Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters

Leukocyte redistribution as immunological biomarker of corticosteroid resistance in severe asthma

BACKGROUND

Earlier studies have suggested that the leukocyte redistribution can be considered as an immunological marker of the clinical response to corticosteroids (CS), representing an easy measurable potential biomarker in severe asthma.

OBJECTIVE

The aim of this study was to determinate the utility of the leukocyte redistribution as a biomarker of disease heterogeneity in patients with severe asthma and as a bioindicator of potential CS resistance.

METHODS

We developed an unbiased clustering approach based on the clinical data and the flow cytometry results of peripheral blood leukocyte phenotypes of 142 patients with severe asthma before and after systemic CS administration.

RESULTS

Based on the differences in the blood count eosinophils, neutrophils and lymphocytes, together with the flow cytometry measurements of basic T cell, B cell and NK cell subpopulations before and after systemic CS administration, we identified two severe asthma clusters, which differed in the cell frequencies, response to CS and atopy status. Patients in cluster 1 had higher frequency of blood eosinophils at baseline, were sensitized to less allergens and had better steroid responsiveness, measured as the pronounced leukocyte redistribution after the administration of systemic CS. Patients in cluster 2 were determined by the higher frequency of B-cells and stronger IgE sensitization status to the multiple allergens. They also displayed higher steroid resistance, as the clinical correlate for the lower leukocyte redistribution after administration of systemic CS.

CONCLUSION

The flow cytometry-based profiling of the basic populations of immune cells in the blood and its analysis before and after systemic corticosteroid administration could improve personalized treatment approaches in patients with severe asthma.

A retrospective observational study on pheno-endotypes of severe asthma among adults attending asthma clinic in a tertiary care centre in India

Background and Objective

Severe asthma phenotyping based on invasive and non-invasive bio-markers assists in a better understanding of heterogeneity of clinical presentations and thereby using targeted therapies. Therefore, the current study was conducted to evaluate phenotypes based on non-invasive bio-markers of severe asthma patients attending a tertiary care hospital in North India.

Methods

This was a retrospective, observational study conducted on the patients who visited the respiratory department of a tertiary care hospital in North India. Patients aged 18 years and above diagnosed with severe asthma were classified into distinct phenotypes, namely, atopic asthma, eosinophilic asthma, and Type 2 low asthma. Patients with their clinical and functional parameters were classified based on the levels of bio-chemical and hematological results [such as total/specific IgE, blood absolute eosinophil count (AEC)], skin prick tests, history of allergy, and the presence of allergic symptoms.

Results

Out of total 100 severe asthmatics, the majority of the patients had an eosinophilic asthma (49%) phenotype, followed by atopic (allergic) asthma (36%) and Type 2 low asthma (15%) phenotypes. However, it was found that 29% of these patients had overlap of both atopy and eosinophilia. The atopic phenotype showed allergic symptoms, positive skin prick tests, and elevated IgE levels. The eosinophilic phenotype had high AEC (≥300 cells/uL) and low IgE (< 30 IU/ml) levels. The Type 2 low phenotype showed low AEC and IgE levels along with the absence of allergic symptoms. However, among these 100 patients, overlapping traits of both atopy and eosinophilia were labelled as overlap phenotypes. 50% of type 2 low severe asthma cases had eosinophils >150 cells/cmm and were eligible for mepolizumab.

Conclusions

Identification of severe asthma pheno-endotypes based on simple non-invasive bio-markers is feasible in Indian settings, and it is of utmost importance for future treatment planning in these patients with available biologicals. Overlap of eosinophilic and atopic endotypes in one-third cases would challenge physicians to choose upfront appropriate biologicals in our country. Type 2 low asthma was least common with only <10% cases of severe asthma being ineligible for any biological.

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.

