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\begin{document}

\title{Machine learning-derived asthma phenotypes in a representative Swedish
adult population}



\author[1]{Muwada Bashir Awad Bashir}%
\author[1]{Daniil Lisik}%
\author[1]{Saliha Selin Özuygur Ermis}%
\author[2]{Rani Basna}%
\author[1]{Reshed Abohalaka}%
\author[1]{Selin Ercan}%
\author[3]{Helena Backman}%
\author[4]{Teet Pullerits}%
\author[1]{Roxana Mincheva}%
\author[5]{Göran Wennergren}%
\author[1]{Madeleine Rådinger}%
\author[1]{Jan Lötvall}%
\author[1]{Linda Ekerljung}%
\author[1]{Hannu Kankaanranta}%
\author[1]{Bright Nwaru}%
\affil[1]{University of Gothenburg}%
\affil[2]{Lunds universitet Institutionen for kliniska vetenskaper Malmo}%
\affil[3]{Umea universitet Institutionen for folkhalsa och klinisk medicin}%
\affil[4]{Goteborgs universitet Avdelningen for invartesmedicin och klinisk nutrition}%
\affil[5]{Goteborgs universitet Pediatrik}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
\textbf{Background} Asthma is a heterogenous airway disease
characterized by multiple phenotypes. Unbiased identification of these
phenotypes is paramount for optimizing asthma management.
\textbf{Objectives} To identify and characterize asthma phenotypes based
on a broad set of attributes using a novel machine learning approach in
a representative sample of Swedish adults. \textbf{Methods} Deep
learning clustering was used to derive asthma phenotypes in a sample of
1,895 subjects aged 16-75, drawn from the ongoing West Sweden Asthma
Study. The algorithm integrated 47 variables encompassing demographics,
risk factors, asthma triggers, pulmonary function, disease severity,
allergy, and comorbidity profiles. The optimal clustering solution was
selected by combining statistical metrics and clinical interpretation.
\textbf{Results} A four-cluster solution was determined to reliably
represent the data, resulting in distinct phenotypes described as: (1)
troublesome, late-onset, non-atopic asthma with smoking ( \emph{n}=458,
24.2\%); 2) female-dominated early adult-onset asthma ( \emph{n}=545,
28.7\%); 3) adult-onset asthma with high inflammation ( \emph{n}=358,
18.9\%); and 4) early-onset, mild, atopic asthma ( \emph{n}=534,
28.2\%). The phenotypes also differed with respect to demographics, risk
factors, asthma triggers, pulmonary function, symptom profiles, and
markers of inflammation. Current asthma was more common in phenotypes
with later age of asthma onset than phenotypes with early onset.
\textbf{Conclusion} Four clinically meaningful asthma phenotypes,
distinguishable by age of onset, severity, risk factors, and prognosis,
were found in Swedish adults. This provides a setting for future
research to profile the immunological basis of the phenotypes, and
further our understanding of their pathophysiology, therapeutic
possibilities, future clinical outcomes, and societal burden.%
\end{abstract}%



\sloppy


\textbf{Machine learning-derived asthma phenotypes in a representative
Swedish adult population}

\textbf{Short title: Machine learning-derived asthma phenotypes}

Muwada Bashir Awad Bashir,\textsuperscript{1} Daniil
Lisik,\textsuperscript{1} Saliha Selin Ozuygur
Ermis,\textsuperscript{1}Rani Basna\textsuperscript{2}, Reshed
Abohalaka,\textsuperscript{1}Selin Ercan,\textsuperscript{1} Helena
Backman,\textsuperscript{3} Teet Pullerits,\textsuperscript{4} Roxana
Mincheva,\textsuperscript{1} G\selectlanguage{ngerman}öran Wennergren,\textsuperscript{5}
Madeleine Rådiinger,\textsuperscript{1}Jan lotvall,\textsuperscript{1}
Linda Ekerljung,\textsuperscript{1}Hannu
Kankaanranta,\textsuperscript{1,6,7} Bright I.
Nwaru\textsuperscript{1,8}.

