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