Multiview Cluster Analysis Identifies Variable Corticosteroid Response Phenotypes in Severe Asthma

Antibody response to SARS-CoV-2 vaccine in patients with asthma

BACKGROUND

Viral infections constitute a primary trigger for asthma exacerbations. While vaccines protect against viral infections by eliciting a Th1 immune response, the impact of the asthmatic immune milieu which is characterized by Th2 cytokine dominance and elevated IgE levels on post-vaccination antibody production remains elusive. Therefore, vaccination protocols tailored to asthma patients need to be formulated.

METHODS

The levels of IgG specific for SARS-CoV-2 S protein were measured in the sera of vaccinated and unvaccinated individuals by ELISA. The differences in antibody titers between asthma patients and healthy controls, as well as among distinct asthma subgroups were analyzed.

RESULTS

The vaccinated individuals had significantly elevated serum antibody levels compared to their unvaccinated counterparts. There were no significant differences in the antibody titers of asthma patients and healthy controls after completion of the three-dose vaccination regimen. Furthermore, no discernible variations in antibody titers were detected among the asthma subgroups.

CONCLUSION

Asthma patients can safely adhere to the same vaccination strategies as the general healthy population, negating the need for any specialized vaccination protocols based solely on the asthmatic immune landscape.

Non-T2 asthma

PURPOSE OF REVIEW

This review provides a comprehensive overview of the non-T asthma phenotypes. Asthma is an umbrella term that defines a complex group of heterogenous airway disorders, which are broadly categorized into predominantly T2 or non-T2 phenotypes depending on the presence and levels of airway and systemic biomarkers associated with a T2 inflammatory response. Individuals with predominant T2 asthma have greater numbers of peripheral blood eosinophils, exhaled nitric oxide and IgE. These patients have more atopy and earlier onset asthma. In contrast, the absence or low levels of these biomarkers define non-T2 asthma. This is a heterogenous group with a later onset of asthma that is also more commonly associated with obesity and with females.

RECENT FINDINGS

This article summarizes new information regarding the plasticity that exists between T2 and non-T2 mechanisms, including their role in exacerbation-prone and nonexacerbating asthma, and many of the risk factors associated with the non-T2 phenotype, such as viral infections, ambient air pollution exposure, smoking, genetic and metabolic factors. It also provides new information on the immunological and metabolic mechanisms associated with non-T2 asthma. We also discuss how to manage this asthma phenotype and how treatment responses differ for these patients.

SUMMARY

Non-T2 asthma defines a heterogenous group of asthma phenotypes. However, acknowledging that the absence of T2 biomarkers is influenced by several factors is important and can longitudinally change in relation to exacerbations, particularly in children.

Application and research progress of artificial intelligence in allergic diseases

Artificial intelligence (AI), as a new technology that can assist or even replace some human functions, can collect and analyse large amounts of textual, visual and auditory data through techniques such as Reinforcement Learning, Machine Learning, Deep Learning and Natural Language Processing to establish complex, non-linear relationships and construct models. These can support doctors in disease prediction, diagnosis, treatment and management, and play a significant role in clinical risk prediction, improving the accuracy of disease diagnosis, assisting in the development of new drugs, and enabling precision treatment and personalised management. In recent years, AI has been used in the prediction, diagnosis, treatment and management of allergic diseases. Allergic diseases are a type of chronic non-communicable disease that have the potential to affect a number of different systems and organs, seriously impacting people's mental health and quality of life. In this paper, we focus on asthma and summarise the application and research progress of AI in asthma, atopic dermatitis, food allergies, allergic rhinitis and urticaria, from the perspectives of disease prediction, diagnosis, treatment and management. We also briefly analyse the advantages and limitations of various intelligent assistance methods, in order to provide a reference for research teams and medical staff.

Chronic Inflammation in Asthma: Looking Beyond the Th2 Cell

Asthma is a common chronic inflammatory disease of the airways. A substantial number of patients present with severe and therapy-resistant asthma, for which the underlying biological mechanisms remain poorly understood. In most asthma patients, airway inflammation is characterized by chronic activation of type 2 immunity. CD4+ T helper 2 (Th2) cells are the canonical producers of the cytokines that fuel type 2 inflammation: interleukin (IL)-4, IL-5, IL-9, and IL-13. However, more recent findings have shown that other lymphocyte subsets, in particular group 2 innate lymphoid cells (ILC2s) and type 2 CD8+ cytotoxic T (Tc2) cells, can also produce large amounts of type 2 cytokines. Importantly, a substantial number of severe therapy-resistant asthma patients present with chronic type 2 inflammation, despite the high sensitivity of Th2 cells for suppression by corticosteroids-the mainstay drugs for asthma. Emerging evidence indicates that ILC2s and Tc2 cells are more abundant in severe asthma patients and can adopt corticosteroid-resistance states. Moreover, many severe asthma patients do not present with overt type 2 airway inflammation, implicating non-type 2 immunity as a driver of disease. In this review, we will discuss asthma pathophysiology and focus on the roles played by ILC2s, Tc2 cells, and non-type 2 lymphocytes, placing special emphasis on severe disease forms.

Allergic Bronchopulmonary Aspergillosis/Mycosis: An Allergic Disease or an Eosinophilic Disease?

Allergic bronchopulmonary aspergillosis/mycosis (ABPA/ABPM) is characterized by increased serum levels of total and fungi-specific immunoglobulin E (IgE) and eosinophilic mucus plugs in the airways. Its classification as either an allergic or eosinophilic disease remains controversial. In the present review, we explored this topic based on three clinical studies that analyzed the clinical characteristics of ABPA/ABPM using a cluster analysis, factor analysis, and comparison between ABPM caused by Schizophyllum commune and ABPA. We also compared therapeutic responses to biologics targeting either IgE (omalizumab) or eosinophils (mepolizumab/benralizumab) to elucidate the role of these components in the pathogenesis of ABPA/ABPM. Based on these analyses, eosinophilic mucus plug formation in the airways is considered a cardinal feature of the development of ABPA/ABPM, whereas IgE responses to fungi are important factors that modulate disease manifestation.

Obesity and hormonal influences on asthma: Mechanisms, management challenges, and emerging therapeutic strategies

Obesity and hormone dysregulation, common comorbidities of asthma, not only influence asthma risk and onset but can also complicate its management. The pathobiologic characteristics of obesity, such as insulin resistance and metabolism alterations, can impact lung function and airway inflammation while highlighting potential opportunities for therapeutic intervention. Likewise, obesity alters immune cell phenotypes and corticosteroid pharmacokinetics. Hormones such as sex hormones, incretins, and thyroid hormones can also affect asthma. This review highlights the mechanisms underlying obesity-related asthma and hormonal pathologies while exploring potential therapeutic strategies and the need for more research and innovative approaches in managing these comorbid conditions.

