Alzheimer's Disease Risk Prediction in the Elderly: A Machine Learning Approach Combining Clinical Characteristics and Polygenic Risk Scores

Motivation: Alzheimer's disease (AD) is the most common type of dementia. Given the lack of a cure, early identification of high-risk populations is crucial for timely prevention. While several studies have focused on AD risk prediction, single feature (e.g., age) may dominate model performance, limiting the discovery of other potential risk factors. This study incorporates key features identified in previous research and applies propensity score matching for age and sex, aiming to improve the predictive performance of AD risk models for older adults.Methods: This study utilized data from the UK Biobank to integrate genetic and clinical data and developed 5-year and 10- year AD risk prediction models for older adults, respectively. The workflow included genome-wide association studies (GWAS) on 433,589 participants were conducted to identify significant Single nucleotide polymorphisms (SNPs) under three p-value thresholds, followed by polygenic risk score (PRS) calculation for 13,282 participants using PRSice-2 and Lassosum, and the integration of multiple features to construct prediction models. Clinical features, PRS, and significant SNPs were then incorporated into four machine learning models: Logistic Regression, LightGBM, XGBoost, and Multi-Layer Perceptron (MLP) for prediction and performance comparison.Results: For the 5-year risk prediction, the MLP model demonstrated the best performance, achieving an AUC of 0.88 based on 37 clinical features and 206 significant SNPs. For the 10-year risk prediction, the MLP model also demonstrated the best performance, achieving an AUC of 0.89 based on 37 clinical features, 206 significant SNPs, and PRS based on these SNPs. SHAP analysis revealed that key contributors across both models included ApoE genotype, urinary tract infection (N390), disorientation, depressive symptoms, and pairs matching time. The 5-year model emphasized immediate clinical and cognitive indicators such as reaction time and number of medications taken, whereas the 10-year model highlighted long-term risk factors including BMI, diabetes, and peak expiratory flow.Conclusion: This study demonstrates that integrating clinical features with PRS can effectively enhance the accuracy of AD risk prediction models for older adults. However, to further validate the utility of PRS, future research should involve collaborations across diverse populations and databases. Additionally, further exploration of other potential risk factors is needed to enhance the clinical applicability of these models.