%0 Journal Article
%T Identifying longitudinal cognitive resilience from cross-sectional amyloid, tau, and neurodegeneration.
%A Boyle R
%A Townsend DL
%A Klinger HM
%A Scanlon CE
%A Yuan Z
%A Coughlan GT
%A Seto M
%A Shirzadi Z
%A Yau WW
%A Jutten RJ
%A Schneider C
%A Farrell ME
%A Hanseeuw BJ
%A Mormino EC
%A Yang HS
%A Papp KV
%A Amariglio RE
%A Jacobs HIL
%A Price JC
%A Chhatwal JP
%A Schultz AP
%A Properzi MJ
%A Rentz DM
%A Johnson KA
%A Sperling RA
%A Hohman TJ
%A Donohue MC
%A Buckley RF
%A  
%J Alzheimers Res Ther
%V 16
%N 1
%D 2024 Jul 3
%M 38961512
暂无%R 10.1186/s13195-024-01510-y
%X <B>BACKGROUND: </B>Leveraging Alzheimer's disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR.<BR><B>METHODS: </B>We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women).<BR><B>RESULTS: </B>The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline.<BR><B>CONCLUSIONS: </B>These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.