The role of DC subgroups in the pathogenesis of asthma

Dendritic cells (DCs), specialized antigen-presenting cells of the immune system, act as immunomodulators in diseases of the immune system, including asthma. The understanding of DC biology has evolved over the years to include multiple subsets of DCs with distinct functions in the initiation and maintenance of asthma. Moreover, most strategies for treating asthma with relevant therapeutic agents that target DCs have been initiated from the study of DC function. We discussed the pathogenesis of asthma (including T2-high and T2-low), the roles played by different DC subpopulations in the pathogenesis of asthma, and the therapeutic strategies centered around DCs. This study will provide a scientific theoretical basis for current asthma treatment, provide theoretical guidance and research ideas for developing and studying therapeutic drugs targeting DC, and provide more therapeutic options for the patient population with poorly controlled asthma symptoms.

High-CBD Extract (CBD-X) in Asthma Management: Reducing Th2-Driven Cytokine Secretion and Neutrophil/Eosinophil Activity

BACKGROUND/OBJECTIVES

Asthma is a chronic inflammatory disorder of the airways affecting over 10% of the global population. It is characterized by airway inflammation, mucus hypersecretion, and bronchial hyperresponsiveness, driven predominantly by type 2 helper T cells (Th2) and type 2 innate lymphoid cells (ILC2s) in a subset of patients. However, a significant portion of asthmatic individuals present with "type 2-low" asthma that is often refractory to standard inhaled corticosteroid (ICS) therapy. Therefore, developing innovative therapeutic strategies has become essential. Recent studies have highlighted cannabidiol (CBD) as a promising anti-inflammatory agent capable of modulating immune responses. This study investigates the therapeutic potential of a high-CBD extract (CBD-X) in asthma.

METHODS

We evaluated the effects of CBD-X on cells involved in asthma pathogenesis using primary human Th2 cells, neutrophils, and asthma mouse model.

RESULTS

Our findings indicate that CBD-X extract inhibits Th2 differentiation and reduces the secretion of IL-5 and IL-13, which are crucial cytokines in asthma. Additionally, CBD-X significantly reduces pro-inflammatory cytokines IL-8 and IL-6 in neutrophils and impairs their migration, a critical step in airway inflammation. In a murine asthma model, CBD-X administration led to marked downregulation of IgE and pro-asthmatic cytokines, along with reduced leukocyte, eosinophil, and neutrophil infiltration in lung tissues.

CONCLUSIONS

These results suggest that CBD-X extract could offer a novel and complementary approach to managing both type 2-high and type 2-low asthma by targeting key inflammatory pathways and modulating immune cell behavior.

M-ClustEHR: A multimodal clustering approach for electronic health records

Sepsis refers to a potentially life-threatening situation where the immune system of the human body has an extreme response to an infection. In the presence of underlying comorbidities, the situation can become even worse and result in death. Employing unsupervised machine learning techniques, such as clustering, can assist in providing a better understanding of patient phenotypes by unveiling subgroups characterized by distinct sepsis progression and treatment patterns. More concretely, this study introduces M-ClustEHR, a clustering approach that utilizes medical data of multiple modalities by employing a multimodal autoencoder for learning comprehensive sepsis patient representations. M-ClustEHR consistently outperforms traditional clustering approaches in terms of several internal clustering performance metrics, as well as cluster stability in identifying phenotypes in the sepsis cohort. The unveiled patterns, supported by existing medical literature and clinicians, highlight the importance of multimodal clustering for advancing personalized sepsis care.

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.

Weighted Breaths: Exploring Biologic and Non-Biologic Therapies for Co-Existing Asthma and Obesity

PURPOSE OF REVIEW

To discuss the effectiveness of biologics, some of which comprise the newest class of asthma controller medications, and non-biologics in the treatment of asthma co-existing with obesity.

RECENT FINDINGS

Our review of recent preliminary and published data from clinical trials revealed that obese asthmatics respond favorably to dupilumab, mepolizumab, omalizumab, and tezepelumab, which are biologics currently indicated as add-on maintenance therapy for severe asthma. Furthermore, clinical trials are ongoing to assess the efficacy of non-biologics in the treatment of obese asthma, including a glucagon-like peptide-1 receptor agonist, a Janus kinase inhibitor, and probiotics. Although many biologics presently indicated as add-on maintenance therapy for severe asthma exhibit efficacy in obese asthmatics, other phenotypes of asthma co-existing with obesity may be refractory to these medications. Thus, to improve quality of life and asthma control, it is imperative to identify therapeutic options for all existing phenotypes of obese asthma.

Ferroptosis contributes to airway epithelial E-cadherin disruption in a mixed granulocytic asthma mouse model

Aberrant expression of airway epithelial E-cadherin is a key feature of asthma, yet the underlying mechanisms are largely unknown. Ferroptosis is a novel form of regulated cell death involved in asthma pathogenesis. This study was aimed to evaluate the role of ferroptosis and to investigate whether ferroptosis mediates E-cadherin disruption in mixed granulocyte asthma (MGA). Two murine models of MGA were established using toluene diisocyanate (TDI) or ovalbumin with Complete Freund's Adjuvant (OVA/CFA). Specific antagonists of ferroptosis, including Liproxstatin-1 (Lip-1) and Ferrostatin-1 (Fer-1) were given to the mice. The allergen-exposed mice displayed markedly shrunk mitochondria in the airway epithelia, with decreased volume and denser staining accompanied by down-regulated GPX4 as well as up-regulated FTH1 and malondialdehyde, which are markers of ferroptosis. Decreased pulmonary expression of E-cadherin was also observed, with profound loss of membrane E-cadherin in the airway epithelia, as well as increased secretion of sE-cadherin. Treatment with Lip-1 not only showed potent protective effects against the allergen-induced airway hyperresponsiveness and inflammatory responses, but also rescued airway epithelial E-cadherin expression and inhibited the release of sE-cadherin. Taken together, our data demonstrated that ferroptosis mediates airway epithelial E-cadherin dysfunction in MGA.

Protein-Protein interactive networks identified in bronchoalveolar lavage of severe compared to nonsevere asthma

INTRODUCTION

Previous bronchoalveolar lavage fluid (BALF) proteomic analysis has evaluated limited numbers of subjects for only a few proteins of interest, which may differ between asthma and normal controls. Our objective was to examine a more comprehensive inflammatory biomarker panel in quantitative proteomic analysis for a large asthma cohort to identify molecular phenotypes distinguishing severe from nonsevere asthma.

