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Updated: Jun 5, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Risk factors and predictive models in the progression from MCI to Alzheimer's disease
Chang Li1, Shike Wang2, Yuwei Xia3
1Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, 400030 China.
Background:
The conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is related to various factors. The causal relationships among these factors remain unclear. This study aims to investigate pathways of the progression by using causal analysis and build a predictive model with high accuracy.
Methods:
162 MCI patients were recruited from the Alzheimer's Disease Neuroimaging Initiative database. 68 patients progressed to AD. 94 patients did not convert to AD. We captured standard T1-weighted images, processed them for feature extraction, and selected relevant features using mRMR and LASSO to calculate cortical and nuclear scores. The computational causal structure discovery and regression analyses were adopted to analyze the intricate relationships among APOE ε4 alleles, P-tau, Aβ1-42, cortical and nuclear scores. The individualized prediction nomogram was constructed.
Results:
Our results indicated that APOE ε4 alleles was the promoter that caused MCI to transform into AD. Three independent pathways were identified, including P-tau, Aβ1-42, and cortical atrophy. P-tau was the cause of nuclear atrophy. The APOE ε4 alleles, P-tau, Aβ1-42, cortical and nuclear scores all had good predictive value for the MCI conversion. The predictive accuracy of the combined model was the highest, with an AUC of 0.918 in the training cohort and 0.908 in the testing cohort. A multi-predictor nomogram was established.
Conclusion:
Our study elucidated the initiating factors and three independent pathways involved in the conversion of MCI to AD. The predictive value of each factor was clarified and a multi-predictor nomogram was established with high accuracy.
Insights
The APOE ε4 allele promotes mild cognitive impairment (MCI) to Alzheimer's disease (AD) conversion through pathways involving P-tau, Aβ1-42, and atrophy. A predictive model achieved high accuracy in identifying at-risk individuals.
Area of Science:
- Neuroscience
- Biomedical data analysis
Background:
- Mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is multifactorial, with unclear causal pathways.
- Understanding these pathways is crucial for early detection and intervention.
Purpose of the Study:
- To investigate the causal relationships among factors influencing MCI to AD progression.
- To develop an accurate predictive model for MCI conversion to AD.
Main Methods:
- Utilized data from 162 MCI patients (68 converted to AD) from the Alzheimer's Disease Neuroimaging Initiative.
- Extracted features from T1-weighted MRI scans and employed mRMR and LASSO for feature selection.
- Applied causal structure discovery and regression analyses to examine relationships between APOE ε4, P-tau, Aβ1-42, and atrophy scores.
Main Results:
- APOE ε4 allele identified as a key promoter of MCI to AD conversion.
- Three independent progression pathways identified: P-tau, Aβ1-42, and cortical atrophy.
- A combined predictive model incorporating these factors achieved high accuracy (AUCtrain=0.918, AUCtest=0.908).
Conclusions:
- Elucidated initiating factors and pathways for MCI to AD conversion.
- Established a highly accurate multi-predictor nomogram for predicting MCI progression.
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