Cognitive Phenotypes of Older Adults with Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment: The Czech Brain Aging Study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F21%3A00075108" target="_blank" >RIV/00159816:_____/21:00075108 - isvavai.cz</a>

Alternative codes found

RIV/00064203:_____/21:10417021 RIV/00216208:11130/21:10417021

Result on the web

<a href="https://www.cambridge.org/core/journals/journal-of-the-international-neuropsychological-society/article/abs/cognitive-phenotypes-of-older-adults-with-subjective-cognitive-decline-and-amnestic-mild-cognitive-impairment-the-czech-brain-aging-study/571FB84DB508924372319E38F333E697" target="_blank" >https://www.cambridge.org/core/journals/journal-of-the-international-neuropsychological-society/article/abs/cognitive-phenotypes-of-older-adults-with-subjective-cognitive-decline-and-amnestic-mild-cognitive-impairment-the-czech-brain-aging-study/571FB84DB508924372319E38F333E697</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/S1355617720001046" target="_blank" >10.1017/S1355617720001046</a>

Result language

angličtina

Original language name

Cognitive Phenotypes of Older Adults with Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment: The Czech Brain Aging Study

Original language description

Objective: To compare cognitive phenotypes of participants with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), estimate progression to MCI/dementia by phenotype and assess classification error with machine learning. Method: Dataset consisted of 163 participants with SCD and 282 participants with aMCI from the Czech Brain Aging Study. Cognitive assessment included the Uniform Data Set battery and additional tests to ascertain executive function, language, immediate and delayed memory, visuospatial skills, and processing speed. Latent profile analyses were used to develop cognitive profiles, and Cox proportional hazards models were used to estimate risk of progression. Random forest machine learning algorithms reported cognitive phenotype classification error. Results: Latent profile analysis identified three phenotypes for SCD, with one phenotype performing worse across all domains but not progressing more quickly to MCI/dementia after controlling for age, sex, and education. Three aMCI phenotypes were characterized by mild deficits, memory and language impairment (dysnomic aMCI), and severe multi-domain aMCI (i.e., deficits across all domains). A dose-response relationship between baseline level of impairment and subsequent risk of progression to dementia was evident for aMCI profiles after controlling for age, sex, and education. Machine learning more easily classified participants with aMCI in comparison to SCD (8% vs. 21% misclassified). Conclusions: Cognitive performance follows distinct patterns, especially within aMCI. The patterns map onto risk of progression to dementia.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30210 - Clinical neurology

Project

<a href="/en/project/EF16_019%2F0000868" target="_blank" >EF16_019/0000868: Molecular, cellular and clinical approach to healthy ageing</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

JOURNAL OF THE INTERNATIONAL NEUROPSYCHOLOGICAL SOCIETY

ISSN

1355-6177

e-ISSN

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Volume of the periodical

27

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

329-342

UT code for WoS article

000636754200003

EID of the result in the Scopus database

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