Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults

Post-Hoc Assessment of Cognitive Efficacy in Alzheimer's Disease Using a Latent Growth Mixture Model in AMBAR, a Phase 2B Randomized Controlled Trial

BACKGROUND

Disease progression in Alzheimer's Dementia (AD) is typically characterized by accelerated cognitive and functional decline, where heterogeneous trajectories can impact the observed treatment response.

METHODS

We hypothesized that unobserved heterogeneity could obscure treatment benefits in AD. The effect of unobserved heterogeneity was empirically quantified within the Alzheimer's Management By Albumin Replacement (AMBAR) phase 2b trial data. The ADAS-Cog 12 cognition endpoint was reanalyzed in a 2-class latent growth mixture model initially fit to the treatment arm. The model with the best fit was then applied across both treatment arms to a larger (n=1000) simulated dataset that was representative of AMBAR trial cognitive data.

RESULTS

Two classes of patients were observed: a stable cognitive trajectory class and a highly variable class. Removal of the latter (n=48, 22%) from the analysis and refitting efficacy models comparing the stable class to full placebo yielded significant treatment efficacy on cognition (p=0.007, Cohen's D=-0.4). Comparison of the stable class of each arm within the simulated dataset revealed a significant difference in treatment efficacy favoring the simulated stable treatment arm.

CONCLUSION

This post hoc exploratory analysis suggests that prespecified strategies for addressing unobserved heterogeneity may yield improved effect detection in AD trials. The generalizability of the analytic strategy is limited by latent stratification in only the treatment arm, a requirement given the small placebo arm in AMBAR. This limitation was partially addressed by the simulation modeling.

Heterogeneity in cognitive profiles of monolingual and bilingual Hispanic/Latino older adults in HABS-HD

OBJECTIVE

The present study characterized heterogeneity in the cognitive profiles of monolingual and bilingual Latino older adults enrolled in the HABS-HD.

METHODS

A total of 859 cognitively unimpaired older adults completed neuropsychological testing. Raw scores for cognitive tests were converted to z-scores adjusted for age, education, sex, and language of testing. A latent profile analysis (LPA) was conducted for monolingual and bilingual speaker groups. A series of 2-5 class solutions were examined, and the optimal model was selected based on fit indices, posterior probabilities, proportion of sample sizes, and pattern of scores. Identified classes were compared on sociodemographic, psychosocial, and health characteristics.

RESULTS

For the monolingual group (n = 365), a 3-class solution was optimal; this consisted of a Low Average Memory group with low average verbal memory performances on the SEVLT Total Learning and Delayed Recall trials, as well as an Average Cognition group and a High Average Cognition group. For the bilingual group (n = 494), a 3-class solution was observed to be optimal; this consisted of a Low Average Memory group, with low average verbal memory performances on the learning and delayed recall trials of Logical Memory; a Low Average Executive group, where performance on Trails A and B and Digit Substitution were the lowest; and a High Average Cognition group, where performance was generally in the high average range across most cognitive measures.

CONCLUSIONS

Cognitive class solutions differed across monolingual and bilingual groups and illustrate the need to better understand cognitive variability in linguistically diverse samples of Latino older adults.

Cognitive Heterogeneity and Risk of Progression in Data-Driven Subtle Cognitive Decline Phenotypes

BACKGROUND

There is increasing recognition of cognitive and pathological heterogeneity in early-stage Alzheimer's disease and other dementias. Data-driven approaches have demonstrated cognitive heterogeneity in those with mild cognitive impairment (MCI), but few studies have examined this heterogeneity and its association with progression to MCI/dementia in cognitively unimpaired (CU) older adults.

OBJECTIVE

We identified cluster-derived subgroups of CU participants based on comprehensive neuropsychological data and compared baseline characteristics and rates of progression to MCI/dementia or a Dementia Rating Scale (DRS) of ≤129 across subgroups.

METHODS

Hierarchical cluster analysis was conducted on individual baseline neuropsychological test scores from 365 CU participants in the UCSD Shiley-Marcos Alzheimer's Disease Research Center longitudinal cohort. Cox regressions examined the risk of progression to consensus diagnosis of MCI or dementia, or to DRS score ≤129, by cluster group.

RESULTS

Cluster analysis identified 5 groups: All-Average (n = 139), Low-Visuospatial (n = 46), Low-Executive (n = 51), Low-Memory/Language (n = 83), and Low-All Domains (n = 46). Subgroups had unique demographic and clinical characteristics. Rates of progression to MCI/dementia or to DRS ≤129 were faster for all subgroups (Low-All Domains progressed the fastest > Low Memory/Language≥Low-Visuospatial and Low-Executive) relative to the All-Average subgroup.

CONCLUSION

Faster progression in the Low-Visuospatial, Low-Executive, and Low-Memory/Language groups compared to the All-Average group suggests that there are multiple pathways and/or unique subtle cognitive decline profiles that ultimately lead to a diagnosis of MCI/dementia. Use of comprehensive neuropsychological test batteries that assess several domains may be a key first step toward an individualized approach to early detection and fewer missed opportunities for early intervention.