\textsuperscript{1}Krefting Research Centre, Institute of Medicine,
Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

\textsuperscript{2}Department of Clinical Sciences in Malmö, Division of
Geriatric Medicine, Lund University, Sweden

\textsuperscript{3} Department of Public Health and Clinical Medicine,
Section of Sustainable Health/the OLIN Unit, Umeå University, Umeå,
Sweden.

\textsuperscript{4}Krefting Research Centre, University of Gothenburg,
Gothenburg, Sweden; Respiratory Medicine and Allergy, Department of
Internal Medicine \& Clinical Nutrition, Institute of Medicine,
University of Gothenburg, Gothenburg, Sweden; Section of Allergology,
Sahlgrenska University Hospital, Gothenburg, Sweden.

\textsuperscript{5}Department of Pediatrics, University of Gothenburg,
Gothenburg, Sweden.

\textsuperscript{6}Department of Respiratory Medicine, Seinäjoki Central
Hospital, Seinäjoki, Finland.

\textsuperscript{7}Tampere University Respiratory Research Group,
Faculty of Medicine and Health Technology, Tampere University, Tampere,
Finland

\textsuperscript{8}Wallenberg Centre for Molecular and Translational
Medicine, Institute of Medicine, University of Gothenburg, Gothenburg,
Sweden.

Word count: 3376

\section*{Conflict of interest}

{\label{conflict-of-interest}}

HK reports personal fees for lectures and consulting from AstraZeneca,
Boehringer-Ingelheim, Chiesi, COVIS Pharma, GSK, Medscape, MSD,
Novartis, Orion Pharma and Sanofi. All other authors of this work
declare no conflict of interest related to current work.

\section*{Funding}

{\label{funding}}

VBG Group Herman Krefting Foundation for Asthma and Allergy Research,
the Swedish Research Council, the Research Foundation of the Swedish
Asthma and Allergy Association, the Nordic Epilung under the support of
Nordforsk, the Swedish Heart-Lung Foundation, and the Swedish government
under the ALF agreement between the Swedish government and the county
councils (Västra Götaland). None of the sponsors had any involvement in
the planning, execution, drafting or write-up of this study.

\section*{Authors' contribution}

{\label{authors-contribution}}

BN conceived the idea of the study. MB processed the data, performed the
analysis, drafted the manuscript, and designed the figures under
supervision of BN. LE, GW, HK, RB contributed to the implementation of
the data processing and analysis. BN contributed to the design and
implementation of the research and data collection. All authors
discussed the results and contributed to the final manuscript. This
study was supervised by BN and HK.

\section*{Ethical approval}

{\label{ethical-approval}}

The study was approved by the regional ethical review board in
Gothenburg, Sweden. Ethical approval number Dnr: 052-16.



\section*{Abstract}

{\label{abstract}}

\textbf{Background}

Asthma is a heterogenous airway disease characterized by multiple
phenotypes. Unbiased identification of these phenotypes is paramount for
optimizing asthma management.

\textbf{Objectives}

To identify and characterize asthma phenotypes based on a broad set of
attributes using a novel machine learning approach in a representative
sample of Swedish adults.

\textbf{Methods}

Deep learning clustering was used to derive asthma phenotypes in a
sample of 1,895 subjects aged 16-75, drawn from the ongoing West Sweden
Asthma Study. The algorithm integrated 47 variables encompassing
demographics, risk factors, asthma triggers, pulmonary function, disease
severity, allergy, and comorbidity profiles. The optimal clustering
solution was selected by combining statistical metrics and clinical
interpretation.