Asthma with Fixed Airflow Obstruction: From Fixed to Personalized Approach

Asthma is generally characterized by variable symptoms such as dyspnea and wheezing and variable airflow obstruction. This review focuses on a subset of patients suffering from asthma with persistent airflow limitation that is not fully reversible (asthma with fixed airflow obstruction, FAO). The pathophysiology, the risk factors and the clinical outcomes associated with FAO are presented, as well as the distinct clinical entity of severe asthma and its inflammatory subtypes (T2 and non-T2). The current strategies for the treatment of these endotypes and treatment of the distinct Asthma/COPD overlap (ACO) phenotype are described. Management and medical interventions in FAO and/or ACO patients demand a holistic approach, which is not yet clearly established in guidelines worldwide. Finally, a treatment algorithm that includes FAO/ACO management based on pharmacological and non-pharmacological treatment, guideline-based management for specific co-morbidities, and modification of the risk factors is proposed.

Physical Activity and Its Association with Traditional Outcome Measures in Pulmonary Arterial Hypertension

Rationale Limitation of physical activity is a common presenting complaint for patients with pulmonary arterial hypertension (PAH). Physical activity is thought to be determined by cardiopulmonary function, yet there are limited data that investigate this relationship. Objective We aimed to study the relationship between right ventricular function and daily activity and its impact on health-related quality of life (HRQoL) in PAH. Methods Baseline data for 55 patients enrolled in PHANTOM, an ongoing multicenter randomized controlled trial of anastrozole in PAH were used. Post-menopausal women and men were eligible and underwent six-minute walk testing, echocardiography and completed HRQoL questionnaires. Each patient wore an accelerometer for 7-days. Multivariable linear regression models were used to study the association between tricuspid annular plane systolic excursion (TAPSE) and vector magnitude counts, and between daily activity and HRQoL. Principal component analysis and K-means clustering were used to identify activity-based phenotypes. K-nearest neighbors' classification was applied to an independent cross-sectional cohort from the University of Pennsylvania. Results The mean age of patients in PHANTOM was 61 years. 67% were women with idiopathic PAH as the most common etiology. A 0.4 cm increase in TAPSE was associated with an increase in daily vector magnitude counts (ß:34000, 95%CI:900-67000, p=0.004) after adjustment for age, sex, body mass index, etiology of PAH and wear time. A 1-standard deviation increase in vector magnitude counts was associated with higher six-minute walk distance (ß: 56.1 meters, 95%CI:28.6-83.7, p<0.001) and lower emPHasis-10 scores (ß:-3.3, 95%CI:0.3-6.4, p=0.03). Three activity phenotypes, low, medium, and high, were identified. The most active phenotype had greater six-minute walk distances (p=0.001) and lower emPHasis-10 scores (p=0.009) after adjustment for age, sex, body mass index, WHO functional class and parenteral prostacyclin use. Phenotypes of physical activity were reproduced in the second cohort and were independently associated with six-minute walk distance. Conclusion Better right ventricular systolic function was associated with increased levels of activity in PAH. Increased daily activity was associated with greater six-minute walk distance and better HRQoL. Distinct activity-based phenotypes may be helpful in risk stratification of PAH patients or provide novel endpoints for clinical trials.

Biomarkers in Different Asthma Phenotypes

Asthma is the most common respiratory disease. It has multiple phenotypes thatcan be partially differentiated by measuring the disease’s specific characteristics—biomarkers. The pathogenetic mechanisms are complex, and it is still a challenge to choose suitable biomarkers to adequately stratify patients, which became especially important with the introduction of biologicals in asthma treatment. Usage of biomarkers and an understanding of the underlying pathobiological mechanisms lead to the definition of endotypes. Asthma can be broadly divided into two endotypes, T2-high and T2-low. The right combination of various biomarkers in different phenotypes is under investigation, hoping to help researchers and clinicians in better disease evaluation since theindividual approach and personalized medicine are imperative. Multiple biomarkers are superior to a single biomarker.

