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Accurate genetic risk prediction may enable earlier identification and stratification of individuals at elevated risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      To 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\n                      <jats:italic>APOE<\/jats:italic>\n                      genotype on model performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We 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\u2009+\u2009T). Models were stratified by age and\n                      <jats:italic>APOE<\/jats:italic>\n                      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\n                      <jats:italic>APOE<\/jats:italic>\n                      genotype where indicated. Classification performance was compared between individuals in the top and bottom PRS quartiles to assess stratified risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the participants (mean age 56.8\u2009\u00b1\u20098.0 years; 46.7% male), 1,328 (0.48%) developed ADRD. In covariate-adjusted models, DDML_PRS achieved the highest predictive accuracy (AUC\u2009=\u20090.847) in individuals aged 65\u201370 years. PHS models showed peak performance at 7 years of follow-up. DDML_PRS significantly improved classification in\n                      <jats:italic>APOE-\u03b54<\/jats:italic>\n                      carriers aged\u2009\u2265\u200965 years and outperformed other models across ADRD subtypes. Significant interactions were observed between PRS performance, age, and\n                      <jats:italic>APOE<\/jats:italic>\n                      genotype.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The DDML_PRS framework showed consistently higher discrimination than standard PRS baselines in this UK Biobank setting, particularly in older adults and\n                      <jats:italic>APOE-\u03b54<\/jats:italic>\n                      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.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13195-026-02011-w","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T05:25:34Z","timestamp":1773293134000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improved polygenic risk prediction for alzheimer\u2019s disease and related dementias using deep learning: age and APOE-stratified analysis"],"prefix":"10.1186","volume":"18","author":[{"given":"Shayan","family":"Mostafaei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel Wikstr\u00f6m","family":"Shemer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan K. 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