Capturing cognitive and behavioral variability among individuals with Down syndrome: a latent profile analysis.

BACKGROUND There is a high degree of inter- and intra-individual variability observed within the phenotype of Down syndrome. The Down Syndrome Cognition Project was formed to capture this variability by developing a large nationwide database of cognitive, behavioral, health, and genetic information on individuals with Down syndrome, ages 6-25 years. The current study used the Down Syndrome Cognition Project database to characterize cognitive and behavioral variability among individuals with Down syndrome. METHODS Latent profile analysis was used to identify classes across a sample of 314 participants based on their cognition (IQ and executive functioning), adaptive and maladaptive behavior, and autism spectrum disorder symptomatology. A multivariate multinomial regression model simultaneously examined demographic correlates of class. RESULTS Results supported a 3-class model. Each class demonstrated a unique profile across the subdomains of cognition and behavior. The "normative" class was the largest (n = 153, 48%) and displayed a relatively consistent profile of cognition and adaptive behavior, with low rates of maladaptive behavior and autism symptomatology. The "cognitive" class (n = 109, 35%) displayed low cognitive scores and adaptive behavior and more autism symptomatology, but with low rates of maladaptive behavior. The "behavioral" class, the smallest group (n = 52, 17%), demonstrated higher rates of maladaptive behavior and autism symptomatology, but with cognition levels similar to the "normative" class; their adaptive behavior scores fell in between the other two classes. Household income and sex were the only demographic variables to differ among classes. CONCLUSIONS These findings highlight the importance of subtyping the cognitive and behavioral phenotype among individuals with Down syndrome to identify more homogeneous classes for future intervention and etiologic studies. Results also demonstrate the feasibility of using latent profile analysis to distinguish subtypes in this population. Limitations and future directions are discussed.