month follow-up. In sensitivity analyses, after excluding the patients who were receiving statins, results did not differ significantly from those of the main analysis.

CONCLUSIONS

We identified the clinical relevance of dyslipidemia, which is associated with specific asthma phenotypes and increased AEs, independent of other components of metabolic syndrome. These findings highlight the importance of considering dyslipidemia as an "extrapulmonary trait" in asthma management.

Age-Related Clinical Characteristics, Inflammatory Features, Phenotypes, and Treatment Response in Asthma

BACKGROUND

Emerging evidence suggests that aging affects asthma outcomes, but the mechanism remains largely unexplored.

OBJECTIVE

To explore age-related clinical characteristics, inflammatory features, phenotypes, and treatment response in asthma.

METHODS

This was a prospective cohort study of asthmatic patients with a 12-month follow-up in a real-world setting. Clinical inflammatory and phenotypic characteristics, future risk for exacerbations, and treatment response were assessed across different age groups (young was defined as age 18 to 39 years; middle-aged, 40 to 64 years; and elderly, 65 years or older).

RESULTS

Compared with young (n = 106) and middle-aged (n = 179) asthmatic patients, elderly patients (n = 55) had worse airway obstruction, more comorbidities including chronic obstructive pulmonary disease and diabetes, less atopy, and lower levels of IgE and FeNO, and were more likely to have late-onset and fixed airflow obstruction asthma and a reduced risk for having type 2 profile asthma. Levels of IFN-gamma, IL-17A, and IL-8 in induced sputum were significantly increased in elderly asthmatic patients (all P < .05). Path analysis indicated that age directly and significantly led to future exacerbations in asthma, partially mediated by an upregulation of airway IFN-gamma. Moreover, elderly patients with asthma had a reduced treatment response (improvement in FEV₁ of 12% or greater, or 200 mL, and a reduction in Borg scores of 1 or greater) (adjusted odds ratio = 0.11; 95% CI, 0.02-0.52; and adjusted odds ratio = 0.12; 95% CI, 0.03-0.49, respectively).

CONCLUSIONS

This study confirms that asthma in the elderly population represents a specific phenotype and indicates that aging can influence asthma in terms of clinical characteristics, inflammatory features, exacerbations, and treatment response.

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.

Cigarette Smoking and Asthma

Globally, around half the adult asthma population are current or former cigarette smokers. Cigarette smoking and asthma interact to induce an "asthma-smoking phenotype(s)," which has important implications for diagnosis, pathogenic mechanisms, and management. The lack of progress in understanding the effects of smoking on adults with asthma is due in part to their exclusion from most investigative studies and large clinical trials. In this review, we summarize the adverse clinical outcomes associated with cigarette smoking in asthma, highlight challenges in diagnosing asthma among cigarette smokers with chronic respiratory symptoms, particularly in older individuals with a long-standing smoking history, and review pathogenic mechanisms involving smoking- and asthma-related airway inflammation, tissue remodeling, corticosteroid insensitivity, and low-grade systemic inflammation. We discuss the key components of management including the importance of smoking cessation strategies, evidence for the effectiveness of the Global Initiative for Asthma recommendations on treatment in cigarette smokers, and the role of treatable traits such as type 2 eosinophilic airway inflammation. Lastly, we provide an algorithm to aid clinicians to manage current and former smokers with asthma. In the future, controlled and pragmatic trials in real-world populations should include cigarette smokers with asthma to provide an evidence base for treatment recommendations.

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

Background

Asthma is a heterogeneous disease and different phenotypes based on clinical parameters have been identified. However, the molecular subgroups of asthma defined by gene expression profiles of induced sputum have been rarely reported.

Methods

We re-analyzed the asthma transcriptional profiles of the dataset of GSE45111. A deep bioinformatics analysis was performed. We classified 47 asthma cases into different subgroups using unsupervised consensus clustering analysis. Clinical features of the subgroups were characterized, and their biological function and immune status were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) network were performed to identify key gene modules and hub genes.

