Enterprise AI Analysis
A machine learning framework for classifying dementia risk in mild cognitive impairment: evidence from a Korean genome-wide association study cohort
This study developed and evaluated machine learning models for classifying mild cognitive impairment (MCI) patients into high- and low-risk groups for dementia, utilizing SNP chip data from a Korean genome-wide association study (GWAS) cohort. The models demonstrated the potential of integrating genetic data with ML-based approaches for personalized dementia risk assessment, showing promising performance in cross-validation and modest, directionally consistent results in temporal validation.
Key Findings & Executive Impact
This research provides critical insights for leveraging genetic data with AI to predict dementia risk, offering significant implications for early intervention and personalized medicine in specific populations.
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AUC (XGBoost, Model 3)
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PR-AUC (XGBoost, Model 3)
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Conversion Rate (2-year follow-up)
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Total Participants
Deep Analysis & Enterprise Applications
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Machine Learning Models
Genetic Factors & GWAS
Clinical Implications
0.881
Highest AUC (XGBoost, Model 3)
| Model (SNP Panel) | Algorithm | Best AUC | Best PR-AUC |
|---|---|---|---|
| Model 1 (54 SNPs) | XGBoost | 0.866 ± 0.077 | 0.939 ± 0.040 |
| Model 2 (60 SNPs) | RF | 0.829 ± 0.085 | 0.782 ± 0.103 |
| Model 3 (76 SNPs, Union) | XGBoost | 0.881 ± 0.074 | 0.924 ± 0.055 |
Individual Heterogeneity in Prediction
While Model 1 showed the most stable predictions, subject-level analysis revealed significant heterogeneity. Some converters were consistently identified by all algorithms (e.g., subjects 1, 2, 9, 10, 12, 13, all with Sensitivityb = 1.0), suggesting a strong genetic load. Others were frequently misclassified (e.g., subjects 6 with Sensitivityb = 0.45, and 11 with Sensitivityb = 0.30), indicating non-genetic factors (vascular, metabolic, hematologic) likely driving progression. This highlights the need for integrating diverse markers.
76
Total SNPs in Union Panel (Model 3)
Enterprise Process Flow
Genotype QC (N=674)
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GWAS (AD vs SCD; AD+VD vs SCD)
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Feature Selection (p < 1x10-5)
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LD Clumping (~4-14 independent loci)
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ML Model Development & Evaluation
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Temporal Validation (2-year follow-up)
Pleiotropic Links and Ancestry Specificity
Functional annotation revealed significant associations with metabolic, cardiovascular, and neurological disorders, suggesting a shared genetic architecture for AD. The APOE-PVRL2–TOMM40 block contained the strongest signals, including rs429358 (APOE ε4). Ancestry-specific allele frequencies and LD structures in Korean populations underscore the need for local models, as European-derived polygenic risk scores often underperform. This study provides crucial proof-of-concept evidence for SNP-based risk stratification in Korean MCI populations.
0.86-0.93
Sensitivity (F1-max threshold)
| Metric | Description | Finding |
|---|---|---|
| Sensitivity | True positive rate | High (0.86-0.93) at F1-max thresholds |
| Specificity | True negative rate | Modest (0.19-0.30) |
| NPV | Negative Predictive Value | High (0.80-0.92) |
| AUROC | Area Under ROC Curve (Temporal Validation) | Modest (0.45-0.55) due to limited power |
Limitations and Future Directions
Limitations include a small cohort (N=674), short 2-year follow-up (14 converters), and lack of external validation in other Korean cohorts, impacting generalizability and statistical power. Future work should integrate multimodal data (neuroimaging, CSF biomarkers, cognitive assessments) and multi-omics strategies. Longer follow-up is needed to clarify false positives and capture latent progression. Ethical implications of genetic risk prediction (psychological impact, privacy) must be carefully considered alongside genetic counseling frameworks.
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