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Updated: Apr 29, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors
Emily C Edmonds1, Lisa Delano-Wood2, Lindsay R Clark3
1Department of Psychiatry, University of California San Diego, School of Medicine, La Jolla, CA, USA.
Background:
We assessed whether mild cognitive impairment (MCI) subtypes could be empirically derived within the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort and examined associated biomarkers and clinical outcomes.
Methods:
Cluster analysis was performed on neuropsychological data from 825 MCI ADNI participants.
Results:
Four subtypes emerged: (1) dysnomic (n = 153), (2) dysexecutive (n = 102), (3) amnestic (n = 288), and (4) cluster-derived normal (n = 282) who performed within normal limits on cognitive testing. The cluster-derived normal group had significantly fewer APOE ε4 carriers and fewer who progressed to dementia compared with the other subtypes; they also evidenced cerebrospinal fluid Alzheimer's disease biomarker profiles that did not differ from the normative reference group.
Conclusions:
Identification of empirically derived MCI subtypes demonstrates heterogeneity in MCI cognitive profiles that is not captured by conventional criteria. The large cluster-derived normal group suggests that conventional diagnostic criteria are susceptible to false-positive errors, with the result that prior MCI studies may be diluting important biomarker relationships.
Insights
Researchers identified distinct subtypes of mild cognitive impairment (MCI) in the ADNI cohort. A significant "normal" subtype suggests current MCI criteria may misdiagnose individuals, impacting Alzheimer's disease research.
Area of Science:
- Neuroscience
- Gerontology
- Biomarkers
Background:
- Mild cognitive impairment (MCI) is a transitional stage to dementia.
- Understanding MCI heterogeneity is crucial for Alzheimer's disease (AD) research.
- The Alzheimer's Disease Neuroimaging Initiative (ADNI) provides valuable data for such studies.
Purpose of the Study:
- To empirically derive subtypes of MCI within the ADNI cohort.
- To investigate the biomarker and clinical outcome associations of these MCI subtypes.
- To evaluate the diagnostic accuracy of conventional MCI criteria.
Main Methods:
- Cluster analysis was applied to neuropsychological data.
- 825 participants from the ADNI MCI cohort were analyzed.
- APOE ε4 carrier status and cerebrospinal fluid (CSF) AD biomarkers were examined.
Main Results:
- Four MCI subtypes were identified: dysnomic, dysexecutive, amnestic, and a cluster-derived normal group.
- The cluster-derived normal group showed fewer APOE ε4 carriers and slower dementia progression.
- This normal group had CSF AD biomarker profiles similar to cognitively normal individuals.
Conclusions:
- Empirically derived MCI subtypes reveal cognitive heterogeneity beyond conventional classifications.
- A substantial cluster-derived normal group indicates potential false-positive errors in current MCI diagnostic criteria.
- Over-diagnosis of MCI may dilute biomarker associations in AD research.
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