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INTRODUCTION: We set out to identify tau PET‐positive (A+T+) individuals among amyloid‐beta (Aβ) positive participants using plasma biomarkers. METHODS: In this cross‐sectional study we assessed 234 participants across the AD continuum who were evaluated by amyloid PET with [18F]AZD4694 and tau‐PET with [18F]MK6240 and measured plasma levels of total tau, pTau‐181, pTau‐217, pTau‐231, and N‐terminal tau (NTA‐tau). We evaluated the performances of plasma biomarkers to predict tau positivity in Aβ+ individuals. RESULTS: Highest associations with tau positivity in Aβ+ individuals were found for plasma pTau‐217 (AUC [CI95%] = 0.89 [0.82, 0.96]) and NTA‐tau (AUC [CI95%] = 0.88 [0.91, 0.95]). Combining pTau‐217 and NTA‐tau resulted in the strongest agreement (Cohen's Kappa = 0.74, CI95% = 0.57/0.90, sensitivity = 92%, specificity = 81%) with PET for classifying tau positivity. DISCUSSION: The potential for identifying tau accumulation in later Braak stages will be useful for patient stratification and prognostication in treatment trials and in clinical practice. Highlights: We found that in a cohort without pre‐selection pTau‐181, pTau‐217, and NTA‐tau showed the highest association with tau PET positivity.We found that in Aβ+ individuals pTau‐217 and NTA‐tau showed the highest association with tau PET positivity.Combining pTau‐217 and NTA‐tau resulted in the strongest agreement with the tau PET‐based classification. [ABSTRACT FROM AUTHOR]

Background: Blood biomarkers have gained much attention in recent years given their increasing ability to indicate amyloid pathology and excellent performance in distinguishing diagnostic groups in the context of Alzheimer's disease (AD). Plasma pTau217 has been considered the best candidate biomarker to serve as diagnostic and prognostic tool for clinical trials. However, biomarker assays targeting tau phosphorylation on Thr217 may differ because of their composition (e.g., targeting multiple or single phosphorylation sites) which may lead to distinct associations with pathology. Thus, we aimed to compare two plasma pTau217 immunoassays that differ in regard to their target specificity. Method: Participants from the TRIAD cohort with available cross‐sectional plasma and imaging data, incorporating within the AD spectrum, were included in this study (Young = 25;CU‐ = 107;CU+ = 32;MCI+ = 42;AD+ = 47;MCI‐ = 19). Plasma pTau was quantified using the assays from Janssen (pTau217+; which exhibits enhanced signal with co‐phosphorylation of pTau212) and from ALZpath (single phosphorylation on pTau217), both performed on the Simoa platform. Amyloid and tau pathologies were indexed by PET using [18F]AZD4694 and [18F]MK6240, respectively. ANCOVA compared biomarker values across groups, Spearman rank tested the correlations between them. Linear regression models (LM) evaluated the association between plasma and PET biomarkers globally and at the voxel level. Result: Biomarker levels (fold mean of CU‐) were comparable across diagnostic groups, showing the expected increases in amyloid positive groups. Both plasma assays were highly correlated with amyloid (Fig.1), which was also observed on the LM adjusted by age and sex, and on the voxel‐wise analysis (Fig.2). Associations with tau PET SUVR (medial temporal) were also significant and showed similar distribution between the two pTau assays. When neocortical tau was evaluated, however, pTau217+ had a better association as well as higher correlation coefficients within amyloid+ (Fig.3) and neocortical tau+ groups than did ALZpath pTau217. Conclusion: In general, pTau217 assays with different antibody specificity showed very similar associations with PET. However, pTau217+ achieved a better association with neocortical tau as compared to ALZpath pTau217. A biomarker targeting multiple phosphorylation sites might be a better proxy of advanced tau, therefore may offer improved ability to detect and exclude participants with advanced AD pathology for clinical trials. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown that microglial activation (MA) plays a key role in the physiopathology and progression of Alzheimer's disease (AD). Unpublished data suggest that MA is highly associated with the development of neuropsychiatric symptoms (NPS) in patients with AD. Thus, we aim to investigate here the contribution of each NPS domain to this association across individuals in the AD continuum. Method: We assessed 132 individuals (86 cognitively unimpaired (CU), 28 MCI, and 18 AD dementia) from the TRIAD cohort who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q), and had positron emission tomography (PET) for amyloid‐β (Aβ) ([18F]AZD4694), tau tangles ([18F]MK6240) and MA ([11C]PBR28) at the same visit. Regions were tailored using Desikan‐Killiany (DK) atlas. SUVRs were calculated using the cerebellum gray matter as a reference. Linear regression tested the association between biomarkers accounting for age, sex, and cognitive status. Result: NPI‐Q total score was significantly associated with [11C]PBR28 in the cingulate, inferior temporal, and precuneus accounting for age, sex, and after false discovery rate (FDR) correction for multiple comparisons (Figure 1A). This association was independent of Aβ and tau levels (Table 1). When we stratify NPI‐Q domains (agitation, irritability, motor disturbance, disinhibition, elation, delusion, hallucinations, nighttime disturbance, depression, anxiety, apathy, and appetite disturbance) severity score, we found that the hyperactivity subdomain (agitation, irritability, motor disturbance, disinhibition, and elation) showed the larger contribution to the results (Figure 1B). Bootstrapping each NPI‐Q domain from the NPI‐Q total score, linear regression analysis reveals that irritability, nighttime disturbance, and agitation are the main contributors to the association between NPS and MA (Figure 1C). Removing these domains, but no other combination of two or three NPI‐Q domains, from the NPI‐Q total score, abolishes this association (Figure 1D). Conclusion: Our results suggest that MA is associated with neuropsychiatric dysfunction in AD. Notably, we found that irritability, nighttime disturbance, and agitation drive the association between NPS and MA. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown that microglial activation (MA) plays a key role in the pathophysiological and clinical progressions of Alzheimer's disease (AD). However, little is known whether MA is also associated with the development of neuropsychiatric symptoms typically found in patients with AD. Thus, we aim to investigate here the association of MA with neuropsychiatric symptoms (NPS) of individuals across the AD continuum. Method: We assessed 132 individuals (86 cognitively unimpaired (CU), 28 MCI, and 18 AD dementia) from the TRIAD cohort who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q), and had positron emission tomography (PET) for amyloid‐β (Aβ) ([18F]AZD4694), tau tangles ([18F]MK6240) and MA ([11C]PBR28) at the same visit. Regions were tailored using Desikan‐Killiany (DK) atlas. SUVRs were calculated using the cerebellum gray matter as a reference. Linear regression tested the association between biomarkers accounting for age, sex, and cognitive status. Result: NPI‐Q total score was significantly associated with [11C]PBR28 in the cingulate, inferior temporal, and precuneus accounting for age, sex, and after false discovery rate (FDR) correction for multiple comparisons (Figures 1A, 1B, 1C). This association was independent of Aβ and tau levels (Table 1). Notably, MA predicted neuropsychiatric dysfunction with higher magnitude than Aß or tau using PET values from overlap region (regional SUVR) and if we use a global measure for all tracers (global SUVR) (Figure 2A). When we stratify NPI‐Q domains (agitation, irritability, motor disturbance, disinhibition, elation, delusion, hallucinations, nighttime disturbance, depression, anxiety, apathy, and appetite disturbance) severity score, we found that the hyperactivity subdomain (agitation, irritability, motor disturbance, disinhibition, and elation) showed the larger contribution to the results (Figure 2B). Conclusion: Our results suggest MA as a key element associated with neuropsychiatric dysfunction in AD independent of Aβ and tau pathologies. These findings provide additional rationale for the therapeutics targeting glial cells activation in AD patients. [ABSTRACT FROM AUTHOR]

Background: The high prevalence of Alzheimer's dementia in females have long puzzled researchers in the field. Despite similar amyloid levels, females show higher load of neurofibrillary tangles (NFTs). Previous literature proposed that amyloid‐β (Aβ) and phosphorylated tau (p‐tau) synergism accelerates biomarker abnormalities. However, it remains to be answered whether this synergism is the driving force behind faster tau progression in females. The overarching goal of the study wa to investigate whether amyloid‐β aggregates differentially impose tau hyperphosphorylation and neurofibrillary tangles formation in a sex‐specific manner. Method: In this longitudinal study, we assessed 287 participants from TRIAD cohort at McGill University Research Centre for Studies in Aging. Cerebral Aβ and tau deposition were assessed with positron emission tomography (PET) radiotracers [18F]AZD4694 ([18F]NAV4694) and [18F]MK6240, respectively. Cerebrospinal fluid (CSF) p‐tau181 and p‐tau217 were also measured (analysed at the Clinical Neurochemistry Laboratory at the University of Gothenburg, Sweden). Regression and voxel‐based models with interaction terms were used to evaluate baseline tau load and NFT accumulation rate as a function of sex and baseline biomarkers (Aβ and p‐tau). Result: We identified sex difference in the relationships between CSF p‐tau, Aβ and NFT (Fig 1). Specifically, voxelwise analyses demonstrated that female presented stronger positive correlations between CSF p‐tau and AD core biomarkers (Aβ and NFT). Furthermore, we discovered that Aβ and CSF ptau181 interactively potentiated tau accumulation in females but not males (Fig 2, Table 1). Together, the present results support that Aβ load imposes higher tau aggregation in females. Conclusion: Aβ‐dependent tau phosphorylation was the key driver for faster NFT accumulation observed in female. [ABSTRACT FROM AUTHOR]

