Developmental profiles of eczema, wheeze, and rhinitis: two population-based birth cohort studies

Abstract

Background: The term "atopic march" has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level.

Methods and findings: Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time. Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). When latent variable modelling was carried out separately for the two cohorts, similar results were obtained. Highly concordant patterns of sensitisation were associated with different profiles of eczema, rhinitis, and wheeze. The main limitation of this study was the difference in wording of the questions used to ascertain the presence of eczema, wheeze, and rhinitis in the two cohorts.

Conclusions: The developmental profiles of eczema, wheeze, and rhinitis are heterogeneous; only a small proportion of children (∼ 7% of those with symptoms) follow trajectory profiles resembling the atopic march. Please see later in the article for the Editors' Summary.

Figure 1. Graphical representation of the independent Markov chain model where transitions within each symptom are assumed to be independent (Model 1).

Shaded circles represent observed variables, and unshaded circles represent latent variables to be inferred. Symptoms are joined together by a latent class disease profile.

Figure 2. Graphical representation of the Markov chain model allowing transition probabilities across symptoms to follow an atopic march progression of symptoms (Model 2).

Shaded circles represent observed variables, and unshaded circles represent latent variables to be inferred. Symptoms are joined together by a latent class disease profile.

Figure 3. Graphical representation of the latent disease profile model taking into account the co-occurrence of symptoms at each time point (Model 3).

Shaded circles represent observed variables, and unshaded circles represent latent variables to be inferred. Symptoms are joined together by a latent class disease profile.

Figure 4. Prevalence of wheeze, eczema, and rhinitis over cross-sectional time points in the ALSPAC and MAAS cohorts.

Figure 5. Distinct disease profile classes.

Using Bayesian machine learning joint modelling of eczema, wheeze, and rhinitis across two population-based birth cohorts, we identified eight distinct disease profile classes. The number of children and the proportion of the study population are indicated for each class. Plots indicate longitudinal trajectories of wheeze, eczema, and rhinitis within each class.

Figure 6. Distinct disease profile classes in MAAS.

Bayesian machine learning joint modelling of eczema, wheeze, and rhinitis for the MAAS cohort. We identified eight distinct disease profile classes that best described the data. Class 1, no disease; class 2, atopic march; class 3, persistent eczema and wheeze; class 4, persistent eczema with later-onset rhinitis; class 5, persistent wheeze with later-onset rhinitis; class 6, transient wheeze; class 7, eczema only; class 8, rhinitis only.

Figure 7. Distinct disease profile classes in ALSPAC.

Bayesian machine learning joint modelling of eczema, wheeze, and rhinitis for the ALSPAC cohort. We identified eight distinct disease profile classes that best described the data. Class 1, no disease; class 2, atopic march; class 3, persistent eczema and wheeze; class 4, persistent eczema with later-onset rhinitis; class 5, persistent wheeze with later-onset rhinitis; class 6, transient wheeze; class 7, eczema only; class 8, rhinitis only.

Figure 8. Proportion of children sensitised in each latent disease profile.

Figure 9. Profile plot showing cross-sectional change in prevalence of eczema, wheeze, and rhinitis in the ALSPAC and MAAS birth cohorts.