e-Article
Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease
Document Type
article
Author
Peter R Millar; Brian A Gordon; Julie K Wisch; Stephanie A Schultz; Tammie LS Benzinger; Carlos Cruchaga; Jason J Hassenstab; Laura Ibanez; Celeste Karch; Jorge J Llibre-Guerra; John C Morris; Richard J Perrin; Charlene Supnet-Bell; Chengjie Xiong; Ricardo F Allegri; Sarah B Berman; Jasmeer P Chhatwal; Patricio A Chrem Mendez; Gregory S Day; Anna Hofmann; Takeshi Ikeuchi; Mathias Jucker; Jae-Hong Lee; Johannes Levin; Francisco Lopera; Yoshiki Niimi; Victor J Sánchez-González; Peter R Schofield; Ana Luisa Sosa-Ortiz; Jonathan Vöglein; for the Dominantly Inherited Alzheimer Network; Randall J Bateman; Beau M Ances; Eric M McDade
Source
Molecular Neurodegeneration, Vol 18, Iss 1, Pp 1-17 (2023)
Subject
Language
English
ISSN
1750-1326
Abstract
Abstract Background “Brain-predicted age” estimates biological age from complex, nonlinear features in neuroimaging scans. The brain age gap (BAG) between predicted and chronological age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD), in which AD progression is highly predictable with minimal confounding age-related co-pathology. Methods We modeled BAG in 257 deeply-phenotyped ADAD mutation-carriers and 179 non-carriers from the Dominantly Inherited Alzheimer Network using minimally-processed structural MRI scans. We then tested whether BAG differed as a function of mutation and cognitive status, or estimated years until symptom onset, and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid-β-42/40), phosphorylated tau (CSF and plasma pTau-181), neurodegeneration (CSF and plasma neurofilament-light-chain [NfL]), and cognition (global neuropsychological composite and CDR-sum of boxes). We compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of ADAD mutation variants, APOE ε4 carrier status, sex, and education. Results Advanced brain aging was observed in mutation-carriers approximately 7 years before expected symptom onset, in line with other established structural indicators of atrophy. BAG was moderately associated with amyloid PET and strongly associated with pTau-181, NfL, and cognition in mutation-carriers. Mutation variants, sex, and years of education contributed to variability in BAG. Conclusions We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG associates well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, BAG offers unique benefits in simplicity of data processing and interpretation. Thus, results in this unique ADAD cohort with few age-related confounds suggest that brain aging attributable to AD neuropathology can be accurately quantified from minimally-processed MRI.