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Blood-based biomarkers have been extensively evaluated for their diagnostic potential in Alzheimer's disease. However, their relative prognostic and monitoring capabilities for cognitive decline, amyloid-β (Aβ) accumulation and grey matter loss in cognitively unimpaired elderly require further investigation over extended time periods. This prospective cohort study in cognitively unimpaired elderly [ n = 185, mean age (range) = 69 (53–84) years, 48% female] examined the prognostic and monitoring capabilities of glial fibrillary acidic protein (GFAP), neurofilament light (NfL), Aβ1-42/Aβ1-40 and phosphorylated tau (pTau)181 through their quantification in serum. All participants underwent baseline Aβ-PET, MRI and blood sampling as well as 2-yearly cognitive testing. A subset additionally underwent Aβ-PET (n = 109), MRI (n = 106) and blood sampling (n = 110) during follow-up [median time interval (range) = 6.1 (1.3–11.0) years]. Matching plasma measurements were available for Aβ1-42/Aβ1-40 and pTau181 (both n = 140). Linear mixed-effects models showed that high serum GFAP and NfL predicted future cognitive decline in memory (βGFAP×Time = −0.021, PFDR = 0.007 and βNfL×Time = −0.031, PFDR = 0.002) and language (βGFAP×Time = −0.021, PFDR = 0.002 and βNfL×Time = −0.018, PFDR = 0.03) domains. Low serum Aβ1-42/Aβ1-40 equally but independently predicted memory decline (βAβ1-42/Aβ1-40×Time = −0.024, PFDR = 0.02). Whole-brain voxelwise analyses revealed that low Aβ1-42/Aβ1-40 predicted Aβ accumulation within the precuneus and frontal regions, high GFAP and NfL predicted grey matter loss within hippocampal regions and low Aβ1-42/Aβ1-40 predicted grey matter loss in lateral temporal regions. Serum GFAP, NfL and pTau181 increased over time, while Aβ1-42/Aβ1-40 decreased only in Aβ-PET-negative elderly. NfL increases associated with declining memory (βNfLchange×Time = −0.030 , PFDR = 0.006) and language (βNfLchange×Time = −0.021, PFDR = 0.02) function and serum Aβ1-42/Aβ1-40 decreases associated with declining language function (βAβ1-42/Aβ1-40×Time = −0.020, PFDR = 0.04). GFAP increases associated with Aβ accumulation within the precuneus and NfL increases associated with grey matter loss. Baseline and longitudinal serum pTau181 only associated with Aβ accumulation in restricted occipital regions. In head-to-head comparisons, serum outperformed plasma Aβ1-42/Aβ1-40 (ΔAUC = 0.10, PDeLong, FDR = 0.04), while both plasma and serum pTau181 demonstrated poor performance to detect asymptomatic Aβ-PET positivity (AUC = 0.55 and 0.63, respectively). However, when measured with a more phospho-specific assay, plasma pTau181 detected Aβ-positivity with high performance (AUC = 0.82, PDeLong, FDR < 0.007). In conclusion, serum GFAP, NfL and Aβ1-42/Aβ1-40 are valuable prognostic and/or monitoring tools in asymptomatic stages providing complementary information in a time- and pathology-dependent manner. [ABSTRACT FROM AUTHOR]

Background: The positron emission tomography (PET) radiotracer [18F]MK‐6240 exhibits high specificity for neurofibrillary tangles (NFTs) of tau protein in Alzheimer's disease (AD), high sensitivity to medial temporal and neocortical NFTs, and low within‐brain background. Objectives were to develop and validate a reproducible, clinically relevant visual read method supporting [18F]MK‐6240 use to identify and stage AD subjects versus non‐AD and controls. Methods: Five expert readers used their own methods to assess 30 scans of mixed diagnosis (47% cognitively normal, 23% mild cognitive impairment, 20% AD, 10% traumatic brain injury) and provided input regarding regional and global positivity, features influencing assessment, confidence, practicality, and clinical relevance. Inter‐reader agreement and concordance with quantitative values were evaluated to confirm that regions could be read reliably. Guided by input regarding clinical applicability and practicality, read classifications were defined. The readers read the scans using the new classifications, establishing by majority agreement a gold standard read for those scans. Two naïve readers were trained and read the 30‐scan set, providing initial validation. Inter‐rater agreement was further tested by two trained independent readers in 131 scans. One of these readers used the same method to read a full, diverse database of 1842 scans; relationships between read classification, clinical diagnosis, and amyloid status as available were assessed. Results: Four visual read classifications were determined: no uptake, medial temporal lobe (MTL) only, MTL and neocortical uptake, and uptake outside MTL. Inter‐rater kappas were 1.0 for the naïve readers gold standard scans read and 0.98 for the independent readers 131‐scan read. All scans in the full database could be classified; classification frequencies were concordant with NFT histopathology literature. Discussion: This four‐class [18F]MK‐6240 visual read method captures the presence of medial temporal signal, neocortical expansion associated with disease progression, and atypical distributions that may reflect different phenotypes. The method demonstrates excellent trainability, reproducibility, and clinical relevance supporting clinical use. Highlights: A visual read method has been developed for [18F]MK‐6240 tau positron emission tomography.The method is readily trainable and reproducible, with inter‐rater kappas of 0.98.The read method has been applied to a diverse set of 1842 [18F]MK‐6240 scans.All scans from a spectrum of disease states and acquisitions could be classified.Read classifications are consistent with histopathological neurofibrillary tangle staging literature. [ABSTRACT FROM AUTHOR]

Purpose: The BACE inhibitor verubecestat was previously found to reduce amyloid load as assessed by 18F-flutemetamol positron emission tomography (PET) composite cortical standard uptake value ratio (SUVr) in patients with mild-to-moderate Alzheimer's disease (AD) in a substudy of the EPOCH trial. Here, we report on additional analyses relevant to the EPOCH PET data, to help inform on the use of PET for assessing amlyloid load in AD clinical trials.Procedures: The analyses addressed (1) identification of an optimal 18F-flutemetamol reference region, (2) determination of the threshold to characterize the magnitude of the longitudinal change, and (3) the impact of partial volume correction (PVC). Pons and subcortical white matter were evaluated as reference regions. The SUVr cutoffs and final reference region choice were determined using 162 18F-flutemetamol PET scans from the AIBL dataset. 18F-flutemetamol SUVrs were computed at baseline and at Week 78 in EPOCH participants who received verubecestat 12 mg (n = 14), 40 mg (n = 20), or placebo (n = 20). Drug effects on amyloid load were computed using either Meltzer (MZ), or symmetric geometric transfer matrix (SGTM) PVC and compared to uncorrected data.Results: The optimal subcortical white matter and pons SUVr cutoffs were determined to be 0.69 and 0.62, respectively. The effect size to detect longitudinal change was higher for subcortical white matter (1.20) than pons (0.45). Hence, subcortical white matter was used as the reference region for the EPOCH PET substudy. In EPOCH, uncorrected baseline SUVr values correlated strongly with MZ PVC (r2 = 0.94) and SGTM PVC (r2 = 0.92) baseline SUVr values, and PVC did not provide improvement for evaluating treatment effects on amyloid load at Week 78. No change from baseline was observed in the placebo group at Week 78, whereas a 0.02 and a 0.04 decrease in SUVr were observed in the 12 mg and 40 mg arms, with the latter representing a 22% reduction in the amyloid load above the detection threshold.Conclusions: Treatment-related 18F-flutemetamol longitudinal changes in AD clinical trials can be quantified using a subcortical white matter reference region without PVC.Clinical Trial Registration: clinicaltrials.gov NCT01739348. [ABSTRACT FROM AUTHOR]

Purpose: End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults. Methods: We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers' diagnostic performance in the asymptomatic Alzheimer's disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK. Results: The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was −0.66 for the classifier trained with neuritic amyloid plaque density, and −0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48–51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0–2 from 3–5 based on the end-of-life dataset and the neuropathological ground truth. Discussion: Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights. [ABSTRACT FROM AUTHOR]