Evolution of Eczema, Wheeze, and Rhinitis from Infancy to Early Adulthood: Four Birth Cohort Studies

Abstract

Rationale: The relationship between eczema, wheeze or asthma, and rhinitis is complex, and epidemiology and mechanisms of their comorbidities is unclear. Objectives: To investigate within-individual patterns of morbidity of eczema, wheeze, and rhinitis from birth to adolescence/early adulthood. Methods: We investigated onset, progression, and resolution of eczema, wheeze, and rhinitis using descriptive statistics, sequence mining, and latent Markov modeling in four population-based birth cohorts. We used logistic regression to ascertain if early-life eczema or wheeze, or genetic factors (filaggrin [FLG] mutations and 17q21 variants), increase the risk of multimorbidity. Measurements and Main Results: Single conditions, although the most prevalent, were observed significantly less frequently than by chance. There was considerable variation in the timing of onset/remission/persistence/intermittence. Multimorbidity of eczema+wheeze+rhinitis was rare but significantly overrepresented (three to six times more often than by chance). Although infantile eczema was associated with subsequent multimorbidity, most children with eczema (75.4%) did not progress to any multimorbidity pattern. FLG mutations and rs7216389 were not associated with persistence of eczema/wheeze as single conditions, but both increased the risk of multimorbidity (FLG by 2- to 3-fold, rs7216389 risk variant by 1.4- to 1.7-fold). Latent Markov modeling revealed five latent states (no disease/low risk, mainly eczema, mainly wheeze, mainly rhinitis, multimorbidity). The most likely transition to multimorbidity was from eczema state (0.21). However, although this was one of the highest transition probabilities, only one-fifth of those with eczema transitioned to multimorbidity. Conclusions: Atopic diseases fit a multimorbidity framework, with no evidence for sequential atopic march progression. The highest transition to multimorbidity was from eczema, but most children with eczema (more than three-quarters) had no comorbidities.

Keywords: asthma; atopic march; birth cohorts; eczema; wheeze.

Figure 1.

Trends in the deviation between observed and expected probabilities for each disease category over time (expressed as percentage point difference). Negative numbers show that observed probabilities were lower than expected probabilities; for example, single diseases were observed less frequently than expected in the population, and eczema+wheeze+rhinitis was observed more than expected. IOW = Isle of Wight; MAAS = Manchester Asthma and Allergy Study. SEATON = Aberdeen cohort.

Figure 2.

Longitudinal sequences of the development of eczema, wheeze, and rhinitis (including comorbid conditions) in individual participants in the four cohorts. Each row is colored by the presence of symptoms and their combinations at each time point. The number of person-unique sequences: 220 SEATON (Aberdeen cohort), 259 Ashford, 295 IOW (Isle of Wight), 351 MAAS (Manchester Asthma and Allergy Study).

Figure 3.

Dynamics of change in eczema, wheeze, and rhinitis over time. Latent Markov modeling in the joint cohort model (2,079 children with complete observations on eczema, wheeze, and rhinitis at five time points). Data were harmonized at overlapping time points to represent five stages of development (infancy: age 1 yr; early childhood: ages 2–3 yr; preschool: ages 4–5 yr; midchildhood: ages 8–10 yr; adolescence: 14–18 yr). (A) Predicted latent Markov states from joint modeling of all four cohorts; each row represents the individual-level latent states across time. (B) Alluvial plot to show relative size of transitions between latent states between time t and t+1 (based on time-homogeneous transition probabilities displayed in Table 4). Children from the eczema (E) state are more likely to persist in the same state. Although relatively small, they are more likely to transition to multimorbidity (MM) than children from other states. Children in the wheeze (W) state are more likely to transition to low risk (LR) than to any other state. R = rhinitis. (C) Dynamics of change in eczema, wheeze, and rhinitis over time. Latent Markov modeling: alluvial plot to show individual-level transitions between predicted latent Markov states at each time point.