METHODS

Bronchoalveolar lavage fluid from 48 severe and 77 nonsevere adult asthma subjects were assessed for 75 inflammatory proteins, normalized to BALF total protein concentration. Validation of BALF differences was sought through equivalent protein analysis of autologous sputum. Subjects' data, stratified by asthma severity, were analysed by standard statistical tests, principal component analysis and 5 machine learning algorithms.

RESULTS

The severe group had lower lung function and greater health care utilization. Significantly increased BALF proteins for severe asthma compared to nonsevere asthma were fibroblast growth factor 2 (FGF2), TGFα, IL1Ra, IL2, IL4, CCL8, CCL13 and CXCL7 and significantly decreased were platelet-derived growth factor a-a dimer (PDGFaa), vascular endothelial growth factor (VEGF), interleukin 5 (IL5), CCL17, CCL22, CXCL9 and CXCL10. Four protein differences were replicated in sputum. FGF2, PDGFaa and CXCL7 were independently identified by 5 machine learning algorithms as the most important variables for discriminating severe and nonsevere asthma. Increased and decreased proteins identified for the severe cluster showed significant protein-protein interactions for chemokine and cytokine signalling, growth factor activity, and eosinophil and neutrophil chemotaxis differing between subjects with severe and nonsevere asthma.

CONCLUSION

These inflammatory protein results confirm altered airway remodelling and cytokine/chemokine activity recruiting leukocytes into the airways of severe compared to nonsevere asthma as important processes even in stable status.

[Modeling T2 high severe asthma using human induced pluripotent stem cells (hiPSC)]

Severe asthma patients with persistent airflow obstruction are characterized by functional obstruction due to mucus plugs containing mucins, fibrin, and eosinophil derived Charcot- Leyden crystals. The molecular mechanisms underlying this endotype are not clearly understood. Developing new models is crucial to respiratory research insofar as critical differences exist between human and rodent airway epithelium. We (and other teams) have shown that it is possible to reconstitute in vitro a complex and functional airway epithelium displaying all the features described in vivo from human-induced pluripotent stem cells (hiPSC). Our aim is to establish a human in vitro model of severe asthma that will recapitulate airway epithelium remodeling and mucus plugs.

Explainable hierarchical clustering for patient subtyping and risk prediction

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.

Cluster features in fibrosing interstitial lung disease and associations with prognosis

BACKGROUND

Clustering is helpful in identifying subtypes in complex fibrosing interstitial lung disease (F-ILD) and associating them with prognosis at an early stage of the disease to improve treatment management. We aimed to identify associations between clinical characteristics and outcomes in patients with F-ILD.

METHODS

Retrospectively, 575 out of 926 patients with F-ILD were eligible for analysis. Four clusters were identified based on baseline data using cluster analysis. The clinical characteristics and outcomes were compared among the groups.

RESULTS

Cluster 1 was characterized by a high prevalence of comorbidities and hypoxemia at rest, with the worst lung function at baseline; Cluster 2 by young female patients with less or no smoking history; Cluster 3 by male patients with highest smoking history, the most noticeable signs of velcro crackles and clubbing of fingers, and the severe lung involvement on chest image; Cluster 4 by male patients with a high percentage of occupational or environmental exposure. Clusters 1 (median overall survival [OS] = 7.0 years) and 3 (OS = 5.9 years) had shorter OS than Clusters 2 (OS = not reached, Cluster 1: p < 0.001, Cluster 3: p < 0.001) and 4 (OS = not reached, Cluster 1: p = 0.004, Cluster 3: p < 0.001). Clusters 1 and 3 had a higher cumulative incidence of acute exacerbation than Clusters 2 (Cluster 1: p < 0.001, Cluster 3: p = 0.014) and 4 (Cluster 1: p < 0.001, Cluster 3: p = 0.006). Stratification by using clusters also independently predicted acute exacerbation (p < 0.001) and overall survival (p < 0.001).

CONCLUSIONS

The high degree of disease heterogeneity of F-ILD can be underscored by four clusters based on clinical characteristics, which may be helpful in predicting the risk of fibrosis progression, acute exacerbation and overall survival.

Characterization of Obesity in Severe Asthma in the German Asthma Net

BACKGROUND

Asthma is increasingly recognized as heterogeneous, characterized by different endotypes, with obesity not only a distinct phenotype but a risk factor for severe asthma.

OBJECTIVE

We sought to understand the associations of obesity with relevant parameters of severe asthma, including asthma control, disease burden, and lung function.

METHODS

The German Asthma Net registry is a multicenter international real-life registry capturing long-term follow-up data. This analysis included 2213 patients (52 ± 16 years, 58% female, 29% with obesity [body mass index ≥30 kg/m2], 4.2 ± 4.3 exacerbations/year). The primary analysis assessed relationships between BMI and variables through univariate tests, followed by a multiple regression model. Secondary outcomes regarded clinically relevant variables in relation to weight groups.

RESULTS

Patients with obesity were more frequently female, more likely to have depression and gastroesophageal reflux, and suffered from worse asthma control, lower quality of life, reduced static lung volumes, more pronounced hypoxemia, and higher blood neutrophil counts, all statistically significant. Blood eosinophils, exhaled nitric oxide, and total IgE were independent of obesity. In the multiple regression analysis, obesity was significantly associated with more frequent reflux and depression, reduced static lung function values, older age, poor asthma control, and long-acting muscarinic antagonist therapy, and inversely associated with bronchiectasis and nonsmoking status.

CONCLUSION

In this large, well-characterized cohort, we identified the association of obesity with a significantly higher disease burden and a similar portfolio of inflammation type 2 markers in patients with and without obesity; therefore, patients with obesity seem similarly eligible for the treatment with biologics targeting these disease endotypes.

Lung epithelial cells: Upstream targets in type 2-high asthma

Over the last years, technological advances in the field of asthma have led to the identification of two disease endotypes, namely, type 2-high and type 2-low asthma, characterized by different pathophysiologic mechanisms at a cellular and molecular level. Although specific immune cells are important contributors to each of the recognized asthma endotype, the lung epithelium is now regarded as a crucial player able to orchestrate responses to inhaled environmental triggers such as allergens and microbes. The impact of the epithelium goes beyond its physical barrier. It is nowadays considered as a part of the innate immune system that can actively respond to insults. Activated epithelial cells, by producing a specific set of cytokines, trigger innate and adaptive immune cells to cause pathology. Here, we review how the epithelium contributes to the development of Th2 sensitization to allergens and asthma with a "type 2-high" signature, in both murine models and human studies of this asthma endotype. We also discuss epithelial responses to respiratory viruses, such as rhinovirus, respiratory syncytial virus, and SARS-CoV-2, and how these triggers influence not only asthma development but also asthma exacerbation. Finally, we also summarize the results of promising clinical trials using biologicals targeting epithelial-derived cytokines in asthmatic patients.