Mixed Sputum Granulocyte Longitudinal Impact on Lung Function in the Severe Asthma Research Program

Rationale: Some reports indicate longitudinal variability in sputum differential cell counts, whereas others describe stability. Highly variable sputum eosinophil percentages are associated with greater lung function loss than persistently elevated eosinophil percentages, but elevated neutrophils are linked to more severe asthma.Objectives: To examine sputum granulocyte stability or variability longitudinally and associations with important clinical characteristics.Methods: The SARP III (Severe Asthma Research Program III) cohort underwent comprehensive phenotype characterization at baseline and annually over 3 years. Adult subjects with acceptable sputum levels were assigned to one of three longitudinal sputum groups: eosinophils predominantly <2%, eosinophils predominantly ≥2%, or highly variable eosinophil percentages (>2 SDs determined from independent, repeated baseline eosinophil percentages). Subjects were similarly assigned to one of three longitudinal neutrophil groups with a 50% cut point.Measurements and Main Results: The group with predominantly <2% sputum eosinophils had the highest lung function (prebronchodilator FEV₁% predicted, P < 0.01; FEV₁/FVC ratio, P < 0.001) at baseline and throughout 3 years compared with other eosinophil groups. Healthcare use did not differ, although the highly variable eosinophil group reported more asthma exacerbations at Year 3. Longitudinal neutrophil groups showed few differences. However, a combination of predominantly ≥2% eosinophil and ≥50% neutrophil groups resulted in the lowest prebronchodilator FEV₁% predicted (P = 0.049) compared with the combination with predominantly <2% eosinophils and<50% neutrophils.Conclusions: Subjects with predominantly ≥2% sputum eosinophils in combination with predominantly ≥50% neutrophils showed greater loss of lung function, whereas those with highly variable sputum eosinophils had greater healthcare use.

A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.

Instability of sputum molecular phenotypes in U-BIOPRED severe asthma

The Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project has described phenotypic differences of severe asthma using a systems biology approach. We obtained three molecular phenotypes termed transcription-associated clusters (TACs) using hierarchical clustering of differentially expressed transcripts between T2-high and T2-low [1]. TAC1 was characterised by receptors IL33R, CCR3 and TSLPR, with the highest enrichment of gene signatures for IL-13/type-2 (T2) inflammation with sputum eosinophilia; TAC2 by inflammasome-associated genes, interferon-α (IFN-α) and tumour necrosis factor-α (TNF-α)-associated genes with sputum neutrophilia; and TAC3 by metabolic and mitochondrial function genes with pauci-granulocytic inflammation. Given that sputum eosinophilia may vary with time in many asthmatic subjects [2, 3], we hypothesised that TAC status may also change with time.

At 1 year, 45% of severe asthma change molecular phenotype as determined by sputum transcriptomic analysis. Together with concomitant shift in sputum granulocytic markers, this may indicate variability of driving mechanisms in this unstable group.https://bit.ly/35aj489

Asthma and fixed airflow obstruction: Long-term trajectories suggest distinct endotypes

BACKGROUND

Long-term trajectories of asthma with fixed airflow obstruction (FAO) may reveal links to inflammatory endotypes.

OBJECTIVE

We investigated whether measures of asthma control and airway inflammation and remodelling differed by long-term FAO status in moderate-to-severe asthma.

METHODS

Adults enrolled in the Difficult Asthma Study assessed initially using serial Asthma Control Questionnaire (ACQ), exacerbation history, spirometry and sputum cytology over 12 months, as well as endoscopic bronchial biopsy with airway smooth muscle (ASM) quantification, were revaluated three or more years later with questionnaires and spirometry. FAO was defined as a persistent post-bronchodilator forced expired volume in one second (FEV₁ )-to-forced vital capacity ratio below 0.70.

