How Does Mild Asthma Differ Phenotypically from Difficult-to-Treat Asthma?

Background: Despite most of the asthma population having mild disease, the mild asthma phenotype is poorly understood. Here, we aim to address this gap in knowledge by extensively characterising the mild asthma phenotype and comparing this with difficult-to-treat asthma. Methods: We assessed two real-world adult cohorts from the South of England using an identical methodology: the Wessex AsThma CoHort of difficult asthma (WATCH) (n=498) and a mild asthma cohort from the comparator arm of the Epigenetics Of Severe Asthma (EOSA) study (n=67). Data acquisition included detailed clinical, health and disease-related questionnaires, anthropometry, allergy and lung function testing, plus biological samples (blood and sputum) in a subset. Results: Mild asthma is predominantly early-onset and is associated with type-2 (T2) inflammation (atopy, raised fractional exhaled nitric oxide (FeNO), blood/sputum eosinophilia) plus preserved lung function. A high prevalence of comorbidities and multimorbidity was observed in mild asthma, particularly depression (58.2%) and anxiety (56.7%). In comparison to difficult asthma, mild disease showed similar female predominance (>60%), T2-high inflammation and atopy prevalence, but lower peripheral blood/airway neutrophil counts and preserved lung function. Mild asthma was also associated with a greater prevalence of current smokers (20.9%). A multi-component T2-high inflammatory measure was comparable between the cohorts; T2-high status 88.1% in mild asthma and 93.5% in difficult asthma. Conclusion: Phenotypic characterisation of mild asthma identified early-onset disease with high prevalence of current smokers, T2-high inflammation and significant multimorbidity burden. Early comprehensive assessment of mild asthma patients could help prevent potential later progression to more complex severe disease.

Comprehensive Characterization of Difficult-to-Treat Asthma Reveals Near Absence of T2-Low Status.

BACKGROUND: Asthma is conventionally stratified as type 2 inflammation (T2)-high or T2-low disease. Identifying T2 status has therapeutic implications for patient management, but a real-world understanding of this T2 paradigm in difficult-to-treat and severe asthma remains limited. OBJECTIVES: To identify the prevalence of T2-high status in difficult-to-treat asthma patients using a multicomponent definition and compare clinical and pathophysiologic characteristics between patients classified as T2-high and T2-low. METHODS: We evaluated 388 biologic-naive patients from the Wessex Asthma Cohort of difficult asthma (WATCH) study in the United Kingdom. Type 2-high asthma was defined as 20 parts per billion or greater FeNO , 150 cells/µL or greater peripheral blood eosinophils, the need for maintenance oral corticosteroids, and/or clinically allergy-driven asthma. RESULTS: This multicomponent assessment identified T2-high asthma in 93% of patients (360 of 388). Body mass index, inhaled corticosteroid dose, asthma exacerbations, and common comorbidities did not differ by T2 status. Significantly worse airflow limitation was found in T2-high compared with T2-low patients (FEV1/FVC 65.9% vs 74.6%). Moreover, 75% of patients defined as having T2-low asthma had raised peripheral blood eosinophils within the preceding 10 years, which left only seven patients (1.8%) who had never had T2 signals. Incorporation of sputum eosinophilia 2% or greater into the multicomponent definition in a subset of 117 patients with induced sputum data similarly found that 96% (112 of 117) met criteria for T2-high asthma, 50% of whom (56 of 112) had sputum eosinophils 2% or greater. CONCLUSIONS: Almost all patients with difficult-to-treat asthma have T2-high disease; less than 2% of patients never display T2-defining criteria. This highlights a need to assess T2 status comprehensively in clinical practice before labeling a patient with difficult-to-treat asthma as T2-low.

The Detrimental Clinical Associations of Anxiety and Depression with Difficult Asthma Outcomes.

