{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:30:08Z","timestamp":1790098208139,"version":"4.0.1"},"reference-count":50,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T00:00:00Z","timestamp":1581379200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alzheimer's &amp;amp; Dementia"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Developing cross\u2010validated multi\u2010biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid\u2010PET and fluorodeoxyglucose positron\u2010emission tomography (FDG\u2010PET) to predict rates of cognitive decline. Prediction models were trained in autosomal\u2010dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross\u2010validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model\u2010based risk enrichment was estimated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A model combining all biomarker modalities and established in ADAD predicted the 4\u2010year rate of decline in global cognition (R<jats:sup>2<\/jats:sup> = 24%) and memory (R<jats:sup>2<\/jats:sup> = 25%) in sporadic AD. Model\u2010based risk\u2010enrichment reduced the sample size required for detecting simulated intervention effects by 50%\u201375%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Our independently validated machine\u2010learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.<\/jats:p><\/jats:sec>","DOI":"10.1002\/alz.12032","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T06:42:40Z","timestamp":1581403360000},"page":"501-511","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease\u2010informed machine\u2010learning"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9736-2283","authenticated-orcid":false,"given":"Nicolai","family":"Franzmeier","sequence":"first","affiliation":[{"name":"Institute for Stroke and Dementia Research Klinikum der Universit\u00e4t M\u00fcnchen Ludwig\u2010Maximilians\u2010Universit\u00e4t LMU  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Koutsouleris","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Psychotherapy Ludwig\u2010Maximilians\u2010Universit\u00e4t LMU  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tammie","family":"Benzinger","sequence":"additional","affiliation":[{"name":"Department of Radiology Washington University in St. Louis  St. Louis Missouri USA"},{"name":"Knight Alzheimer's Disease Research Center Washington University in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alison","family":"Goate","sequence":"additional","affiliation":[{"name":"Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai  New York New York USA"},{"name":"Ronald M. 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