!!!!!!!! Phenotypes of Childhood Asthma - Are They Real 2010

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REVIEW

Phenotypes of childhood asthma: are they real?

B. D. Spycher1, M. Silverman2,3 and C. E. Kuehni1

1Swiss Paediatric Respiratory Research Group, Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland, 2 Department of Infection,

Immunity & Inflammation, The Leicester Children’s Asthma Centre, Division of Child Health, University of Leicester, Leicester, UK and 3 Department of Respiratory

Medicine, Institute for Lung Health, University of Leicester, Leicester, UK

Clinical &Experimental

Allergy

Correspondence:

Claudia E. Kuehni, Institute of Social

and Preventive Medicine, University of

Bern, Finkenhubelweg 11, CH-3012

Bern, Switzerland.

E-mail: [email protected]

Cite this as : B. D. Spycher, M. Silverman

and C. E. Kuehni, Clinical &

Experimental Allergy , 2010 (40)

1130–1141.

Summary

It has been suggested that there are several distinct phenotypes of childhood asthma or

childhood wheezing. Here, we review the research relating to these phenotypes, with a focus

on the methods used to define and validate them. Childhood wheezing disorders manifest

themselves in a range of observable (phenotypic) features such as lung function, bronchial

responsiveness, atopy and a highly variable time course (prognosis). The underlying causes

are not sufficiently understood to define disease entities based on aetiology. Nevertheless,

there is a need for a classification that would (i) facilitate research into aetiology andpathophysiology, (ii) allow targeted treatment and preventive measures and (iii) improve the

prediction of long-term outcome. Classical attempts to define phenotypes have been one-

dimensional, relying on few or single features such as triggers (exclusive viral wheeze vs.

multiple trigger wheeze) or time course (early transient wheeze, persistent and late onset

wheeze). These definitions are simple but essentially subjective. Recently, a multi-

dimensional approach has been adopted. This approach is based on a wide range of features

and relies on multivariate methods such as cluster or latent class analysis. Phenotypes

identified in this manner are more complex but arguably more objective. Although

phenotypes have an undisputed standing in current research on childhood asthma and

wheezing, there is confusion about the meaning of the term ‘phenotype’ causing much

circular debate. If phenotypes are meant to represent ‘real’ underlying disease entities rather

than superficial features, there is a need for validation and harmonization of definitions. Themulti-dimensional approach allows validation by replication across different populations and

may contribute to a more reliable classification of childhood wheezing disorders and to

improved precision of research relying on phenotype recognition, particularly in genetics.

Ultimately, the underlying pathophysiology and aetiology will need to be understood to

properly characterize the diseases causing recurrent wheeze in children.

What is a phenotype?

Childhood asthma, or wheezing disease, is highly variable inboth its clinical presentation and time course [1]. Throughout

the last century, there have been conflicting views of wheez-

ing disorders in childhood as either a group of several

discrete disease entities or a single although variable disease

[2–6]. In particular, it was unclear whether wheezing asso-

ciated with recurrent respiratory infection in the first years of

life (wheezy bronchitis), is a precursor of asthma or whether

it is a different disease [2–4, 7, 8]. In the past few decades, a

growing number of cohort studies have documented the

natural history of these disorders in more detail, and results

have been used to support one or the other view [2, 3, 9].

Currently, the prevailing paradigm is that asthma (and

wheezing) in children consists of several discrete disease

entities or syndromes [10–13]. For lack of a definitebiological basis for disease heterogeneity – genetics or

causal agent – classification has been based on pheno-

types. Some phenotypic classifications such as that by the

Tucson group into early transient, late onset and persis-

tent wheeze [9] have found wide popularity. The number

of publications containing the terms ‘phenotype’, ‘asthma

or wheeze’ and ‘paediatric or child’ in Pubmed (excluding

articles with ‘genetics’ as a keyword, because of the

alternative use of the term phenotype) has increased

steeply, from less than five publications annually until

the mid-1990s to well over 40 in recent years (Fig. 1).

ECdoi: 10.1111/j.1365-2222.2010.03541.x Clinical & Experimental Allergy , 40, 1130–1141

c 2010 Blackwell Publishing Ltd

mailto:[email protected]















Currently, phenotypes have an undisputed standing in the

research on childhood asthma, and their existence is often

uncritically accepted. They are also increasingly used for

clinical decision making in treatment guidelines [14–18].