Gaps and Future Directions in Clinical Research on Obesity-Related Asthma

Obesity is a major comorbidity for the development and worsening of asthma. It is associated with increased disease incidence, reduced response to inhaled and systemic steroids, increased asthma exacerbations, and poor disease control. Over the past two decades, we have learned that there are clinical asthma phenotypes associated with obesity, which have unique immune, inflammatory, and metabolic disease mechanisms. The objectives of this review are to provide a brief overview of the associations and gaps between these chronic inflammatory diseases and the role that traditional therapies have on treating patients with obesity-related asthma, and to describe new clinical research of therapeutic developments targeting mechanisms that are more specific to this patient population.

Prospective Real-World Analysis of Asthma Patients With Preserved and Reduced Physical Activity

BACKGROUND

Asthma is a highly heterogeneous airway disease, and the clinical characteristics of patients with asthma with preserved and reduced physical activity are poorly understood.

OBJECTIVE

We aimed to investigate the risk factors and clinical phenotypes associated with reduced physical activity in a wide range of patients with asthma.

METHODS

We conducted a prospective observational study of 138 patients with asthma, including patients with asthma without chronic obstructive pulmonary disease (COPD) (n = 104) and asthma-COPD overlap (n = 34), and 42 healthy controls. Physical activity levels were measured for 2 weeks using a triaxial accelerometer at baseline and 1 year later.

RESULTS

Higher eosinophils and body mass index (BMI) were associated with reduced physical activity in patients with asthma without COPD. Cluster analysis of asthma without COPD revealed 4 asthma phenotypes. We identified a cluster with preserved physical activity (n = 43) that was characterized by good symptom control and lung function and included a high proportion of biologics users (34.9%). Multivariate regression analysis revealed that patients with late-onset eosinophilic (n = 21), high-BMI noneosinophilic (n = 14), and symptom-predominant asthma phenotypes (n = 26) had lower levels of physical activity than controls. Patients with asthma-COPD overlap also had significantly lower physical activity levels than controls. Similar trends in physical activity levels were observed in each asthma group at 1-year follow-up.

CONCLUSION

This study showed the clinical features of patients with asthma with preserved and reduced physical activity. Reduced physical activity was observed in various asthma phenotypes and in asthma-COPD overlap.

Longitudinal Asthma Phenotypes from Childhood to Middle-Age: A Population-based Cohort Study

Rationale: Asthma is a heterogeneous condition, and longitudinal phenotyping may provide new insights into the origins and outcomes of the disease. Objectives: We aimed to characterize the longitudinal phenotypes of asthma between the first and sixth decades of life in a population-based cohort study. Methods: Respiratory questionnaires were collected at seven time points in the TAHS (Tasmanian Longitudinal Health Study) when participants were aged 7, 13, 18, 32, 43, 50, and 53 years. Current-asthma and ever-asthma status was determined at each time point, and group-based trajectory modeling was used to characterize distinct longitudinal phenotypes. Linear and logistic regression models were fitted to investigate associations of the longitudinal phenotypes with childhood factors and adult outcomes. Measurements and Main Results: Of 8,583 original participants, 1,506 had reported ever asthma. Five longitudinal asthma phenotypes were identified: early-onset adolescent-remitting (40%), early-onset adult-remitting (11%), early-onset persistent (9%), late-onset remitting (13%), and late-onset persistent (27%). All phenotypes were associated with chronic obstructive pulmonary disease at age 53 years, except for late-onset remitting asthma (odds ratios: early-onset adolescent-remitting, 2.00 [95% confidence interval (CI), 1.13-3.56]; early-onset adult-remitting, 3.61 [95% CI, 1.30-10.02]; early-onset persistent, 8.73 [95% CI, 4.10-18.55]; and late-onset persistent, 6.69 [95% CI, 3.81-11.73]). Late-onset persistent asthma was associated with the greatest comorbidity at age 53 years, with increased risk of mental health disorders and cardiovascular risk factors. Conclusions: Five longitudinal asthma phenotypes were identified between the first and sixth decades of life, including two novel remitting phenotypes. We found differential effects of these phenotypes on risk of chronic obstructive pulmonary disease and nonrespiratory comorbidities in middle age.

Effects of a Peptide Derived from the Primary Sequence of a Kallikrein Inhibitor Isolated from Bauhinia bauhinioides (pep-BbKI) in an Asthma-COPD Overlap (ACO) Model

(1) There are several patients with asthma-COPD overlap (ACO). A peptide derived from the primary sequence of a kallikrein inhibitor isolated from Bauhinia bauhinioides (pep-BbKI) has potent anti-inflammatory and antioxidant effects. Purpose: To investigate the effects of pep-BbKI treatment in an ACO model and compare them with those of corticosteroids. (2) BALB/c mice were divided into groups: SAL (saline), OVA (ovalbumin), ELA (elastase), ACO (ovalbumin + elastase), ACO-pep-BbKI (treated with inhibitor), ACO-DX (dexamethasone treatment), ACO-DX-pep-BbKI (both treatments), and SAL-pep-BbKI (saline group treated with inhibitor). We evaluated: hyperresponsiveness to methacholine, bronchoalveolar lavage fluid (BALF), exhaled nitric oxide (eNO), IL-1β, IL-4, IL-5, IL-6, IL-10, IL-13, IL-17, IFN-γ, TNF-α, MMP-9, MMP-12, TGF-β, collagen fibers, iNOS, eNO, linear mean intercept (Lm), and NF-κB in airways (AW) and alveolar septa (AS). (3) ACO-pep-BbKI reversed ACO alterations and was similar to SAL in all mechanical parameters, Lm, neutrophils, IL-5, IL-10, IL-17, IFN-γ, TNF-α, MMP-12 (AW), collagen fibers, iNOS (AW), and eNO (p > 0.05). ACO-DX reversed ACO alterations and was similar to SAL in all mechanical parameters, Lm, total cells and differentials, IL-1β(AS), IL-5 (AS), IL-6 (AS), IL-10 (AS), IL-13 (AS), IFN-γ, MMP-12 (AS), TGF-β (AS), collagen fibers (AW), iNOS, and eNO (p > 0.05). SAL was similar to SAL-pep-BbKI for all comparisons (p > 0.05). (4) Pep-BbKI was similar to dexamethasone in reducing the majority of alterations of this ACO model.

FoxO4 mediates macrophage M2 polarization by promoting LXA4R expression in an ovalbumin-induced allergic asthma model in mice

BACKGROUND

Asthma imposes a heavy burden due to its high prevalence. Forkhead box O4 (FoxO4) proteins participate in the modulation of cell progression. However, the role and mechanism of FoxO4 in asthma remains uncharted.