Background: The ATN classification system assumes a sequential model of disease progression. However, there are groups of individuals in the same ATN category that exhibit a predominance of abnormality (higher burden) of one of the biomarkers, creating heterogeneous ATN groups regarding pathological predominance. Thus, we tested the hypothesis that individuals clustered by ATN biomarker abnormality predominance may offer an alternative to groups defined using biomarkers cut‐offs. Method: We assessed 103 cognitively impaired individuals(CDR> = 0.5) from the TRIAD cohort with available measures of plasma phosphorylated tau‐181, brain MRI, amyloid PET, and tau PET. We used the K‐means algorithm to stratify participants into three clusters. We compared the clusters on composite measures of memory, executive functioning, language, and visuospatial processing. To examine the utility of the discovered clusters, we compared them to traditional ATN categories in the prediction of neuropsychological measures. We did so by creating three categories: patients positive on all three ATN biomarkers, patients positive on two of the three biomarkers, and patients positive on either one or none. Additionally, we created an inflammation, amyloid and tau deposition probabilistic map anchored on young controls(n = 51, mean age = 23.74). Results: We uncovered 3 clusters: an amyloid predominant (AP) cluster, a tau/phosphor‐tau predominant cluster (TP), and a cluster showing no predominance with low levels on all biomarkers (CN)(figure 1). Notably, levels of neurodegeneration and inflammation were similar between the AP and TP clusters. The AP cluster significantly differed from the CN cluster in memory only. Participants in the TP cluster had significantly lower scores in memory, executive functioning, language, and visuospatial processing than the other two clusters. In comparison, using threshold‐based ATN groups showed milder differences in memory and executive functioning, and no differences in language and visuospatial processing(figure 2). Furthermore, cluster membership moderated the relationship between various biomarkers, to the point of reversing the direction of correlation(figure 3). Conclusion: Our results highlight the biological heterogeneity present within the Alzheimer's disease continuum and that the pathological predominance of amyloid and tau is associated with different disease phenotypes. Approaching dementia patients with an eye on the predominance of pathology rather than cutoffs for abnormality may provide a better understanding of AD pathological subtypes. [ABSTRACT FROM AUTHOR]

Background: The 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‐β (Aβ) pathology and the apolipoprotein E ε4 (APOEε4) genotype have been shown to accelerate tau accumulation, it is still not clear whether assessing both APOEε4 genotype and Aβ positivity is useful to enrich tau‐targeting trials using tau positron emission tomography (PET) as outcome. Here, we investigated the implications of considering APOEε4 carriership for population enrichment in trials testing drug effects on tau tangle deposition in cognitively impaired (CI) individuals across the AD continuum. Method: We studied 29 Aβ 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 Aβ ([18F]AZD4694) and tau ([18F]MK6240) at baseline, as well as a follow‐up tau‐PET scan (mean follow‐up, 2.2 years). Aβ positivity was determined as global [18F]AZD4694 SUVR ≥ 1.55. Result: No demographic differences were observed between APOEε4 carriers and noncarriers (Table 1). Regression analysis revealed that APOEε4 carriers had higher tau‐PET SUVR increase in temporal regions compared to APOEε4 noncarriers (Figure 1). The use of Aβ 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 Aβ positivity plus APOEε4 carriership would require a sample size of as few as 158 individuals per study arm (reduction of 64% in relation to using only Aβ positivity) to test the same drug effect (Figure 2). Conclusion: Our results reveal that APOEε4 carriership is associated with increased tau tangle accumulation in CI individuals who are Aβ positive. Clinical trials testing drug effects on tangle deposition may benefit from assessing both APOEε4 carriership and Aβ positivity statuses as enrollment criteria to select individuals at higher risk of fast tau accumulation, resulting in a more cost‐effective trial. [ABSTRACT FROM AUTHOR]

Background: The mechanisms by which the apolipoprotein E ε4 (APOEε4) allele influences Alzheimer's disease (AD) pathophysiological progression are poorly understood. Here, we tested the association of APOEε4 carriership and amyloid‐β (Aβ) burden with longitudinal tau pathology progression. Method: We studied 104 individuals across the aging and AD clinical spectrum from the McGill TRIAD cohort. Study participants underwent clinical assessments, APOE genotyping, magnetic resonance imaging, positron emission tomography (PET) for Aβ ([18F]AZD4694) and tau ([18F]MK6240) at baseline, as well as an additional follow‐up tau‐PET scan (mean follow‐up, 2.4 years). We further assessed longitudinal changes in tau phosphorylation (plasma phosphorylated tau at threonine 217 [p‐tau217+]), brain atrophy (gray matter density), and