Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Karanth, Shama D.a; b; * | Schmitt, Frederick A.b; c | Nelson, Peter T.b; d | Katsumata, Yurikob; e | Kryscio, Richard J.b; e; f | Fardo, David W.b; e | Harp, Jordan P.b; c | Abner, Erin L.a; b; e
Affiliations: [a] Department of Epidemiology, University of Kentucky, Lexington, KY, USA | [b] Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA | [c] Department of Neurology, University of Kentucky, Lexington, KY, USA | [d] Department of Pathology, University of Kentucky, Lexington, KY, USA | [e] Department of Biostatistics, University of Kentucky, Lexington, KY, USA | [f] Department of Statistics, University of Kentucky, Lexington, KY, USA
Correspondence: [*] Correspondence to: Shama D. Karanth, MDS, PhD, Department of Epidemiology, Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA. Tel.: +1 415 988 2994; E-mail: [email protected].
Abstract: Background:Late-life cognitive function is heterogeneous, ranging from no decline to severe dementia. Prior studies of cognitive trajectories have tended to focus on a single measure of global cognition or individual tests scores, rather than considering longitudinal performance on multiple tests simultaneously. Objective:The current study aimed to examine cognitive trajectories from two independent datasets to assess whether similar patterns might describe longitudinal cognition in the decade preceding death, as well as what participant characteristics were associated with trajectory membership. Methods:Data were drawn from autopsied longitudinally followed participants of two cohorts (total N = 1,346), community-based cohort at the University of Kentucky Alzheimer’s Disease Research Center (n = 365) and National Alzheimer’s Coordinating Center (n = 981). We used group-based multi-trajectory models (GBMTM) to identify cognitive trajectories over the decade before death using Mini-Mental State Exam, Logical Memory-Immediate, and Animal Naming performance. Multinomial logistic and Random Forest analyses assessed characteristics associated with trajectory groups. Results:GBMTM identified four similar cognitive trajectories in each dataset. In multinomial models, death age, Braak neurofibrillary tangles (NFT) stage, TDP-43, and α-synuclein were associated with declining trajectories. Random Forest results suggested the most important trajectory predictors were Braak NFT stage, cerebral atrophy, death age, and brain weight. Multiple pathologies were most common in trajectories with moderate or accelerated decline. Conclusion:Cognitive trajectories associated strongly with neuropathology, particularly Braak NFT stage. High frequency of multiple pathologies in trajectories with cognitive decline suggests dementia treatment and prevention efforts must consider multiple diseases simultaneously.
Keywords: Cognitive decline, dementia, neurodegenerative disorders, neuropsychological tests, trajectories
DOI: 10.3233/JAD-210293
Journal: Journal of Alzheimer's Disease, vol. 82, no. 2, pp. 647-659, 2021
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]