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Luckett, Patrick H.; Chen, Charlie; Gordon, Brian A.; Wisch, Julie; Berman, Sarah B.; Chhatwal, Jasmeer P.; Cruchaga, Carlos; Fagan, Anne M.; Farlow, Martin R.; Fox, Nick C.; Jucker, Mathias; Levin, Johannes; Masters, Colin L.; Mori, Hiroshi; Noble, James M.; Salloway, Stephen; Schofield, Peter R.; Brickman, Adam M.; Brooks, William S.; Cash, David M.; Fulham, Michael J.; Ghetti, Bernardino; Jack, Clifford R.; Voeglein, Jonathan; Klunk, William E.; Koeppe, Robert; Su, Yi; Weiner, Michael; Wang, Qing; Marcus, Daniel; Koudelis, Deborah; Joseph-Mathurin, Nelly; Cash, Lisa; Hornbeck, Russ; Xiong, Chengjie; Perrin, Richard J.; Karch, Celeste M.; Hassenstab, Jason; McDade, Eric; Morris, John C.; Benzinger, Tammie L. S.; Bateman, Randall J. and Ances, Beau M. (2022): Biomarker clustering in autosomal dominant Alzheimer's disease. In: Alzheimer'S & Dementia, Vol. 19, No. 1: pp. 274-284

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Abstract

INTRODUCTION As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.

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