Asthma diagnosis, precision medicine and biomarkers
Asthma should be correctly diagnosed as early in life as possible according to the latest clinical guidelines. Branco et al. reported that 1.3% of children were previously undiagnosed asthmatics, providing evidence of under-diagnosed asthma in both pre- and primary school children in both urban and rural areas.10
Postma et al. reported the baseline data from the multi-centred, international study specifically designed to explore the relevance and extent of small airway dysfunction (SAD) in asthma. In the largest such study to date, the team developed a clinical SAD score and showed that SAD is present across all asthma severities, but consistently more so in severe asthma. The clinical impact of SAD in asthma is further explored in the longitudinal study.110 A study proposed the addition of oscillometry together with spirometry as it proved useful to assess earlier changes in responders to treatment with benralizumab.111
Dunn et al.112 evaluated asthma in the elderly and the late onset asthma in adults. Elderly asthmatics have higher rates of morbidity and mortality compared to younger patients. It is more likely that the disease is undiagnosed and undertreated. Elderly patients more often have a non-type 2 asthma endotype, with a Th17 mediated pathology, which is also less responsive to traditional therapies such as ICS. Therefore, it is important that more elderly patients are included in clinical trials.
Non-steroidal anti-inflammatory drug (NSAID) -exacerbated respiratory disease (NERD) is a severe eosinophilic asthma phenotype.113 It has been well defined as an inflammatory phenotype responsive to corticosteroids. Although no absolute/consistent cut-off values have been established, sub-analyses show an overall better response in patients with more inflammation, defined by higher blood eosinophil levels.114Recently, Celejewska‐Wójcik et al. demonstrated that three distinct NERD sub phenotypes reflect differences in inflammatory response measured by airway eosinophils and the ratio of logLTE4/logPGE2 in induced sputum.115 Mastalerz et al.116studied airways PGE2 in induced sputum and reported that NERD subjects had higher levels of PGE2 before aspirin challenge compared to controls. After exposure to aspirin, PGE2 levels significantly dropped, which was not the case for aspirin tolerant asthmatic individuals. The inhibition of bronchial PGE2 biosynthesis can trigger bronchoconstriction in NERD. Chen et al.117 showed that the levels of exhaled PGE2, LTB4, LXA4, and LTE4 may efficiently differentiate asthmatic children from healthy controls. Especially the measurement of LTB4 and lipoxin A4 together with FeNO and FEV1, might help for the better diagnosis of asthma.
A nationwide Japanese prospective study has investigated 190 patients with near-fatal asthma exacerbation. By analysing asthma symptoms over the 2-week period before their admission, the authors could define 3 different clusters of symptoms. Analysis of clusters indicated the most relevant factors to be assigned to 3 different clusters, such as a degree of ICS or ICS/LABA compliance, low or high perception of dyspnea and hypersensitivity to environmental stimuli.118
Additional to clinical trials, the analysis of real-world data is very important to confirm effectiveness also in larger, more diverse patient groups. Jutel et al. analysed changes in AR progression and asthma status after HDM allergen immunotherapy (AIT) based on data on prescription medicine consumption. They found that treatment of AR patients with HDM allergoid can ameliorate asthma symptoms, slow asthma progression and reduce the general incidence of asthma as compared to untreated control groups.119
Asthma inducing agents are also present at many workplaces. Occupational asthma is often hard to diagnose since the reference test (specific inhalation challenge) can only be conducted in a few centres worldwide. Recently, a model to improve diagnosis of occupational asthma without specific inhalation challenge was published on basis of a Canadian dataset and was successfully validated in a European population.120 Clinicians could use this model with decision making on referral to a specialized centre in the future. Van der plas et al. 121 studied the differences between sensitization to high molecular-weight proteins and low-molecular-weight chemicals in occupational asthma. Asthma