Related Experiment Video
Updated: Jul 16, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Early Detection of Dementia using Risk Classification in MCI: Outcomes of Shanghai Mild Cognitive Impairment Cohort
Bin Zhou1, Qianhua Zhao2, Shinsuke Kojima3
1Foundation of Learning Health Society Institute, Nagoya, Japan.
Introduction:
The purpose of this study is to identify the risk factors and risk classification associated with the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia to facilitate early intervention and the design of clinical trials for AD.
Methods:
The study comprised a prospective cohort study of 400 subjects with MCI who had annual follow-ups for 3 years.
Results:
During an average follow-up period of 3.5 years, 109 subjects were diagnosed with all cause of dementia, of whom 104 subjects converted to Alzheimer's dementia and 5 subjects converted to other types of dementia. The cumulative conversion rate was 5.5% (95% CI: 3.4, 8.6), 16.3% (95% CI: 12.9, 21.1), and 31.0% (95% CI: 25.4, 36.5) in each of the first 3 follow-up years, respectively. The factors associated with a greater risk of conversion from MCI to AD included smoking status, ApoE4 carrier status, right hippocampal volume (rt. HV), left temporal lobe volume, and scores on the Revised Chinese version of the Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog-C). The risk classification of the ADAS-Cog-C or Preclinical Alzheimer Cognitive Composite (PACC) score combined with the rt. HV showed a conversion difference among the groups at every annual follow-up.
Conclusion:
A simple risk classification using the rt. HV and neuropsychological test scores, including those from the ADAS-Cog-C and PACC, could be a practicable and efficient approach to indentify individuals at risk of all-cause dementia.
Insights
Identifying risk factors for Alzheimer's disease (AD) dementia conversion from mild cognitive impairment (MCI) is crucial. Simple risk scores using cognitive tests and brain volume can predict dementia progression.
Area of Science:
- Neurology
- Gerontology
- Cognitive Science
Background:
- Mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease (AD) dementia.
- Early identification of individuals at high risk for MCI to AD conversion is critical for timely intervention and clinical trial design.
Purpose of the Study:
- To identify risk factors for progression from MCI to AD dementia.
- To develop a risk classification system for predicting MCI to AD conversion.
Main Methods:
- Prospective cohort study of 400 individuals with MCI.
- Annual follow-ups over 3 years with clinical and neuroimaging assessments.
- Analysis of factors including smoking, ApoE4 carrier status, hippocampal volume, and cognitive scores (ADAS-Cog-C, PACC).
Main Results:
- The cumulative conversion rate to AD dementia was 31.0% by the third year.
- Significant risk factors for conversion included smoking, ApoE4 carrier status, right hippocampal volume, left temporal lobe volume, and ADAS-Cog-C scores.
- Risk classification combining ADAS-Cog-C or PACC scores with right hippocampal volume showed significant conversion differences.
Conclusions:
- A practical risk classification using right hippocampal volume and neuropsychological tests (ADAS-Cog-C, PACC) can identify individuals at risk of dementia.
- This approach facilitates early detection and intervention for individuals with MCI at risk of progressing to AD dementia.
More Related Videos
06:23The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease
Published on: October 13, 2016
08:43Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
Related Concept Videos
01:30Dementia
The progression of dementia is generally gradual....
01:26Alzheimer's Disease: Overview
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...