Despite this widespread usage, there is confusion about

what the term phenotype actually means. In particular, it

is unclear whether phenotypes are simply pragmatic

constructs or whether they have a more fundamental

meaning, standing for separate disease entities. This

matters: if they describe something real, the classification

should be harmonized so that it best represents the ‘true’

underlying entities. If, however, asthma phenotypes aremerely utilitarian, any classification may be valid.

As introduced by Johannsen and Shull early last

century [19–20], the term phenotype was intended to

characterize different ‘types’ of organisms distinguishable

by their observable characteristics such as their shape,

structure, size or colour. Today, the term ‘phenotype’ is

used in a variety of ways. To geneticists, a phenotype can

be any observable trait of an organism, such as its

morphology, biochemical or physiological properties or

behaviour, without assuming the existence of distinct

‘types’ (Table 1, row 1). In the literature on phenotypes of

wheezing illness, the meaning of the term is vague andrarely specified. Usage ranges from the view that pheno-

types are useful but entirely artificial constructs (Table 1,

row 2) to the belief that they represent underlying disease

entities that, like biological species, await discovery (Table

1, row 3).

Here, we adopt the view that phenotypes are hypothe-

sized disease entities but use the term more loosely when

reviewing some of the literature. To qualify as a disease

entity, we would require a condition to affect a significant

number of patients and be aetiologically distinguishable

from other conditions.

This review is a critical summary of past and current

approaches to categorizing children who wheeze into

phenotypes. In particular, we: (1) briefly describe child-

hood wheezing disease with its associated symptoms and

traits; (2) explain why it might be important to distinguish

phenotypes; (3) describe different methodological ap-

proaches to define phenotypes; (4) discuss methods for

validating phenotypes as true disease entities or at least

clinically useful constructs and (5) make some suggestions

for further research.

The clinical spectrum of wheezing disease and asthma –

where are the boundaries?

Wheezing disease in children manifests itself in a wide

spectrum of measurable features, including symptoms,signs and associated traits (Table 2). These features are

not the main focus of the present review and the list in

Table 2 is not exhaustive. Important, however, are the

following observations.

First, these features are associated with each other (i.e.

they are not independent) but the associations are modest.

For instance, children with wheeze will be more likely to

have bronchial hyperresponsiveness (BHR) than children

without wheeze. This means there are more children with

both wheeze and BHR than would be expected if these

features were independent that is, if each feature were

randomly distributed among all children regardless of thepresence of other features. However, quite surprisingly,

the overlap between the features is only moderately

greater than it would be assuming independence (Fig. 2)

[21, 22]. A consequence of this is that if the definition of

asthma or of an asthma phenotype requires the presence

of more than one of these features, group sizes quickly

become small. For instance, a phenotype requiring

wheeze, atopy and BHR would include only 7.3% of

children who have any one of these features (Fig. 2).

Second, the presence of these features, particularly

symptoms, varies by age [3, 23–25] and over short periods

0

10

20

30

40

50

1 9 8 0

1 9 8 5

1 9 9 0

1 9 9 5

2 0 0 0

2 0 0 5

2 0 1 0

Fig. 1. Annual tally of articles found in Pubmed using the terms

‘ phenotypÃ AND (wheez Ã OR asthma) AND (child Ã OR pediatric Ã)’ (all

terms searched in all fields, excluding articles with ‘genetics’ as a

keyword).

Table 1. Different usages of the term ‘phenotype’

Usage Description Example of usage

Any observable

trait (partial

phenotype)

Includes signs, symptoms,

measurements and

biological markers

Bronchial

hyperresponsiveness

[103], see also Table 2

Clinically useful

grouping

Defines groups that differ

with respect to features of

interest: e.g. risk factors,

response to treatment,

prognosis

Difficult or severe

asthma [104] see also

Tables 3 and 4

Hypothesized

disease entity

Defines a condition that is

thought to represent a

distinct disease entity

Atopic asthma and

virus-induced wheeze

[71] see also Tables 3

and 4

c 2010 Blackwell Publishing Ltd, Clinical & Experimental Allergy , 40 : 1130–1141

Wheezing phenotypes in childhood 1131



of time [26]. Third, many of the listed measurements are

costly, difficult to perform in infants and young children

or pose ethical problems. Bronchial biopsy is an example.

