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

Published online by Cambridge University Press:  21 December 2023

Kelsey R Thomas*
Affiliation:
VA San Diego Healthcare System, San Diego, CA, USA. University of California, San Diego, La Jolla, CA, USA.
Katherine J Bangen
Affiliation:
VA San Diego Healthcare System, San Diego, CA, USA. University of California, San Diego, La Jolla, CA, USA.
Alexandra J Weigand
Affiliation:
San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA.
Gema Ortiz
Affiliation:
University of California, Los Angeles, Los Angeles, CA, USA.
Kayla S Walker
Affiliation:
VA San Diego Healthcare System, San Diego, CA, USA.
David P Salmon
Affiliation:
University of California, San Diego, La Jolla, CA, USA.
Mark W Bondi
Affiliation:
VA San Diego Healthcare System, San Diego, CA, USA. University of California, San Diego, La Jolla, CA, USA.
Emily C Edmonds
Affiliation:
Banner Alzheimer’s Institute, Tucson, AZ, USA. University of Arizona, Tucson, AZ, USA
*
Correspondence: Kelsey R. Thomas, PhD, VA San Diego Healthcare System & University of California, San Diego, kthomas@health.ucsd.edu
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Abstract

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Objective:

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. 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.

Participants and Methods:

A hierarchical cluster analysis was conducted using 11 baseline neuropsychological test scores from 365 CU participants in the UCSD Shiley-Marcos Alzheimer’s Disease Research Center (age M=71.93 years, SD=7.51; 55.9% women; 15.6% Hispanic/Latino/a/x/e). A discriminate function analysis was then conducted to test whether the individual neuropsychological scores predicted cluster-group membership. 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). The discriminant function analysis using the neuropsychological measures to predict group membership into these 5 clusters correctly classified 85.2% of the participants. Subgroups had unique demographic and clinical characteristics. Relative to the All-Average group, the Low-Visuospatial (hazard ratio [HR] 2.39, 95% CI [1.03, 5.56], p=.044), Low-Memory/Language (HR 4.37, 95% CI [2.24, 8.51], p<.001), and Low-All Domains (HR 7.21, 95% CI [3.59, 14.48], p<.001) groups had greater risk of progression to MCI/dementia. The Low-Executive group was also twice as likely to progress to MCI/dementia compared to the AllAverage group, but did not statistically differ (HR 2.03, 95% CI [0.88,4.70], p=.096). A similar pattern of results was found for progression to DRS score <129, with the Low-Executive (HR 2.82, 95% CI [1.26, 6.29], p=.012), Low-Memory/Language (HR 3.70, 95% CI [1.80, 7.56], p<.001) and Low-All Domains (HR 5.79, 95% CI [2.74, 12.27], p<.001) groups at greater risk of progression to a DRS score <129 than the All-Average group. The Low-Visuospatial group was also twice as likely to progress to DRS <129 compared to the All-Average group, but did not statistically differ (HR 2.02, 95% CI [0.80, 5.06], p=.135).

Conclusions:

Our results add to a growing literature documenting heterogeneity in the earliest cognitive and pathological presentations associated with Alzheimer’s disease and related disorders. Participants with subtle memory/language, executive, and visuospatial weaknesses all declined at faster rates than the All-Average group, suggesting that there are multiple pathways and/or unique subtle cognitive decline profiles that ultimately lead to a diagnosis of MCI/dementia. These results have important implications for early identification of individuals at risk for MCI/dementia. Given that the same classification approach may not be optimal for everyone, determining profiles of subtle cognitive difficulties in CU individuals and implementing neuropsychological test batteries that assess multiple cognitive domains may be a key step towards an individualized approach to early detection and fewer missed opportunities for early intervention.

Type
Poster Session 01: Medical | Neurological Disorders | Neuropsychiatry | Psychopharmacology
Copyright
Copyright © INS. Published by Cambridge University Press, 2023