METHODS

An allergic asthma model was constructed by the induction of ovalbumin and interleukin (IL)-4 in mice and monocyte/macrophage-like Raw264.7 cells, respectively. The role and mechanism of FoxO4 in asthma was determined by pathological staining, immunofluorescence assay, measurement of inflammatory cells in the blood, reverse transcription quantitative polymerase chain reaction (RT-qPCR), Western blot analysis, and flow cytometry.

RESULTS

Ovalbumin treatment triggered an obvious inflammatory cell infiltration with a prominent increase in F4/80⁺ cell numbers. The relative messenger RNA (mRNA) and protein expressions of FoxO4 were increased in both ovalbumin-induced mice and interleukin-4 (IL-4)-induced Raw264.7 cells. Inhibition of FoxO4 via AS1842856 reduced inflammatory cell infiltration, the number of Periodic Acid Schiff+ (PAS+) goblet cells, the numbers of inflammatory cells in the blood, and the airway resistance in ovalbumin-induced mice. Besides, interference of FoxO4 decreased the number of F4/80⁺CD206⁺ cells, and the relative protein expressions of CD163 and Arg1 in vivo and in vitro. Mechanically, suppression of FoxO4 diminished the relative mRNA and protein expressions of LXA4R in both ovalbumin-induced mice and IL-4-induced Raw264.7 cells. Overexpression of LXA4R reversed the outcomes caused by repression of FoxO4, including airway resistance, the number of F4/80+ cells, the proportion of CD206+ cells in ovalbumin-induced mice, and the proportion of F4/80⁺CD206⁺ cells in IL-4-induced Raw264.7 cells.

CONCLUSION

FoxO4/LXA4R axis mediated macrophage M2 polarization in allergic asthma.

Severe Asthmatic Responses: The Impact of TSLP

Asthma is a chronic inflammatory disease that affects the lower respiratory system and includes several categories of patients with varying features or phenotypes. Patients with severe asthma (SA) represent a group of asthmatics that are poorly responsive to medium-to-high doses of inhaled corticosteroids and additional controllers, thus leading in some cases to life-threatening disease exacerbations. To elaborate on SA heterogeneity, the concept of asthma endotypes has been developed, with the latter being characterized as T2-high or low, depending on the type of inflammation implicated in disease pathogenesis. As SA patients exhibit curtailed responses to standard-of-care treatment, biologic therapies are prescribed as adjunctive treatments. To date, several biologics that target specific downstream effector molecules involved in disease pathophysiology have displayed superior efficacy only in patients with T2-high, eosinophilic inflammation, suggesting that upstream mediators of the inflammatory cascade could constitute an attractive therapeutic approach for difficult-to-treat asthma. One such appealing therapeutic target is thymic stromal lymphopoietin (TSLP), an epithelial-derived cytokine with critical functions in allergic diseases, including asthma. Numerous studies in both humans and mice have provided major insights pertinent to the role of TSLP in the initiation and propagation of asthmatic responses. Undoubtedly, the magnitude of TSLP in asthma pathogenesis is highlighted by the fact that the FDA recently approved tezepelumab (Tezspire), a human monoclonal antibody that targets TSLP, for SA treatment. Nevertheless, further research focusing on the biology and mode of function of TSLP in SA will considerably advance disease management.

Adenophora Stricta Root Extract Alleviates Airway Inflammation in Mice with Ovalbumin-Induced Allergic Asthma

Adenophora stricta Miq. (Campanulaceae family) is a traditional herb used for relieving cough and phlegm in East Asia. This study explored the effects of A. stricta root extract (AsE) in ovalbumin (OVA)-induced allergic asthma and lipopolysaccharide (LPS)-stimulated macrophages. Administration of 100-400 mg/kg AsE dose-dependently decreased pulmonary congestion and suppressed the reduction of alveolar surface area in mice with OVA-mediated allergic asthma. Histopathological analysis of lung tissue and cytological analysis of bronchioalveolar lavage fluid showed that AsE administration significantly attenuated inflammatory cell infiltration into the lungs. In addition, AsE also alleviated OVA-specific immunoglobulin E, interleukin (IL)-4, and IL-5 production, which are essential for OVA-dependent activation of T helper 2 lymphocytes. In Raw264.7 macrophage cells, AsE significantly blocked nitric oxide, tumor necrosis factor-α, IL-1β, IL-6, and monocyte chemoattractant factor-1 production in response to LPS. Results from an immunoblot assay revealed that AsE inhibited the phosphorylation of c-jun N-terminal kinase, inhibitory-κB kinase α/β, and p65 in LPS-stimulated cells. Furthermore, 2-furoic acid, 5-hydroxymethylfurfural, and vanillic acid 4-β-D-glucopyranoside in AsE were shown to inhibit the production of proinflammatory mediators by LPS. Taken together, the present results suggest that A. stricta root will be a useful herb for relieving allergic asthma through managing airway inflammation.

Aging-Related Mechanisms Contribute to Corticosteroid Insensitivity in Elderly Asthma

Asthma in elderly populations is an increasing health problem that is accompanied by diminished lung function and frequent exacerbations. As potent anti-inflammatory drugs, corticosteroids are commonly used to reduce lung inflammation, improve lung function, and manage disease symptoms in asthma. Although effective for most individuals, older patients are more insensitive to corticosteroids, making it difficult to manage asthma in this population. With the number of individuals older than 65 continuing to increase, it is important to understand the distinct mechanisms that promote corticosteroid insensitivity in the aging lung. In this review, we discuss corticosteroid insensitivity in asthma with an emphasis on mechanisms that contribute to persistent inflammation and diminished lung function in older individuals.

In vitro human cell-based models to study airway remodeling in asthma

Airway remodeling, as a predominant characteristic of asthma, refers to the structural changes that occurred both in the large and small airways. These pathological changes not only contribute to airway hyperresponsiveness and airway obstruction, but also predict poor outcomes of patients. In vitro models are the alternatives to animal models that facilitate airway remodeling research. Current approaches to mimic airway remodeling in vitro include mono cultures of cell lines and primary cells that are derived from the respiratory tract, and co-culture systems that consist of different cell subpopulations. Moreover, recent advances in microfluid chips and organoids show promise in simulating the complex architecture and functionality of native organs. According, they enable highly physiological-relevant investigations of human diseases in vitro. Here we aim to detail the current human cell-based models regarding their key pros and cons, and to discuss how they may be used to facilitate our understanding of airway remodeling in asthma.

