Phenotypes of atopic dermatitis and long-term body mass index trajectories

Background Evidence linking atopic dermatitis (AD) to obesity is inconsistent, partly because prior studies overlooked the disease’s clinical heterogeneity. This study aimed to identify longitudinal AD and body mass index (BMI) trajectories from childhood to adolescence and examine their concurrent associations. Methods Data from 20,158 children in a Japanese nationwide birth cohort followed for 13 years were analyzed. Group-based trajectory modeling identified five AD phenotypes and three BMI z-score trajectories. Associations were analyzed using sex-stratified multinomial logistic regression. Results Three BMI trajectories with similar patterns were identified for both sexes: “Stable,” “High-Decreasing,” and “Low-Increasing.” With BMI as outcome, in boys, “Early-onset transient” (adjusted odds ratio [aOR], 1.37; 95% confidence interval [CI], 1.16–1.63) and “Early-onset persistent” (aOR, 1.18; 1.06–1.31) AD phenotypes were associated with the “High-Decreasing” BMI, while “Late-onset” AD was associated with the “Low-Increasing” BMI trajectory (aOR, 1.51; 1.07–2.14). In girls, “Late-onset” AD was associated with the “Low-Increasing” (aOR, 1.88; 1.35–2.64) and “High-Decreasing” (aOR, 1.45; 1.06–1.98) BMI; early-onset phenotypes showed no significant associations. Conclusion The longitudinal clinical course of childhood AD is concurrently associated with distinct, sex-specific BMI trajectories, highlighting a parallel relationship between cutaneous symptom development and somatic growth. Impact Longitudinal phenotypes of childhood atopic dermatitis (AD) are associated with distinct, sex-specific body mass index (BMI) trajectories. Using a 13-year nationwide birth cohort, we show a parallel relationship between the AD course and somatic growth. Early-onset AD phenotypes are associated with a “catch-down” BMI in boys, whereas late-onset AD phenotypes are associated with upward BMI shifts in both sexes. Recognizing these parallel developmental patterns helps clinicians holistically monitor children with AD.