The trajectory of cognitive decline in patients with late-onset Alzheimer’s disease varies widely. Genetic variations in CLU, PICALM, and CR1 are associated with Alzheimer’s disease, but it is unknown whether they exert their effects by altering cognitive trajectory in elderly individuals at risk for the disease.
The authors developed a Bayesian model to fit cognitive trajectories in a cohort of elderly subjects and test for genetic effects. They first validated the model’s ability to detect the previously established effects of APOE ε4 alleles on age at cognitive decline and of psychosis on the rate of cognitive decline in 802 subjects from the Cardiovascular Health Cognition Study who did not have dementia at study entry and developed incident dementia during follow-up. The authors then evaluated the effects of CLU, PICALM, and CR1 on age and rate of decline in 1,831 subjects who did not have dementia at study entry and then did or did not develop incident dementia by study’s end.
The model generated robust fits to the observed cognitive trajectory data, and validation analysis supported the model’s utility. CLU and CR1 were associated with more rapid cognitive decline. PICALM was associated with an earlier age at midpoint of cognitive decline. Associations remained after accounting for the effects of APOE and demographic factors.
Evaluation of cognitive trajectories provides a powerful approach to dissecting genetic effects on the processes leading to cognitive deterioration and Alzheimer’s disease.