Results

Unsupervised consensus clustering of gene expression profiles in asthma identified two distinct subgroups (Cluster I/II), which were significantly associated with eosinophilic asthma (EA) and paucigranulocytic asthma (PGA). The differentially expressed genes (DEGs) between the two subgroups were primarily enriched in immune response regulation and signal transduction. The ssGSEA suggested the different immune infiltration and function scores between the two clusters. The WGCNA and PPI analysis identified three hub genes: THBS1, CCL22 and CCR7. ROC analysis further suggested that the three hub genes had a good ability to differentiate the Cluster I from the Cluster II.

Conclusions

Based on the gene expression profiles of the induced sputum, we identified two asthma subgroups, which revealed different clinical characteristics, gene expression patterns, biological functions and immune status. The transcriptional classification confirms the molecular heterogeneity of asthma and provides a framework for more in-depth research on the mechanisms of asthma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01824-3.

Switch from Omalizumab to Benralizumab in Allergic Patients with Severe Eosinophilic Asthma: A Real-Life Experience from Southern Italy

Background. The wide availability of monoclonal antibodies for the add-on therapy of severe asthma currently allows for the personalization of biologic treatment by selecting the most appropriate drug for each patient. However, subjects with overlapping allergic and eosinophilic phenotypes can be often eligible to more than one biologic, so that the first pharmacologic choice can be quite challenging for clinicians. Within such a context, the aim of our real-life investigation was to verify whether allergic patients with severe eosinophilic asthma, not adequately controlled by an initial biologic treatment with omalizumab, could experience better therapeutic results from a pharmacologic shift to benralizumab. Patients and methods. Twenty allergic patients with severe eosinophilic asthma, unsuccessfully treated with omalizumab and then switched to benralizumab, were assessed for at least 1 year in order to detect eventual changes in disease exacerbations, symptom control, oral corticosteroid intake, lung function, and blood eosinophils. Results. In comparison to the previous omalizumab therapy, after 1 year of treatment with benralizumab our patients experienced significant improvements in asthma exacerbation rate (p < 0.01), rescue medication need (p < 0.001), asthma control test (ACT) score (p < 0.05), forced expiratory volume in the first second (FEV₁) (p < 0.05), and blood eosinophil count (p < 0.0001). Furthermore, with respect to the end of omalizumab treatment, the score of sino-nasal outcome test-22 (SNOT-22) significantly decreased after therapy with benralizumab (p < 0.05). Conclusion. The results of this real-life study suggest that the pharmacologic shift from omalizumab to benralizumab can be a valuable therapeutic approach for allergic patients with severe eosinophilic asthma, not adequately controlled by anti-IgE treatment.

Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma

Background

Asthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA.

Methods

Based on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein–protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset.

Results

Compared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268.

Conclusions

Our study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01711-3.

Interaction effects of asthma and rhinitis control on work productivity and activity impairment: A cross-sectional study

Background: Symptomatic asthma and rhinitis negatively impact patients' work productivity and activity. However, little is known about the potential interaction effect of both asthma and rhinitis control on work productivity and activity impairment. Objective: This study aimed to explore whether there are interaction effects of asthma and rhinitis control on work productivity and activity impairment in patients with asthma and with rhinitis. Methods: A total of 206 adult patients were prospectively recruited and were divided into four groups: both poorly controlled (BPC) n = 53), poorly controlled asthma (PCA) with controlled rhinitis (CR) (n = 38), well controlled asthma with uncontrolled rhinitis (n = 43), and both well controlled (BWC) (n = 72) based on the symptom control status of asthma and rhinitis. Work productivity loss and activity impairment, asthma control, and rhinitis control were assessed by using work productivity and activity impairment questionnaire: general health, the asthma control test, and the rhinitis control assessment test, respectively. General linear regression models were used to study the contribution of asthma control, rhinitis control, and the interaction effect on work productivity and activity impairment. Results: Work productivity loss was most frequently reported in patients in the BPC group. Compared with the patients in the BWC group, the patients in the PCA-CR group had significantly higher activity impairment and worse asthma-related quality of life (both p < 0.001). There were significant interaction effects of asthma and rhinitis control, which accounted for the increase in presenteeism, work productivity loss, and activity impairment (all p < 0.001). Although differences in absenteeism were not significant among the groups, there was a significant interaction effect of control levels accounted for absenteeism (p = 0.035). Conclusion: Distinct interaction effects of asthma and rhinitis control reflected a link between upper and lower airways, which indicated that rhinitis control and the interaction effects of asthma and rhinitis control cannot be neglected during asthma management.

