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Updated: Aug 9, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Cognitive Trajectories and Associated Biomarkers in Patients with Mild Cognitive Impairment
Bum Soo Kim1, Sungmin Jun1, Heeyoung Kim1
1Department of Nuclear Medicine, Kosin University Gospel Hospital, University of Kosin College of Medicine, Busan, Republic of Korea.
Background:
To diagnose mild cognitive impairment (MCI) patients at risk of progression to dementia is clinically important but challenging.
Objective:
We classified MCI patients based on cognitive trajectories and compared biomarkers among groups.
Methods:
This study analyzed amnestic MCI patients with at least three Clinical Dementia Rating (CDR) scores available over a minimum of 36 months from the Alzheimer's Disease Neuroimaging Initiative database. Patients were classified based on their progression using trajectory modeling with the CDR-sum of box scores. We compared clinical and neuroimaging biomarkers across groups.
Results:
Of 569 eligible MCI patients (age 72.7±7.4 years, women n = 223), three trajectory groups were identified: stable (58.2%), slow decliners (24.6%), and fast decliners (17.2%). In the fifth year after diagnosis, the CDR-sum of box scores increased by 1.2, 5.4, and 11.8 points for the stable, slow, and fast decliners, respectively. Biomarkers associated with cognitive decline were amyloid-β 42, total tau, and phosphorylated tau protein in cerebrospinal fluid, hippocampal volume, cortical metabolism, and amount of cortical and subcortical amyloid deposits. Cortical metabolism and the amount of amyloid deposits were associated with the rate of cognitive decline.
Conclusion:
Data-driven trajectory analysis provides new insights into the various cognitive trajectories of MCI. Baseline brain metabolism, and the amount of cortical and subcortical amyloid burden can provide additional information on the rate of cognitive decline.
Insights
Identifying mild cognitive impairment (MCI) progression is key. Brain metabolism and amyloid levels predict cognitive decline rates in MCI patients, aiding dementia risk assessment.
Area of Science:
- Neurology
- Neuroscience
- Geriatrics
Background:
- Diagnosing mild cognitive impairment (MCI) patients at risk of progression to dementia is clinically significant yet challenging.
- Understanding diverse MCI progression pathways is crucial for timely intervention.
Purpose of the Study:
- Classify MCI patients based on distinct cognitive trajectories.
- Compare clinical and neuroimaging biomarkers across identified MCI progression groups.
Main Methods:
- Analyzed amnestic MCI patients from the Alzheimer's Disease Neuroimaging Initiative database with longitudinal Clinical Dementia Rating (CDR) scores.
- Utilized trajectory modeling based on CDR-sum of box scores to classify patients.
- Compared clinical and neuroimaging biomarkers, including cerebrospinal fluid (CSF) markers, hippocampal volume, and amyloid burden.
Main Results:
- Identified three MCI trajectory groups: stable (58.2%), slow decliners (24.6%), and fast decliners (17.2%).
- Observed significant increases in CDR-sum of box scores over five years, particularly in fast decliners.
- Found associations between cognitive decline rate and biomarkers such as amyloid-β 42, tau proteins, hippocampal volume, cortical metabolism, and amyloid deposits.
Conclusions:
- Data-driven trajectory analysis offers novel insights into heterogeneous MCI progression.
- Baseline cortical metabolism and amyloid burden are significant predictors of cognitive decline rate in MCI.
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