Objective: Given prior work demonstrating that mild cognitive impairment (MCI) can be empirically differentiated into meaningful cognitive subtypes, we applied actuarial methods to comprehensive neuropsychological data from the University of California San Diego (UCSD) Alzheimer’s Disease Research Center (ADRC) in order to identify cognitive subgroups within nondemented ADRC participants, and to examine cognitive, biomarker, and neuropathological trajectories.

Methods: Cluster analysis was performed on baseline neuropsychological data (n=738; mean age=71.8). Survival analysis examined progression to dementia (mean follow-up=5.9 years). CSF AD biomarker status and neuropathological findings at follow-up were examined in a subset with available data.

Results: Five clusters were identified: "optimal" cognitively normal (CN; n=130) with above-average cognition, "typical" CN (n=204) with average cognition, non-amnestic MCI (naMCI; n=104), amnestic MCI (aMCI; n=216), and mixed MCI (mMCI; n=84). Progression to dementia differed across MCI subtypes (mMCI>aMCI>naMCI), with the mMCI group demonstrating the highest rate of CSF biomarker positivity and AD pathology at autopsy. Actuarial methods classified 29.5% more of the sample with MCI and outperformed consensus diagnoses in capturing those who had abnormal biomarkers, progressed to dementia, or had AD pathology at autopsy.

Conclusions: We identified subtypes of MCI and CN with differing cognitive profiles, clinical outcomes, CSF AD biomarkers, and neuropathological findings over more than 10 years of follow-up. Results demonstrate that actuarial methods produce reliable cognitive phenotypes, with data from a subset suggesting unique biological and neuropathological signatures. Findings indicate that data-driven algorithms enhance diagnostic sensitivity relative to consensus diagnosis for identifying older adults at risk for cognitive decline.