caused by high molecular-weight chemicals showed a stronger association to rhinitis, conjunctivitis, atopy, early asthmatic reactions and had a higher risk of airflow limitation, while low-molecular-weight agents were associated with chest tightness, late asthmatic reactions, and increased risk of severe exacerbations. Beretta et al. highlighted that the measurement of nonspecific bronchial hyperresponsiveness alone is often not enough to diagnose occupational asthma. Combining it with the assessment of FeNO levels and sputum eosinophils count significantly increases the sensitivity and accuracy of the method, improving the identification of subjects, who may have occupational asthma and therefore require further testing.122
Multiple omics, big data, and systems biology have demonstrated a profound complexity and dynamic variability in asthma between individuals, as well as between regions. Reliable diagnosis of asthma and the monitoring of its severity are challenging particularly in daily clinics. Asthma is an umbrella term, including several distinct phenotypes and endotypes, which are characterized by specific cellular and molecular immune response patterns.47,114,123,124The main asthma endotypes: type 2 and non-type 2 inflammation, are broader described in the section below. Type 2 allergic asthma is defined by IgE, IL-13, IL-4, IL-5, and eosinophil responses and covers more than 50% of asthma endotypes.114
Boudier et al. highlighted the value of unsupervised asthma phenotypes research, including multiple asthma characteristics, for understanding the long‐term evolution of asthma patients. Using a cluster‐based model developed for longitudinal data, asthma phenotypes were identified in a large population of adults with asthma 20 years after recruitment.125 This model was developed by taking into account two time points and nine variables combining clinical and functional characteristics, such as respiratory symptoms, asthma treatment, allergic characteristics, lung function and bronchial hyper‐responsiveness. These cluster‐based asthma phenotypes showed a stronger long‐term clinical prognosis compared to phenotypes classically used in epidemiological studies, allowing a strong tracking of lung function over the life course to better tailor asthma management strategies.125
Ivanova et al. reviewed the role of ‘omics’ technologies in asthma.126 Although omics data studies have several limitations, usually due to limited sample size and the complexity of the data and all its interactions, different insights were acquired. Another review also highlighted the importance of omics data for molecular phenotyping, defining the endotypes and identifying pathways and mechanisms, such as type 2-high and type 2-low.1In regard to this purpose, the transcriptome and protein levels in three different mouse models for eosinophilic, mixed, and neutrophilic asthma were analyzed. The authors found that differential expression of tight junctions, mucin and inflammasome-related molecules in distinct inflammatory phenotypes of asthma may be linked to the pathophysiology and might reflect the differences observed in the clinic.124 Eosinophil and neutrophil dominant phenotypes were described in children with asthma as well. The neutrophil dominant phenotype was associated with the biggest differences compared to the other asthma phenotypes. The vast majority of the differentially expressed genes was associated with corticosteroid response, and the neutrophilic phenotype with corticosteroid non-responsiveness.127
Severe asthma is a heterogeneous disorder, including different clinical characteristics (phenotypes) and immunopathological pathways (endotypes). The identification of non-invasive biomarkers is able to predict treatment response and assist in designing personalized therapies for severe asthma patients is demanding.
Eguiluz-Gracia et al. broadly reviewed recent developments in biomarkers in allergic diseases, highlighting the importance of eosinophils in allergic asthma diagnosis and management.123
According to an EAACI position paper in 2019, biomarkers for the clinical and inflammatory phenotype of asthma were summarized as follows 1) type 2 asthma: a) serum IgE, b) blood and sputum eosinophils, and c) FeNO; 2) Non-type 2 asthma: a) sputum neutrophils and b) blood and sputum eosinophils.114 However, the etiology of asthma with non-type 2 inflammation is less clear.