There is therefore a paucity of data on children that would

allow the interdependencies of these measurements and

their association with wheezing illness throughout child-

hood to be examined.Selection of the features defining asthma has been

much debated. While the diagnosis of asthma may be easy

to make in a patient with all the classical features of

asthma such as recurrent attacks of wheeze, reversible

airway obstruction, sensitization to aeroallergens and

bronchial hyperresponsiveness, it can be quite difficult in

patients who have only some of these features. In fact,

only a few features are necessary for a clinical diagnosis

of asthma in children and none are sufficient. At some

stage, the prevailing definition of asthma has required

recurrent wheeze, reversible airway obstruction, BHR and

airway inflammation as necessary elements [27]. The

current trend is again, as proposed decades ago by Dame

Turner–Warwick, towards a simpler more pragmatic defi-

nition (variable wheeze with no other identifiable causes

and responding to asthma treatment) [28, 29] requiring

only wheeze (or dry cough assuming that a cough-variant

form of asthma exists) and evidence of reversible airway

obstruction (including bronchodilator response) [18, 30].

Even these features may be absent during long, symptom-

free intervals. Other features such as atopy, BHR and

Table 2. Features of wheezing illness in children

Dimensions Features Diagnostic process

Symptoms Wheeze Self, parental or clinical

observationCough

Shortness of breath/

difficulty breathing/

chest tightness

Symptom history/age of

onset

Signs Expiratory polyphonic

wheeze

Physical examination

Barrel chest, etc.

Lung function Reversible airway

obstruction

Spirometry plus

bronchodilator response

Air flow variability Twice daily peak flow

meter/serial spirometry

Bronchial

responsiveness

Bronchial

hyperresponsiveness

Diverse direct and indirect

bronchial challenge

tests

Atopy Skin sensitivity Skin prick testsIncreased total IgE Serum IgE

Increased specific IgE RAST, ELISA

Blood eosinophilia Differential white cell

count

Airway

inflammation

Raised exhaled nitric

oxide fraction (FeNO)

FeNO measurement

Sputum eosinophilia

and/or other

inflammatory markers

Bronchoalveolar lavage,

sputum induction or

exhaled breath

Airway wall

inflammation and/or

remodelling

Bronchial biopsy

Response to

therapy

Bronchodilator response Spirometry

Corticosteroid

responsiveness

Treatment trial

Fig. 2. Venndiagramsshowingoverlap of the features wheeze, atopy and

bronchial hyperresponsiveness (BHR) in: (a) a real general population;

and (b) a hypothetical population in which these features have the same

prevalence but are totally independent. The prevalences ( pb) in (b) were

computed by multiplying the prevalences ( pa) of respective features in

the original real population with each other, e.g. pb(Current wheeze, BHR

but no atopy) = pa(Current wheeze)Â pa(BHR)Â(1À pa(atopy)). Source :

Unpublished data from the 1990 Leicester respiratory cohorts. Current

wheeze,Z1 episode of wheeze in the past 12 months; atopy,Z1 positive

skin prick test (dog, cat, grass or house dust mite); BHR, bronchial

hyperresponsiveness (lowest quintile of PC20).


1132 B. D. Spycher et al



measures of airway inflammation, although typical of

asthma, are often absent in children with clinically

relevant disease [31–33] and occur in many asymptomatic

children (Fig. 2).

Why distinguish phenotypes?

Ideally, we would like to define groups of patients that are

homogenous in their clinical features, risk factor profiles,

responses to treatment and prognoses. Such coherent

groups would greatly facilitate research and clinical

management by (A) improving precision in epidemiologi-

cal and clinical studies, (B) enabling targeted treatment

and preventive strategies and (C) improving the prediction

of long-term outcomes. The most coherent groups with

regard to these aspects are those representing different

underlying diseases (Table 1, row 3). If underlying biolo-

gical processes were clearly understood, precise diagnos-

tic markers could be used to define these groups. However,

in the absence of such gold standards, there is a need for a

phenotypic classification. Incidentally, even within a

well-characterized disease for which a diagnostic gold

standard exists, such as cystic fibrosis, there may be large

phenotypic variability and a need to define more homo-

genous subgroups.

(A) Improving precision in genetic and epidemiological studies

Poor phenotype definition has been recognized as one of

the main limitations for studying the genetics of asthma

[34–36]. The vast majority of genetic association studies

have used the broad category asthma (usually doctor diagnosed) as outcome [37] and only few studies [38, 39]

have focused on more specific phenotypes of childhood

wheezing. Causative genes discovered to date explain

only a small fraction of asthma heritability despite the

considerable investment in asthma genetic research

[40, 41]. Improved phenotype definition may help in

identifying the missing heritability [42].

