| Autor: |
Edmonds, Emily C., Thomas, Kelsey R., Rapcsak, Steven Z., Lindemer, Shannon L., Delano‐Wood, Lisa, Salmon, David P., Bondi, Mark W. |
| Zdroj: |
Alzheimer's & Dementia: The Journal of the Alzheimer's Association; May2024, Vol. 20 Issue 5, p3442-3454, 13p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
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