학술논문
Biomarker clustering in autosomal dominant Alzheimers disease.
Document Type
article
Author
Luckett, Patrick; Chen, Charlie; Gordon, Brian; Wisch, Julie; Berman, Sarah; Chhatwal, Jasmeer; Cruchaga, Carlos; Fagan, Anne; Farlow, Martin; Fox, Nick; Jucker, Mathias; Levin, Johannes; Masters, Colin; Mori, Hiroshi; Noble, James; Salloway, Stephen; Schofield, Peter; Brickman, Adam; Brooks, William; Cash, David; Fulham, Michael; Ghetti, Bernardino; Jack, Clifford; Vöglein, Jonathan; Klunk, William; Koeppe, Robert; Su, Yi; Weiner, Michael; Wang, Qing; Marcus, Daniel; Koudelis, Deborah; Joseph-Mathurin, Nelly; Cash, Lisa; Hornbeck, Russ; Xiong, Chengjie; Perrin, Richard; Karch, Celeste; Hassenstab, Jason; McDade, Eric; Morris, John; Benzinger, Tammie; Bateman, Randall; Ances, Beau
Source
Alzheimers and Dementia. 19(1)
Subject
Language
Abstract
INTRODUCTION: As the number of biomarkers used to study Alzheimers 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.