ArticleAlzheimer's research & therapy2026
Improved polygenic risk prediction for alzheimer's disease and related dementias using deep learning: age and APOE-stratified analysis.
Article in Alzheimer's research & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Integrative prediction of Alzheimer's disease and related dementias using multi-omics aging clocks and genetic data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
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5 authors.
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Abstract
backgroundAlzheimer’s disease and related dementias (ADRD) are complex, polygenic conditions with substantial public health impact. Accurate genetic risk prediction may enable earlier identification and stratification of individuals at elevated risk.
objectiveTo evaluate the predictive performance of polygenic risk scores (PRS) for ADRD using a Bayesian variational autoencoders approach and to assess the modifying effects of age and APOE genotype on model performance.
methodsWe analyzed data from 276,566 unrelated individuals of European ancestry in the UK Biobank, with a median follow-up of 9.19 years. PRS and polygenic hazard scores (PHS) were constructed using genome-wide association study summary statistics, with PHS incorporating age-at-onset information. Three PRS methods were compared: DDML (Bayesian variational autoencoders), SBayesR (Bayesian multiple regression), and clumping and thresholding (C + T). Models were stratified by age and APOE genotype. Predictive performance was evaluated using time-dependent AUC, C-index, and hazard ratios (HRs), using a prespecified 2:1 training/testing split with identical ADRD case proportion across splits. All primary results are based on covariate-adjusted models, incorporating PRS together with age, sex, and 10 genetic principal components, and APOE genotype where indicated. Classification performance was compared between individuals in the top and bottom PRS quartiles to assess stratified risk.
resultsAmong the participants (mean age 56.8 ± 8.0 years; 46.7% male), 1,328 (0.48%) developed ADRD. In covariate-adjusted models, DDML_PRS achieved the highest predictive accuracy (AUC = 0.847) in individuals aged 65–70 years. PHS models showed peak performance at 7 years of follow-up. DDML_PRS significantly improved classification in APOE-ε4 carriers aged ≥ 65 years and outperformed other models across ADRD subtypes. Significant interactions were observed between PRS performance, age, and APOE genotype.
conclusionThe DDML_PRS framework showed consistently higher discrimination than standard PRS baselines in this UK Biobank setting, particularly in older adults and APOE-ε4 carriers, supporting its potential for individualized ADRD risk stratification. However, the observed classification accuracy remains modest, limiting immediate clinical utility and underscoring the need for external replication and multi-modal validation (e.g., biomarkers and clinical adjudication) to translate these predictive gains into practical early detection strategies.
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