Background: The heterogeneity of childhood atopic dermatitis (AD) underscores the need to understand latent phenotypes that may inform risk stratification and disease prognostication.
Objective: To identify AD trajectories across the first 8 years of life and investigate risk factors associated with each trajectory and their relationships with other comorbidities.
Methods: Data were collected prospectively from 1152 mother-offspring dyads in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort from ages 3 months to 8 years. AD was defined based on parent-reported doctor's diagnosis. An unsupervised machine learning technique was used to determine AD trajectories.
Results: Three AD trajectories were identified as follows: early-onset transient (6.3%), late-onset persistent (6.3%) and early-onset persistent (2.1%), alongside a no AD/reference group (85.2%). Early-onset transient AD was positively associated with male gender, family history of atopy, house dust mite sensitization and some measures of wheezing. Early-onset persistent AD was associated with antenatal/intrapartum antibiotic use, food sensitization and some measures of wheezing. Late-onset persistent AD was associated with a family history of atopy, some measures of house dust mite sensitization and some measures of allergic rhinitis and wheezing.
Conclusion and clinical relevance: Three AD trajectories were identified in this birth cohort, with different risk factors and prognostic implications. Further work is needed to understand the molecular and immunological origins of these phenotypes.
Keywords: atopic dermatitis; machine learning; rhinitis; trajectories; wheezing.
© 2021 John Wiley & Sons Ltd.