RESULTS

Sixty-two participants (mean ± SD age 48 ± 11 years; 50% female; 75% atopic; asthma duration 24 ± 14 years) returned for follow-up assessment (median interval 7.9 years; IQR: 5.4-8.8 years). Compared to participants without FAO (n = 28), those with FAO at baseline and long-term follow-up (n = 18) had higher baseline sputum neutrophil content and ASM, and a higher exacerbation frequency that persisted at long-term follow-up. Sputum eosinophils, ACQ and long-term FEV₁ decline did not differ. Participants with incident FAO at long-term follow-up (n = 16) had higher baseline exacerbation frequency, sputum eosinophil content, higher ACQ scores and greater decline in FEV₁ , whereas baseline ASM was similar to those without FAO.

CONCLUSION

In moderate-to-severe asthma, long-term FAO is characterized by neutrophilic sputum inflammation and airway remodelling, but FEV₁ decline is similar to those without FAO. Long-term incident FAO is preceded by higher exacerbation frequency, higher sputum eosinophil content and significant FEV₁ decline.

The Future of Asthma Care: Personalized Asthma Treatment

Although once considered a single disease entity, asthma is now known to be a complex inflammatory disease engaging a range of causal pathways. The most frequent forms of asthma are identified by sputum/blood eosinophilia and activation of type 2 inflammatory pathways involving interleukins-3, -4, -5, and granulocyte-macrophage colony-stimulating factor. The use of diagnostics that identify T2 engagement linked to the selective use of highly targeted biologics has opened up a new way of managing severe disease. Novel technologies, such as wearables and intelligent inhalers, enable real-time remote monitoring of asthma, creating a unique opportunity for personalized health care.

Development and initial validation of the Asthma Severity Scoring System (ASSESS)

BACKGROUND

Tools for quantification of asthma severity are limited.

OBJECTIVE

We sought to develop a continuous measure of asthma severity, the Asthma Severity Scoring System (ASSESS), for adolescents and adults, incorporating domains of asthma control, lung function, medications, and exacerbations.

METHODS

Baseline and 36-month longitudinal data from participants in phase 3 of the Severe Asthma Research Program (NCT01606826) were used. Scale properties, responsiveness, and a minimally important difference were determined. External replication was performed in participants enrolled in the Severe Asthma Research Program phase 1/2. The utility of ASSESS for detecting treatment response was explored in participants undergoing corticosteroid responsiveness testing with intramuscular triamcinolone and participants receiving biologics.

RESULTS

ASSESS scores ranged from 0 to 20 (8.78 ± 3.9; greater scores reflect worse severity) and differed among 5 phenotypic groups. Measurement properties were acceptable. ASSESS was responsive to changes in quality of life with a minimally important difference of 2, with good specificity for outcomes of asthma improvement and worsening but poor sensitivity. Replication analyses yielded similar results, with a 2-point decrease (improvement) associated with improvements in quality of life. Participants with a 2-point or greater decrease (improvement) in ASSESS scores also had greater improvement in lung function and asthma control after triamcinolone, but these differences were limited to phenotypic clusters 3, 4, and 5. Participants treated with biologics also had a 2-point or greater decrease (improvement) in ASSESS scores overall.

CONCLUSIONS

The ASSESS tool is an objective measure that might be useful in epidemiologic and clinical research studies for quantification of treatment response in individual patients and phenotypic groups. However, validation studies are warranted.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Stability of peripheral blood immune markers in patients with asthma

Background

Asthma is a complex disease with variable course. Efforts to identify biomarkers to predict asthma severity, the course of disease and response to treatment have not been very successful so far. We have previous suggested that PAR-2 and CRTh2 expression on specific peripheral blood cell subtypes may be biomarkers of asthma severity. We reasoned that parameters that remain stable when asthma symptoms are controlled would be the most appropriate to evaluate for their utility to predict loss of asthma control and/or severity of the disease.

Methods

Nineteen stable asthmatics were recruited from the University of Alberta Asthma clinic and followed in clinic every 3 months for a total of 4 visits. Patients had spirometry and completed the ACQ questionnaire in every visit. Blood was drawn in every visit and analyzed for a number of immune parameters by flow cytometry. These parameters included PAR-2 and CRTh2 expression on monocyte subgroups and T lymphocytes respectively, as well as numbers of eosinophils, innate lymphoid type-2 cells (ILC2) and dendritic cells. Within person stability of immune and physiological parameters was calculated using the intraclass correlation (ICC) using R version 3.4.0.

