Data-driven Classification of Cognitively Normal and Mild Cognitive Impairment Subtypes Predicts Progression in the NACC Dataset

Overview

Journal Alzheimers Dement

Specialties Neurology
Psychiatry

Date 2024 Apr 4

PMID 38574399

Authors

Emily C Edmonds

Kelsey R Thomas

Steven Z Rapcsak

Shannon L Lindemer

Lisa Delano-Wood

David P Salmon

Mark W Bondi

Affiliations

Soon will be listed here.

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.

Citing Articles

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PMID: 39132326 PMC: 11315225. DOI: 10.1016/j.nbas.2024.100123.

Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset.

Edmonds E, Thomas K, Rapcsak S, Lindemer S, Delano-Wood L, Salmon D Alzheimers Dement. 2024; 20(5):3442-3454.

PMID: 38574399 PMC: 11095435. DOI: 10.1002/alz.13793.

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