Difficult asthma describes asthma in which comorbidities, inadequate treatment, suboptimal inhaler technique and/or poor adherence impede good asthma control. The association of anxiety and depression with difficult asthma outcomes (exacerbations, hospital admissions, asthma control, etc.) is unclear. This study assessed the clinical associations of anxiety and depression with difficult asthma outcomes in patients with a specialist diagnosis of difficult asthma. Using real-world data, we retrospectively phenotyped patients from the Wessex Asthma Cohort of Difficult Asthma (N = 441) using clinical diagnoses of anxiety and depression against those without anxiety or depression (controls). Additionally, we stratified patients by severity of psychological distress using the Hospital Anxiety and Depression Scale (HADS). We found that depression and/or anxiety were reported in 43.1% of subjects and were associated with worse disease-related questionnaire scores. Each psychological comorbidity group showed differential associations with difficult asthma outcomes. Anxiety alone (7.9%) was associated with dysfunctional breathing and more hospitalisations [anxiety, median (IQR): 0 (2) vs. controls: 0 (0)], while depression alone (11.6%) was associated with obesity and obstructive sleep apnoea. The dual anxiety and depression group (23.6%) displayed multimorbidity, worse asthma outcomes, female predominance and earlier asthma onset. Worse HADS-A scores in patients with anxiety were associated with worse subjective outcomes (questionnaire scores), while worse HADS-D scores in patients with depression were associated with worse objective (ICU admissions and maintenance oral corticosteroid requirements) and subjective outcomes. In conclusion, anxiety and depression are common in difficult asthma but exert differential detrimental effects. Difficult asthma patients with dual anxiety and depression experience worse asthma outcomes alongside worse measures of psychological distress. There is a severity-gradient association of HADS scores with worse difficult asthma outcomes. Collectively, our findings highlight the need for holistic, multidisciplinary approaches that promote early identification and management of anxiety and depression in difficult asthma patients.

MicroRNAs-A Promising Tool for Asthma Diagnosis and Severity Assessment: A Systematic Review.

Micro RNAs (miRNAs) are short, non-coding RNAs (Ribonucleic acids) with regulatory functions that could prove useful as biomarkers for asthma diagnosis and asthma severity-risk stratification. The objective of this systematic review is to identify panels of miRNAs that can be used to support asthma diagnosis and severity-risk assessment. Three databases (Medline, Embase, and SCOPUS) were searched up to 15 September 2020 to identify studies reporting differential expression of specific miRNAs in the tissues of adults and children with asthma. Studies reporting miRNAs associations in animal models that were also studied in humans were included in this review. We identified 75 studies that met our search criteria. Of these, 66 studies reported more than 200 miRNAs that are differentially expressed in asthma patients when compared to non-asthmatic controls. In addition, 16 studies reported 17 miRNAs that are differentially expressed with differences in asthma severity. We were able to construct two panels of miRNAs that are expressed in blood and can serve as core panels to further investigate the practicality and efficiency of using miRNAs as non-invasive biomarkers for asthma diagnosis and severity-risk assessment, respectively.

Prediction models for childhood asthma: A systematic review.

BACKGROUND: The inability to objectively diagnose childhood asthma before age five often results in both under-treatment and over-treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school-age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school-age asthma. METHODS: Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school-age children (6-13 years). Validation studies were evaluated as a secondary objective. RESULTS: Twenty-four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression-based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression-based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62-0.83). CONCLUSION: Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school-age asthma prediction.

Real-time measurement of the intracellular pH of yeast cells during glucose metabolism using ratiometric fluorescent nanosensors.

Intracellular pH is a key parameter that influences many biochemical and metabolic pathways that can also be used as an indirect marker to monitor metabolic and intracellular processes. Herein, we utilise ratiometric fluorescent pH-sensitive nanosensors with an extended dynamic pH range to measure the intracellular pH of yeast (Saccharomyces cerevisiae) during glucose metabolism in real-time. Ratiometric fluorescent pH-sensitive nanosensors consisting of a polyacrylamide nanoparticle matrix covalently linked to two pH-sensitive fluorophores, Oregon green (OG) and 5(6)carboxyfluorescein (FAM), and a reference pH-insensitive fluorophore, 5(6)carboxytetramethylrhodamine (TAMRA), were synthesised. Nanosensors were functionalised with acrylamidopropyltrimethyl ammonium hydrochloride (ACTA) to confer a positive charge to the nanoparticle surfaces that facilitated nanosensor delivery to yeast cells, negating the need to use stress inducing techniques. The results showed that under glucose-starved conditions the intracellular pH of yeast population (n ≈ 200) was 4.67 ± 0.15. Upon addition of d-(+)-glucose (10 mM), this pH value decreased to pH 3.86 ± 0.13 over a period of 10 minutes followed by a gradual rise to a maximal pH of 5.21 ± 0.26, 25 minutes after glucose addition. 45 minutes after the addition of glucose, the intracellular pH of yeast cells returned to that of the glucose starved conditions. This study advances our understanding of the interplay between glucose metabolism and pH regulation in yeast cells, and indicates that the intracellular pH homestasis in yeast is highly regulated and demonstrates the utility of nanosensors for real-time intracellular pH measurements.