Numerous studies have shown that phenotypes can

differ in their association with asthma risk factors [9, 33,

43–48]. Phenotypic differences in the outcomes used may

explain some of the contradictory findings from studies

relating to the effects of risk factors on the development of wheeze, such as breastfeeding [49], or exposure to pets

[50]. In the Tucson cohort, for instance, breastfeeding was

associated with a lower overall incidence of wheeze in the

first 2 years of life. However, breastfed children of atopic

mothers had an increased risk of recurrent wheeze at

age 6 [51].

(B) Improving treatment

Importantly for clinical practice, phenotypes can differ in

their response to treatment [52–54]. For instance, main-

tenance treatment with inhaled corticosteroids appears to

be more effective in children with risk factors for later

asthma than in children with intermittent wheezing [53].

Increasingly, asthma guidelines therefore recommend

phenotype-specific treatment [14–16, 55–58]. This might

be particularly important with regard to very severe

asthma. Patient selection becomes essential for cytokine-specific therapies such as monoclonal antibodies against

interleukin-5 [59, 60]. Although these therapies are cur-

rently mainly targeted at adults, future paediatric usage is

likely for children with very severe disease.

(C) Improving the prediction of long-term outcomes

Identifying phenotypes that would allow predicting long-

term disease outcome from an early age has been a long

sought goal. Phenotypes defined in early childhood have

been shown to differ markedly in their prognosis into later

childhood [61], adolescence [62] and adulthood [63, 64].

Even wheezing conditions that appear to be transient in

early childhood may be precursors of obstructive airway

disease in later life [65].

How are phenotypes defined?

Phenotype definitions have followed one of two

approaches. The first uses a single or few disease char-

acteristics to define phenotypes, here referred to as one-

dimensional. The second approach uses a wide range of

characteristics or disease dimensions, referred to as multi-

dimensional.

The one-dimensional approach

The common way to define phenotypes has been to

classify children with wheeze into groups based on simple

criteria involving one or few clinical features. Popular

criteria include triggers of wheezing episodes (or short-

term temporal pattern), severity of wheezing (usually

based on the frequency of episodes), or retrospective

symptom history since early childhood (long-term tem-

poral pattern) (Table 3). These phenotype definitions were

typically introduced in single cohort studies.

In the Melbourne cohort, one of the oldest respiratory cohort studies, children with wheeze at age 7 years were

divided into three groups based on frequency and triggers

of wheezing episodes in the past year: (1) ‘mild wheezy

bronchitis’ (less than five episodes, only associated with

apparent respiratory tract infection (RTI); (2) ‘wheezy

bronchitis’ (greater than or equal to five episodes, only

associated with RTI); and (3) ‘asthma’ (at least one episode

not associated with RTI) (Table 3, part D) [3]. Compared

with controls (asymptomatic at age 7) the groups 1–3 had

increasingly worse prognosis at later follow ups to the age

of 42 [64, 66, 67] leading to the conclusion that these





phenotypes represented varying grades of severity of the

same disease [3, 68, 69]. This ‘all is one’ disease concept

contradicted earlier claims that ‘wheezy bronchitis’ was a

separate disease, distinct from asthma, and propagated a

change in the treatment of ‘wheezy bronchitis’, from

antibiotics to inhaled bronchodilators and steroids.

In the early 1990s, Wilson and Silverman reintroduced

the hypothesis that several diseases exist within the

spectrum of wheezing disorders in childhood [4, 8, 26,

70]. They distinguished children who experience episodes

of wheeze only during viral RTI (exclusive viral wheeze)

from those who also wheeze between viral RTI, i.e. have

interval symptoms (multiple-trigger wheeze) and re-

viewed evidence showing that these groups differ in many

aspects, including association with atopy, measures of

bronchial inflammation, response to treatment and long-

term prognosis (Table 3, part A) [71, 72]. This phenotype

classification has been applied in different cohort studies

[65, 73, 74] and has recently been recommended as an aid

for treatment decisions [15].

