Latent class analysis of early developmental trajectory in baby siblings of children with autism

Abstract

Background: Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used.

Methods: Sibs-A (N = 204) were assessed with the Mullen Scales of Early Learning from age 6 to 36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (N = 52); non-ASD social/communication delay (broader autism phenotype; BAP; N = 31); and unaffected (N = 121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification.

Results: A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4.

Conclusions: Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A.

Figure 1

Parallel Process Latent Class Growth Curve Model Parallel process latent class growth curve model of Mullen scale growth processes across six measurement occasions. The model is described notationally in the Methods section. Latent variable intercepts, slopes, and quadratic terms, represented in the graph above as in “growth process” circles, for each score, are used to construct latent classes.

Figure 2

Observed Mean Trajectories by Mullen Scale from a Four-Class Parallel Process Latent Class Growth Model (n = 204) Observed mean trajectories by latent class status from a four-class parallel process latent class growth model (general model setup displayed graphically in Figure 1 and notationally in equations in the Methods when k = 4). In this display, class membership is assigned based on most likely posterior probabilities of class membership.