\textbf{Results}

A four-cluster solution was determined to reliably represent the data,
resulting in distinct phenotypes described as: (1) troublesome,
late-onset, non-atopic asthma with smoking (\emph{n} =458, 24.2\%); 2)
female-dominated early adult-onset asthma (\emph{n} =545, 28.7\%); 3)
adult-onset asthma with high inflammation (\emph{n} =358, 18.9\%); and
4) early-onset, mild, atopic asthma (\emph{n} =534, 28.2\%). The
phenotypes also differed with respect to demographics, risk factors,
asthma triggers, pulmonary function, symptom profiles, and markers of
inflammation. Current asthma was more common in phenotypes with later
age of asthma onset than phenotypes with early onset.

\textbf{Conclusion}

Four clinically meaningful asthma phenotypes, distinguishable by age of
onset, severity, risk factors, and prognosis, were found in Swedish
adults. This provides a setting for future research to profile the
immunological basis of the phenotypes, and further our understanding of
their pathophysiology, therapeutic possibilities, future clinical
outcomes, and societal burden.

Keywords: asthma, clinical, deep learning, phenotypes, population

\section*{introduction}

{\label{introduction}}

Asthma is a heterogeneous disease, and a clear-cut characterization of
its various phenotypes has historically remained daunting for both
clinical practice and research purposes \textsuperscript{1-3}.
Conventionally, asthma phenotypes have been defined based on timing of
onset, atopic origin, eosinophilic inflammation, and presence of
obesity, to name a few \textsuperscript{4}. Such phenotypic
characterization has been described as primarily based on clinical
insights and experiences of the attending clinician. However, it has
been suggested that such asthma phenotyping is largely subjective as the
classification may vary from clinician to
clinician\textsuperscript{4,5}. Additionally, asthma phenotyping has
mostly been attempted in selected cohorts, example hospital-based asthma
patients or those with severe asthma, with less data from
population-representative samples.

The advancements being made by computational science at elucidating
biological processes have been welcomed in the field of asthma,
particularly in identifying asthma phenotypes \textsuperscript{4-6}. In
this context, various features of asthma are inputted into algorithms
that learn from unlabelled data, with less artefact bias, to produce
meaningful asthma phenotypes. This data-driven approach is believed to
be more objective and can, with relevant clinical inputs, produce
phenotypes that are clinically meaningful \textsuperscript{4,5,7}.
Characterizing asthma at a more granular level is in parallel with
efforts towards precision medicine, subsequently enabling prevention and
optimal, tailored management \textsuperscript{8}.

In this work, by including a broad range of clinical, biological, and
epidemiological parameters that are relevant to asthma, we employed a
novel machine learning approach to identify and describe asthma
phenotypes in an adult representative sample in western Sweden.

\section*{Methods}

{\label{methods}}

\subsection*{Study population}

{\label{study-population}}

The study sample was derived from the ongoing West Sweden Asthma Study
(WSAS), a longitudinal cohort study investigating different aspects of
airway diseases among a representative sample of adults of western
Sweden. WSAS started in 2008 in which a random sample of 30,000
individuals aged 16-75 years were invited to participate in a postal
survey. Of those who responded to the questionnaire, 2,000 randomly
selected subjects and 1,524 individuals with self-reported asthma were
further invited to undergo detailed clinical investigations, conducted
between 2009 and 2012 (WSAS I). A total of 2,006 subjects participated
in this initial clinical examination. In 2016, a non-overlapping sample
from western Sweden was added to WSAS, using the same survey and
clinical investigation methods. Put together, a total of 3,101
individuals underwent clinical investigations before the start of
COVID-19, at which time clinical data collection was put on hold due to
the pandemic, of which 1,895 subjects who had ever had asthma were
included in the current phenotyping work. A detailed description of the
WSAS cohort design and characteristics has been reported
previously\textsuperscript{9}.

\subsection*{Assessment and
measurements}

{\label{assessment-and-measurements}}

\emph{Definition of asthma}

Asthma was defined as self-reported history of ever having asthma or
physician-diagnosed asthma. This definition was used to capture the
aspect of timing of asthma onset. Thus, a follow-up question ``at what
age did your asthma start'' was included.

