Search

BACKGROUND: This study investigated the potential of phosphorylated plasma Tau217 ratio (pTau217R) and plasma amyloid beta (Aβ) 42/Aβ40 in predicting brain amyloid levels measured by positron emission tomography (PET) Centiloid (CL) for Alzheimer's disease (AD) staging and screening. METHODS: Quantification of plasma pTau217R and Aβ42/Aβ40 employed immunoprecipitation‐mass spectrometry. CL prediction models were developed on a cohort of 904 cognitively unimpaired, preclinical and early AD subjects and validated on two independent cohorts. RESULTS: Models integrating pTau217R outperformed Aβ42/Aβ40 alone, predicting amyloid levels up to 89.1 CL. High area under the receiver operating characteristic curve (AUROC) values (89.3% to 94.7%) were observed across a broad CL range (15 to 90). Utilizing pTau217R‐based models for low amyloid levels reduced PET scans by 70.5% to 78.6%. DISCUSSION: pTau217R effectively predicts brain amyloid levels, surpassing cerebrospinal fluid Aβ42/Aβ40's range. Combining it with plasma Aβ42/Aβ40 enhances sensitivity for low amyloid detection, reducing unnecessary PET scans and expanding clinical utility. CLINICALTRIALS.GOV IDENTIFIERS: NCT02956486 (MissionAD1), NCT03036280 (MissionAD2), NCT04468659 (AHEAD3‐45), NCT03887455 (ClarityAD) Highlights: Phosphorylated plasma Tau217 ratio (pTau217R) effectively predicts amyloid‐PET Centiloid (CL) across a broad spectrum.Integrating pTau217R with Aβ42/Aβ40 extends the CL prediction upper limit to 89.1 CL.Combined model predicts amyloid status with high accuracy, especially in cognitively unimpaired individuals.This model identifies subjects above or below various CL thresholds with high accuracy.pTau217R‐based models significantly reduce PET scans by up to 78.6% for screening out individuals with no/low amyloid. [ABSTRACT FROM AUTHOR]

BACKGROUND: Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring. METHODS: Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures. RESULTS: The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2‐year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model‐based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%. DISCUSSION: Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications. [ABSTRACT FROM AUTHOR]

Background: Tau positron emission tomography (PET) is increasingly used in the clinical evaluation of patients and as an outcome measure in Alzheimer's disease (AD) clinical trials. Due to differences in tracer properties, instrumentation, and methods of analysis, however, tau‐PET outcome data cannot currently be meaningfully compared or combined. Here, we tested i) the feasibility of adapting the Centiloid method—an approach originally developed to standardize amyloid PET—to harmonize tau‐PET quantification (CenTauRs); ii) the performance of a non‐linear mixed model‐based approach (Joint Propagation Model) that does not require the use of a reference tracer. Methods: Head‐to‐head tau‐PET data (Table 1) was included from two cohorts ([18F]RO948 vs [18F]flortaucipir, n = 37 [BioFINDER‐2]; [18F]flortaucipir [Avid A05] vs [18F]MK‐6240, n = 15, University of Pittsburgh) in which each participant was scanned with two tau‐PET tracers. Standardized uptake value ratio (SUVR) values were calculated using the inferior cerebellar cortex as the reference region. Anchor point data (Table 1) was derived for each tracer using the following criteria: CenTauR‐0, cognitively unimpaired (CU), amyloid PET negative (<10 Centiloids); CenTauR‐100, amyloid PET positive (>50 Centiloids), typical (temporoparietal) AD pattern on tau‐PET visual read, age<65 andMMSE>20. Regions‐of‐interest (ROIs) included a universal tau‐PET ROI—based on the intersection of tracer specific ([18F]flortaucipir, [18F]MK‐6240, [18F]PI‐2620, [18F]PM‐PBB3, [18F]GTP1 and [18F]RO948) masks that had been derived by subtracting average of amyloid‐negative CU images from the average AD image—as well as four subregions delineated within this ROI (medial temporal, meta‐temporal, temporoparietal and frontal) (Figure 1A). An overview of the adapted Centiloid‐like approach and the joint propagation model is shown in Figure 1B. Results: High R2 values were observed between tracers across all ROIs: [18F]RO948 vs [18F]flortaucipir, average 0.965 [range 0.923 (frontal) to 0.986 (universal)]; [18F]MK‐6240 vs [18F]flortaucipir, average 0.985 [range, 0.923 (medial temporal) to 0.991 (frontal)]. The Centiloid‐like and joint propagation model approaches provided near identical CenTauR values (Figure 2). Conclusions: Preliminary findings support the development of standardized scale for tau‐PET using both a Centiloid‐like or joint propagation model approach. Additional data and consideration of the advantages and disadvantages of each will be needed to recommend one approach over the other. [ABSTRACT FROM AUTHOR]

Background: Tau positron emission tomography (PET) is increasingly used in the clinical evaluation of patients and as an outcome measure in Alzheimer's disease (AD) clinical trials. Due to differences in tracer properties, instrumentation, and methods of analysis, however, tau‐PET outcome data cannot currently be meaningfully compared or combined. Here, we tested i) the feasibility of adapting the Centiloid method—an approach originally developed to standardize amyloid PET—to harmonize tau‐PET quantification (CenTauRs); ii) the performance of a non‐linear mixed model‐based approach (Joint Propagation Model) that does not require the use of a reference tracer. Method: Head‐to‐head tau‐PET data (Table 1) was included from two cohorts ([18F]RO948 vs [18F]flortaucipir, n = 37 [BioFINDER‐2]; [18F]flortaucipir [Avid A05] vs [18F]MK‐6240, n = 15, University of Pittsburgh) in which each participant was scanned with two tau‐PET tracers. Standardized uptake value ratio (SUVR) values were calculated using the inferior cerebellar cortex as the reference region. Anchor point data (Table 1) was derived for each tracer using the following criteria: CenTauR‐0, cognitively unimpaired (CU), amyloid PET negative (<10 Centiloids); CenTauR‐100, amyloid PET positive (>50 Centiloids), typical (temporoparietal) AD pattern on tau‐PET visual read, age<65 andMMSE>20. Regions‐of‐interest (ROIs) included a universal tau‐PET ROI—based on the intersection of tracer specific ([18F]flortaucipir, [18F]MK‐6240, [18F]PI‐2620, [18F]PM‐PBB3, [18F]GTP1 and [18F]RO948) masks that had been derived by subtracting average of amyloid‐negative CU images from the average AD image—as well as four subregions delineated within this ROI (medial temporal, meta‐temporal, temporoparietal and frontal) (Figure 1A). An overview of the adapted Centiloid‐like approach and the joint propagation model is shown in Figure 1B. Result: High R2 values were observed between tracers across all ROIs: [18F]RO948 vs [18F]flortaucipir, average 0.965 [range 0.923 (frontal) to 0.986 (universal)]; [18F]MK‐6240 vs [18F]flortaucipir, average 0.985 [range, 0.923 (medial temporal) to 0.991 (frontal)]. The Centiloid‐like and joint propagation model approaches provided near identical CenTauR values (Figure 2). Conclusion: Preliminary findings support the development of standardized scale for tau‐PET using both a Centiloid‐like or joint propagation model approach. Additional data and consideration of the advantages and disadvantages of each will be needed to recommend one approach over the other. [ABSTRACT FROM AUTHOR]

Background: [18F]MK6240 is a second‐generation radiotracer for imaging tau pathology in vivo. Regional thresholds that allow for an assessment of Braak stage have yet to be established and importantly may vary between regions. In this work we use a Z‐score approach to define regional thresholds and estimate Braak stage in Alzheimer's Disease. Method: All static MK6240 images used were downloaded from the Lantheus/Cerveau database, spatially normalised into MNI space using DARTEL and SUVR images were computed using an inferior cerebellum reference region. Three Braak meta‐ROIS, Braak I/II, Braak III/IV and Braak V/VI (Figure 1), were applied to 275 amyloid negative healthy control images to define regional thresholds for positivity in each ROI at the level of mean SUVR + 2 SD. These thresholds were then applied to 232 scans from subjects across the AD spectrum with associated MMSE data (88 healthy control, 68 MCI and 76 AD: 131 Aβ+ / 101 Aβ‐). Result: [18F]MK6240 showed similar mean values and low variability across all ROIs in the amyloid negative healthy controls (Figure 1). The regional SUVR cut‐off values were estimated as: Braak I/II = 1.29, Braak III/IV = 1.15 and Braak V/VI = 1.15. When these cut‐offs were applied to subjects across the AD spectrum with MMSE data (Figure 2), 118 out of 232 were negative for Braak pathology. These subjects were predominantly healthy controls and their mean MMSE was 27.3. For subjects that demonstrated Braak positivity in at least one region, 104 subjects demonstrated an ordering consistent with Braak staging whilst 10 subjects had an atypical ordering consistent with hippocampal sparing. 64 subjects were positive in all Braak regions with a mean MMSE of 23.4 and all but one of these subjects was either MCI or AD. Conclusion: Analysis of [18F]MK6240 images with thresholds that allow for the determination of Braak stage status provides data that are consistent with clinical status and MMSE score. This approach may be useful in natural history studies and clinical trials. [ABSTRACT FROM AUTHOR]

Background: Amyloid PET VR is the most common method to determine Aβ pathology in clinical practice. Amyloid positivity by VR or CL cut‐off generally shows a good concordance, however, it is possible to have discordant cases whereby the VR is positive, but CL is below the cut‐off of amyloid positivity, or vice versa. The objective of this analysis was to assess the rate and cause of discordance in defining amyloid pathology using VR and CL in Eisai's Elenbecestat MissionAD Phase 3 program. Method: Florbetaben, Florbetapir, and Flutemetamol amyloid PET scans from 3412 participants with MCI due to AD or mild AD dementia were visually read at screening by trained neuroradiologists and quantitated using CLs. VR classification of the amyloid status is based on the higher or equal grey matter (GM) uptake relative to uptake in the white matter (WM) in at least one area, as per manufacturer's guidance. Quantitatively, amyloid negativity was defined as CL<30. The number of positive cortical regions (SUVR>1.17) was also calculated. Result: The positivity concordance between VR and CL was 92%, while the negativity concordance was 94.6%. VR+/CL‐ discordance was 8% (n = 1814 VR+, n = 145 CL‐) whereas VR‐/CL+ discordance was 5.4% (n = 1598 VR‐, n = 86 CL+). VR+/CL‐ discordant cases had significantly fewer positive cortical regions than both VR+/CL+ and VR‐/CL+ cases (Figure 1). VR‐/CL+ had a significantly higher WM uptake than both VR‐/CL‐ and VR+/CL‐ cases (Figure 2). Conclusion: VR+/CL‐ discordant cases show fewer amyloid positive cortical regions, computing CLs results in below cut‐off values due to a dilution effect. These cases are considered VR+ as per manufacturer's guidelines as they show at least one area of amyloid accumulation. On the other hand, VR‐/CL+ discordant cases result from an increased WM uptake, reducing GM/WM contrast required to determine visual read positivity. VR represents a robust and validated method to determine the presence of amyloid deposition and enables enrolling patients with amyloid beta pathology, as visible on amyloid PET scans, while CLs are optimal for assessing longitudinal changes over time and are a more sensitive measure to assess disease progression. [ABSTRACT FROM AUTHOR]

Background: Prediction of longitudinal cognitive decline for patients with mild dementia using baseline characteristics will be useful for designing optimal clinical trials and real‐world patient monitoring. This can be accomplished via machine‐learning models using baseline clinical characteristics. Adding plasma pTau181 and brain region volumes may improve the predictions. Method: The training cohort (TC) included 905 amyloid‐positive (A+) patients with mild dementia from two clinical trials. The validation cohort (VC) included 230 A+ patients from another clinical trial. Over 85% of these patients had MCI. The longitudinal cognitive decline was defined by the change from baseline in CDR‐SB at months 3, 6, 9, 12, 15, and 18. Plasma pTau181 was measured using Simoa assay in a subset of 159 patients in TC. Structural brain network (SBN) modules and hubs were derived using 207 regional volumetric MRI measures in TC via an algorithm from genomics called "multiscale embedded gene co‐expression network analysis". A signature for predicting longitudinal cognitive trajectory for each patient was first derived using baseline cognitive assessments and demographics within TC via the Stochastic Gradient Boosting algorithm. The added value of SBNs and plasma pTau181 was then evaluated within this framework. Prediction performance was evaluated in VC via the correlation between predicted versus observed cognitive trajectory. Result: Predictions of the longitudinal cognitive trajectory using baseline cognitive assessments achieved 43.4% and 42.5% correlation with observed values at months 12 and 18 respectively. Key baseline cognitive function predictors were ADAS‐Cog‐14, CDR‐SB, and sub‐scores such as ideational praxis and word recall. Adding baseline SBNs significantly improved the correlation between predicted versus observed cognitive trajectory to 46.3% and 49.7% at months 12 and 18 respectively (p<0.05). Key baseline SBN predictors were middle and inferior temporal, inferior parietal, and superior frontal cortex, along with a module comprising the entorhinal cortex and temporal pole. Adding baseline plasma pTau181 to cognitive assessments yielded similar improvements, but adding both pTau181 and SBNs did not improve the predictions further. Conclusion: Longitudinal cognitive decline of A+ patients with mild dementia can be predicted using baseline cognitive assessments. Adding specific structural brain networks or plasma pTau181 significantly improves the predictions. [ABSTRACT FROM AUTHOR]

Background: Non‐invasive methods for detecting neurofibrillary tangles that spread throughout the brain are needed for efficiently screening patients in Alzheimer's disease (AD) clinical trials. This can be accomplished via machine‐learning models using baseline clinical characteristics. Adding plasma pTau181 and brain region volumes may improve detection accuracy. Method: Regional MK6240 Tau‐PET SUVR data were available for 312 Amyloid‐positive patients from a clinical trial (182 MCI, 130 mild AD, 131 Tau‐negative, 181 Tau‐positive). Over 90% of the Tau‐positive subjects were in Braak 3‐6. Plasma pTau181 was measured using Simoa assay. Data were split randomly into 70%‐30% for training and testing purposes. Tau positivity for each Braak stage was defined via the one‐sided upper 99% confidence limit of a subset of 70 subjects with background SUVR. Signatures for detecting Tau positivity in Braak 3‐6 and for further discriminating Braak 3‐4 and 5‐6 were derived via the stochastic gradient boosting algorithm and Bayesian ordinal logistic regression respectively. Demographics (age, gender, BMI), ApoE4 status, and cognitive assessments were included, and the added value of plasma pTau181 and volumetric MRI measures were assessed. Prediction performance was assessed via 10‐fold cross‐validation in the training set followed by evaluation in the test set. Result: Signatures comprising psychometric data, ApoE4 status, and demographics achieved 76% accuracy (ROC AUC = 80.4%) for detecting Tau positivity in Braak 3‐6, and 60.8% accuracy for discriminating Braak 3‐4 versus 5‐6. Adding plasma pTau181 significantly improved the accuracy to 82.2% (ROC AUC = 86.6%, p<0.05) in Braak 3‐6, and to 67.6% accuracy (p<0.05) for Braak 3‐4 versus 5‐6. Besides plasma pTau181, key predictors were delayed word recall, ADAS‐cog‐14, BMI, CDR‐SB, and ApoE4 genotype. Detection accuracy of Tau positivity in Braak 3‐4 versus the spread to Braak 5‐6 improved significantly from 67.6% to 74.6% (p<0.05) when combining MRI data with plasma pTau181, ApoE4, and demographics. Key MRI predictors were the thickness and volume of the inferior parietal cortex. Adding cognitive assessments did not improve the detection accuracy. Conclusion: These results demonstrate the potential of non‐invasive investigations such as plasma pTau181, cognitive performance, and MRI data for detecting Tau deposition throughout the brain along the Braak stages. [ABSTRACT FROM AUTHOR]

Background: Anti‐amyloid antibodies administered to patients with Alzheimer's disease can result in marked reduction of brain amyloid as measured by neuroimaging with amyloid PET ligands. However, there is a lack of pathological data. Here we present neuropathological findings in a patient who received ∼173 weeks of (interrupted) treatment with lecanemab (BAN2401), a humanized IgG1 monoclonal antibody that preferentially targets soluble aggregated β‐amyloid species (protofibrils) and insoluble fibrils, in a Phase 2 study (NCT01767311, Alz Res Therapy 2021;13:80). Method: Brain autopsy was performed on an ∼85 year old whose lecanemab treatment history over 6 years was: 79 weeks 10mg/kg monthly in the double‐blind (DB) phase, then 98 weeks without treatment, then 94 weeks at 10mg/kg biweekly in the open‐label extension (OLE) phase, followed by ∼12 weeks of treatment discontinuation prior to death. Result: Clinically, memory symptoms started age ∼77. Study entry was at age ∼80, with mild dementia, CDR 0.5, CDR‐SB 3, and MMSE 24. Florbetapir PET scan was positive at baseline, and negative after 53 weeks DB treatment and at start of OLE. These findings and clinical measures are shown in Table 1. ARIA was not observed during the study. Autopsy showed low brain weight (1052g) with diffuse atrophy, including hippocampi, consistent with atrophy seen on clinical MRI scans. Microscopic findings showed tau pathology including tangles and threads (by AT8 antibody and Bielschowsky staining) throughout neocortex (Braak NF stage VI; B3). Beta‐amyloid immunohistochemical stain (6E10) showed only modest amyloid load (Thal phase 2; A1), with near‐absence of diffuse amyloid deposits and only scattered clusters of neuritic plaques (CERAD stage B; C2). Conclusion: In this autopsy of an individual with Alzheimer's disease treated with lecanemab, neuropathological changes showed tau and β‐amyloid deposits. However, the pattern of significant tau pathology, accompanied by only a mild degree of β‐amyloid deposits, (A1B3C2), is uncommon in typical (untreated) Alzheimer's disease. For example, in the NACC neuropathology dataset, only 2% of brains with Braak B2 or B3 show Thal stage A0 or A1. This pattern suggests that lecanemab treatment resulted in a reduction in brain amyloid, consistent with the amyloid PET scanning results in this patient. [ABSTRACT FROM AUTHOR]

Background: Non‐invasive methods for brain amyloid‐b (Ab) detection are needed for effective patient care, and for faster and more cost‐effective screening of patients in clinical trials. Using MRI and other patient screening data from internal clinical trials, we employ artificial intelligence (AI) algorithms to develop brain Aβ positivity detection tools and validate their performance in independent datasets. Method: Training dataset included structural MRI and several cognitive assessments obtained during screening of 1960 patients from a clinical trial. Volumetric measures from 207 brain regions were generated by BioClinica using their proprietary imaging pipeline. Over 85% of these patients had mild cognitive impairment (MCI), and approximately 50% were Aβ positive based PET visual read (mostly using Florbetaben). Signatures for detecting brain Aβ positivity were derived via several AI algorithms such as Bayesian elastic‐net, neural network, regularized random forests, and stochastic gradient boosting. These models included demographics and clinical features, and the added value of ApoE and MRI features were assessed. Performance accuracy was first assessed via 10‐fold cross‐validation within the training set. Signatures from the best performing algorithm were evaluated in two independent test sets: a clinical trial cohort (n=1853) and an ADNI cohort (n=1092). Result: Signatures using cognitive assessments and demographics achieved 62.2% accuracy for detecting brain Aβ positivity when evaluated via cross‐validation, which improved to 75.2% when adding ApoE, and to 77.9% when including MRI. When tested in two independent cohorts, signatures with cognitive assessments and demographics achieved accuracy of 64.5% and 65.8% respectively, which improved to 71.9% and 74.3% when adding ApoE, to 76.7% and 77.5% when including MRI, and to 83.6% and 84.8% when considering 2/3 of the test‐sets that were predicted with high‐confidence (i.e., subjects predicted with > 70% probability as brain Aβ positive or negative). Conclusion: These results demonstrate the potential of non‐invasive AI tools based on cognitive assessments, ApoE and MRI for detecting brain Aβ positivity in clinical trials and patient care. Results reported here may be refined further using impending additional data prior to final presentation. [ABSTRACT FROM AUTHOR]

Background: Neurofibrillary tangles represent a pathological hallmark of AD that spreads throughout the brain in a relatively stereotypical spatio‐temporal fashion along Braak stages. Understanding the impact of regional Tau distribution and spread on future cognitive decline would be highly valuable for targeted patient selection and treatment strategy in drug development. Method: Regional flortaucipir (FTP) PET SUVR in 77 brain regions, and Braak stage regions I‐VI, were available from 638 ADNI subjects (89 AD, 117 MCI, 432 age‐matched normal controls). Baseline FTP data, processed at UC Berkeley, was partial volume corrected to reduce off‐target binding contamination. Braak stage II (i.e., hippocampus) was excluded from the analysis due to known off‐target binding in this region. A machine‐learning (ML) approach via stochastic gradient boosting was applied to determine the characteristics of regional Tau distribution that uniquely reflect AD relative to control subjects. This signature was then applied to PET scans of an independent group of 117 MCI subjects to predict their 24‐month cognitive decline, as defined by an increase in the clinical dementia rating sum of boxes (CDR‐SB). MCI subjects predicted to have 'control‐like' Tau deposition signature were considered as slow cognitive decliners while those predicted to have 'AD‐like' signature were considered as fast decliners. Prediction performance was evaluated and the regions of Tau deposition indicative of faster cognitive decline were identified. Result: MCI subjects predicted as fast cognitive decliners had odds ratio of 7.3 relative to those predicted as slow decliners (7.3‐fold greater odds of decline), with the median and 75th percentile increase in CDR‐SB of 1 and 3 respectively, compared to an increase of 0 and 0.5 among those predicted as slow decliners. Subjects with Tau deposition in Braak I (entorhinal cortex) and Braak III at baseline, and particularly in the amygdala, were predicted to be fast decliners. Further presence of Tau in parahippocampal, fusiform and precuneus regions at baseline were strongly associated with fast cognitive decline. Conclusion: The nature of baseline regional Tau distribution in MCI subjects is a strong indicator of fast cognitive decline that can help inform targeted patient selection and treatment strategy in drug development. [ABSTRACT FROM AUTHOR]

ABSTRACT BACKGROUND AND PURPOSE The pathological differences underlying the clinical disease phases in multiple sclerosis (MS) are poorly characterized. We sought to explore the relationship between the distribution of white matter (WM) lesions in relapsing-remitting (RR) and secondary progressive (SP) MS and the normal regional variability of cerebral perfusion. METHODS WM lesions were identified and quantified on a single magnetic resonance imaging scan from 1,249 patients with MS. The spatial distribution of lesions was compared between early RR, late RR, and SP MS in the context of normal cerebral perfusion patterns provided by a single-photon emission-computed tomography atlas of healthy individuals. RESULTS Patients with SP MS had more distinct and larger lesions than patients with RR MS. Across all subjects, lesions were present in regions of relatively lower normal perfusion than normal appearing WM. Further, lesions in SP MS were more common in areas of lower perfusion as compared to the lesion distribution in early and late RR MS. CONCLUSION Chronic plaques were more prevalent in WM regions with lower relative perfusion. Lesions in more highly perfused regions were more commonly observed in early RR MS and therefore, may be more likely to successfully remyelinate and resolve. J Neuroimaging 2012;22:129-136. [ABSTRACT FROM AUTHOR]