Discussion
By including a large sample of asthma subjects with ever reported or
physician-diagnosed asthma and a comprehensive set of parameters,
covering demographic/risk factors/triggers, clinical, and
pathophysiological aspects into a novel machine learning algorithm, we
could derive a four-cluster solution that captured clinically meaningful
asthma phenotypes. The derived asthma phenotypes could be distinguished
based on age at asthma onset, ranging from those with onset in childhood
to those with onset in adulthood. They could also be distinguished on
the basis of level of severity, ranging from the childhood atopic mild
asthma to the late adulthood more troublesome asthma. The phenotypes
could also be differentiated on the basis of several demographic/risk
factors/triggers, clinical aspects, symptom profiles, and various
measures of inflammation.
WSAS is representative of the adult population of western Sweden; as
such our findings have a reliable generalizability to the underlying
target population. The population-based sampling also constitutes an
advantage over some previous studies that have relied primarily on
hospital-based setting in their phenotyping 7,17-19 .
With a population-based sample, the whole spectrum of asthma severity
can be captured. We selected a comprehensive set of variables for the
phenotyping exercise, which ensured that multiple dimensions of asthma
were captured, providing an advantage over approaches that utilize fewer
sets of variables or that focus primarily on clinical variables. We
employed a novel and robust machine learning approach, deep embedded
learning, which is particularly adept at managing complex,
multi-dimensional data, offering an advantage over conventional
clustering approaches. The selection and description of the derived
phenotypes represent a hybrid of data science and clinical experience,
ensuring that the phenotypes accurately align to both clinical and
statistical expectations. Our study, however, may be limited by the
absence of certain inflammatory markers, like sputum measures, which are
valuable in defining asthma endotypes. In addition, the lack of certain
co-morbidities could also mean potential aspects of phenotype
characterization were not captured. Nevertheless, the alignment of our
findings to previous studies indicates that our approaches were largely
valid and reliable.
The first phenotype in our work (Cluster 1) was characterized by
substantially older age, late onset and troublesome asthma with
increased smoking, which had high symptom and health care use burden,
compared to other clusters. It also has a higher proportion of patients
that can be classified as having severe asthma based on medication
usage. This phenotype overlaps with findings from previous studies among
adults 20-25. For instance, a similar phenotype
derived by Kaneko et al.22 carried same
characteristics as our first phenotype. Kim et al.23also reported a phenotype with high airway obstruction, non-atopy, and
older age. The phenotype derived by Loureiro et al. 26was similar to ours by being late onset and severe, uncontrolled asthma,
dominated by obese women, and had high eosinophil, neutrophil and
monocyte counts. Different characteristics of this phenotype have been
described in a similar phenotype derived by other studies, including
systemic inflammation 27. late-onset and severe
asthma28, high comorbidity burden29,
need for more medication 30, 31, and
increased cigarette smoking30,32 .
Our second phenotype (Cluster 2) that was characterized by female
dominance and early adult-onset asthma with high breathlessness and
moderate symptoms, nearly normal lung function, and moderate healthcare
use also closely aligns with findings from previous
studies22,26,29,33-35. This phenotype closely mirrors
the phenotype identified by Dudchenko et al.36, which
was notably sensitive to weather as a trigger, while our phenotype had
exercise and infections as important triggers. Ilmarinen et
al.32 reported a similar phenotype that was described
as ‘female asthma’ and marked by near normal lung function but being
moderately symptomatic and using health care services. A similar
phenotype was described by Kim et al.23 as early
adulthood-onset, mild, female asthma, featuring persistent normal lung
function and a gentle disease progression in young women.
The phenotype of adult-onset asthma with high inflammation (Cluster 3)
also aligns with a phenotype found in previous
studies20,28,33,37-39. For example, Bochnek and
colleagues20 reported a moderate asthma phenotype with
elevated eosinophil levels. However, their study did not address the age
of asthma onset. Boudir and colleagues also identified a moderate asthma
phenotype characterized by significant bronchodilator reversibility and
pronounced respiratory symptoms with a high rate of atopy, consistent
with our results. Hsaio and colleagues 33 also
described a similar phenotype to our findings, which was further
distinguished by a history of smoking.
Similarly, our fourth phenotype of early-onset, mild asthma with atopy
(Cluster 4) was frequently reported in previous studies22,23,32,36,37,39-42. In addition to overlapping
characteristics of mild disease course, good control status, high atopy,
and relatively early onset, Dudchenko et al.36reported high impairment on physical activity that additionally
characterize this phenotype. Two studies additionally reported younger
age of subjects belonging to this phenotype, which is in line with our
observation of young mean age among members of this phenotype. Loza et
al.39 additionally reported this phenotype to be
associated with low inflammation of high T2 cell pattern.
The first phenotype (Cluster 1), characterized by late onset troublesome
asthma, with older age, high rate of smoking, COPD as a comorbidity, and
reduced diffusion capacity, may point to presence of emphysematous
changes. Clinically, this phenotype may present asthma and COPD
co-existing in the same patient 32,43-45.
Additionally, compared to the other phenotypes, with high BMI, high
proportion of females, more healthcare use, hospital emergencies, and
systemic inflammation, this phenotype could also be reflecting the group
of severe female obesity-related asthma with mixed inflammation patterns
that have been reportedly associated with severe presentation at late
age 26,32,33. The presentation of more comorbidities
amongst such a group of asthma patients also aligns with the greatest
impairment to quality of life that had been associated with such
phenotype previously 26.
The second phenotype (Cluster 2) that constituted a group of women with
moderate asthma seemed to have better overall health because they had
fewer other health problems, had low smoking rates, and generally
demonstrated good lung function. This group also showed relatively
moderate asthma symptoms, which might be because women tend to notice
their symptoms more and seek medical help sooner 46,
which is demonstrated by high utilization of emergency service among
this group compared to others. Further, low smoking history, low BMI,
low count of comorbidities may have influenced the good overall
prognosis of this female cluster. Additionally, these women were
particularly good at noticing what triggered their asthma, like changes
in weather or infections, which may help them avoid these triggers and
have fewer symptoms.
The asthma phenotype that typically begins in early adulthood (Cluster
3) was characterized by significant inflammation and moderate symptom
severity, with a prominent feature being an elevated FeNO and eosinophil
count. These are associated with type 2 immune response39,47. Such group with high eosinophiles and FeNo
levels may represent a sensitive treatment group with ICS therapy,
however they may be undertreated. Additionally, this phenotype tends to
have greater exposure to smoking, which has been linked to increased
eosinophilic inflammation 48. This phenotype also
exhibited a high occurrence of rhinitis and allergic conditions, such as
chronic nasal problems accompanying asthma. Nonetheless, the observation
that this group reported the fewest symptoms of drug-induced asthma
contradicts this hypothesis.26
The early onset, mild atopic asthma phenotype (Cluster 4) possibly
represents the traditional childhood-onset asthma characterized by high
allergic sensitization, better asthma control, and low symptom burden.
Childhood onset-asthma has greater propensity for remission than asthma
starting in adulthood 49. Our data suggests that this
phenotype also may have the highest rate of remission as it had the
lowest proportion of those who have current asthma as defined by recent
symptoms and medication use, indicating that although members of this
phenotype developed asthma during childhood, some of them might be
transient in part of patients in this cluster.