In the mid-1990s, the Tucson group introduced a

classification of children based on retrospective symptom

Table 3. Summary of frequently used phenotypes of childhood wheeze and asthma

Labels Synonyms Definitions

Related but not

synonymous labels Published associations

(A) Triggers/short-term temporal pattern

Exclusive viral wheeze

[4, 71, 72]

Episodic (viral) wheeze [15]

Recurrent bronchiolitis

(USA)

Wheezy bronchitis [3]

Episodes of wheeze only

during viral RTI

Non-atopic wheeze

Early transient wheeze

Risk factors [26, 96]

Other clinical features [3]

Gene expression [93]

Prognosis [64–66, 73]

Response to treatment

[52, 53]

Multiple trigger wheeze [26] Asthma [3]

Chronic wheeze

Wheeze during but also

apart from viral RTI

Atopic asthma

(B) Long-term temporal pattern

Early transient wheeze [9] Wheeze only during the

first 3 years of life with

remission

Exclusive viral wheeze

Episodic viral wheeze

Risk factors [9, 25, 43–48,

77, 81, 82, 94, 95,

105–107]

Other clinical features

[9, 32, 44, 45, 77]

Prognosis [62, 63]

Persistent wheeze [9] Persistent early onset

wheezing

Wheeze during the first 3

years of life persisting

into school-age

Non-atopic persistent

wheeze (GINA) [30]

Intrinsic asthma

Late-onset wheeze [9] Wheeze beginning after 3

years of age

Multiple trigger wheeze

Atopic wheeze/asthma

Extrinsic asthma(C) Atopy

Non-atopic wheeze/asthma Intrinsic asthma [108]

Non-atopic persistent

wheeze [109]

Wheeze/reversible airflow

obstruction without

allergic sensitization

Exclusive viral-induced

wheeze

In older children/adults:

Exercise-induced asthma

Drug-induced asthma

Risk factors [97, 110–112]


Prognosis [63, 95]

Response to treatment [113]

Atopic wheeze/asthma Extrinsic asthma [108]

Atopic persistent wheeze

[109]

Wheeze/reversible airflow

obstruction with allergic

sensitization

Multiple trigger wheeze

(D) Severity

Childhood wheeze (e.g.

Melbourne cohort [3])

Childhood asthma Childhood wheeze as a

single disorder varying

by degrees of severity

Grades defined by number

of episodes, and

association or not with

RTI

Asthma Risk factors [114]


Prognosis [3, 64, 66, 67, 114]

Response to treatment [115]

Asthma (e.g. GINA [30]) Asthma as a single disorder

varying by degrees of

severity

Grades defined by

frequency of symptoms,

or lung function deficit

RTI, respiratory tract infection.





history in the first 6 years of life into ‘transient early

wheezing’ (wheeze in first 3 years of life but no wheeze at

6 years), ‘late-onset wheezing’ (no wheeze until age 3

years of life, but wheeze at 6 years) and ‘persistent

wheezing’ (wheeze both before age 3 and at age 6) (Table

3, part B) [9]. As this classification is based on retro-

spective symptom history at the age of 6, it cannot be usedto guide treatment decisions for younger children. How-

ever, it has become the most widely used model in

epidemiological studies [25, 43–46, 75–82].

Pros and cons of the one-dimensional approach

The obvious advantage of one-dimensional phenotype

definitions is that they are simple. If they are useful,

simple phenotypic definitions are preferable to complex

ones in both clinical and epidemiological settings. More

questionable is whether they represent underlying disease

entities. It is possible, but unlikely, that disease processes

specifically affect only one or a few clinical features. And

clearly, selecting only a few features from among many to

define the phenotypes requires subjective choices.

Including more features as necessary components of

asthma phenotypes is difficult because this results in

smaller groups. For instance, if all combinations of only

three dichotomous features, say age of onset (late, early),

persistence (transient, persistent) and triggers (only with,

also without RTI), are considered, eight separate pheno-

types result. With four dichotomous features 16 pheno-

types result and so on. Of course, some of these groups

could be combined, but the choice of which ones to

combine is again subjective. To include more features, weneed a classification that is less rigid and does not require

arbitrary selection.

The multi-dimensional approach

The multi-dimensional approach attempts to include a

wide range of features (Table 2) in the definition of

phenotypes. This approach was recently proposed by

Wardlaw et al. [83] as a means to develop a new taxonomy

of asthma and airways disease in general. The authors

suggested the use of cluster analysis, a group of techni-

ques that seek to organize the subjects of a multivariatedataset into relatively homogenous groups. Such methods

allow phenotypes to be identified in a data-driven manner

and might therefore minimize the subjectivity involved in

selecting the features [61, 83–85].