clinical function (clinical dementia rating scale sum of boxes). Result: We found that APOEε4 carriership potentiates Aβ effects on longitudinal tau tangle accumulation over two years (Figure 1). Interestingly, the APOEε4‐potentiated Aβ effects on tangles were mediated by longitudinal plasma p‐tau217+ increase (Figure 2). In addition, this longitudinal tau accumulation as measured by PET was accompanied by brain atrophy and clinical decline during the follow‐up period (Figure 3). Conclusion: Our results support a model in which the APOEε4 allele plays a key role in Aβ downstream effects on the aggregation of phosphorylated tau in the form of neurofibrillary tangles in the living human brain, which is a key factor in the development of dementia. These observations have important implications for the design of future trials by suggesting that the combination of therapies targeting both ApoeE4 and Aβ pathology might have the potential to synergistically halt tau progression in AD. [ABSTRACT FROM AUTHOR]

Background: Neuropathological observations report incipient tau accumulation in the human brain starting in the third decade of life, with the proportion of individuals with some degree of tau deposition increasing with age. Given the high prevalence of tau positive cases among cognitively unimpaired (CU) older adults, one may question whether these individuals constitute an ideal reference group when assessing tau load in symptomatic population. Here, we compare tau biomarker levels of participants with Alzheimer's disease (AD) and CU of different ages. Method: We evaluated 35 CU young individuals (aged <26 years) and 124 amyloid‐beta (Aβ)‐ elderly, considered the two control groups. We also assessed 42 Aβ+ CU and 122 Aβ+ cognitively impaired (CI) individuals, diagnosed with either mild cognitive impairment or AD dementia. Participants were assessed with 18F‐MK6240 positron emission tomography (PET) and CSF and plasma pTau181, 217, 231 and 235. We used linear regression to perform voxel‐wise comparisons of tau‐PET signal between groups. We compared the standardized uptake value ratio (SUVR) in medial‐temporal and Braak‐like regions‐of‐interest (ROI) and fluid biomarkers levels using the Wilcoxon rank sum test. Result: In the voxel‐wise comparisons (Figure 1A‐E), CI participants presented more widespread cortical signal than control groups. Less signal in the parietal cortex was observed in the comparison with CU young. Higher t‐values were observed in Aβ+ CU individuals in the medial temporal region, compared to both control groups, and adjacent neocortex, especially compared to CU young. Temporal and Braak V‐VI SUVR were higher in CI but not in Aβ+ CU individuals compared to controls. Moreover, Braak I‐II and III‐IV SUVR and the fluid biomarkers levels were higher in both Aβ+ groups compared to controls (Figures 2A‐D; 3A‐G). Importantly, Braak I‐II SUVR and the CSF biomarkers levels were higher in CU old Aβ‐ versus young. Conclusion: Here, we provide in vivo evidence that tau‐load in age‐matched Aβ‐ controls constitutes a bias for identifying tau pathology, particularly in individuals at early stages of AD pathophysiology. Age‐related tau load should be considered when selecting control groups to assess tau in symptomatic populations, especially those with early AD, and when defining criteria for tau abnormality. [ABSTRACT FROM AUTHOR]

Background: Healthy synapses are the key to proper brain function, ensuring communication between neurons. Recent studies have associated synaptic dysfunction with Alzheimer's disease (AD) proteins, however, little is known about the role of glial reactivity, another pathology closely linked to AD, in brain synaptic dysfunction (Figure 1). Method: We evaluated 123 individuals (67 cognitively unimpaired (CU) and 56 cognitively impaired (CI)) who had available Aβ‐ and Tau‐PET as well as cerebrospinal fluid measures of glial fibrillary acidic protein (GFAP), chitinase‐3‐like protein 1 (YKL‐40), soluble triggering receptor expressed on myeloid cells 2 (sTREM2), synaptic markers (growth‐associated protein 43 (GAP‐43), neurogranin (Ng), synaptotagmin 1 (SYT1), and presynaptic protein synaptosomal‐associated protein 25 (SNAP‐25)). ANCOVA adjusted for clinical diagnosis, age, and sex was used to compare levels of CSF biomarkers; whereas linear regressions adjusted for age, sex, clinical diagnosis, and Aβ/tau‐PET were used to test the associations between glial reactivity and synaptic markers. Result: Demographic information is shown in Table 1. Increased levels of GAP‐43, SNAP‐25, and Ng were observed in CI compared to CU individuals. CSF GFAP was highly associated with both presynaptic and postsynaptic biomarkers in CU and CI groups. CSF YKL‐40 was associated only with presynaptic biomarkers in both clinical groups. On the other hand, CSF sTREM2 showed an association with all synaptic markers but only in the CI group (Figure 2). Conclusion: We found a heterogeneous association between synaptic markers and glial activation. The presence of GFAP+ astrocytes was associated with dysfunction of presynaptic/postsynaptic markers, whereas YKL‐40+ astrocytes specifically reflected presynaptic dysfunction across aging