Eosinophils as biomarkers: Sputum eosinophilia is the most useful biomarker in asthma. In general, sputum eosinophilia is associated with steroid responsiveness. Although there is no standardized cut-off, a blood eosinophil count of 300 cells/μL and the normal range for sputum eosinophilia defined as 1–2% have commonly been used as a threshold to indicate eosinophilic asthma.114 Higher blood or sputum eosinophil count has been assessed to be a sensitive and practical predictive biomarker for biological therapies targeting allergic and/or eosinophilic pathways in patients with severe asthma.128-130 Systematic reviews showed the efficacy and safety of benralizumab, dupilumab, mepolizumab, omalizumab, and reslizumab for severe eosinophilic asthma and allergic asthma.128,129 A high blood eosinophil count (>300 cells/μL) has been reported as a potential biomarker to predict successful treatment effects of omalizumab in children with severe allergic asthma.114 Sputum eosinophilia also adequately predicts response to biologics. Patients with refractory asthma are more likely to respond to anti–IL-5 or anti–IL-4/IL-13 targeted treatment if they have sputum eosinophils of >3%, or ≥ 300 cells/μL blood eosinophils.114,128-130
For evaluating treatment success with mepolizumab and patient stratification, possible biomarkers were investigated in a post-hoc study of Phase III clinical trial data. The results of this study reinforce the use of peripheral blood eosinophil counts and eosinophil-derived neurotoxin as predictive biomarkers.131
The transcriptomic data from bronchial biopsies of European U-BIOPRED cohort patients showed that MMP-10 and MET genes were significantly overexpressed in severe asthma. These results demonstrated that MMP-10 and MET play an important role in pathways of airway remodeling and cellular inflammation that are associated with submucosal eosinophilia.132
Recent studies have shown that eosinophils can also display protective regulatory properties in asthma. In a recent study Pineros et al.133 provided ex vivo and in vivoevidence that mouse and human eosinophils are capable of rapid capture and inactivation of respiratory viruses. They also showed that eosinophils from asthma patients displayed a reduced capacity to bind virus, which may lead to a less effective virus inactivation.133 These results underlie the in vivo antiviral activity of eosinophils, and the pathogenesis of virus-induced asthma exacerbations. Another study conducted by Tarancon et al., evaluated eosinophils during Mycobacterium tuberculosisinfection in an experimental model. They observed that eosinophil production in the bone marrow is weakened in Mycobacterium tuberculosis infection and protects against asthma.134
The role of eosinophils in personalized asthma treatment remains controversial. In a real-life study Bagnasco et al. showed no correlation between peripheral blood eosinophils count with the clinical, functional, biological outcome changes in asthma patients.135,136
Not only frequencies of eosinophils can serve as a biomarker of asthma severity 123 and response to the treatment.137 Rodrigo-Muñoz et al.138 recently reported a set of 14 miRNAs stratifying eosinophils from asthmatic patients and controls. Interestingly, 3 of those miRNAs (miR-144-5p, miR-185-5p, and miR-320a) were validated as asthma biomarkers in the serum, distinguishing not only asthma status but also the severity. 138
Other immune cells as biomarkers: The level of human circulating neutrophil extracellular trap but not eosinophil extracellular trap can act as a potential marker for asthma severity and poor control.139 Another candidate marker for asthma severity could be the frequencies of circulating chemokine receptor (CCR)10+ ILC2s and plasma CCL27 level. Conversely, CCR10+ ILC2s resemble the characteristics of ILC1s and display higher levels of T-bet expression and increased production of interferon (IFN)-γ compare to the CCR10-ILC2 counterpart, which favours the controlling of allergic inflammation in asthma.140
IgE as a biomarker: IgE exerts several biological functions as an Fc-receptor-bound antigen sensor for mast cells, basophils, dendritic cells (DCs), T and B cells and other cells in the allergic inflammation. Total serum IgE and allergen-specific IgE have been strongly associated with asthma.114 Omalizumab is the recombinant humanized mAb that binds to the Fc region of IgE. Correlations between treatment response and baseline total serum IgE or antigen specific IgE levels are not clear, but serum IgE is used to dose omalizumab.114,128,129