Literature DB >> 27978792

Predicting Stability of Mild Cognitive Impairment (MCI): Findings of a Community Based Sample.

Sinika Ellendt1, Bianca Voβ1, Nils Kohn2, Lisa Wagels1, Katharina S Goerlich1, Eva Drexler1, Frank Schneider3, Ute Habel1.   

Abstract

BACKGROUND: Mild Cognitive Impairment (MCI) is a risk factor for Alzheimer's disease (AD) and other forms of dementia. However, much heterogeneity concerning neuropsychological measures, prevalence and progression rates impedes distinct diagnosis and treatment implications.
OBJECTIVE: Aim of the present study was the identification of specific tests providing a high certainty for stable MCI and factors that precipitate instability of MCI in a community based sample examined at three measurement points.
METHOD: 130 participants were tested annually with an extensive test battery including measures of memory, language, executive functions, intelligence and dementia screening tests. Exclusion criteria at baseline comprised, severe cognitive deficits (e.g. diagnosis of dementia, psychiatric or neurological disease). Possible predictors for stability or instability of MCI-diagnosis were analyzed using Regression and Receiver Operating Characteristic (ROC) curve analysis. Age, IQ and APOE status were tested for moderating effects on the interaction of test performances and group membership.
RESULTS: A high prevalence of MCI (49%) was observed at baseline with a reversion rate of 18% after two years. Stability of MCI was related to performances in four measures (VLMT: delayed recall, CERAD: recall drawings, CERAD: Boston Naming Test, Benton Visual Retention Test: number of mistakes). Conversion to MCI is associated with language functions. Reversion to 'normal' was primarily predicted by single domain impairment. There was no significant influence of demographic, medical or genetic variables.
CONCLUSION: The results highlight the role of repeated measurements for a reliable identification of functional neuropsychological predictors and better diagnostic reliability. In cases of high uncertainty close monitoring over time is needed in order of estimating outcome. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Entities:  

Keywords:  Alzheimer dementia (AD); Mild cognitive impairment (MCI); cognitive functions; longitudinal survey; memory; neuropsychology

Mesh:

Year:  2017        PMID: 27978792     DOI: 10.2174/1567205014666161213120807

Source DB:  PubMed          Journal:  Curr Alzheimer Res        ISSN: 1567-2050            Impact factor:   3.498


  7 in total

1.  A Color-Picture Version of Boston Naming Test Outperformed the Black-and-White Version in Discriminating Amnestic Mild Cognitive Impairment and Mild Alzheimer's Disease.

Authors:  Dan Li; Yue-Yi Yu; Nan Hu; Min Zhang; Li Liu; Li-Mei Fan; Shi-Shuang Ruan; Fen Wang
Journal:  Front Neurol       Date:  2022-04-25       Impact factor: 4.086

2.  Genome-wide association study of rate of cognitive decline in Alzheimer's disease patients identifies novel genes and pathways.

Authors:  Richard Sherva; Alden Gross; Shubhabrata Mukherjee; Ryan Koesterer; Philippe Amouyel; Celine Bellenguez; Carole Dufouil; David A Bennett; Lori Chibnik; Carlos Cruchaga; Jorge Del-Aguila; Lindsay A Farrer; Richard Mayeux; Leanne Munsie; Ashley Winslow; Stephen Newhouse; Andrew J Saykin; John S K Kauwe; Paul K Crane; Robert C Green
Journal:  Alzheimers Dement       Date:  2020-06-23       Impact factor: 16.655

3.  Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer's disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database.

Authors:  Fayçal Ben Bouallègue; Denis Mariano-Goulart; Pierre Payoux
Journal:  Alzheimers Res Ther       Date:  2017-04-26       Impact factor: 6.982

4.  Neuroanatomical and Neuropsychological Markers of Amnestic MCI: A Three-Year Longitudinal Study in Individuals Unaware of Cognitive Decline.

Authors:  Katharina S Goerlich; Mikhail Votinov; Ellen Dicks; Sinika Ellendt; Gábor Csukly; Ute Habel
Journal:  Front Aging Neurosci       Date:  2017-02-22       Impact factor: 5.750

5.  Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods.

Authors:  Jaime Gómez-Ramírez; Marina Ávila-Villanueva; Miguel Ángel Fernández-Blázquez
Journal:  Sci Rep       Date:  2020-11-26       Impact factor: 4.379

6.  Longitudinal Study-Based Dementia Prediction for Public Health.

Authors:  HeeChel Kim; Hong-Woo Chun; Seonho Kim; Byoung-Youl Coh; Oh-Jin Kwon; Yeong-Ho Moon
Journal:  Int J Environ Res Public Health       Date:  2017-08-30       Impact factor: 3.390

7.  Simple Quantification of Surface Uptake in F-18 Florapronol PET/CT Imaging for the Validation of Alzheimer's Disease.

Authors:  Do-Hoon Kim; Junik Son; Chae Moon Hong; Ho-Sung Ryu; Shin Young Jeong; Sang-Woo Lee; Jaetae Lee
Journal:  Diagnostics (Basel)       Date:  2022-01-06
  7 in total

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