and AD spectrums. On the other hand, microglial activation reflected synaptic dysfunction associated with dementia symptoms. Our results support recent experimental observations suggesting that clarifying the heterogeneity of different glial cell phenotypes is crucial to advancing our understanding of the role of immune cells in cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Tau plays a prominent role in the synapse and has been shown to be closely related to neurodegeneration. Recent evidence show that cerebrospinal fluid (CSF) synaptic markers are related to CSF tau and degeneration. However, it is not clear whether synaptic dysfunction in combination with tau tangles is associated with neurodegeneration in the same brain regions. Our study aims to map in the brain the association of synaptic markers with tau and degeneration. Method: We evaluated 147 individuals from the TRIAD cohort with CSF GAP43, SYT1, SNAP25, neurogranin (Ng), ptau217, neurofilament light protein (NfL) quantification, as well as tau positron emission tomography (PET), magnetic resonance imaging, and clinical assessments. One‐way ANOVA tested group differences on synaptic levels. Spearman correlation tested the association among synaptic proteins. Linear regressions tested the association between biomarkers. Voxel‐based morphometry (VBM) was considered as an index of neurodegeneration. Result: We observed an increase in synaptic markers across age and the AD spectrum, reaching a plateau when cognition is affected (Figure 1A). Among the synaptic markers, SNAP25 showed the greatest magnitude of change, on average, between young, aged cognitively unimpaired, mild cognitive impaired, and AD individuals (mean 62.44%). (Figure 1B‐C). In addition, we found that all synaptic markers are significantly intercorrelated (Figure 1D). Moreover, CSF ptau217 correlated with all synaptic biomarkers, GAP43 (β = ‐044), SYT1 (β = ‐0.41), SNAP25 (β = ‐0.59) and Ng (β = ‐0.25). Conversely, CSF NfL was strongly but only correlated with GAP43 (β = 0.62) (Figure 2). Voxel‐wise correlation revealed that tau tangles measured by [18F]‐MK6240 PET, was positively associated with SNAP25 in AD‐related regions, and VBM was associated with all synaptic markers, but more closely with GAP43. Furthermore, the interaction between synaptic markers and temporal meta‐ROI [18F]‐MK6240 on VBM was significant for Ng and GAP43 (Figure 3). Conclusion: Our results support a heterogenous role of synaptic markers in the AD continuum and suggest that synaptic dysfunction in the presence of tau pathology may not be driving atrophy. [ABSTRACT FROM AUTHOR]

Background: Astrocyte reactivity is a common finding in individuals across the Alzheimer's disease (AD) continuum. Recently, we demonstrated that individuals with higher levels of plasma glial fibrillary acidic protein (GFAP), a marker of astrocyte reactivity, present a stronger association between amyloid‐β (Aβ) and tau pathologies in preclinical AD. Because cohort studies and clinical trials show sex differences in AD biomarkers, we investigated whether the effects of astrocyte reactivity on the association between Aβ and tau differs in men and women Method: We assessed 1,016 CU individuals from three cohorts with available Aβ (plasma/PET), plasma p‐tau181 and GFAP measures. A subset of individuals also has plasma p‐tau217 available (n = 136).Individuals were classified as positive (Ast+) or negative (Ast‐) for astrocyte reactivity using a cutoff based on plasma GFAP of younger Aβ‐ individuals.Linear regressions accounting for age and sex were used to evaluate the association between plasma p‐tau epitopes and Aβ burden.An interaction between term between Aβ*sex was also added to the models.Voxel‐wise associations between biomarkers were tested using linear regressions accounting for age, sex, and adjusting for multiple comparisons. Result: We found that Aβ burden was associated with plasma p‐tau in Ast+ but not in Ast‐. Men in the Ast+ group presented a much increased association between Aβ with plasma p‐tau181 (β = 0.71,p<0.0001,Fig.1a) or p‐tau217 (β = 1.23,p = 0.00086,Fig.1b) compared to women. Similarly, a significant interaction between Aβ and sex on plasma p‐tau181 (β = 0.47,p = 0.005,Fig1a) and p‐tau217 (β = 1.01,p = 0.002,Fig.1b) was observed only in the Ast+ individuals. Voxel‐wise analysis showed the differences in the topographical association between Aβ and plasma p‐tau epitopes between men and women. In men but not women, Aβ‐PET associates with plasma p‐tau181 (Fig.2) and plasma p‐tau217 (Fig.3) in important AD regions such as precuneus, posterior cingulate and orbitofrontal cortex. Conclusion: A sex effect was observed in the association between Aβ burden and plasma p‐tau epitopes in individuals with increased astrocyte reactivity, with the association being stronger in men than women. The greater effect of Aβ on tau phosphorylation in the presence of Ast+ in men than in women may have implications for interpretation of biomarkers in clinical trials testing as anti‐Aβ therapies already showed different effects between men and women. [ABSTRACT FROM AUTHOR]

