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Keret, Ophir; Staffaroni, Adam M.; Ringman, John M.; Cobigo, Yann; Goh, Sheng-Yang M.; Wolf, Amy; Allen, Isabel Elaine; Salloway, Stephen; Chhatwal, Jasmeer; Brickman, Adam M.; Reyes-Dumeyer, Dolly; Bateman, Randal J.; Benzinger, Tammie L. S.; Morris, John C.; Ances, Beau M.; Joseph-Mathurin, Nelly; Perrin, Richard J.; Gordon, Brian A.; Levin, Johannes; Vöglein, Jonathan; Jucker, Mathias; Fougere, Christian; Martins, Ralph N.; Sohrabi, Hamid R.; Taddei, Kevin; Villemagne, Victor L.; Schofield, Peter R.; Brooks, William S.; Fulham, Michael; Masters, Colin L.; Ghetti, Bernardino; Saykin, Andrew J.; Jack, Clifford R.; Graff-Radford, Neill R.; Weiner, Michael; Cash, David M.; Allegri, Ricardo F.; Chrem, Patricio; Yi, Su; Miller, Bruce L.; Rabinovici, Gil D. and Rosen, Howard J. (2021): Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease. In: Alzheimer'S & Dementia: Diagnosis, Assessment & Disease Monitoring, Vol. 13, No. 1, e12197

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Introduction Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials.

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