A-105: Early Cognitive and Behavioral Outcomes in Child Carriers and Non-Carriers: Preliminary Findings From the Longitudinal NeuroKids Autosomal Dominant Alzheimer’s Disease Study

Abstract

Objective: Individuals carrying the Presenilin-1 (PSEN1) E280A mutation are nearly certain to develop autosomal dominant Alzheimer’s disease (ADAD) by their forties. Although presymptomatic differences have been observed in adults decades before symptom onset, the earliest divergence remains unknown. This ongoing longitudinal study tracks cognitive, behavioral, pubertal, and neural development in child PSEN1 E280A carriers and noncarriers to identify early trajectories preceding AD onset. Here, we focus on baseline cognitive and behavioral differences between groups. Method/Data Selection: A total of 61 non-carriers (M age=11.0 (3.0); 46% female) and 24 carriers (M age=11.5 (2.7); 63% female) were included in these analyses. Participants completed the Evaluación Neuropsicológica Infantil (ENI), a comprehensive neuropsychological battery, and the Behavior Assessment System for Children-2 (BASC-2; parent and self-report), a measure of behavioral and emotional functioning. Linear regression models, controlling for sex, examined associations between carrier status and ENI and BASC scores. Results/Data Synthesis: Groups did not differ significantly on age, sex, or education. Carriers and non-carriers also showed no significant differences across ENI cognitive domains or BASC behavioral measures. However, carriers demonstrated a trend toward lower scores on story memory recall (p= 0.08). Conclusions: Preliminary baseline findings support the hypothesis that cognitive and behavioral differences between carriers and noncarriers are not yet evident in childhood, although these results are limited by the small number of carriers. The absence of group differences underscores the importance of longitudinal follow-up to characterize the timing and trajectory of early cognitive changes in ADAD. Ongoing collection of pubertal and biomarker data will help identify biological correlates as divergence emerges over time.