Background: A significant percentage of Aβ‐positive cognitively unimpaired (CU) individuals do not develop detectable downstream tau pathology and, consequently, cognitive decline.Experimental literature suggest that reactive astrocytes are necessary to unleashing Aβ effects in pathological tau phosphorylation.Here we aimed to investigate whether astrocyte reactivity is key to determining the association of Aβ burden with early tau phosphorylation in preclinical Alzheimer's disease (AD). Method: We assessed 1,016 CU individuals from two research and one population‐based cohort (TRIAD, Pittsburgh and MYHAT) with Aβ (plasma or PET), plasma p‐tau and GFAP measures. Individuals were classified as positive (Ast+) or negative (Ast‐) for astrocyte reactivity using a cutoff based on plasma GFAP of younger Aβ‐ individuals.Lowess method and linear regressions accounting for age and sex were used to model the trajectories of plasma p‐tau epitopes as a function of Aβ burden. Cohen's d corrected for age and sex was used to estimate effect sizes between groups. Result: We observed that plasma p‐tau181 levels increased as a function of Aβ only in CU Ast+ individuals from Pittsburgh (β = ‐0.35,t = 3.10,p = 0.003,Fig.1a,b), MYHAT (β = ‐0.20,t = 2.26,p = 0.026, Fig.1d,e) and TRIAD (β = 0.46,t = 2.92,p = 0.004;Fig.1g,h) cohorts. A significant interaction between Aβ burden and astrocyte reactivity status on plasma p‐tau181 levels was observed in the Pittsburgh (β = ‐0.29,t = 2.30,p = 0.022;Fig.1b), MYHAT (β = ‐0.19,t = 2.07,p = 0.038;Fig.1e) and TRIAD (β = 0.46,t = 2.92,p = 0.004;Fig.1h) cohorts.Cohen's d analysis revealed that the presence of Aβ+ and Ast+ has a large magnitude of effect on tau phosphorylation (Cohen's d:Pittsburgh = 0.67; MYHAT = 0.69;TRIAD = 0.98;Fig.1c,f,i), whereas Aβ+ in the absence of Ast+ presented a negligible effect size. Similar results were observed for plasma p‐tau231 and p‐tau217.Voxel‐wise analysis confirmed that Aβ levels in brain regions known to present early Aβ accumulation in AD associated with plasma p‐tau181 only in Ast+ individuals (Fig.2).Tau‐PET deposition occurred as a function of Aβ burden only in CU Ast+ (Fig.3a), affecting 100% and 62% of the extension of the Braak I and II regions, respectively (Fig.3b) Conclusion: We observed biomarker evidence across multiple cohorts that the presence of astrocyte reactivity, measured by plasma GFAP, plays a key role in the association of Aβ with early tau pathology in preclinical AD.Our results might have implications for the biological definition of preclinical AD and selecting individuals for early preventive clinical trials. [ABSTRACT FROM AUTHOR]

Background: It has been suggested that phosphorylated tau(p‐tau) at threonine 231 and 217 are markers of amyloid‐β(Aβ) rather than tau pathology. However, most of the studies were conducted in cohorts composed mainly of preclinical Alzheimer Disease(AD) individuals. It remains to be elucidated if p‐tau is still more associated with Aβ than tau pathology in symptomatic individuals. Here, we evaluate the contribution of each plasma biomarker to the demographics in identifying brain Aβ and tau pathologies across the AD spectrum. Method: We evaluated 138 cognitively unimpaired(CU) and 87 cognitively impaired(CI) individuals with available Aβ[18F]AZD4694 PET and tau[18F]MK‐6340 PET, plasma Aβ42/40, p‐tau (at threonine 181, 217, and 231), neurofilament light chain, and glial fibrillary acidic protein(GFAP), from the McGill TRIAD cohort(Table1). The performance of plasma biomarkers in predicting Aβ‐ and tau‐PET abnormalities over that one provided by demographics‐only(age and sex) was evaluated using logistic and linear regression, receiver operating characteristic analysis, and goodness‐of‐fit metrics. Using voxel‐wise linear regression models, we assessed the brain regions where plasma biomarker contribution to the demographic‐only model overlapped to detect Aβ and tau‐PET signals. Result: Our results demonstrated that in the CU only plasma p‐tau231 and p‐tau217+ significantly added to the demographics‐only model to detect Aβ pathology(Figure 1A), while no plasma biomarker added information to identify tau pathology(Figure 1B). In the CI, plasma p‐tau217+ and GFAP significantly added to the demographic‐only model to identify tau and Aβ pathology(Figure 1C), while p‐tau231 only added to detect tau deposition(Figure 1D). P‐tau181, Aβ42/40, and NfL did not significantly add to the demographics‐only in CU or CI group (Figure 1). Voxel‐wise analysis demonstrated that in CU, p‐tau231 and p‐tau217+ were only regionally associated with Aβ‐PET(Figure 2A‐C). In CI, p‐tau231 provided additional information on tau tangle accumulation in AD‐related regions(Figure 2D). On the other hand, for p‐tau217+, 3% of brain regions were only associated with Aβ‐PET, 35% with only tau‐PET, and 39% overlapped with both(Figure 2E). Conclusion: Our results support plasma p‐tau231 and p‐tau217+ as state markers of Aβ deposition in preclinical AD. In CI, plasma p‐tau231 is mainly related to tau pathology, and p‐tau217+ appears to be linked to both Aβ and tau pathology. [ABSTRACT FROM AUTHOR]

