Data-Driven vs Consensus Diagnosis of MCI: Enhanced Sensitivity for Detection of Clinical, Biomarker, and Neuropathologic Outcomes

Abstract

Background and objectives: 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 Alzheimer's Disease Research Center (ADRC) in order to identify cognitive subgroups within ADRC participants without dementia and to examine cognitive, biomarker, and neuropathologic 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 Alzheimer disease (AD) biomarker status and neuropathologic 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, nonamnestic 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.

Discussion: We identified subtypes of MCI and CN with differing cognitive profiles, clinical outcomes, CSF AD biomarkers, and neuropathologic 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 neuropathologic signatures. Findings indicate that data-driven algorithms enhance diagnostic sensitivity relative to consensus diagnosis for identifying older adults at risk for cognitive decline.

Figure 1. Neuropsychological Performance of the Cluster-Derived Groups

Distribution of cognitive z-scores for each neuropsychological domain across cluster-derived groups. The points denote group mean and lines denote 1 SD. The dotted line represents impairment at 1 SD below the mean. aMCI = amnestic mild cognitive impairment; CN = cognitively normal; mMCI = mixed mild cognitive impairment; naMCI = nonamnestic mild cognitive impairment.

Figure 2. Kaplan-Meier Survival Curves Showing Risk of Progression to Dementia in the Cluster-Derived Groups

All groups differed significantly from one another, with the exception of the optimal cognitively normal (CN) and typical CN groups. aMCI = amnestic mild cognitive impairment; mMCI = mixed mild cognitive impairment; naMCI = nonamnestic mild cognitive impairment.

Figure 3. Concentrations of CSF β-Amyloid (Aβ)1–42 (pg/mL), Tau (pg/mL), and the Tau/Aβ1–42 Ratio for Each Cluster-Derived Group

The dotted line represents the cut point for biomarker positivity for the tau/Aβ_1–42 ratio (>0.52). aMCI = amnestic mild cognitive impairment; CN = cognitively normal; mMCI = mixed mild cognitive impairment; naMCI = nonamnestic mild cognitive impairment.

Figure 4. Neuropathologic Findings Showing Alzheimer Disease (AD) Pathology Severity Based on National Institute on Aging–Reagan Consensus Criteria in the Mild Cognitive Impairment (MCI) and Cognitively Normal (CN) Groups

Note that the optimal and typical CN groups had a significantly longer interval between baseline neuropsychological evaluation and autopsy (15 years) relative to the 3 MCI groups (7–12 years). aMCI = amnestic mild cognitive impairment; mMCI = mixed mild cognitive impairment; naMCI = nonamnestic mild cognitive impairment.