To advance asthma cohort research, we need a method that can use longitudinal data, including when collected at irregular intervals, to model multiple phenotypes of wheeze and identify both time-invariant (eg, sex) and time-varying (eg, environmental exposure) risk factors.
To demonstrate the use of latent class growth analysis (LCGA) in defining phenotypes of wheeze and examining the effects of causative factors, using repeated questionnaires in an urban birth cohort study.
We gathered repeat questionnaire data on wheeze from 689 children ages 3 through 108 months (n = 7,048 questionnaires) and used LCGA to identify wheeze phenotypes and model the effects of time-invariant (maternal asthma, ethnicity, prenatal environmental tobacco smoke, and child sex) and time-varying (cold/influenza [flu] season) risk factors on prevalence of wheeze in each phenotype.
LCGA identified four wheezing phenotypes: never/infrequent (47.1%), early-transient (37.5%), early-persistent (7.6%), and late-onset (7.8%). Compared with children in the never/infrequent phenotype, maternal asthma was a risk factor for the other 3 phenotypes; Dominican versus African American ethnicity was a risk factor for the early-transient phenotype; and male sex was a risk factor for the early-persistent phenotype. The prevalence of wheeze was higher during the cold/flu season than otherwise among children in the early-persistent phenotype (P = .08).
This is the first application of LCGA to identify wheeze phenotypes in asthma research. Unlike other methods, this modeling technique can accommodate questionnaire data collected at irregularly spaced age intervals and can simultaneously identify multiple trajectories of health outcomes and associations with time-invariant and time-varying causative factors.