Initial investigation of molecular phenotypes of airway mast cells and cytokine profiles in equine asthma

Equine asthma is a naturally occurring lung disease characterized by chronic, partially reversible airway obstruction, pulmonary remodeling, and lower airway inflammation. Asthma is currently divided into two major groups, mild to moderate asthma (mEA) and severe asthma (sEA), but further subtyping by phenotype (i.e., clinical presentation) and/or endotype (i.e., cellular mechanisms) may be warranted. For this study, we were interested in further investigation of cellular and inflammatory characteristics of EA, including airway mast cells. The purpose of this study was to: (1) compare mast cell protease mRNA expression between healthy and asthmatic horses, (2) analyze the cytokine profile present in BALF of currently defined equine asthma groups, and (3) use these data to evaluate potential biomarkers of defined asthma groups. We hypothesized that there would be significant differences in the cellular mast cell phenotypes (i.e., mucosal vs. connective tissue) and cytokine profiles in the BALF of asthmatic vs. healthy horses and across asthma groups. We assert these characteristics may inform additional subtypes of equine asthma. Adult horses were recruited from the institution's teaching herd and clinical caseload. Mast cell protease gene expression of the BALF cellular component and multiplex bead immunoassay for cytokine concentrations in the BALF supernatant were investigated. Airway mast cells primarily expressed tryptase, with low levels of chymase. No significant changes in protease expression were detected across groups. Horses with severe asthma had increased TNF-α, CXCL-8, and IFN-γ concentrations in BALF supernatant. Multidimensional analysis demonstrated healthy and mEA horses have overlapping characteristics, with sEA separating from the other groups. This difference was primarily due to BALF neutrophil and lymphocyte concentrations. These study results further inform understanding of EA immunopathology, and future studies designed to investigate asthma phenotypes and endotypes. Ultimately, a better understanding of these groups could help identify novel therapeutic strategies.

Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.

Multi-channel AgNWs-doped interdigitated organic electrochemical transistors enable sputum-based device towards noninvasive and portable diagnosis of lung cancer

Biochemical monitoring of bodily fluidics such as sweat, urine, and tears have been extensively developed, but reliable biochemical analysis of sputum biospecimens remains limited and challenging due to the low abundance of biomarkers in intrinsically viscous sputum. We reported a portable multi-channel sputum-based interdigitated organic electrochemical transistors (SiOECTs) device for noninvasive sputum diagnosis. We tailored the AgNWs-doped organic electrochemical transistors, integrating with multiplexed aptamer-antigen assays, to realize the signal amplification and simultaneous detection of biomarkers in raw sputum biospecimens from lung cancer patients. Clinical validation studies demonstrated favorable correlation coefficients between the sputum and serum biospecimens. By utilizing our portable multi-channel iOECTs devices, lung cancer patients were differentiated from health control with an optimum area under the curve (AUC) of 0.931, sensitivity of 87.0%, and specificity of 86.5%. Our miniaturized and portable device could even realize the continuous in-home tracking of the biomarkers change for lung cancer patients after radiotherapy/chemotherapy. It is envisaged that the SiOECTs will shed light on noninvasive diagnostics platforms for sputum-related diseases.

Uso de glucocorticoides sistémicos para el tratamiento del asma grave: Consenso multidisciplinar español

Background and aim

Since their effectiveness was initially demonstrated, oral corticosteroids (OCS) have been routinely used to treat asthma. We now know that their usage is linked to the development of side effects such osteoporosis and adrenal insufficiency. This is an observational study based on Delphi methodology. The questionnaire was divided into 4 sections: OCS generalities, maintenance treatment, short-term treatment, and adverse events.

Materials and methods

Two rounds of a 68-item questionnaire were completed by a panel of 48 allergists and pneumologists.

Results

Definitions were agreed upon, as was the proper use of OCS in the treatment of severe asthma. The experts agreed that the use of OCS should be minimized as much as possible and that in the event of maintenance treatments, a slow and progressive tapering strategy should be used. They also emphasized the importance of standardizing the technique for measuring the amount of SCG delivered in both cases.

Conclusions

This consensus document attempts to bring together scientifically supported suggestions from specialists in the management of asthma to reduce the use of OCS in Spain.

Identification of potential biomarkers and pathogenesis in neutrophil-predominant severe asthma: A comprehensive bioinformatics analysis

BACKGROUND

Airway neutrophilia has been associated with asthma severity and asthma exacerbations. This study attempted to identify biomarkers, pathogenesis, and therapeutic molecular targets for severe asthma in neutrophils using bioinformatics analysis.

METHODS

Fifteen healthy controls and 3 patients with neutrophilic severe asthma were screened from the Gene Expression Omnibus (GEO) database. Based on the analysis of differentially expressed genes (DEGs), functional and pathway enrichment analyses, gene set enrichment analysis, protein-protein interaction network construction, and analysis were performed. Moreover, small-molecule drug candidates have also been identified.

RESULTS

Three hundred and three upregulated and 59 downregulated genes were identified. Gene ontology function enrichment analyses were primarily related to inflammatory response, immune response, leukocyte migration, neutrophil chemotaxis, mitogen-activated protein kinase cascade, Jun N-terminal kinase cascade, I-kappaB kinase/nuclear factor-κB, and MyD88-dependent toll-like receptor signaling pathway. Pathway enrichment analyses and gene set enrichment analysis were mainly involved in cytokine-cytokine receptor interaction, the TNF signaling pathway, leukocyte transendothelial migration, and the NOD-like receptor signaling pathway. Furthermore, 1 important module and 10 hub genes (CXCL8, TLR2, CXCL1, ICAM1, CXCR4, FPR2, SELL, PTEN, TREM1, and LEP) were identified in the protein-protein interaction network. Moreover, indoprofen, mimosine, STOCK1N-35874, trapidil, iloprost, aminoglutethimide, ajmaline, levobunolol, ethionamide, cefaclor, dimenhydrinate, and bethanechol are potential drugs for the treatment of neutrophil-predominant severe asthma.

CONCLUSION

This study identified potential biomarkers, pathogenesis, and therapeutic molecular targets for neutrophil-predominant severe asthma.