Results

FEV₁ (% predicted), FEV₁/FVC ratio, ACQ5 and ACQ7 did not differ significantly over the 4 visits, as would be expected for patients with stable asthma. Peripheral blood eosinophil numbers by Kimura stain and by flow cytometry showed ICC scores of 0.44 and 0.52 respectively, indicating moderate stability. The % of ILC2 cells in peripheral blood also showed moderate stability [ICC score of 0.45 (0.14–0.67)]. The stability for all other immune parameters was poor.

Conclusion

Among the peripheral blood immune parameters we studied, only numbers of eosinophils and ILC2 in peripheral blood were moderately stable over a year in stable asthmatics. Further studies are required to understand the reasons for the variability of the other cell types.

Reconceptualizing developmental language disorder as a spectrum disorder: issues and evidence

BACKGROUND

There is considerable variability in the presentation of developmental language disorder (DLD). Disagreement amongst professionals about how to characterize and interpret the variability complicates both the research on understanding the nature of DLD and the best clinical framework for diagnosing and treating children with DLD. We describe and statistically examine three primary possible models for characterizing the variability in presentation in DLD: predictable subtypes; individual differences; and continuum/spectrum.

AIMS

To test these three models of DLD in a population-based sample using two distinct types of cluster analyses.

METHODS & PROCEDURES

This study included children with DLD (n = 505) from the US Epidemiological Study of Language Impairment database. All available language and cognitive measures were included. Two cluster methods were used: Ward's method and K-means. Optimal cluster sizes were selected using Bayesian information criteria (BIC). Bootstrapping and permutation methods were used to evaluate randomness of clustering.

OUTCOMES & RESULTS

Both clustering analyses yielded more than 10 clusters, and the clusters did not have spatial distinction: many of these clusters were not clinically interpretable. However, tests of random clustering revealed that the cluster solutions obtained did not arise from random aggregation.

CONCLUSIONS & IMPLICATIONS

Non-random clustering coupled with a large number of non-interpretable subtypes provides empirical support for the continuum/spectrum and individual differences models. Although there was substantial support for the continuum/spectrum model and weaker support for the individual differences model, additional research testing these models should be completed. Based on these results, clinicians working with children with DLD should focus on creating treatment plans that address the severity of functioning rather than seeking to identify and treat distinct subtypes. Additional consideration should be given to reconceptualizing DLD as a spectrum condition.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Progress in the understanding of the pathology of allergic asthma and the potential of fruit proanthocyanidins as modulators of airway inflammation

Allergic asthma is a chronic inflammatory lung disease characterized by sensitization of the airways, and the development of immunoglobulin E antibodies, to benign antigens. The established pathophysiology of asthma includes recurrent lung epithelial inflammation, excessive mucus production, bronchial smooth muscle hyperreactivity, and chronic lung tissue remodeling, resulting in reversible airflow restriction. Immune cells, including eosinophils and the recently characterized type 2 innate lymphoid cells, infiltrate into the lung tissue as part of the inflammatory response in allergic asthma. It is well established that a diet high in fruits and vegetables results in a reduction of the risk of developing inflammatory diseases. Secondary plant metabolites, such as proanthocyanidins which are found in apples, blackcurrants, boysenberries, cranberries, and grapes, have shown promising results in reducing or preventing allergic asthma airway inflammation. Recent evidence has also highlighted the importance of microbiome-mediated metabolism of plant polyphenols in modulating the immune system. In this review, we will discuss advances in our understanding of the pathophysiology of allergic asthma, including the role of the microbiome in lung immune function, and how proanthocyanidins modulate the airway inflammation. We will highlight the potential of dietary proanthocyanidins to impact on allergic asthma and the immune system.

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

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