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Identifying individuals with rapid proteinopathy progression is crucial for timely intervention and trial enrichment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>We analyzed longitudinal data from 456 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, including 375 with digital cognitive test scores. Amyloid and tau accumulation rates were estimated from positron emission tomography (PET) imaging using linear mixed\u2010effects models. Participants were classified as fast or slow accumulators via Gaussian modeling. Predictors of accumulation and clinical conversion were assessed with logistic and Cox regression models, incorporating demographics, cognitive measures and plasma biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Plasma p\u2010tau217 and A\u03b242\/A\u03b240 predicted rapid accumulation and conversion, with p\u2010tau217 the strongest marker (odds ratio [OR] up to 6.6). Baseline digital cognitive measures contributed significantly to the prediction, achieving comparable or superior predictive accuracy to traditional cognitive tests (area under the curve [AUC] up to 0.92; C\u2010index 0.82).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>Plasma p\u2010tau217 and A\u03b242\/A\u03b240 emerged as robust predictors of the progression of disease pathology, supported by cognitive measures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70281","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T23:02:56Z","timestamp":1773010976000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Digital cognition plus plasma p\u2010Tau217 and A\u03b242\/40 powerfully predict Alzheimer's progression"],"prefix":"10.1002","volume":"18","author":[{"given":"Ahmet","family":"Begde","sequence":"first","affiliation":[{"name":"Department of Psychiatry University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Psychiatry University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Raymont","sequence":"additional","affiliation":[{"name":"Department of Psychiatry University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James B.","family":"Rowe","sequence":"additional","affiliation":[{"name":"Department of Clinical Neurosciences University of Cambridge  Cambridge UK"},{"name":"Medical Research Council Cognition and Brain Sciences Unit  Cambridge UK"},{"name":"Cambridge University Hospitals NHS Foundation Trust  Cambridge 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