\emph{Variables included in deriving the phenotypes}

We included a list of 47 variables, which were selected based on
clinical experience and previous studies. This comprehensive list of
variables was included to capture a full representation of the clinical
presentation, pathophysiological mechanisms, and potential risk factors
related to asthma. The variables were grouped as follows: (1)
demographics/triggers/risk factors; (2) symptom profiles; (3) measures
of pulmonary function; (4) disease prognosis and severity indicators;
and (5) measures of inflammation. All variables were self -reported by
participants except for lung function, reversibility, measures of lung
diffusion capacity, and inflammation biomarkers which were measured
objectively in the clinic. A detailed list of all variables is presented
in the \textbf{Supplementary file} .

\subsection*{Statistical analysis}

{\label{statistical-analysis}}

\textbf{Handling of missing data}

Missing items in the data set was imputed using multiple imputation with
random forest. Random forests is an ensemble learning method, primarily
used for classification and regression, which operate by constructing a
multitude of decision trees at training time and outputting the mode of
the classes (classification) or mean prediction (regression) of the
individual trees \textsuperscript{10}. When applied to data imputation,
random forests leverage their inherent ability to handle non-linear
relationships and interactions between variables to predict missing
values with high accuracy \textsuperscript{10,11}. The imputation
process was implemented using miceRanger package in
R\textsuperscript{12}. Details regarding the imputation process can be
found in the \textbf{supplementary material} .

\textbf{Unsupervised clustering}

The derivation of the asthma phenotypes was done using Deep Embedded
Clustering (DEC) \textsuperscript{13}. DEC is a novel approach that
combines deep learning, which is an advanced form of machine learning
technology, with clustering, allowing for the discovery of complex
patterns in data and providing a robust, scalable solution for
clustering large datasets without the need for labelled
data\textsuperscript{13}. This makes it particularly valuable for
applications where the true cluster structure is unknown or hard to
define a priori \textsuperscript{13}. DEC has an advantage over
traditional clustering methods because of its ability to learn a
lower-dimensional representation (feature space) of data using deep
autoencoders. This feature space is more suitable for clustering due to
compact representation at lower dimensionality, allowing DEC to
outperform traditional methods that either do not involve feature
learning or rely on simpler, linear dimensionality reduction
techniques\textsuperscript{13}. Secondly, DEC's iterative optimization
process that utilizes distance metrices to optimize both the feature
representation and cluster assignments in a way that traditional
methods, such as k-means or spectral clustering,
cannot\textsuperscript{13}. These qualities make DEC particularly
effective for complex datasets, offering improved clustering accuracy,
efficiency in handling large datasets.

After the data was processes, The R package NbClust was used to decide
the optimal number of clusters using voting consensus
methods\textsuperscript{14}. Additionally, the optimal number of cluster
was confirmed using Monte Carlo reference based consensus clustering
approach \textsuperscript{15}, implemented through M3C R
package\textsuperscript{16}. The output was further fed into the DEC
algorithm to perform the clustering. The cluster were later validated
using prediction strength approach. The final numbers proposed by such
metrics were then evaluated in conjunction with clinical experience
before a final determination of the optimal number of clusters were
decided to represent the data. The cluster solution determined were then
named based on their distribution with regards to the variables used to
derive the clusters. A detailed statistical implementation is presented
in the\textbf{Supplementary file} .

\textbf{Other statistical analysis}

Continuous data were expressed as means and standard deviations (SD).
Group comparisons were performed by 1-way analysis of variance with the
Tukey post hoc test, the Kruskal- Wallis test, or the chi-square, for
categorical and continuous variables as suitable. Graphical
representation of variation between cluster was performed through radar
plots, where categorical variables were represented as proportions.

\textbf{Reproducibility and data availability}

The full information describing the analysis approach implemented as
well as the analysis codes are available in the online repository
(https://github.com/ranibasna/AsthmaClustering).

