Dataset Information

Predicting Regional Pattern of Longitudinal ?-Amyloid Accumulation by Baseline PET.

ABSTRACT: Knowledge about spatial and temporal patterns of ?-amyloid (A?) accumulation is essential for understanding Alzheimer disease (AD) and for design of antiamyloid drug trials. Here, we tested whether the regional pattern of longitudinal A? accumulation can be predicted by baseline amyloid PET. Methods: Baseline and 2-y follow-up ¹⁸F-florbetapir PET data from 58 patients with incipient and manifest dementia due to AD were analyzed. With the determination of how fast amyloid deposits in a given region relative to the whole-brain gray matter, a pseudotemporal accumulation rate for each region was calculated. The actual accumulation rate of ¹⁸F-florbetapir was calculated from follow-up data. Results: Pseudotemporal measurements from baseline PET data explained 87% (P < 0.001) of the variance in longitudinal accumulation rate across 62 regions. The method accurately predicted the top 10 fast and slow accumulating regions. Conclusion: Pseudotemporal analysis of baseline PET images is capable of predicting the regional pattern of longitudinal A? accumulation in AD at a group level. This approach may be useful in exploring spatial patterns of A? accumulation in other amyloid-associated disorders such as Lewy body disease and atypical forms of AD. In addition, the method allows identification of brain regions with a high accumulation rate of A?, which are of particular interest for antiamyloid clinical trials.

SUBMITTER: Guo T

PROVIDER: S-EPMC5373505 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

Predicting Regional Pattern of Longitudinal β-Amyloid Accumulation by Baseline PET.

Guo Tengfei T Brendel Matthias M Grimmer Timo T Rominger Axel A Yakushev Igor I

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20161006 4

Knowledge about spatial and temporal patterns of β-amyloid (Aβ) accumulation is essential for understanding Alzheimer disease (AD) and for design of antiamyloid drug trials. Here, we tested whether the regional pattern of longitudinal Aβ accumulation can be predicted by baseline amyloid PET. <b>Methods:</b> Baseline and 2-y follow-up <sup>18</sup>F-florbetapir PET data from 58 patients with incipient and manifest dementia due to AD were analyzed. With the determination of how fast amyloid deposi ...[more]

PMID: 27754901

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Project description:Clinical trials with anti-tau drugs will need to target individuals at risk of accumulating tau. Our objective was to identify variables available in a research setting that predict future rates of tau PET accumulation separately among individuals who were either cognitively unimpaired or cognitively impaired. All 337 participants had: a baseline study visit with MRI, amyloid PET, and tau PET exams, at least one follow-up tau PET exam; and met clinical criteria for membership in one of two clinical diagnostic groups: cognitively unimpaired (n = 203); or cognitively impaired (n = 134, a combined group of participants with either mild cognitive impairment or dementia with Alzheimer's clinical syndrome). Our primary analyses were in these two clinical groups; however, we also evaluated subgroups dividing the unimpaired group by normal/abnormal amyloid PET and the impaired group by clinical phenotype (mild cognitive impairment, amnestic dementia, and non-amnestic dementia). Linear mixed effects models were used to estimate associations between age, sex, education, APOE genotype, amyloid and tau PET standardized uptake value ratio (SUVR), cognitive performance, cortical thickness, and white matter hyperintensity volume at baseline, and the rate of subsequent tau PET accumulation. Log-transformed tau PET SUVR was used as the response and rates were summarized as annual per cent change. A temporal lobe tau PET meta-region of interest was used. In the cognitively unimpaired group, only higher baseline amyloid PET was a significant independent predictor of higher tau accumulation rates (P < 0.001). Higher rates of tau accumulation were associated with faster rates of cognitive decline in the cognitively unimpaired subgroup with abnormal amyloid PET (P = 0.03), but among the subgroup with normal amyloid PET. In the cognitively impaired group, younger age (P = 0.02), higher baseline amyloid PET (P = 0.05), APOE ε4 (P = 0.05), and better cognitive performance (P = 0.05) were significant independent predictors of higher tau accumulation rates. Among impaired individuals, faster cognitive decline was associated with faster rates of tau accumulation (P = 0.01). While we examined many possible predictor variables, our results indicate that screening of unimpaired individuals for potential inclusion in anti-tau trials may be straightforward because the only independent predictor of high tau rates was amyloidosis. In cognitively impaired individuals, imaging and clinical variables consistent with early onset Alzheimer's disease phenotype were associated with higher rates of tau PET accumulation suggesting this may be a highly advantageous group in which to conduct proof-of-concept clinical trials that target tau-related mechanisms. The nature of the dementia phenotype (amnestic versus non-amnestic) did not affect this conclusion.

Dataset Information

Predicting Regional Pattern of Longitudinal ?-Amyloid Accumulation by Baseline PET.

Publications

Predicting Regional Pattern of Longitudinal β-Amyloid Accumulation by Baseline PET.

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