A Pragmatic Primary Practice Approach to Using Specific IgE in Allergy Testing in Asthma Diagnosis, Management, and Referral

Asthma afflicts an estimated 339 million people globally and is associated with ill health, disability, and early death. Strong risk factors for developing asthma are genetic predisposition and environmental exposure to inhaled substances that may provoke allergic reactions. Asthma guidelines recommend identifying causal or trigger allergens with specific IgE (sIgE) testing after a diagnosis of asthma has been made. Allergy testing with sIgE targets subpopulations of patients considered at high risk, such as those with frequent exacerbations, emergency visits or hospitalizations, or uncontrolled symptoms. Specific recommendations apply to preschool children, school-age children, patients with persistent or difficult-to-control asthma, patients needing oral corticosteroids or high-dose inhaled steroids, patients seeking understanding and guidance about their disease, and candidates for advanced therapies (biologics, allergen immunotherapy). Allergen skin testing is common in specialized settings but less available in primary care. Blood tests for total and sIgE are accessible and yield quantifiable results for tested allergens, useful for detecting sensitization. Results are interpreted in the context of the patient’s clinical presentation, age, and relevant allergen exposures. Incorporating sIgE testing into asthma management adds objective information to identify specific allergies and can guide personalized treatment plans, which reinforce patient-doctor communication. Test results can also be used to predict exacerbations and response to therapies. Additional diagnostic information can be gleaned from (i) eosinophil count ≥300 μL, which significantly increases the odds of having exacerbations, and emerging eosinophil biomarkers (eg, eosinophil-derived neurotoxin), which can be measured in plasma or serum samples, and (ii) fractional exhaled nitric oxide (FeNO), with values ≥25 ppb regarded as the cutoff for diagnosis, evaluating inhaled corticosteroid response, and of probable response to anti-IgE, anti-IL4 and anti-IL5 receptor biologics. Referral to asthma/allergy specialists is warranted when the initial diagnosis is uncertain, and when asthma symptoms, impairment, or exacerbations are repeated or severe.

Severe asthma and personalized approach in the choice of biologic

PURPOSE OF REVIEW

Severe asthma requires intensive pharmacological treatment to achieve disease control. Oral corticosteroids are effective, but their use is burdened with important side effects. Biologics targeting the specific inflammatory pathways underpinning the disease have been shown to be effective but not all patients respond equally well. As we treat more patients than those who can respond, our inability to predict responders has important healthcare costs considering that biologics are expensive drugs. Thus, a more precise choice of the 'right patients' to be prescribed with the 'right biologics' would be desirable.

RECENT FINDINGS

Machine learning techniques showed that it is possible to increase our ability to predict outcomes in patients treated with biologics. Recently, we identified by cluster analysis four different clusters within the T2 high phenotype with differential benralizumab response. Two of these clusters, characterized by higher levels of inflammatory markers, showed the highest response rate (80-90%).

SUMMARY

Machine learning holds promise for asthma research enabling us to predict which patients will respond to which drug. These techniques can facilitate the diagnostic workflow and increase the chance of selecting the more appropriate treatment option for the individual patient, enhancing patient care and satisfaction.

Diversities of allergic pathologies and their modifiers: Report from the second DGAKI-JSA meeting

In October 2021, researchers from the German Society of Allergy and Clinical Immunology (DGAKI) and from the Japanese Society of Allergology (JSA) focused their attention on the pathological conditions and modifiers of various allergic diseases. Topics included 1) the pathophysiology of IgE/mast cell-mediated allergic diseases; 2) the diagnosis and prevention of IgE/mast cell-mediated diseases; 3) the pathophysiology, diagnosis, and treatment of eosinophilic airway diseases; and 4) host-pathogen interaction and allergic diseases. This report summarizes the panel discussions, which highlighted the importance of recognizing the diversity of genetics, immunological mechanisms, and modifying factors underlying allergic diseases.

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

OBJECTIVE

The heterogeneity of asthma has inspired widespread application of statistical clustering algorithms to a variety of datasets for identification of potentially clinically meaningful phenotypes. There has not been a standardized data analysis approach for asthma clustering, which can affect reproducibility and clinical translation of results. Our objective was to identify common and effective data analysis practices in the asthma clustering literature and apply them to data from a Southern California population-based cohort of schoolchildren with asthma.

METHODS

As of January 1, 2020, we reviewed key statistical elements of 77 asthma clustering studies. Guided by the literature, we used 12 input variables and three clustering methods (hierarchical clustering, k-medoids, and latent class analysis) to identify clusters in 598 schoolchildren with asthma from the Southern California Children's Health Study (CHS).

RESULTS

Clusters of children identified by latent class analysis were characterized by exhaled nitric oxide, FEV1/FVC, FEV1 percent predicted, asthma control and allergy score; and were predictive of control at two year follow up. Clusters from the other two methods were less clinically remarkable, primarily differentiated by sex and race/ethnicity and less predictive of asthma control over time.

CONCLUSION

Upon review of the asthma phenotyping literature, common approaches of data clustering emerged. When applying these elements to the Children's Health Study data, latent class analysis clusters-represented by exhaled nitric oxide and spirometry measures-had clinical relevance over time.

Corticosteroid resistance in asthma: Cellular and molecular mechanisms

Inhaled glucocorticoids (GCs) are drugs widely used as treatment for asthma patients. They prevent the recruitment and activation of lung immune and inflammatory cells and, moreover, have profound effects on airway structural cells to reverse the effects of disease on airway inflammation. GCs bind to a specific receptor, the glucocorticoid receptor (GR), which is a member of the nuclear receptor superfamily and modulates pro- and anti-inflammatory gene transcription through a number of distinct and complementary mechanisms. Targets genes include many pro-inflammatory mediators such as chemokines, cytokines, growth factors and their receptors. Inhaled GCs are very effective for most asthma patients with little, if any, systemic side effects depending upon the dose. However, some patients show poor asthma control even after the administration of high doses of topical or even systemic GCs. Several mechanisms relating to inflammation have been considered to be responsible for the onset of the relative GC resistance observed in these patients. In these patients, the side-effect profile of GCs prevent continued use of high doses and new drugs are needed. Targeting the defective pathways associated with GC function in these patients may also reactivate GC responsiveness.

Insights gained in the pathology of lung disease through single cell transcriptomics

The human lung is a relatively quiescent organ in the normal healthy state but contains stem/progenitor cells that contribute to normal tissue maintenance and either repair or remodeling in response to injury and disease. Maintenance or repair lead to proper restoration of functional lung tissue and maintenance of physiological functions, with remodeling resulting in altered structure and function that is typically associated with disease. Knowledge of cell types contributing to lung tissue maintenance and repair/remodeling have largely relied on mouse models of injury-repair and lineage tracing of local progenitors. Therefore, many of the functional alterations underlying remodeling in human lung disease, have remained poorly defined. However, the advent of advanced genomics approaches to define the molecular phenotype of lung cells at single cell resolution has paved the way for rapid advances in our understanding of cell types present within the normal human lung and changes that accompany disease. Here we summarize recent advances in our understanding of disease-related changes in the molecular phenotype of human lung epithelium that have emerged from single-cell transcriptomic studies. We focus attention on emerging concepts of epithelial transitional states that characterize the pathological remodeling that accompanies chronic lung diseases, including idiopathic pulmonary fibrosis, chronic obstructive pulmonary disease, cystic fibrosis, and asthma. Concepts arising from these studies are actively evolving and require corroborative studies to improve our understanding of disease mechanisms. Whenever possible we highlight opportunities for providing a unified nomenclature in this rapidly advancing field of research. This article is protected by copyright. All rights reserved.