Haldar and colleagues applied cluster analysis to three

independent adult asthma populations using measure-

ments of symptoms, atopy, eosinophilic inflammation,

psychological status and variable airflow obstruction

[84]. In each dataset, they identified three or four clusters,

two of which showed a feature profile that was consistent

over the three populations. We recently used latent class

analysis (LCA), a type of cluster analysis based on a formal

statistical model to identify phenotypes of childhood

wheeze using data from a cohort study in Leicester, UK

(skin prick tests, airway responsiveness, lung function

and repeated assessment of symptoms of wheeze and

cough) [61, 85]. The model distinguished two phenotypes

of chronic cough, a persistent and a transient form, andthree phenotypes of wheeze, a transient form associated

with viral infections, a persistent form associated with

other triggers, and a non-atopic but mostly persistent

form (Fig. 3). Recently Clarisse and colleagues used a

different clustering method (partitioning around medoids)

to identify phenotypes of bronchial obstructive symptoms

in infants from the Paris birth cohort [86]. Two sympto-

matic groups emerged, one characterized by dyspnoea

with sleep disturbance and the other by nocturnal dry

cough.

Using data from the Avon Longitudinal Study of

Parents and Children (ALSPAC), Henderson and colleagues

applied LCA to seven repeated assessments of current

wheeze during the first 6 years of life [76], thus refining

the approach from the Tucson group. Their approach was

essentially one-dimensional because it included only one

feature (the occurrence of wheeze attacks) measured at

different points. However, the phenotypes were derived in

data-driven manner from the symptom histories of a large

number of children from the general population

(n = 6265). The analysis identified five types of longitudi-

nal trajectories distinguished by age at onset and persis-

tence of symptoms (Fig. 4).

Multivariate techniques can also be used to model

variability along a continuum rather than by discretephenotypes. A commonly used method is factor analysis,

which assumes that the observed features are related

through one or few unobserved continuous variables,

called factors. Features that are highly correlated with

each other contribute to the same factor and the factor

becomes a composite measure of these features. Several

studies have applied factor analysis and related methods

to data on wheezing and asthma in childhood [87–89].

Pros and cons of the multi-dimensional approach

The main advantage of the multi-dimensional approach is

that it attempts to involve all features of the disease

without making presumptions about which features are

the most distinguishing ones. Proponents argue that

resulting phenotypes might better reflect underlying dis-

ease entities as they are defined in a more objective

manner using information contained in the data [61, 76,

84, 85]. However, these methods are not entirely objective

because the investigator needs to make choices about

which particular method to use and which variables to

include.





A disadvantage is that the resulting phenotypes are not

defined by a simple combination of features. The pheno-

type of a particular group identified by cluster analysis is

defined by the characteristic features of this group. Such a

definition is more like a description, and cannot be

reduced to a single feature. For example, a phenotype

might be characterized by a tendency for early onset of

wheeze, usually persisting into the school age or later,

with attacks usually occurring not only during colds but

also apart from colds. This type of ‘soft’ definition may

resemble processes going on in the minds of clinicianswho combine multi-dimensional observations to make a

clinical diagnosis.

Validating phenotypes as real clinical entities

If phenotypes were visible objects, then their existence

and characteristics would be undisputed. However, we still

lack such a gold standard against which phenotypes could

be validated. Because the underlying disease process is not

fully understood, it is not possible to prove that a

particular phenotype classification correctly represents it.

Therefore, rather than proofs, other indications that phe-

notypes could represent disease entities are sought. Such

indications include whether disease phenotypes (a) are

associated with features not used to define them (usually

physiological measurements), (b) predict future outcomes,

(c) have distinctive risk factors, (d) have specific responses

to treatment, (e) are stable over time or (f) are consistent

across populations.

A large number of studies have tested whether different

phenotypes (varyingly defined) of wheezing or asthma in

children are differently associated with concurrent or later physiological measurements (e.g. lung function, bronchial

responsiveness, atopy and airway inflammation) [3, 9, 23,

32, 33, 44, 45, 47, 68, 69, 76, 77, 90–93], with distinctive

risk factors [9, 23, 25, 33, 43–46, 48, 81, 82, 94–96], with

later disease outcomes (prognosis) [61–66, 73] or with

response to treatment [52–54] (Table 3). A general rule is

that features used to define phenotypes cannot be used for

validation, meaning that for phenotypes defined by a

multi-dimensional approach fewer features remain for

this purpose (Table 4). However, the multi-dimensional

approach accounts for associations existing between many

of the features in the process of phenotype definition. Validating phenotypes by testing for associations with

other features can be misleading. If, for example, the

phenotype-persistent wheezing is associated with a ma-

ternal history of asthma [9] while the transient phenotype

is not [9, 82], this does not mean that the phenotypic

distinction transient vs. persistent is superior to other

distinctions. Other classifications, for instance by atopy

[97] or by triggers [26], produce similar results. The

question should rather be whether certain phenotype

classifications predict the presence of features (earlier,

concurrent or later) better than other classifications. Such

319(100)

208(65.2)

111(34.8)

159(49.8)

97(30.4)

63(19.7)

86(27.0)

86(27.0)

84(26.3)

63(19.7)

97(30.4)

82(25.7)

58(18.2)

47(14.7)

35(11.0)

Persistentcough

Transientcough

Atopicpersistent

wheeze

Non−atopicpersistent

Transientviral

wheeze

Fig. 3. Formation of phenotypes in a study of symptomatic children by

Spycher et al. [61]: Phenotypes were identified from multivariate data

including repeated symptom measurements, atopy, lung function and

BHR using latent class analysis. The branching between layers shows

how children are redistributed to new phenotypes as the number of

phenotypes in the model is increased. Box widths are proportional to

numbers of children assigned to the phenotypes (number and percentage

reported). A model with five phenotypes (bottom layer) was selected

based on statistical criteria but the branchingpattern shows that essential

differences between these phenotypes were identified in earlier stages.

The phenotypes of the final model are labelled according to their main

distinctive characteristics. Data are presented as n (%). Adapted from

Spycher BD et al. [61] with permission from European Respiratory

Society Journals Ltd.

Fig. 4. Developmental trajectories in six phenotypes of wheezing identi-

fied by Henderson et al. [76]: estimated prevalence of wheezing at each

time-point from birth to 81 months for each of the six wheezing

phenotypes identified by latent class analysis in 6265 children with

complete data on wheezing in the past 12 months over seven sequential

surveys. Reproduced from Henderson J et al. [76] with permission from

BMJ Publishing Group Ltd.





comparisons could be made using statistics such as

sensitivity, specificity or the likelihood ratio [98]. Because

this is rarely, if ever, done the observed associations, even

if well replicated across different studies, do not provide

support for any particular phenotypic classification over

the others.

Recently, a study investigated whether children with a

certain phenotype continued to have the same phenotype

at a later time-point, i.e. whether phenotypes were stable

over time [99]. One would expect phenotypes representing

actual diseases to persist for the duration of these diseases.

However, given the possibility of remission and relapse itis unclear over how long a period a phenotype should be

stable. Also, stability may be an artefact of phenotype

definition. For instance, a phenotype defined by the

wheezing pattern of the past year is by definition stable

over 1 year. The Tucson classification is necessarily stable

over the first 6 years of life because it is based on the

symptom history of this period.

A possibility for validating phenotypes identified by

data-driven methods is the replication of results in differ-

ent datasets. For instance, Haldar et al. [84] found similar

results of the same clustering method across three popula-

tions. Phenotypes reflecting true biological entities shouldreappear in a similar form in different populations. Such

comparisons are complicated by differences in study

design, such as differences in the age when children were

examined or in features assessed. ‘True’ phenotypes may,

however, be robust against such differences.

The way forward

In spite of its popularity, the meaning of the term

‘phenotype’, as used in the context of childhood asthma,

is vague and rarely specified. It is usually unclear whether

phenotypes are simply practical definitions or are in-

tended to represent real entities. Stating this clearly could

avoid much confusion and circular debate. If phenotypes

are just useful definitions, for instance because they are

associated with different response to treatment, then only

their usefulness needs to be shown, not whether or not

they represent real diseases. If, on the other hand, pheno-

types are thought to be real, then there is a need for

validation and for harmonization of definitions. Cur-

rently, there is a wide range of concepts among clinicians

[13] and among researchers. Despite numerous validation

studies, no phenotype model stands out as more valid thanothers.

