Residual decline in cognition after adjustment for common neuropathologic conditions

Lei Yu*, Patricia A. Boyle, Eisuke Segawa, Sue Leurgans, Julie A. Schneider, Robert S. Wilson, David A. Bennett

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Objective: Neurodegenerative and cerebrovascular conditions are common in old age and are associated with cognitive decline. However, considerable heterogeneity remains in residual decline (i.e., personspecific trajectories of cognitive decline adjusted for these common neuropathologic conditions). The present study aimed to characterize profiles of residual decline in late life cognition. Method: Up to 19 waves of longitudinal cognitive data were collected from 876 autopsied participants from 2 ongoing clinical-pathologic cohort studies of aging. Uniform neuropathologic examinations quantified measures of Alzheimer's disease, cerebral infarcts, Lewy body disease, and hippocampal sclerosis. Random effects mixture models characterized latent profiles of residual decline in global cognition. Results: We identified 4 latent groups, and each group demonstrated distinct residual decline profiles. On average, 44% of the participants had little or no decline, 35% showed moderate decline, 13% showed severe decline and the rest (8%) had substantial within-subject fluctuation of longitudinal cognitive measures. These latent groups differed in psychological, experiential and neurobiologic factors that have been previously shown to be associated with cognitive decline. Specifically, compared with nondecliners, decliners had more depressive symptoms, were more socially isolated; were less engaged in cognitive or physical activities; and had lower density of noradrenergic neurons in locus ceruleus. Conclusions: After controlling for common dementia related pathologies, considerable residual variability remains in cognitive aging trajectories and this variability is not random but rather is related to markers of cognitive and neural reserve. The mixture modeling approach provides a powerful tool to identify latent groups with distinct cognitive trajectories.

Original languageEnglish (US)
Pages (from-to)335-343
Number of pages9
Issue number3
StatePublished - May 1 2015


  • Cognitive aging
  • Common neuropathologies
  • Random effects mixture model
  • Reserve
  • Residual decline

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology


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