Clinical Characteristics and Management Strategies for Adult Obese Asthma Patients

The rates of asthma and obesity are increasing concurrently in the United States. Epidemiologic studies demonstrate that the incidence of asthma increases with obesity. Furthermore, obese individuals have asthma that is more severe, harder to control, and resistant to standard medications. In fact, specific asthma-obesity phenotypes have been identified. Various pathophysiologic mechanisms, including mechanical, inflammatory, metabolic and microbiome-associated, are at play in promulgating the obese-asthma phenotypes. While standard asthma medications, such as inhaled corticosteroids and biologics, are currently used to treat obese asthmatics, they may have limited effectiveness. Targeting the underlying aberrant processes, such as addressing steroid resistance, microbiome, metabolic and weight loss approaches, may be helpful.

Lung function, airway and peripheral basophils and eosinophils are associated with molecular pharmacogenomic endotypes of steroid response in severe asthma

INTRODUCTION

Asthma is a complex disease with heterogeneous expression/severity. There is growing interest in defining asthma endotypes consistently associated with different responses to therapy, focusing on type 2 inflammation (Th2) as a key pathological mechanism. Current asthma endotypes are defined primarily by clinical/laboratory criteria. Each endotype is likely characterised by distinct molecular mechanisms that identify optimal therapies.

METHODS

We applied unsupervised (without a priori clinical criteria) principal component analysis on sputum airway cells RNA-sequencing transcriptomic data from 19 asthmatics from the Severe Asthma Research Program at baseline and 6-8 weeks follow-up after a 40 mg dose of intramuscular corticosteroids. We investigated principal components PC1, PC3 for association with 55 clinical variables.

RESULTS

PC3 was associated with baseline Th2 clinical features including blood (rank-sum p=0.0082) and airway (rank-sum p=0.0024) eosinophilia, FEV₁ change (Kendall tau-b R=-0.333 (-0.592 to -0.012)) and follow-up FEV₁ albuterol response (Kendall tau-b R=0.392 (0.079 to 0.634)). PC1 with blood basophlia (rank-sum p=0.0191). The top 5% genes contributing to PC1, PC3 were enriched for distinct immune system/inflammation ontologies suggesting distinct subject-specific clusters of transcriptomic response to corticosteroids. PC3 association with FEV₁ change was reproduced in silico in a comparable independent 14-subject (baseline, 8 weeks after daily inhaled corticosteroids (ICS)) airway epithelial cells microRNAome dataset.

CONCLUSIONS

Transcriptomic PCs from this unsupervised methodology define molecular pharmacogenomic endotypes that may yield novel biology underlying different subject-specific responses to corticosteroid therapy in asthma, and optimal personalised asthma care. Top contributing genes to these PCs may suggest new therapeutic targets.

Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension

Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.

Immunobiology of Steroid-Unresponsive Severe Asthma

Asthma is a heterogeneous respiratory disease characterized by airflow obstruction, bronchial hyperresponsiveness and airway inflammation. Approximately 10% of asthma patients suffer from uncontrolled severe asthma (SA). A major difference between patients with SA from those with mild-to-moderate asthma is the resistance to common glucocorticoid treatments. Thus, steroid-unresponsive uncontrolled asthma is a hallmark of SA. An impediment in the development of new therapies for SA is a limited understanding of the range of immune responses and molecular networks that can contribute to the disease process. Typically SA is thought to be characterized by a Th2-low and Th17-high immunophenotype, accompanied by neutrophilic airway inflammation. However, Th2-mediated eosinophilic inflammation, as well as mixed Th1/Th17-mediated inflammation, is also described in SA. Thus, existing studies indicate that the immunophenotype of SA is diverse. This review attempts to summarize the interplay of different immune mediators and related mechanisms that are associated with airway inflammation and the immunobiology of SA.

Applications of artificial intelligence in common pulmonary diseases

Artificial intelligence (AI) is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks. AI can provide accurate results as well as analyze vast amounts of data that cannot be analyzed via conventional statistical methods. AI has been utilized in pulmonary medicine for almost two decades and its utilization continues to expand. AI can help in making diagnoses and predicting outcomes in pulmonary diseases based on clinical data, chest imaging, lung pathology, and pulmonary function testing. AI-based applications enable physicians to use enormous amounts of data and improve their precision in the treatment of pulmonary diseases. Given the growing role of AI in pulmonary medicine, it is important for practitioners caring for patients with pulmonary diseases to understand how AI can work in order to implement it into clinical practices and improve patient care. The goal of this mini-review is to discuss the use of AI in pulmonary medicine and imaging in cases of obstructive lung disease, interstitial lung disease, infections, nodules, and lung cancer.

Lung Function Variation during the Estrus Cycle of Mares Affected by Severe Asthma

Simple Summary

The estrus cycle and sex hormones influence asthma development and severity in humans, but whether the same is occurring in the asthma of horses is unknown. Severe equine asthma (SEA) is characterized by breathing difficulty, even at rest, and although it can be controlled by management and medication, it remains incurable. Stabling and hay feeding are the main contributors to disease exacerbation, but other factors could possibly alter the respiratory compromise. Therefore, the objective of this study was to evaluate the effects of the estrus cycle on airway dysfunction in five mares affected by SEA by assessing the lung function during the follicular and luteal phases of the reproductive cycle. The inspiratory obstruction improved during the luteal phase and the variation in progesterone and the dominant follicle size correlated with lung function parameters, suggesting a role for sex hormones in asthma pathophysiology. This first description of the estrus cycle’s modulation of airway obstruction in horses supports further studies to uncover the effects of sex hormones in asthma in horses and humans.

Abstract

While the prevalence of asthma is higher in boys than in girls during childhood, this tendency reverses at puberty, suggesting an effect of sex hormones on the disease pathophysiology. Fluctuations of asthma severity concurring with the estrus cycle are reported in women, but this phenomenon has never been investigated in mares to date. The objective of this exploratory study was to determine whether the estrus cycle modulates airway obstruction in severe equine asthma (SEA). Five mares with SEA during exacerbation of the disease were studied. The whole breath, expiratory and inspiratory resistance, and reactance were compared during the follicular and luteal phases of the estrus cycle. The reproductive tract was evaluated by rectal palpation, ultrasound, and serum progesterone levels. The inspiratory resistance and reactance improved during the luteal phase of the estrus cycle, and variation in progesterone levels and the dominant follicle size correlated with several lung function parameters. The fluctuation of airway dysfunction during the estrus cycle is noteworthy as deterioration of the disease could perhaps be expected and prevented by horse owners and veterinarians. Further studies are required to determine if the equine species could be a suitable model to evaluate the effects of sex hormones on asthma.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.