\section*{Results}

{\label{results}}

\subsection*{Baseline characteristics of asthma versus non-asthma
subjects in the
sample}

{\label{baseline-characteristics-of-asthma-versus-non-asthma-subjects-in-the-sample}}

Of the 3,101 subjects who participated in the WSAS clinical
investigations prior to COVID-19, 1,206 had never had asthma, while the
remaining 1,895 who had ever had asthma were included in the phenotype
derivation. General characteristics of subjects with asthma and without
asthma, respectively, can be seen in Table 1. Those who had ever had
asthma were younger, had a higher proportion of women, were more obese,
had poorer lung function, more inflammation, and more respiratory
symptoms, and were less atopic, had a higher proportion of family
history of asthma/allergy, less rhinitis, and more COPD than those who
had never had asthma.

\textbf{Derived asthma phenotypes and their descriptive names}

Based on the DEC computational metrics, a three-cluster solution had the
best metrics, but when subjected to a clinical review, we determined
that a four-cluster solution was the most meaningful and valid
representation of the data, both clinically and scientifically,
capturing the diversity of phenotypes, hence we chose the four-cluster
solution. The clusters were given the following descriptive names:

\begin{itemize}
\tightlist
\item
  Phenotype 1 (n=458, 24.2\%): troublesome late-onset, non-atopic asthma
  with more women and smokers
\item
  Phenotype 2 (n=545, 28.7\%): Female-dominated early adult-onset asthma
\item
  Phenotype 3 (n=358, 18.9\%): Adult-onset asthma with high inflammation
\item
  Phenotype 4 (n=534, 28.2\%): Early-onset, mild, atopic asthma
\end{itemize}

Detailed information on differences between clusters can be found in the
supplementary material.

\textbf{Demographic, risk factors and asthma triggers in derived
phenotypes}

Cluster 1 had the highest average age at asthma onset 35, while Cluster
4 had the youngest average age at asthma onset 13 \textbf{(Table
1).}Cluster 1 had the highest average calendar age (65 and the highest
average BMI (28.5) than the other clusters. While it had a lower
urbanization rate (38.9\%) than the other clusters, the proportion of
smokers and average pack-year history was the highest. Cluster 1 also
had the highest proportion of individuals exposed to smoking at home and
at work than other clusters (\textbf{Table 1} and \textbf{Figure 1:A and
B} ). On the other hand, Cluster 2 had the second youngest average age
at asthma onset, was the most female-dominated cluster, the second least
urbanized, highest proportion of never smokers. In this cluster, the
subjects had experienced more household mould, plastic-carpet or water
damage, the highest rate of exertion-induced respiratory symptoms, and
the highest rate of infection-triggered asthma symptoms than other
clusters (\textbf{Figure 1: A and B} ). Cluster 3 had the second oldest
age at asthma onset and had a higher proportion of males than other
clusters (\textbf{Figure 1} and \textbf{Figure 2} ). Cluster 4 had the
youngest average age at asthma onset, had the lowest average calendar
age and BMI, more equally distributed between men and women, most
urbanized, lowest pack-years of smoking, lowest rates of smoking
exposure at home and at work (29.8\%), and the lowest rates of exercise
and infection as asthma triggers (\textbf{Table 1} and \textbf{Figure 1:
A} ).

\textbf{Derived asthma phenotypes by markers of inflammation}

Cluster 3 had the highest FeNO levels and eosinophil count. Cluster 1
had the highest neutrophil count. Cluster 4 had the lowest FeNO levels
and neutrophil count. There were no marked differences between cluster
1, 2 and 4 regarding FeNO levels. (\textbf{Figure 2)} .

\textbf{Derived asthma phenotypes by symptom profiles, allergic status,
and comorbidities}

Cluster 1 had the highest reports of respiratory symptoms but had the
lowest rates of allergic sensitization and family history of asthma or
allergy. It also had the highest proportion of individuals with asthma
related nasal polyps, rhinitis, and COPD (\textbf{Figure 1: C} ). On the
other hand, cluster 4 had the least respiratory symptoms, the highest
rate of allergic sensitization, and the lowest proportion of individuals
with co-existing COPD (\textbf{Figure 1: C} ). Clusters 2 and 3 were
in-between clusters 1 and 4 in these aspects.

