Predicting the time to tau onset in Down syndrome using amyloid chronicity: Neuroimaging: Other neurodegenerative disorders. (7th December 2020)

• Record Type: Journal Article
• Authors: Zammit, Matthew D; Laymon, Charles M; Koscik, Rebecca L.; Tudorascu, Dana L; Betthauser, Tobey J; Ellison, Paul; Cohen, Ann; Minhas, Davneet S; Zaman, Shahid; Ances, Beau M; Mathis, Chester; Johnson, Sterling C.; Klunk, William E; Handen, Benjamin L; Christian, Bradley T
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.046060
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Adults with Down syndrome (DS) are predisposed to Alzheimer's disease (AD) with the majority developing clinical dementia by their 60s. This study aims to estimate the time to tau onset in DS using the metric of Aβ chronicity (estimated duration of Aβ(+)) as a predictor. Method: N=140 adults with DS (age=39.3±8.4 years) were evaluated for Aβ and tau using [C‐11]PiB and [F‐18]AV‐1451 PET, respectively. 50‐70 minute PiB SUVr and 80‐100 minute AV‐1451 SUVr images were created (cerebellar gray matter reference region). Longitudinal PiB scans were conducted for n=68 (3.0±0.7 scans; 2.4±0.6 years apart). The Amyloid Load (AβL ), calculated from PiB SUVr images, indicates global Aβ burden. Group‐based trajectory modeling (GBTM) was applied across longitudinal AβL data to identify Aβ accumulation groups. The trajectories with the lowest BIC were solved to determine the estimated age of Aβ(+) onset (AβL cutoff: 20.0% (Zammit, DADM 2020)) and Aβ chronicity was calculated as chronological age minus estimated Aβ(+) age (Koscik and Betthauser, DADM 2020). Regional AV‐1451 SUVr values were extracted based on ROIs encompassing the Braak staging of NFT pathology. Preliminary thresholds for elevated tau (tau(+)) were determined for each Braak composite region using k‐means clustering with resampling. Linear regression models were performed between Aβ chronicity and AV‐1451 SUVr. The functional form of the regression fit was solved to estimate average Aβ(+) chronicity at tau(+) in each Braak region. Result: GBTM identified two Aβ accumulator groups in our DS cohort with estimated ages of Aβ(+) onset of 32 and 49 years (Figure 1). Of the early accumulators, only n=2 carried the APOE E4 allele. Aβ chronicity and AV‐1451 SUVr displayed a linear relationship for Braak regions 1‐2, and a quadratic relationship for Braak regions 3‐6 (Figure 2). The pattern of tau deposition in DS followed the conventional Braak NFT staging observed in late‐onset AD. Estimated Aβ(+) chronicity at tau(+) onset increased as tau pathology progressed through each Braak stage (Table 1). Conclusion: These preliminary findings reveal insight on the time course of tau deposition relative to Aβ in DS. Longitudinal AV‐1451 scans are currently underway to further refine the model and characterize tau progression in this population.