In future, multi-dimensional approaches to phenotype

definition could complement the traditional one-dimen-

sional approach. Multi-dimensional approaches allow to

include more disease features and to define phenotypes

that can be validated across populations. They could be

used to assess the importance of the defining features of

traditional phenotypes in context with other features. For

instance, in the three wheezing phenotypes identified in

our study using LCA, both triggers and the persistence of

symptoms, the defining features of the popular classifica-

tions by the Tucson group and by Silverman and collea-gues, were important in conjunction with other features

such as atopy and BHR [61]. Such additional features

could be incorporated into traditional classifications. This

will require ‘softer’ and more complex definitions, for

instance, using decision trees or a probabilistic assign-

ment of children to phenotypes.

Different phenotypes may be needed for clinical prac-

tice and for research. One size is not likely to fit all. From

the clinician’s perspective, phenotypes should be clini-

cally useful and preferably simple. They need to be

relevant for treatment decisions or prognosis and be

Table 4. Variables used to define phenotypes or analysed for association with phenotypes (validation) in different studies

Disease variables or dimensions Description Used for phenotype definition

Examined for associations

with phenotypes

Aetiology Environmental risk factors

Genetics

Martinez et al. [9]

Silverman [26]

Disease characteristics Symptoms

Signs

Pathophysiology

Short-term time course

Silverman [26, 116]Ã

Spycher et al. [61]z‰

Martinez et al. [9]

Henderson et al. [76]

Long-term time course Previous time course (symptom

history)

Martinez et al. [9]w

Henderson et al. [76]z

Spycher et al. [61]z‰

Subsequent time course

(prognosis)

Martinez [62, 63]

Spycher et al. [61]

ÃTable 3, row A.

wTable 3, row B.

zData-driven definition using latent class analysis.

‰Multi-dimensional approach.





defined using readily accessible clinical information (fa-

mily and patient history, symptoms, signs and simple

measurements). Symptom control may be similar in dif-

ferent patients even if the symptoms have different under-

lying causes. In research, however, the main focus is the

underlying disease processes and their mechanisms. Hav-

ing phenotypes that are specific for the main underlyingdisease processes can greatly improve the precision of

research into mechanisms and causes.

The importance of precise phenotype definitions is

becoming increasingly clear in genetic research. Pheno-

types of childhood wheezing have rarely been used in

genetic association studies although they may reflect

important susceptibilities to respiratory disease through-

out life [63–65]. A question that should be addressed is

whether phenotypic variability between children is better

represented by discrete groups (distinct phenotypes) or by

a continuum (one or more continuous factors). This is

important because if children are categorized into discrete

groups when in fact they lie along a single continuum,

information is lost and statistical power to detect genetic

associations is reduced [100, 101]. A study by the Tucson

group demonstrated stronger associations between a con-

tinuous score of atopy obtained by factor analysis with

specific genetic markers than with individual dichoto-

mous results of skin tests [87]. Using a cross-sectional

dataset, we determined whether a model with continuous

factors or one with discrete groups better represented

variability of parent-reported symptoms. Preliminary re-

sults [102] point to a continuum of disease rather than to

discrete phenotypes; however, further work is required to

see whether the inclusion of time course and objectivemeasurements might reveal discrete patterns.

Ultimately, only knowledge of genetic and environ-

mental determinants and pathophysiological pathways

can reveal the true nature of the diseases causing recur-

rent wheeze in children. Reaching this goal is an iterative

process: better phenotype definitions increase the preci-

sion of research into causes and mechanisms, while a

better understanding of the underlying processes leads to

more meaningful labels, until eventually new diseases

might be defined based on aetiology. In this process, we

may need to discard old distinctions and possibly even

shift our focus away from wheezing as the cardinalsymptom. Like all hypotheses, current phenotypes will

eventually be superseded. In the meantime, we should

treat phenotypes as exactly that: the best current working

hypotheses.

Acknowledgements

The authors would like to thank: all the children and

families participating in Leicestershire cohort studies for

contributing data; Urs Frey and Philip Latzin for critical

discussions on this topic; and Lutz Dumbgen, John

Thompson and Marie Pierre Strippoli for their methodo-

logical and technical support in our work on phenotypes

of wheeze.

Funding:

The work was supported by Asthma UK (grant 07/048)

and the Swiss National Science Foundation (PROSPER

grant 3233-069348 and 3200-069349, and SNF grant823B - 046481).

Competing interests:

There are no competing interests.

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