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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30210 - Clinical neurology

Projekt

<a href="/cs/project/EF16_019%2F0000868" target="_blank" >EF16_019/0000868: Molekulární, buněčný a klinický přístup ke zdravému stárnutí</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF THE INTERNATIONAL NEUROPSYCHOLOGICAL SOCIETY

ISSN

1355-6177

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

329-342

Kód UT WoS článku

000636754200003

EID výsledku v databázi Scopus

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