Effectiveness of Switching Biologics for Severe Asthma Patients in Japan: A Single-Center Retrospective Study

BACKGROUND

In Japan, biologic therapy was initiated for patients with severe asthma in 2009. In recent years, four biologics with different mechanisms of action have become available in the clinical setting. However, the efficacy of switching between biologics remains uncertain.

METHODS

To elucidate the efficacy of switching between biologics, 97 patients were enrolled who had received any biologic therapy for severe asthma at Jikei University Hospital, Tokyo, Japan, from July 2009 to December 2020. We retrospectively examined the patient characteristics, biomarkers, pulmonary function test results, selected biologics, and efficacy.

RESULTS

Thirty-one males and 66 females received any biologics. The mean age was 53.3 years at the initiation of biologic therapy. Initially, 33, 41, 15 and eight patients received omalizumab, mepolizumab, benralizumab, and dupilumab, respectively. Among three representative indicators for biologics administration, the peripheral blood eosinophil count, serum IgE levels and fractional exhaled nitric oxide, 64% of the patients had two indicators, and 28% had three indicators. Thirty-four patients (35%) switched from the initial biologic to another, and the reasons for switching included persistent asthmatic symptoms (n=22), schedule of hospital visits (n=5), and other reasons. Thus, the treatment was effective in 11 patients after switching. In addition, two patients received combination therapy with different biologics. Eighteen patients (19%) interrupted treatment for various reasons. Regardless of whether the biologic was the initial therapy, the overall efficacy of the four biologics was 60% based on the global evaluation of treatment effectiveness.

CONCLUSION

Switching between biologics can be a promising option for severe asthma patients in whom treatment with an initial biologic is ineffective.

Saudi Arabian real-life experience with biologic therapy in severe asthma

BACKGROUND

Severe asthma (SA) is a common health problem associated with increased morbidity and mortality and high medical costs. Biological therapies have emerged in recent decades as promising treatment options for patients with high type 2 (T2) SA. This retrospective observational study from Saudi Arabia aimed to investigate the effects of additional biologics therapy on reducing oral corticosteroid (OCS) consumption, frequency of asthma exacerbations, improvement in lung function, and asthma control.

METHODS

This multicenter observational study enrolled a cohort of 97 patients from March 2019 to February 2021. Outcomes of anti-IgE, anti-IL5/IL5R, and anti-IL4R therapies in severe type 2 asthma were recorded and analyzed in terms of number of exacerbations (emergency visits or hospitalizations required), asthma symptoms, and use of oral corticosteroids, blood eosinophil count, asthma control according to GINA classification, and FEV1 before and during biologic therapy.

RESULTS

Ninety-seven patients were included in the analysis The mean age was 46.7±14.1 years, and 69.1% of them were female. The average duration of biological treatment was 16.4±6.8 months. At the time of data collection, the four biologic therapies reduced the exacerbation rate per year from 82/97 (84.5%) to 14/97 (14.4%) with a percent improvement of 83% from 2.9 per year in the year before biologic treatment to 1.6 per year (p<0.001). OCS was reduced from 75/97 (77.3%) to 10/97 (10.3%) for a percent improvement of 86.7%, and the average OCS dose decreased from 7.12 mg to 6.8 mg. Mean blood eosinophil count also decreased after biologic therapy from 750.5±498.5 to 188.0±122.4 cells/μl, most significant result achieved with benralizumab, and mean FEV1 improved from 59.0±12.9% to 76.0±10.2%, most significant result achieved with omalizumab. ll patients had uncontrolled asthma before biologics therapy, but asthma control improved by 91.8% after treatment.

CONCLUSIONS

Biologic as add-on therapy for high T2 SA was found to reduce asthma exacerbations, systemic glucocorticoid doses, and SA symptoms.