Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset.
Alzheimers Dement
; 20(5): 3442-3454, 2024 05.
Article
en En
| MEDLINE
| ID: mdl-38574399
ABSTRACT
INTRODUCTION:
Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods.METHODS:
Cluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the "normal cognition" subsample (n = 16,005). Survival analyses examined MCI or dementia progression.RESULTS:
Five clusters were identified "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN < tCN < aMCI < mMCI-Mild < mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All < Low-WM = Low-Memory < aMCI < naMCI).DISCUSSION:
Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Progresión de la Enfermedad
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Disfunción Cognitiva
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Pruebas Neuropsicológicas
Límite:
Aged
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Aged80
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Female
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Humans
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Male
Idioma:
En
Revista:
Alzheimers Dement
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos