Dataset Information

Estimation of amyloid distribution by [18F]flutemetamol PET predicts the neuropathological phase of amyloid ?-protein deposition.

ABSTRACT: The deposition of the amyloid ?-protein (A?) in senile plaques is one of the histopathological hallmarks of Alzheimer's disease (AD). A?-plaques arise first in neocortical areas and, then, expand into further brain regions in a process described by 5 phases. Since it is possible to identify amyloid pathology with radioactive-labeled tracers by positron emission tomography (PET) the question arises whether it is possible to distinguish the neuropathological A?-phases with amyloid PET imaging. To address this question we reassessed 97 cases of the end-of-life study cohort of the phase 3 [18F]flutemetamol trial (ClinicalTrials.gov identifiers NCT01165554, and NCT02090855) by combining the standardized uptake value ratios (SUVRs) with pons as reference region for cortical and caudate nucleus-related [18F]flutemetamol-retention. We tested them for their prediction of the neuropathological pattern found at autopsy. By defining threshold levels for cortical and caudate nucleus SUVRs we could distinguish different levels of [18F]flutemetamol uptake termed PET-A? phase estimates. When comparing these PET-A? phase estimates with the neuropathological A?-phases we found that PET-A? phase estimate 0 corresponded with A?-phases 0-2, 1 with A?-phase 3, 2 with A?-phase 4, and 3 with A?-phase 5. Classification using the PET-A? phase estimates predicted the correct A?-phase in 72.16% of the cases studied here. Bootstrap analysis was used to confirm the robustness of the estimates around this association. When allowing a range of?±?1 phase for a given A?-phase correct classification was given in 96.91% of the cases. In doing so, we provide a novel method to convert SUVR-levels into PET-A? phase estimates that can be easily translated into neuropathological phases of A?-deposition. This method allows direct conclusions about the pathological distribution of amyloid plaques (A?-phases) in vivo. Accordingly, this method may be ideally suited to detect early preclinical AD-patients, to follow them with disease progression, and to provide a more precise prognosis for them based on the knowledge about the underlying pathological phase of the disease.

SUBMITTER: Thal DR

PROVIDER: S-EPMC6132944 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimation of amyloid distribution by [<sup>18</sup>F]flutemetamol PET predicts the neuropathological phase of amyloid β-protein deposition.

Thal Dietmar Rudolf DR Beach Thomas G TG Zanette Michelle M Lilja Johan J Heurling Kerstin K Chakrabarty Aruna A Ismail Azzam A Farrar Gill G Buckley Christopher C Smith Adrian P L APL

Acta neuropathologica 20180819 4

The deposition of the amyloid β-protein (Aβ) in senile plaques is one of the histopathological hallmarks of Alzheimer's disease (AD). Aβ-plaques arise first in neocortical areas and, then, expand into further brain regions in a process described by 5 phases. Since it is possible to identify amyloid pathology with radioactive-labeled tracers by positron emission tomography (PET) the question arises whether it is possible to distinguish the neuropathological Aβ-phases with amyloid PET imaging. To ...[more]

PMID: 30123935

Similar Datasets

Project description:BackgroundAlthough amyloid PET of typical Alzheimer's disease (AD) shows diffuse ß-amyloid (Aß) deposition, some patients show focal deposition. The clinical significance of this focal Aß is not well understood. We examined the clinical significance of focal Aß deposition in terms of cognition as well as Aß and tau cerebrospinal fluid (CSF) levels. We further evaluated the order of Aß accumulation by visual assessment.MethodsWe included 310 subjects (125 cognitively unimpaired, 125 mild cognitive impairment, and 60?AD dementia) from 9 referral centers. All patients underwent neuropsychological tests and 18F-flutemetamol (FMM) PET. Seventy-seven patients underwent CSF analysis. Each FMM scan was visually assessed in 10 regions (frontal, precuneus and posterior cingulate, lateral temporal, parietal, and striatum of each hemisphere) and was classified into three groups: No-FMM, Focal-FMM (FMM uptake in 1-9 regions), and Diffuse-FMM (FMM uptake in all 10 regions).Results53/310 (17.1%) subjects were classified as Focal-FMM. The cognitive level of the Focal-FMM group was better than that of Diffuse-FMM group and worse than that of No-FMM group. Among the Focal-FMM group, those who had FMM uptake to a larger extent or in the striatum had worse cognitive levels. Compared to the Diffuse-FMM group, the Focal-FMM group had a less AD-like CSF profile (increased Aß42 and decreased t-tau, t-tau/Aß42). Among the Focal-FMM group, Aß deposition was most frequently observed in the frontal (62.3%) and least frequently observed in the striatum (43.4%) and temporal (39.6%) regions.ConclusionsWe suggest that focal Aß deposition is an intermediate stage between no Aß and diffuse Aß deposition. Furthermore, among patients with focal Aß deposition, those who have Aß to a larger extent and striatal involvement show clinical features close to diffuse Aß deposition. Further longitudinal studies are needed to evaluate the disease progression of patients with focal Aß deposition.

Project description:Background: Blood biomarkers may aid in recruitment to clinical trials of Alzheimer's disease (AD) modifying therapeutics by triaging potential trials participants for amyloid positron emission tomography (PET) or cerebrospinal fluid (CSF) A? and tau tests. Objective: To discover a plasma proteomic signature associated with CSF and PET measures of AD pathology. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics were performed in plasma from participants with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD, recruited to the Amsterdam Dementia Cohort, stratified by CSF Tau/A?42 (n = 50). Technical replication and independent validation were performed by immunoassay in plasma from SCD, MCI, and AD participants recruited to the Amsterdam Dementia Cohort with CSF measures (n = 100), MCI participants enrolled in the GE067-005 study with [18F]-Flutemetamol PET amyloid measures (n = 173), and AD, MCI and cognitively healthy participants from the EMIF 500 study with CSF A?42 measurements (n = 494). Results: 25 discovery proteins were nominally associated with CSF Tau/A?42 (P < 0.05) with associations of ficolin-2 (FCN2), apolipoprotein C-IV and fibrinogen ? chain confirmed by immunoassay (P < 0.05). In the GE067-005 cohort, FCN2 was nominally associated with PET amyloid (P < 0.05) replicating the association with CSF Tau/A?42. There were nominally significant associations of complement component 3 with PET amyloid, and apolipoprotein(a), apolipoprotein A-I, ceruloplasmin, and PPY with MCI conversion to AD (all P < 0.05). In the EMIF 500 cohort FCN2 was trending toward a significant relationship with CSF A?42 (P ? 0.05), while both A1AT and clusterin were nominally significantly associated with CSF A?42 (both P < 0.05). Conclusion: Associations of plasma proteins with multiple measures of AD pathology and progression are demonstrated. To our knowledge this is the first study to report an association of FCN2 with AD pathology. Further testing of the proteins in larger independent cohorts will be important.

Project description:BACKGROUND:A long dynamic scanning protocol may be required to accurately measure longitudinal changes in amyloid load. However, such a protocol results in a lower patient comfort and scanning efficiency compared to static scans. A compromise can be achieved by implementing dual-time-window protocols. This study aimed to optimize these protocols for quantitative [18F]flutemetamol and [18F]florbetaben studies. METHODS:Rate constants for subjects across the Alzheimer's disease spectrum (i.e., non-displaceable binding potential (BPND) in the range 0.02-0.77 and 0.02-1.04 for [18F]flutemetamol and [18F]florbetaben, respectively) were established based on clinical [18F]flutemetamol (N?=?6) and [18F]florbetaben (N?=?20) data, and used to simulate tissue time-activity curves (TACs) of 110?min using a reference tissue and plasma input model. Next, noise was added (N?=?50) and data points corresponding to different intervals were removed from the TACs, ranging from 0 (i.e., 90-90?=?full-kinetic curve) to 80 (i.e., 10-90) minutes, creating a dual-time-window. Resulting TACs were fitted using the simplified reference tissue method (SRTM) to estimate the BPND, outliers (??1.5?×?BPND max) were removed and the bias was assessed using the distribution volume ratio (DVR?=?BPND?+?1). To this end, acceptability curves, which display the fraction of data below a certain bias threshold, were generated and the area under those curves were calculated. RESULTS:[18F]Flutemetamol and [18F]florbetaben data demonstrated an increased bias in amyloid estimate for larger intervals and higher noise levels. An acceptable bias (??3.1%) in DVR could be obtained with all except the 10-90 and 20-90-min intervals. Furthermore, a reduced fraction of acceptable data and most outliers were present for these two largest intervals (maximum percentage outliers 48 and 32 for [18F]flutemetamol and [18F]florbetaben, respectively). CONCLUSIONS:The length of the interval inversely correlates with the accuracy of the BPND estimates. Consequently, a dual-time-window protocol of 0-30 and 90-110?min (=maximum of 60?min interval) allows for accurate estimation of BPND values for both tracers. [18F]flutemetamol: EudraCT 2007-000784-19, registered 8 February 2007, [18F]florbetaben: EudraCT 2006-003882-15, registered 2006.

Project description:Current approaches to the early detection of Alzheimer's disease (AD) rely upon classifying individuals as "positive" or "negative" for biomarkers related to the core pathology of ?-amyloid (A?). However, the accumulation of A? begins slowly, years before biomarkers become abnormal. We used longitudinal [11C] Pittsburgh Compound B PET scanning and neuropsychological assessment to investigate the earliest changes in AD pathology and how it affects memory in cognitively normal older humans (N = 71; mean age 75 years; 35% male). We used [18F] AV-1451 PET scanning at the end of the observation period to measure subsequent tau deposition in a subset of our sample (N = 37). We found evidence for an inverted-U relationship between baseline A? levels and A? slope in asymptomatic older adults, suggesting a slowing of A? accumulation even in cognitively normal adults. In participants who were nominally amyloid negative, both the rate of amyloid accumulation and the baseline levels of A? predicted early tau deposition in cortical Braak regions associated with AD. Amyloid measures were only sensitive to memory decline as baseline levels of A? increased, suggesting that pathological accumulation occurs before impacting memory. These findings support the necessity of early intervention with amyloid-lowering therapies even in those who are amyloid negative.SIGNIFICANCE STATEMENT The progressive nature of Alzheimer's disease (AD) necessitates the earliest possible detection of pathological or cognitive change if disease progression is to be slowed. We examined cognitively normal older adults in whom AD pathology is starting to develop, with the goal of early detection of AD pathology or cognitive changes. We found amyloid measures to be sensitive early on in predicting subsequent early tau deposition. Further, it appears that rates of amyloid accumulation already begin to slow in preclinical AD, suggesting that it is a relatively late stage of AD progression. Thus, it is crucial to examine older adults early, before amyloid levels have saturated, to intervene to slow disease progression.

Dataset Information

Estimation of amyloid distribution by [18F]flutemetamol PET predicts the neuropathological phase of amyloid ?-protein deposition.

Publications

Estimation of amyloid distribution by [<sup>18</sup>F]flutemetamol PET predicts the neuropathological phase of amyloid β-protein deposition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets