Aβ Positivity Research Articles

The role of biofluid markers in predicting near‐term cognitive impairment

AbstractBackgroundAlzheimer’s disease (AD) has been defined as a biological construct that can be determined using in vivo biomarkers. Recent studies confirmed the clinical value of the AT(N) framework when using Aβ and tau positron emission tomography (PET) scans in predicting progression from cognitively unimpaired to MCI. Our study aims to investigate if plasma biomarkers can yield similar predictive performance to PET biomarkers, and to also compare against cerebrospinal fluid (CSF) biomarkersMethodIndividuals from the PREVENT‐AD cohort were included in the study. Participants had Aβ and tau plasma measurements and at least one year of clinical follow‐up thereafter (n = 278). A subset of participants had CSF measurements (n = 102) and/or PET scans (n = 133) also with 1‐year clinical follow‐up. Participants were cognitively unimpaired at the time of all biomarker measurements. MCI diagnoses were made by clinical consensus among expert clinical and research staff blind to plasma, CSF, PET, MRI and APOE genotype. We established thresholds of plasma and CSF Aβ and tau positivity based on >80% specificity in identifying individuals with significant levels of Aβ‐PET (plasma Aβ42/40 = 0.057; plasma pTau181 = 7.53 pg/mL; CSF Aβ42 = 961.37; CSF pTau181 = 66.59 pg/mL). Aβ ([18F]NAV4694) and tau ([18F]AV1451)PET thresholds were defined by the mean plus 2 standard deviations from young participants (Aβ SUVR cut‐off = 1.17; tau SUVR cut‐off = 1.24).Result36.8% (7/19) of the A+T+ plasma group progressed to MCI, 50% (4/8) of the A+T+ CSF group progressed to MCI, and 46.6% (7/15) A+T+ PET group progressed to MCI (Fig. 1).We found differences in MCI progression status between the A+T+ group compared to the A‐T‐ and A+T‐ groups across all biomarkers’ measurements, with the A+T+ group always showing a higher rate of progression than the other two groups (post‐hoc chi‐square analyses; p<0.05).ConclusionAbnormal levels of Aβ and tau plasma biomarkers can help predict near‐ term progression to MCI. When plasma thresholds are derived based on PET data, however, plasma biomarkers have a slightly lower specificity than PET and CSF biomarkers, and both plasma and CSF biomarkers are less sensitive than PET biomarkers.

Amyloid and Tau PET Positivity in Progressive Apraxia of Speech and Agrammatic Aphasia

AbstractBackgroundThe spectrum of what is referred to as nonfluent/agrammatic primary progressive aphasia includes patients with isolated apraxia of speech (PPAOS), progressive agrammatic aphasia (PAA), or a combination thereof (AOS‐PAA). When AOS is present, there is typically an underlying 4R tauopathy (corticobasal degeneration; progressive supranuclear palsy); isolated PAA is more often associated with a 3R tauopathy (Pick’s disease). While pathological studies confirm the clinical syndrome is the sequela of these proteinopathies, beta‐amyloid (Aβ) deposition has also been observed. Given the growing number of treatment options, it is important to better understand the incidence of Alzheimer’s disease (AD) in PPAOS‐PAA spectrum disorders.Methods65 patients recruited by the Neurodegenerative Research Group at Mayo Clinic underwent Aβ‐PET imaging using Pittsburgh Compound B (PiB) and tau‐PET imaging with [18F]flortaucipir at the same visit (PPAOS = 23; PAA = 6; AOS‐PAA = 36). A global PiB SUVR (standardized uptake value ratio)≥1.48 and temporal lobe tau meta‐ROI SUVR≥1.25 were deemed Aβ and tau positive, respectively. The incidence of Aβ and tau positivity and clinical‐demographic profiles were compared among groups.ResultsThere was no difference in age, sex, APOE status, tau/Aβ positivity or PET SUVRs across clinical groups (Table 1). Patients differed on indices of aphasia and AOS severity, in concordance with group membership; there was no difference on a memory measure. Eight patients (12%) were both Aβ and tau positive (indicative of the presence of AD), with an additional 20 patients (31%) positive on only one biomarker (Table 1). Of the eight Aβ and tau positive patients, seven had AOS (six prosodic predominant AOS). Aβ more than tau SUVRs increased with age and were related to each other (Figure 1).ConclusionsAβ and tau positivity is not uncommon in AOS and/or PAA patients, particularly in older patients. Enrollment criteria for Aβ or tau targeted pharmacological trials should consider concordance of biological variables and clinical presentations, rather than simply biological biomarkers. Alternatively, if such patients are enrolled, the outcome measures should reflect the clinical impairments, as it is not yet known if or how concomitant AD influences the clinical presentation.

Towards a clinical timeline of Alzheimer’s disease; a multivariate cognitive perspective

AbstractBackgroundAlzheimer’s disease (AD) is a multifactorial disease characterised by a long period of neuronal loss which manifests clinically as cognitive decline and ultimately dementia. Understanding the nature and magnitude of cognitive decline prior to dementia is critical to understanding the disease course. Here we quantify change in six cognitive composite scores to compare their temporal sequence and rates of change early in the AD disease course.MethodAmyloid‐beta positive (Aβ+) cognitively unimpaired (CU), MCI and AD participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing with ≥three study visits (N = 857), were used to derive composite scores for executive function, language, attention and processing, recognition, visuospatial, and a general composite used in clinical trials (AIBL‐PACC). A multivariate method for combining cognitive change trajectory curves was used to place each composite score trajectory in a common numerical space. Using the mean AIBL‐PACC scores for the prodromal group (MCI‐Aβ+) as a reference, change over a three‐year period for each composite was estimated. Given the AIBL requirement for a minimum 1.5 SD change in two or more cognitive domains to justify progression to MCI/AD, disease time was set with the zero point at a ‐1.5 AIBL‐PACC score, representing a 1.5 SD decline in AIBL‐PACC from the mean CU Ab‐ population at baseline.ResultThe AIBL‐PACC, language and visuospatial functioning scores showed the steepest trajectories indicating AD related decline in these scores was fastest (Figure 1). AIBL‐PACC reached the ‐1.5 point on the y‐axis first followed closely by memory recognition, with steeper trajectory of decline for the AIBL‐PACC across the age range. Focusing on AIBL‐PACC, it took 2.15 years to transition from ‐1.5 to the mean of the prodromal group (‐1.87). At five years past the start of the prodromal stage, the data suggests a drop in the AIBL‐PACC to ‐2.54, an approximate decline of ∼0.22 points per year.ConclusionThis is the first presentation of a novel multivariate disease trajectory method examining expected change across the AD continuum among Aβ+ persons. A 3‐year change in the PACC composite demonstrated here will be relevant to measure disease progression during clinical trials.

Preclinical Alzheimer’s disease is associated with distinct gut microbial metagenomic profiles

AbstractBackgroundAlterations in gut microbiome in individuals with Alzheimer’s disease (AD) may contribute to development or exacerbation of AD pathology via the microbiota‐gut‐brain axis. Composition and diversity of gut microbiota have been studied previously in AD dementia; here we investigate the gut microbial genes involved and their association with AD pathology prior to development of dementia.MethodCerebrospinal fluid (CSF; N = 98) and fecal samples (N = 333) were collected from participants enrolled in the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center studies. CSF biomarkers of AD, β‐amyloid (Aβ)42/Aβ40 and phosphorylated‐tau‐181 (phospho‐tau [181P]), were quantified using robust prototype assays from the Roche NeuroToolKit (Roche Diagnostics International Ltd, Rotkreuz, Switzerland). Fecal samples were analyzed by whole genome sequencing. Sequences were filtered, quality controlled, and profiled for taxonomy and functional classification at species level using MetaPhlAn 4.0/HUMAnN 3.0 software. Compositional/functional differential abundance, alpha‐/beta‐diversity, correlation between feature abundance and sample metadata, gene‐set enrichment (GSE), and network analyses were performed using R packages (phyloseq/vegan/DESeq2/NetCoMi/rstatix/clusterProfiler). Analyses compared clinical status and biomarker groups.ResultThe relative abundance of phylum Firmicutes and Bacteroidetes decreased and increased, respectively, in individuals with AD versus cognitively unimpaired (CU) individuals. In CU individuals, functional profiles differed in Aβ‐positive (A+) and phospho‐tau [181P]‐positive (T+) groups versus Aβ‐ and phospho‐tau [181P]‐negative (A−T−) individuals. In the microbial metagenome, 64 genes were significantly increased (p < 0.05) in the A+T+ group versus the A−T− group; 21/64 genes had ≥1 significant correlation with the AD biomarkers (e.g., toxic protein SymE positively correlated with phospho‐tau [181P] and negatively correlated with Aβ42/Aβ40 in CU individuals, suggesting that it may be a contributor to pathophysiological metabolic pathways in AD). Three KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways identified in GSE analyses, including fatty acid metabolism/degradation and cationic antimicrobial peptide resistance, were significantly associated (p < 0.05) with genes that were enriched in the AD population versus CU individuals.ConclusionIndividuals with AD have distinct differential abundance in microbiota composition, in addition to an increase and enrichment of genes associated with AD pathogenesis pathways. These genes were associated with AD biomarkers prior to development of AD dementia.

Signatures of abnormal deviations in excitation‐inhibition balance for amyloid positive individuals

AbstractBackgroundDysfunctional excitation‐inhibition balance (EIB) is hypothesized to precede cognitive impairment in Alzheimer’s disease (AD); however, overlapping neuropathology (like amyloid plaques) can develop in cognitively normal (CN) and impaired (CI) individuals. We investigate EIB trajectories for individuals with significant amyloid (Aβ+).MethodMultimodal imaging was used to construct hybrid resting‐state structural connectomes (Fortel, Igor, et al. Network Neuroscience 6.2 (2022): 420‐444.), computing whole‐brain EIB based on 105 ROIs. This cohort (N = 185, 94 Female) aged 55‐90 (μ FEMALES = 74.9, and μ MALES 73) was limited to Aβ+ individuals with APOE genotyping (ε3/ε3, ε3/ε4, ε4/ε4); grouped further by clinical dementia rating (CDR), with CDR > 0 indicating CI. Imaging, Aβ+ classifications, CDR, and APOE were obtained from OASIS‐3: Longitudinal Multimodal Neuroimaging: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P30 AG066444, P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352. AV‐45 doses were provided by Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly.ResultResults presented in Figure 1 suggest whole‐brain EIB in both CN sexes with Aβ+ decreases with increasing age (APOE‐ε4 agnostic). However, sex differences are highlighted with CI; regression analyses produce a marginally significant 4‐way interaction of age, sex, cognitive status, and APOE (P = 0.085, β = ‐0.013). Partial correlation of EIB with age, controlling for sex, APOE and cognitive status is also significant (P = 0.016, r = ‐0.18). Results indicate sex‐mediated EIB differences in individuals with CI, dominated by APOE‐ε4 in women rather than amyloid.ConclusionWe’ve demonstrated EIB deviations in cognitively impaired men and women that may be associated with AD neuropathology. Our findings are consistent with known AD sex differences and suggest novel multimodal connectomics may capture subtle disruptions in brain dynamics that traditional methods may not. Figure. 1. Whole‐brain excitation‐Inhibition balance (EIB) trajectories for amyloid positive (Aβ+) men and women. The cognitively normal (CN) group (rows A‐B) suggests no sex differences in EIB trajectories with age, while the cognitively impaired (CI) group (rows C‐D) begins to reveal distinct patterns of dysfunction (more significantly in women with APOE‐ε4). Note a value of 0.5 indicates perfect balance.

Fatigue Interacts with Amyloid Status To Predict Daily Working Memory Performance in Preclinical Alzheimer’s Disease

AbstractBackgroundSmartphone‐based cognitive assessment has the potential to reduce barriers to cognitive screening and increase sensitivity to detect preclinical Alzheimer’s disease (AD). Ecological momentary assessment of cognition is novel, and it is not yet well understood how psychological factors (i.e., mood, stress, and fatigue) may impact cognitive performance in remote, real‐world settings. We aimed to evaluate whether psychological factors influence daily cognitive performance differently between older adults with and without AD‐related pathologic change.MethodParticipants (N = 73) were cognitively unimpaired adults aged 60‐80 years (71% female, 89% White, 16.5 mean years of education) recruited for a digital cognitive assessment study. Participants had amyloid (Aβ) PET imaging results available from prior research participation [(n = 25 Aβ positive (Aβ+) and n = 48 Aβ negative (Aβ‐)]. A modified Telephone Interview for Cognitive Status (TICSm) score of ≥34 was used to indicate normal cognition. Participants completed 8 consecutive days of cognitive assessment using Mobile Monitoring of Cognitive Change (M2C2), a smartphone‐based testing platform developed as part of the National Institute of Aging’s Mobile Toolbox. Brief (i.e., 3‐4 minute) M2C2 sessions were assigned three times daily (morning, afternoon, and evening). Tasks included measures of visual working memory (WM, “Color shapes”), processing speed (PS, “Symbol search”), and episodic memory (EM, “Prices”). Multilevel modeling was used to examine the impact of self‐reported mood, stress, fatigue, and Aβ status on daily cognitive performance while controlling for age, sex, education, and time of day.ResultDaily fatigue was found to interact with amyloid status such that Aβ+ individuals performed worse on the WM task when experiencing higher levels of fatigue (Est. = .004, p = .01) Higher levels of daily stress were associated with poorer performance on the PS task (Est. = ‐.0005, p = .03) but no interaction with Aβ status was observed. There were no significant relationships between mood and cognitive performance.ConclusionWe demonstrated that psychological factors including fatigue and stress may impact performance on remote cognitive assessments. Results also suggested that fatigue may impact daily cognitive performance more among those with AD pathological change. These preliminary results suggest that psychological factors should be considered when determining optimal windows for remote cognitive screening.

Predicting of β‐amyloid Positivity Using Integer‐based Risk Scores with Blood‐based Biomarkers

AbstractBackgroundβ‐amyloid (Aβ), a pathologic hallmark of Alzheimer’s disease (AD) is the target of recently FDA‐approved drugs. In trials and in practice, identifying individuals likely to be β‐amyloid positive (Aβ+) with accessible and affordable tests will be increasingly important. Blood‐based biomarkers (BBMs) have shown early promise in determining amyloid status. We aimed to develop machine learning‐based integer risk scores using BBMs and other accessible measures to predict Aβ positivity by PET in older, non‐demented (cognitively unimpaired or mild cognitive impairment) adults.MethodsWe used data from 722 individuals (72.1 ± 7.0 years old) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to develop integer risk scores for predicting Aβ positivity. The sample was divided into training (75%) and test (25%) sets. Variables were categorized as demographics (D: age, sex, and education), genetic information (G: APOE4 status), cognitive measures (C: ADAS‐Cog‐13 and MMSE), and plasma biomarkers of p‐Tau181(T) and neurofilament light (N: NfL). Feature‐sets were formed as hierarchically constructed combinations of these categorized variables (see Figure 1). Using the AutoScore algorithm, variables were ordered according to their Random Forest Gini Index and were iteratively included in the construction of logistic regression‐derived risk score models. Variables were included based on increases in Area Under the ROC Curves (AUCs).ResultsFour models were developed based on four feature‐sets. All models utilized age and APOE4 status and, when considered, all models retained ADAS‐Cog‐13, p‐Tau, and NfL. The best performing model corresponded to the DGCTN feature‐set with performance on the dedicated test set of AUC = 0.810 (C.I.: 0.745‐0.876). Using the DGCTN model on the test set, 97% of individuals with scores in the top quartile of possible risk score points were correctly identified as Aβ+.ConclusionsWe developed risk score models utilizing accessible and affordable measures to predict Aβ positivity. Plasma biomarkers improve performance of risk scores indicating their value. Applications include screening in clinical settings for therapeutic care and enrichment of clinical trials. These data can be used to optimize the efficient use of PET in determining eligibility for clinical trials and amyloid targeted therapies in practice.

Effects of pre‐analytical sample handling on the performance of plasma phospho‐tau217

AbstractBackgroundPre‐analytical variables can greatly influence biomarkers concentration. Till now, only two studies have examined the effects of pre‐analytical factors on plasma concentrations of phosphorylated tau (p‐tau)1,2. Hence, in this study we investigated how pre‐analytical factors affect the performance of one of the most promising plasma biomarkers of Alzheimer´s Disease, p‐tau217.MethodWe included 50 β‐amyloid positive (Aβ+) and 50 Aβ− participants from the Swedish BioFINDER‐1 cohort. Plasma samples were either thawed at room temperature (RT) or on ice, centrifuged or not centrifuged and freeze‐thawed once, twice or three times. Plasma and CSF p‐tau217 were measured using immunoassay developed by Lilly Research Laboratories. Aβ‐status was determined by CSF the Aβ42/Aβ40 ratio.ResultIn the whole cohort, we found significant correlations between plasma p‐tau217 and CSF Aβ42/Aβ40 and p‐tau217 for all tested conditions (Table 1). Correlations between plasma p‐tau217 and CSF p‐tau217 were also significant for all conditions in the Aβ+ group (Table 1). However, in this group, correlations with CSF Aβ42/Aβ40 were only significant in the centrifuged samples (thawed at RT, Rs = ‐0.394, p = 0.005; thawed on ice, Rs = ‐0.406; p = 0.003). In Aβ− participants, significant correlations between plasma p‐tau217 and CSF p‐tau217 were found again only in the centrifuged samples (thawed at RT, Rs = 0.394, p = 0.005; thawed on ice, Rs = 0.334; p = 0.018, Table 1), with no significant correlations between plasma p‐tau217 and CSF Aβ42/Aβ40 for any of the conditions. Even though the accuracy to identify individuals with abnormal CSF Aβ42/Aβ40 or CSF p‐tau217 status was high for all conditions, the AUCs of samples that were thawed at RT without centrifugation were numerically lower than AUCs of other conditions (CSF Aβ42/Aβ40: 0.845 vs 0.872‐0.884; CSF p‐tau217: 0.866 vs 0.908‐0.924, Table 2). Furthermore, no differences were found in plasma p‐tau217 concentration between the samples that were freeze‐thawed once, twice or three times.ConclusionWe found that while centrifugation improved the performance of plasma p‐tau217, but thawing temperatures and upto three freeze‐thaw cycles did not have significant impact. These results might be of importance for future development of standard protocols for pre‐analytical blood sample handling.

Brain‐age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum

AbstractBackgroundNeuroimaging‐derived brain‐age is a useful biomarker to study the brain’s biological aging process. Brain‐age has shown cross‐sectional associations with cognitive function and modifiable risk factors for dementia. We aimed to study, in cognitively unimpaired (CU) individuals, the mediating role of brain‐age in the association between modifiable risk factors and cognitive changes, and the impact of AD pathology on this role.MethodWe included 416 CU individuals from the ALFA+ study with available structural MRI, measurements of the global cognitive Preclinical Alzheimer’s Cognitive Composite (PACC) (370 individuals had a follow‐up PACC assessment 3.28±0.27 years later), and lifestyle and cardiovascular risk factors assessments. We computed brain‐age delta as the difference between chronological and predicted brain‐age using a previously pre‐trained machine learning algorithm on structural MRI data. Partial Least Squares Path Modeling (PLS‐PM) was employed to investigate the mediation effect of brain‐age delta between a computed latent variable from modifiable risk factors (cardiovascular, mental health and mood, metabolic/endocrine disease history, and alcohol consumption factors; Table 1) and a latent variable from longitudinal PACC. Statistical bias adjustment was performed to control for the confounding effects of age and sex by using multiple linear regression. The analysis was performed on the whole sample (ALL) and after stratification by amyloid‐β (Aβ) status. Participants were classified as amyloid‐β positive (Aβ+) if CSF Aβ42/40<0.071 (Milà‐Alomà et al, 2020). Significance of mediation was examined by 95% confidence interval bootstrap.ResultThe effect of risk factors (latent risk) on longitudinal cognition (latent cognition) was partially mediated by brain‐age delta only in Aβ+ individuals (57.286%) (Figure 1C). Higher latent risk was associated with older brain‐age delta (p = 0.003) and older brain‐age delta was associated with worse latent cognition (p = 0.007). Conversely, the indirect effect was not significant in ALL nor in Aβ‐ individuals, where brain‐age delta was not significantly associated with latent cognition (Figure 1A‐B). Still, higher latent risk was associated with older brain‐age delta (ALL: p<0.001; Aβ‐: p = 0.004) and associated with worse latent cognition (ALL: p = 0.012, Aβ‐: p = 0.001).ConclusionIn CU individuals in the AD continuum, biological brain aging mediates the association between modifiable risk factors and cognitive decline.

Spatial Extent as a more sensitive amyloid biomarker for early stages of Alzheimer’s disease

AbstractBackgroundDespite considerable advances in beta amyloid (Aβ)‐PET imaging over the last decade, the standard approach of estimating the average neocortical Aβ burden remains largely unchanged. However, as research and clinical trials increasingly shift earlier in the disease process, measures of how far Aβ has spread throughout the cortex (spatial extent) may prove more sensitive than average neocortical magnitude for detecting and quantifying early Aβ deposits and their association with future tau proliferation and cognitive decline.MethodClinically‐normal individuals (n = 214) were included from the Harvard Aging Brain Study with longitudinal PIB‐PET (2‐4 scans, median = 4.7±2.7 years) and annual cognitive data (median = 5.2±2 years), as well as a subset (n = 181) with flortaucipir (FTP)‐PET. Spatial extent (EXT) was computed as the number of cortical ROIs (n = 62, Desikan atlas) above their ROI‐specific threshold for Aβ positivity. EXT was compared to a traditional mean neocortical DVR using logistic growth modeling. Receiver Operator Characteristic (ROC) curve analyses evaluated EXT’s ability to identify baseline PIB‐ individuals (<1.19DVR/24CL) who progressed to PIB+ at 3‐year follow‐up. Linear Mixed Effects (LME) modeling assessed baseline EXT (or DVR) as a predictor of increasing inferior temporal tau (IT FTP SUVR) and cognitive decline on the Preclinical Alzheimer’s Cognitive Composite (PACC).ResultEXT begins rising below the neocortical DVR threshold (Figure1A), reaching a maximal growth rate of 14 ROIs per 0.1 DVR increase (∼10CL) and plateauing as full cortical EXT is achieved starting at ∼1.5DVR/68CL. A 3‐ROI EXT threshold predicts progression from PIB‐ to PIB+ in 3 years (AUC = .97, SE = .82,SP = .97), outperforming neocortical DVR (AUC = .92, SE = .65,SP = .94, Figure1B). EXT provides a stronger biomarker of Aβ change than DVR (lower coefficient of variation, Figure2) across the Aβ continuum due to its low variance, even after EXT has plateaued. Baseline EXT is also a stronger predictor of increases in IT FTP SUVR (Figure 3A, η2 EXT = .25, η2 DVR = .20) and PACC decline (Figure3B, η2 EXT = .28, η2 DVR = .22).ConclusionBy describing the spread of Aβ throughout the cortex rather than average neocortical Aβ burden, spatial extent provides a more sensitive measure of Aβ at early, preclinical stages of AD that may improve design of AD prevention trials and open new avenues for research into AD pathogenesis.

Voxel‐level of abnormal β‐amyloid accumulation and spatiotemporal distribution patterns in Alzheimer’s Disease

AbstractBackgroundThe spatial patterns of β‐amyloid (Aβ) accumulation are critical for early diagnosis and clinical trials of Alzheimer’s Disease (AD). In this study, we explored a new voxel‐level Aβ index and investigated spatial patterns of voxels with abnormal Aβ deposition in different stages of AD.MethodWe identified 944 ADNI participants with Aβ 18F‐florbetapir positron emission tomography (PET) image. All Aβ PET images were co‐registered with their corresponding magnetic resonance imaging (MRI) scans, intensity‐normalized to the whole cerebellum, and spatial‐normalized to the Montreal Neurological Institute (MNI) space. We defined the threshold of each voxel as the mean+2SD of 18F‐florbetapir PET images of 51 participants who were Aβ PET negative (A‐), CSF p‐Tau181 negative (T‐), hippocampal atrophy negative (N‐), and cognitively unimpaired (CU) at both baseline and follow‐up. The positive voxel number (NoVoxel+) was counted based on the threshold of each voxel. The cross‐sectional and longitudinal correlations between NoVoxel+ and cortical summary Aβ standard uptake value ratio (SUVR) were determined in A‐ and Aβ‐positive (A+) separately. We compared baseline NoVoxel+ among different stages of AD, as well as how it related to longitudinal changes of Aβ SUVR, CSF p‐Tau181, residual hippocampal volume (rHCV), temporal meta‐ROI cortical thickness, and preclinical Alzheimer cognitive composite (PACC) score, and follow‐up tau PET levels, controlling for age, sex and APOE4. Finally, we investigated the spatial patterns of NoVoxel+ in different stages of AD.ResultThe new Aβ index NoVoxel+ was strongly correlated with the cortical summary Aβ SUVR cross‐sectionally and longitudinally regardless of Aβ positivity (Figure 1). Cognitively impaired (MCI and dementia) individuals showed larger (p<0.001) NoVoxel+ than the CU individuals, A+ group had greater NoVoxel+ than the A‐ group (Figure 2). NoVoxel+ was related to faster rates of increases in Aβ SUVR and CSF p‐Tau, decreases in rHCV, temporal meta‐ROI cortical thickness, and PACC. Abnormal Aβ voxels were primarily located in precuneus, medial orbital frontal and anterior/posterior cingulate regions in A‐/CU group (Figure 3).ConclusionThe proposed voxel‐level Aβ deposition NoVoxel+ may be useful to investigate the spatial and temporal patterns of Aβ accumulation in AD, particularly in early amyloidosis stage.

Potential utility of using both APOEε4 and Aβ positivity to enrich clinical trials of tau‐targeting therapies

AbstractBackgroundThe use of enrichment strategies is crucial for selecting individuals with the highest probability of Alzheimer’s disease (AD)‐related progression in typical clinical trial time frames. Although both amyloid‐ß (Aß) pathology and the apolipoprotein E e4 (APOEe4) genotype have been shown to accelerate tau accumulation, it is still not clear whether assessing both APOEe4 genotype and Aß positivity is useful to enrich tau‐targeting trials using tau positron emission tomography (PET) as outcome. Here, we investigated the implications of considering APOEe4 carriership for population enrichment in trials testing drug effects on tau tangle deposition in cognitively impaired (CI) individuals across the AD continuum.MethodWe studied 29 Aß positive CI individuals (16 with mild cognitive impairment [MCI] and 13 with AD dementia) from the McGill Translational Biomarkers in Aging and Dementia (TRIAD) cohort. Study participants underwent clinical assessments, APOE genotyping, magnetic resonance imaging, PET for Aß ([18F]AZD4694) and tau ([18F]MK6240) at baseline, as well as a follow‐up tau‐PET scan (mean follow‐up, 2.2 years). Aß positivity was determined as global [18F]AZD4694 SUVR = 1.55.ResultNo demographic differences were observed between APOEe4 carriers and noncarriers (Table 1). Regression analysis revealed that APOEe4 carriers had higher tau‐PET SUVR increase in temporal regions compared to APOEe4 noncarriers (Figure 1). The use of Aß positivity alone for population enrichment of a clinical trial focusing on CI individuals would require a sample size of 436 individuals per study arm to test a 25% drug effect on tau‐PET accumulation (Figure 2). A similar clinical trial with a population enrichment strategy using Aß positivity plus APOEe4 carriership would require a sample size of as few as 158 individuals per study arm (reduction of 64% in relation to using only Aß positivity) to test the same drug effect (Figure 2).ConclusionOur results reveal that APOEe4 carriership is associated with increased tau tangle accumulation in CI individuals who are Aß positive. Clinical trials testing drug effects on tangle deposition may benefit from assessing both APOEe4 carriership and Aß positivity statuses as enrollment criteria to select individuals at higher risk of fast tau accumulation, resulting in a more cost‐effective trial.

Preliminary results investigating synaptic density across the Alzheimer’s Disease continuum

AbstractBackgroundProgressive loss of synapses occurs throughout the Alzheimer’s Disease (AD) continuum and is closely associated with cognitive impairment. Here, we present pilot data investigating variation in synaptic density across aging and AD using 18F‐SynVesT‐1 molecular imaging, a ligand for the SV2A site.Method26 participants underwent 18F‐SynVesT‐1 imaging to estimate synaptic density in‐vivo. Participants were young adults (Y) (n = 7; Age = 26.1+‐3.8; Sex(F) = 4), cognitively unimpaired older adults (O) (n = 13; Age = 79.7+‐4.27; Sex(F) = 7) or patients (P) (n = 6; Age = 66.3 +‐8.2; Sex (F) = 1; Diagnoses EOAD = 3, MCI = 1, PCA(AD) = 1; lvPPA(AD)). We collected a 90 min dynamic sequence after injection of 5 mCi of tracer. Participant whole brain Distribution Volume Ratio (DVR) images were generated using a cerebellar grey matter reference region. O and P groups also underwent tau and β‐amyloid (Aβ) PET imaging (O: 18F‐flortaucipir,11C‐PiB; P: 18F‐PI‐2620, 18F‐Florbetaben) and a visual assessment of tau and Aβ positivity for AD was determined by an expert reviewer (DSM).ResultWe contrasted 18F‐SynVesT‐1 uptake in four cortical regions for Y, O and P. In each region, we observed a significant effect of group (Hippocampus F(1,23) = 4.35, p = 0.025 (uncorrected); Meta‐Temporal ROI F(1,23) = 4.36, p = 0.025 (uncorrected); Temporal F(1,23) = 9, p<0.01(FWE corrected); F(1,23) = 14.4, p<0.001 (FWE corrected)) Figure 1. Further, we observe non‐significant numerical differences suggesting lower synaptic density in participants with biomarker evidence of Aβ and tau Figure 1. Finally, we observe a positive partial correlation between temporal cortical thickness and temporal 18F‐SynVesT‐1 uptake controlling for age (r(23) = 0.54,p = 0.006) Figure 2. We observe no relationship between age and tracer uptake in the cerebral white matter (r(24) = ‐0.01,p = 0.95), suggesting the reference region selected does not introduce systematic biases in the DVR image.ConclusionThis demonstrates 18F‐SynVesT‐1 PET can capture age and AD related decreases in synaptic density across the cortex. Further, we show an association between cortical thickness and synaptic density in the temporal cortex controlling for biases due to age. Future work will incorporate additional participants and investigate participant level associations between regional 18F‐SynVesT‐1 uptake, cognition, and in‐vivo AD pathologies.

In vivo tau deposition in the brain stem nuclei and circadian rest‐activity rhythm change in non‐demented older adults

AbstractBackgroundCircadian rest‐activity rhythm (RAR) alterations are common in the early stage of Alzheimer’s disease (AD) disease. Given that the brainstem nuclei, the locus coeruleus (LC) and dorsal raphe nucleus (DRN) in particular, play key roles in modulating arousal and the sleep‐wake cycle, tau pathology in the brain stem nuclei, which occurs even before cortical tau progression, may relate to circadian RAR alteration in older individuals. Therefore, we aimed to test the hypothesis that in vivo tau deposition in the LC and DRN correlates with circadian RAR in non‐demented older adults.MethodA total of 63 non‐demented older adults from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) were included. All participants underwent [11C] PiB PET and [18F] AV1451‐PET. Circadian RAR variables were measured using actigraphy for 8 consecutive days. Tau deposition of the LC and DRN (LC‐tau and DRN‐tau, respectively) was measured in the regions of interest (ROI) masks for each participant prepared in native space using the Harvard arousal network atlas for the LC and DRN. For the purpose of comparison, tau deposition in the substantia nigra (SN‐tau) was also measured in the neuromelanin sensitive MRI‐based ROI for the SN.ResultIncreased LC‐tau and DRN‐tau were significantly correlated with delayed acrophase (β = 0.303, p = 0.024 for LC‐tau and β = 0.398, p = 0.003 for DRN‐tau; Table 1 & Figure 1). In contrast, SN‐tau did not related with any RAR variables. Even after controlling for global tau deposition, the significance did not changed (β = 0.321, p = 0.021 for LC‐tau and β = 0.367, p = 0.008 for DRN‐tau, Table 1). In addition, Aβ positivity showed a significant moderation effect on the relationship between LC‐tau with acrophase. Subgroup analyses showed that increased LC‐tau significantly related with delayed acrophase in Aβ‐positivegroup (β = 0.427, p = 0.004), but did not in Aβ‐negative group (β = 0.099, p = 0.615)(Figure 2)ConclusionOur findings support the possibility that the tau pathology in the LC and DRN underlie circadian RAR alteration, more specifically delayed phase, in non‐demented older adults. In addition, such relationship appears to be more prominent in individuals with brain Aβ pathology.

Anterior and posterior default mode network functional connectivity and segregation in aging

AbstractBackgroundThe default mode network (DMN) supports memory performance and has been shown to be vulnerable to the effects of aging. The posterior DMN (pDMN) is an early region of amyloid‐β (Aβ) accumulation, which may lead to disrupted communication within/between subnetworks of the DMN. Using resting state functional connectivity (FC), we tested whether communication within and between the pDMN and anterior DMN (aDMN) were altered by aging and Aβ pathology and related to memory performance. We also tested segregation FC between the aDMN and pDMN.MethodData from 27 cognitively normal adults (69.6 years, 59% females) with resting state fMRI (rsfMRI), 18F‐florbetapir (FBP) PET to measure Aβ, and the Rey Auditory Verbal LearningTest (RAVLT) delayed recall to assess memory were analyzed. The rsfMRI data were preprocessed using CONN toolbox. ROI‐to‐ROI FC matrices were constructed for regions included in the aDMN and pDMN. We calculated: (1) the average FC within each network (Zw), (2) average FC between the aDMN and pDMN (Zb), and (3) network segregation [(Zw‐Zb)/Zw] for each DMN network. Global FBP SUVR was calculated as the mean across association cortical regions, and a cutoff of 1.11 was used to determine Aβ positivity. Analyses included the FC variables and their associations with age, Aβ, and memory.ResultOlder age was associated with reduced network segregation of the pDMN (r = ‐0.53, p = 0.004), and a trend level association for reduced within‐pDMN FC (r = ‐0.37, p = 0.057). Global FBP SUVR was not associated with FC or network segregation, and there was no significant difference in FC between Aβ groups (controlling for age). There was a trend‐level association between better memory performance and stronger FC between aDMN and pDMN (r = 0.37, p = 0.066; controlling for age and education).ConclusionResults demonstrated that aging is associated with specific FC disruptions of pDMN, leading to reduced specialization of processing as indexed by network segregation. Moreover, increased communication between DMN subnetworks may support memory in aging. Future analyses in a larger sample will further test the association between Alzheimer’s pathology and functional changes to DMN subnetworks.

An accelerated rate of Beta‐amyloid Measured with [C‐11]PiB in Down Syndrome compared to Neurotypical Populations

AbstractBackgroundDown syndrome (DS) is characterized by a higher risk and earlier onset of beta‐amyloid (Aβ) plaque accumulation and Alzheimer’s disease (AD). The triplication of chromosome 21 containing the amyloid precursor protein (APP) gene is implicated in this process and in this study we compare the accumulation rates of Aβ between DS and neurotypical (NT) populations quantified by longitudinal [C‐11]PiB imaged at the same site to minimize experimental variation.MethodsIndividuals with three or more [C‐11]PiB scans spanning eleven years our site with at least one Aβ+ (global SUVR>1.40) and Aβ‐ [C‐11]PiB scan were selected. 12 of 74 DS and 15 of 246 NT participants fit these criteria. An identical imaging procedure was conducted for all participants imaged on either an ECAT HR+ or Biograph mCT PET scanner 50‐70 minutes post‐injection. Reconstructed PET images were summed and spatially normalized into template space and converted into 50‐70min SUVR images using a cerebellar grey matter reference region. Global SUVR composed of grey matter regions from the AAL atlas was used as a metric for Aβ deposition. SUVR data were fit against age with a logistic growth curve (f(t) = NS+K/(1+e(‐r(t‐t50)))) to determine Aβ accumulation as subjects transitioned to Aβ+. The rate of accumulation was found using the slope of the curve at Aβ positivity threshold based on the model fit. Our model fixed NS = 1.12, the average baseline SUVR of all Aβ‐ subject scans (n = 491), and K = 1.28, the highest specific binding in any participant.ResultsPlotting [C‐11]PiB SUVR against age provides visual comparisons of cohorts (Fig 1) and transforming the x‐axis to amyloid chronicity (t = 0 at Aβ+) illustrates differences in accumulation rates between groups (Fig 2). The average rate of amyloid accumulation (ΔSUVR/yr) when becoming Aβ+ in the DS cohort is 0.071(0.021) versus 0.043(0.009) for NT (p = 4.31×10−4).ConclusionsAβ accumulation rate was higher in the DS cohort compared to NT. This is consistent with the additional APP gene copy in DS, and we observed an accumulation rate 65% faster in this cohort. There was a higher variability in the DS population requiring further investigation towards understanding the influence of genetic and lifestyle factors on Aβ deposition.

Plasma p‐tau181 and nfl as surrogates biomarkers in clinical trials targeting preclinical stage of Alzheimer’s disease

AbstractBackgroundThe preclinical stage of Alzheimer’s disease (AD) is one of the main focuses of clinical trials. Plasma and positron emission tomography (PET) biomarkers have already been proposed to monitor participants’ disease progression in clinical trials targeting cognitively unimpaired (CU) individuals. Although longitudinal changes in plasma phosphorylated tau 181 (p‐tau181) and neurofilament light chain(NfL) correlate with AD progression, it is unknown whether these changes can be used to monitor drug effects in preventive clinical trials. Here, we aimed to evaluate the utility of using changes in plasma p‐tau181 and NfL as surrogate biomarkers for clinical trials targeting preclinical AD.MethodWe access 257 CU older individuals that had available Aβ‐PET at baseline, as well as the baseline, up to 24‐month plasma p‐tau181 and NfL measures from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We calculate the estimated sample size needed to test a 25% drug effect with 80% of power at a 0.05 level on reducing changes in plasma markers.ResultWe demonstrated that therapeutical clinical trials of 24‐month follow‐up using p‐tau181 and NfL would require 78% and 63% smaller sample sizes compared with a 12‐month follow‐up(Figure 1). The use of intermediate levels of Aβ(CL20‐40), rather than merely Aβ positivity, as an enrichment strategy reduced the sample size by 73% for p‐tau181 and 59% for NfL over 24 months. As demonstrated in Figure 2A the estimated cost of a clinical trial using only plasma biomarkers is lower than using neuroimaging biomarkers for surrogacy(Figure 2A). However, as showed by Figure 2B a trial including all Aβ positive individuals, the total estimated cost when considering surrogate biomarkers plus other related assessments are numerically higher using plasma than neuroimaging biomarkers; while trials including only individuals with intermediate Aβ levels, the cost was similar using plasma and neuroimaging biomarkers for surrogacy.ConclusionOur results suggest that to monitor large scale large‐scale population interventions in CU Aβ positive individuals’ plasma p‐tau181/ NfL could potentially be used. Furthermore, using a strategy that has been proposed in recent clinical trials, we demonstrated that the use of intermediate levels of Aβ is more cost‐effective than trials using Aβ‐positive individuals in clinical.

Association of β‐amyloid, microglial activation, hypermetabolism and hypertrophy in Alzheimer’s disease

AbstractBackgroundPlasma β‐amyloid (Aβ) Aβ42/Aβ40 measured by immunoprecipitation and liquid chromatography‐mass spectrometry assay has been demonstrated useful to identify Aβ PET positive individuals, but the concordance and discordance about the positivity of plasma Aβ42/Aβ40 (Plasma+/‐) and Aβ PET (PET+/‐) have not been fully characterized. Moreover, the association of Aβ deposition, cortical thickness (CTh), glucose metabolism, and microglial activation among different Plasma/PET profiles is poorly understood.MethodIn this study, we classified 173 ADNI participants into Plasma‐/PET‐ (N = 48), Plasma+/PET‐ (N = 43), and Plasma+/PET+ (N = 73) group based on thresholds of composite Aβ PET (18F‐florbetapir, FBP) standardized uptake value ratio (SUVR) (≥1.11) and plasma Aβ42/Aβ40 (≤0.1218). Part of them had longitudinal 18F‐fluorodeoxyglucose (FDG) PET, structural MRI, and CSF soluble Triggering Receptor Expressed on Myeloid Cell 2 (sTREM2) data. Baseline and slopes of FBP SUVRs of 68 Freesurfer‐defined ROIs were compared between different Plasma/PET groups. The associations of regional FBP SUVRs, CSF sTREM2, regional glucose metabolism (reflected in FDG‐SUVR), and CTh (measured using structural MRI) were investigated in different Plasma/PET groups.ResultPlasma Aβ42/Aβ40 might become positive earlier than Aβ PET (Fig.1A, McNemar’s test, p<0.001). Longitudinally, Plasma+/PET‐ participants showed faster rates of Aβ accumulation in bilateral BANKSSTS, supramarginal, and superior parietal cortices whereas demonstrated no difference in baseline FBP SUVRs than Plasma‐/PET‐ participants (Fig.1D‐E). Higher CSF sTREM2 level and faster rate of CSF sTREM2 increase were related to slower rates of Aβ accumulation in BANKSSTS (standardized β (βstd) = ‐0.418, p = 0.002) in Plasma+/PET+ but not in Plasma+/PET‐ individuals (Fig.2A and D). In contrast, thicker CTh (Fig. 4B‐C) and higher glucose metabolism (Fig. 4H‐I) in supramarginal and superior parietal cortices were coupled to faster rates of CSF sTREM2 increase in Plasma+/PET‐ rather than in Plasma+/PET+ individuals.ConclusionThese findings suggest that plasma Aβ42/Aβ40 might become abnormal earlier than Aβ PET, and plasma Aβ42/Aβ40 positive Aβ PET negative (Plasma+/PET‐) individuals accumulate Aβ plaques faster than Plasma‐/PET‐ individuals. Activated microglia may play a role in reducing fibrillar‐Aβ increasing rates in late amyloidosis stage, whereas it may be related to hypertrophy and hypermetabolism in early amyloidosis stages.

Multimodal assessment of brain fluid clearance is associated with amyloid-beta deposition in humans

PurposeThe present study investigates a multimodal imaging assessment of glymphatic function and its association with brain amyloid-beta deposition. MethodsTwo brain CSF clearance measures (vCSF and DTI-ALPS) were derived from dynamic PET and MR diffusion tensor imaging (DTI) for 50 subjects, 24/50 were Aβ positive (Aβ+). T1W, T2W, DTI, T2FLAIR, and 11C-PiB and 18F-MK-6240 PET were acquired. Multivariate linear regression models were assessed with both vCSF and DTI-ALPS as independent variables and brain Aβ as the dependent variable. Three types of models were evaluated, including the vCSF-only model, the ALPS-only model and the vCSF+ALPS combined model. Models were applied to the whole group, and Aβ subgroups. All analyses were controlled for age, gender, and intracranial volume. ResultsSample demographics (N=50) include 20 males and 30 females with a mean age of 69.30 (sd=8.55). Our results show that the combination of vCSF and ALPS associates with Aβ deposition (p < 0.05, R2 = 0.575) better than either vCSF (p < 0.05, R2 = 0.431) or ALPS (p < 0.05, R2 = 0.372) alone in the Aβ+ group. We observed similar results in whole-group analyses (combined model: p < 0.05, R2 = 0.287; vCSF model: p <0.05, R2 = 0.175; ALPS model: p < 0.05, R2 = 0.196) with less significance. Our data also showed that vCSF has higher correlation (r = -0.548) in subjects with mild Aβ deposition and DTI-ALPS has higher correlation (r=-0.451) with severe Aβ deposition subjects. ConclusionThe regression model with both vCSF and DTI-ALPS is better associated with brain Aβ deposition. These two independent brain clearance measures may better explain the variation in Aβ deposition than either term individually. Our results suggest that vCSF and DTI-ALPS reflect complementary aspects of brain clearance functions.

Regional Tau Deposition Reflects Different Pathways of Subsequent Neurodegeneration and Memory Decline in Cognitively Normal Older Adults.

Tau pathology is recognized as a primary contributor to neurodegeneration and clinical symptoms in Alzheimer's disease (AD). This study aims to localize the early tau pathology in cognitively normal older people that is predictive of subsequent neurodegeneration and memory decline, and delineate factors underlying tau-related memory decline in individuals with and without β-amyloid (Aβ). A total of 138 cognitively normal older individuals from the Berkeley Aging Cohort Study underwent 11 C-Pittsburgh Compound-B (PiB) positron emission tomography (PET) to determine Aβ positivity and 18 F-Flortaucipir (FTP) PET to measure tau deposition, with prospective cognitive assessments and structural magnetic resonance imaging. Voxel-wise FTP analyses examined associations between baseline tau deposition and longitudinal memory decline, longitudinal hippocampal atrophy, and longitudinal cortical thinning in AD signature regions. We also examined whether hippocampal atrophy and cortical thinning mediate tau effects on future memory decline. We found Aβ-dependent tau associations with memory decline in the entorhinal and temporoparietal regions, Aβ-independent tau associations with hippocampal atrophy within the medial temporal lobe (MTL), and that widespread tau was associated with mean cortical thinning in AD signature regions. Tau-related memory decline was mediated by hippocampal atrophy in Aβ- individuals and by mean cortical thinning in Aβ+ individuals. Our results suggest that tau may affect memory through different mechanisms in normal aging and AD. Early tau deposition independent of Aβ predicts subsequent hippocampal atrophy that may lead to memory deficits in normal older individuals, whereas elevated cortical tau deposition is associated with cortical thinning that may lead to more severe memory decline in AD. ANN NEUROL 2024;95:249-259.