\textbf{Derived asthma phenotypes by lung function}

Cluster 1 had the lowest post-bronchodilator FEV\textsubscript{1}/FVC
ratio and the second highest reversibility after bronchodilator therapy
among the clusters. Conversely, cluster 4 had the highest
FEV\textsubscript{1}/FVC ratio and the lowest reversibility score.
Clusters 2 and 3 were in-between clusters 1 and 4 in lung function
(\textbf{Figure 3} ).

\textbf{Derived asthma phenotypes by disease prognosis and severity}

Cluster 1 had the highest proportion of individuals with uncontrolled
asthma based on GINA classification and the highest proportion of
individuals on GINA treatment steps 4 and 5. Cluster 1 also had the
second-highest proportion of individuals who only used short-acting
beta-agonists (SABA) or never had used asthma medication. On the other
hand, cluster 4 had the lowest proportion of individuals with
uncontrolled asthma, the lowest proportion of individuals on GINA
treatment steps 4 and 5, and the highest proportion of individuals who
only used short-acting beta-agonists (SABA) or never had been prescribed
asthma medication. Clusters 2 and 3 were in-between clusters 1 and 4 in
regarding asthma severity (\textbf{Figure 1: B} ).

\textbf{Description of derived asthma phenotypes by current asthma}

The prevalence of current asthma among both men and women was higher for
clusters with later onset-age of asthma than for those with early onset.
For instance, while almost all individuals (both in men and women) in
cluster 1 (the cluster with the highest age at asthma onset) had current
asthma, the proportion was less than 80\% in cluster 4 (the cluster with
the earliest age of asthma onset) \textbf{(Figure 3).}

\section*{Discussion}

{\label{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 \textsuperscript{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 \textsuperscript{20-25}. For instance, a similar phenotype
derived by Kaneko et al.\textsuperscript{22} carried same
characteristics as our first phenotype. Kim et
al.\textsuperscript{23}also reported a phenotype with high airway
obstruction, non-atopy, and older age. The phenotype derived by Loureiro
et al. \textsuperscript{26}was 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 \textsuperscript{27}.
late-onset and severe asthma\textsuperscript{28}, high comorbidity
burden\textsuperscript{29}, need for more medication
\textsuperscript{30}, \textsuperscript{31}, and increased cigarette
smoking\textsuperscript{30},\textsuperscript{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
studies\textsuperscript{22,26,29,33-35}. This phenotype closely mirrors
the phenotype identified by Dudchenko et al.\textsuperscript{36}, which
was notably sensitive to weather as a trigger, while our phenotype had
exercise and infections as important triggers. Ilmarinen et
al.\textsuperscript{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.\textsuperscript{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
studies\textsuperscript{20,28,33,37-39}. For example, Bochnek and
colleagues\textsuperscript{20} 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 \textsuperscript{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
studies\textsuperscript{22,23,32,36,37,39-42}. In addition to
overlapping characteristics of mild disease course, good control status,
high atopy, and relatively early onset, Dudchenko et
al.\textsuperscript{36}reported 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.\textsuperscript{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 \textsuperscript{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 \textsuperscript{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 \textsuperscript{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 \textsuperscript{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
response\textsuperscript{39,47}. 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 \textsuperscript{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.\textsuperscript{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 \textsuperscript{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.

\textbf{Conclusion}

In a representative sample of adults who had ever had asthma, four
clinically relevant asthma phenotypes can be seen, which could be
distinguished on the basis of age at asthma onset, level of severity,
and other characterization, including demographics, risk factors,
triggers, lung function, respiratory symptom profiles, and inflammation
markers. The derived asthma phenotypes provide a novel setting for
catalyzing important asthma research that will include detailed
profiling of the immunological aspects of the phenotypes, modelling
putative risk factors, characterizing their comorbidity profiles, and
assessing future clinical outcomes and societal burden of each asthma
phenotype.

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