TY - JOUR
T1 - Capturing cognitive and behavioral variability among individuals with Down syndrome
T2 - a latent profile analysis
AU - The Down Syndrome Cognition Project
AU - Channell, Marie Moore
AU - Mattie, Laura J.
AU - Hamilton, Debra R.
AU - Capone, George T.
AU - Mahone, E. Mark
AU - Sherman, Stephanie L.
AU - Rosser, Tracie C.
AU - Reeves, Roger H.
AU - Kalb, Luther G.
N1 - Funding Information:
First and foremost, we thank the families who devoted their time and effort to participate in this study. We also thank the numerous Down Syndrome Cognition Project staff and students who supported participant recruitment, data collection, and data management efforts at each site. We thank Michael Harpold whose tireless efforts on behalf of people with Down syndrome and his specific advice and encouragement were critical to the success of this project. The vision and support of the Anna and John Sie Foundation helped to establish the LuMind Foundation and to sustain this effort.
Funding Information:
Funding provided by LuMind Down Syndrome Research Foundation (PI: RR and SS) supported the implementation of the Down Syndrome Cognition Project. The Yellow Crayon Funds, provided by the family of Joanna Elisabeth Stone, supported recruitment efforts for the Down Syndrome Cognition Project. Data analysis and interpretation was supported by the NIH-funded Intellectual & Developmental Disabilities Research Centers grant U54 HD079123 awarded to the Kennedy Krieger Institute (PI: Schlaggar).
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Adaptive behavior
KW - Autism symptomatology
KW - Cognition
KW - Down syndrome
KW - Intellectual disability
KW - Latent profile analysis
KW - Maladaptive behavior
KW - Phenotypes
UR - http://www.scopus.com/inward/record.url?scp=85105159278&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105159278&partnerID=8YFLogxK
U2 - 10.1186/s11689-021-09365-2
DO - 10.1186/s11689-021-09365-2
M3 - Article
C2 - 33874886
AN - SCOPUS:85105159278
SN - 1866-1947
VL - 13
JO - Journal of Neurodevelopmental Disorders
JF - Journal of Neurodevelopmental Disorders
IS - 1
M1 - 16
ER -