Childhood patterns of overweight and wheeze and subsequent risk of current asthma and obesity in adolescence

Paediatr Perinat Epidemiol. 2021 Sep;35(5):569-577. doi: 10.1111/ppe.12760. Epub 2021 Mar 22.

Abstract

Background: Obesity and asthma in childhood often co-occur. Few studies have examined this relationship using repeated measures of body mass index (BMI) or asthma symptoms (such as wheeze).

Objective: We compared two analytic approaches for repeated measures data to investigate this relationship.

Methods: Our baseline sample consisted of 1277 children enrolled in a Boston-area cohort with BMI or wheeze at age 1 year and no missing covariates. We used latent class growth models (LCGM) and inverse probability weighting (IPW) of marginal structural models to examine the extent to which presence of overweight across childhood was associated with early adolescent current asthma, and conversely of repeated measures of wheeze across childhood with early adolescent obesity.

Results: Using LCGM, a "persistent" childhood overweight class (vs "never") was associated with higher risk of asthma in early adolescence (RR 1.9; 95% CI 1.1, 3.0), while "persistent" childhood wheeze (vs "never") was associated with higher risk of obesity in early adolescence (RR 2.7; 95% CI 1.0, 6.4) after adjusting for baseline covariates. An IPW analysis treating childhood overweight and wheeze as time-varying exposures and adjusting for baseline and time-varying covariates resulted in weaker and less precise associations of "persistent" (vs "never") overweight with adolescent asthma (RR 1.3; 95% CI 0.3, 3.0), and of "persistent" (vs "never") wheeze with adolescent obesity (RR 2.3; 95% CI 0.4, 5.3).

Conclusion: Our point estimates from both approaches suggest an association between "persistent" childhood overweight and adolescent asthma, and between "persistent" childhood wheeze and adolescent obesity. LCGM results were stronger and more precise, whereas IPW results were less conclusive with wider 95% confidence intervals containing the null. The precision gained from LCGM may be at the expense of bias, and the use of both approaches helps to shed some light on this tradeoff.

Keywords: asthma; inverse probability weighting; latent class growth models; obesity.