Background: Tau plays a prominent role in the synapse and has been shown to be closely related to neurodegeneration. Recent evidence show that cerebrospinal fluid (CSF) synaptic markers are related to CSF tau and degeneration. However, it is not clear whether synaptic dysfunction in combination with tau tangles is associated with neurodegeneration in the same brain regions. Our study aims to map in the brain the association of synaptic markers with tau and degeneration. Method: We evaluated 147 individuals from the TRIAD cohort with CSF GAP43, SYT1, SNAP25, neurogranin (Ng), ptau217, neurofilament light protein (NfL) quantification, as well as tau positron emission tomography (PET), magnetic resonance imaging, and clinical assessments. One‐way ANOVA tested group differences on synaptic levels. Spearman correlation tested the association among synaptic proteins. Linear regressions tested the association between biomarkers. Voxel‐based morphometry (VBM) was considered as an index of neurodegeneration. Result: We observed an increase in synaptic markers across age and the AD spectrum, reaching a plateau when cognition is affected (Figure 1A). Among the synaptic markers, SNAP25 showed the greatest magnitude of change, on average, between young, aged cognitively unimpaired, mild cognitive impaired, and AD individuals (mean 62.44%). (Figure 1B‐C). In addition, we found that all synaptic markers are significantly intercorrelated (Figure 1D). Moreover, CSF ptau217 correlated with all synaptic biomarkers, GAP43 (ß = ‐044), SYT1 (ß = ‐0.41), SNAP25 (ß = ‐0.59) and Ng (ß = ‐0.25). Conversely, CSF NfL was strongly but only correlated with GAP43 (ß = 0.62) (Figure 2). Voxel‐wise correlation revealed that tau tangles measured by [18F]‐MK6240 PET, was positively associated with SNAP25 in AD‐related regions, and VBM was associated with all synaptic markers, but more closely with GAP43. Furthermore, the interaction between synaptic markers and temporal meta‐ROI [18F]‐MK6240 on VBM was significant for Ng and GAP43 (Figure 3). Conclusion: Our results support a heterogenous role of synaptic markers in the AD continuum and suggest that synaptic dysfunction in the presence of tau pathology may not be driving atrophy. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Fluid biomarkers capable of specifically tracking tau tangle pathology in vivo are greatly needed. METHODS: We measured cerebrospinal fluid (CSF) and plasma concentrations of N‐terminal tau fragments (NTA‐tau), using a novel immunoassay (NTA) in the TRIAD cohort, consisting of 272 individuals assessed with amyloid beta (Aβ) positron emission tomography (PET), tau PET, magnetic resonance imaging (MRI) and cognitive assessments. RESULTS: CSF and plasma NTA‐tau concentrations were specifically increased in cognitively impaired Aβ‐positive groups. CSF and plasma NTA‐tau concentrations displayed stronger correlations with tau PET than with Aβ PET and MRI, both in global uptake and at the voxel level. Regression models demonstrated that both CSF and plasma NTA‐tau are preferentially associated with tau pathology. Moreover, plasma NTA‐tau was associated with longitudinal tau PET accumulation across the aging and Alzheimer's disease (AD) spectrum. DISCUSSION: NTA‐tau is a biomarker closely associated with in vivo tau deposition in the AD continuum and has potential as a tau tangle biomarker in clinical settings and trials. Highlights: An assay for detecting N‐terminal tau fragments (NTA‐tau) in plasma and CSF was evaluated.NTA‐tau is more closely associated with tau PET than amyloid PET or neurodegeneration.NTA‐tau can successfully track in vivo tau deposition across the AD continuum.Plasma NTA‐tau increased over time only in cognitively impaired amyloid‐β positive individuals. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Plasma biomarkers are promising tools for Alzheimer's disease (AD) diagnosis, but comparisons with more established biomarkers are needed. METHODS: We assessed the diagnostic performance of p‐tau181, p‐tau217, and p‐tau231 in plasma and CSF in 174 individuals evaluated by dementia specialists and assessed with amyloid‐PET and tau‐PET. Receiver operating characteristic (ROC) analyses assessed the performance of plasma and CSF biomarkers to identify amyloid‐PET and tau‐PET positivity. RESULTS: Plasma p‐tau biomarkers had lower dynamic ranges and effect sizes compared to CSF p‐tau. Plasma p‐tau181 (AUC = 76%) and p‐tau231 (AUC = 82%) assessments performed inferior to CSF p‐tau181 (AUC = 87%) and p‐tau231 (AUC = 95%) for amyloid‐PET positivity. However, plasma p‐tau217 (AUC = 91%) had diagnostic performance indistinguishable from CSF (AUC = 94%) for amyloid‐PET positivity. DISCUSSION: Plasma and CSF p‐tau217 had equivalent diagnostic performance for biomarker‐defined AD. Our results suggest that plasma p‐tau217 may help reduce the need for invasive lumbar punctures without compromising accuracy in the identification of AD. Highlights: p‐tau217 in plasma performed equivalent to p‐tau217 in CSF for the diagnosis of AD, suggesting the increased accessibility of plasma p‐tau217 is not offset by lower accuracy.p‐tau biomarkers in plasma had lower mean fold‐changes between amyloid‐PET negative and positive groups than p‐tau biomarkers in CSF.CSF p‐tau biomarkers had greater effect sizes than plasma p‐tau biomarkers when differentiating between amyloid‐PET positive and negative groups.Plasma p‐tau181 and plasma p‐tau231 performed worse than p‐tau181 and p‐tau231 in CSF for AD diagnosis. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Phosphorylated tau (p‐tau) biomarkers have been recently proposed to represent brain amyloid‐β (Aβ) pathology. Here, we evaluated the plasma biomarkers' contribution beyond the information provided by demographics (age and sex) to identify Aβ and tau pathologies in individuals segregated as cognitively unimpaired (CU) and impaired (CI). METHODS: We assessed 138 CU and 87 CI with available plasma p‐tau231, 217+, and 181, Aβ42/40, GFAP and Aβ‐ and tau‐PET. RESULTS: In CU, only plasma p‐tau231 and p‐tau217+ significantly improved the performance of the demographics in detecting Aβ‐PET positivity, while no plasma biomarker provided additional information to identify tau‐PET positivity. In CI, p‐tau217+ and GFAP significantly contributed to demographics to identify both Aβ‐PET and tau‐PET positivity, while p‐tau231 only provided additional information to identify tau‐PET positivity. DISCUSSION: Our results support plasma p‐tau231 and p‐tau217+ as state markers of early Aβ deposition, but in later disease stages they inform on tau tangle accumulation. Highlights: It is still unclear how much plasma biomarkers contribute to identification of AD pathology across the AD spectrum beyond the information already provided by demographics (age + sex).Plasma p‐tau231 and p‐tau217+ contribute to demographic information to identify brain Aβ pathology in preclinical AD.In CI individuals, plasma p‐tau231 contributes to age and sex to inform on the accumulation of tau tangles, while p‐tau217+ and GFAP inform on both Aβ deposition and tau pathology. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Amyloid‐β (Aβ) and tau can be quantified in blood. However, biological factors can influence the levels of brain‐derived proteins in the blood. The blood‐brain barrier (BBB) regulates protein transport between cerebrospinal fluid (CSF) and blood. BBB altered permeability might affect the relationship between brain and blood biomarkers. METHODS: We assessed 224 participants in research (TRIAD, n = 96) and clinical (BIODEGMAR, n = 128) cohorts with plasma and CSF/positron emission tomography Aβ, p‐tau, and albumin measures. RESULTS: Plasma Aβ42/40 better identified CSF Aβ42/40 and Aβ‐PET positivity in individuals with high BBB permeability. An interaction between plasma Aβ42/40 and BBB permeability on CSF Aβ42/40 was observed. Voxel‐wise models estimated that the association of positron emission tomography (PET), with plasma Aβ was most affected by BBB permeability in AD‐related brain regions. BBB permeability did not significantly impact the relationship between brain and plasma p‐tau levels. DISCUSSION: These findings suggest that BBB integrity may influence the performance of plasma Aβ, but not p‐tau, biomarkers in research and clinical settings. HIGHLIGHTS: BBB permeability affects the association between brain and plasma Aβ levels.BBB integrity does not affect the association between brain and plasma p‐tau levels.Plasma Aβ was most affected by BBB permeability in AD‐related brain regions.BBB permeability increases with age but not according to cognitive status. [ABSTRACT FROM AUTHOR]

Background: Recent epidemiological studies showed that patients with attention‐deficit/hyperactivity disorder (ADHD) are more likely to be diagnosed with Alzheimer's Disease (AD). Additionally, increased genetic risk for ADHD, measured with ADHD polygenic risk scores (ADHD‐PRS), was associated with amyloid‐dependent cognitive decline in older adults. However, it is unclear whether higher genetic risk for ADHD is associated with worse cognitive function in patients with AD dementia. Method: We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate the association between cognitive function (Preclinical Alzheimer Cognitive Composite [PACC], executive function, and memory) and ADHD‐PRS in subjects with AD dementia. Additionally, we tested whether ADHD‐PRS potentiated brain hypometabolism measured with [18F]FDG‐PET. Analyses were controlled by age, sex, years of study, and number of APOE ε4 alleles. Result: We evaluated baseline data from 264 AD patients (114 women [43.2%], mean [SD] age of 75 [7.6] years). ADHD‐PRS was associated with decreased cognitive function (p‐value =.04, η2 =.01, Figure 1a), more specifically in executive function (p‐value =.04, η2 =.01, Figure 1c). Higher ADHD‐PRS was associated with brain hypometabolism in frontal, parietal, and temporal regions (Figure 2a,b). Brain hypometabolism correlated with worse cognitive function in the following regions: postcentral gyrus (p‐value =.01, η2 =.03), superior parietal gyrus (p‐value =.001, η2 =.07), precentral gyrus (p‐value =.004, η2 =.05), and fusiform gyrus (p‐value<.0001, η2 =.08, Figure 2c,d,e,f). Finally, decreased metabolism in these four regions mediated the effect of ADHD‐PRS on cognitive function (Figure 3). Conclusion: Findings indicate that a higher genetic risk for ADHD is correlated with impaired cognitive function with small effect sizes. Moreover, the effects of the genetic risk of ADHD on cognitive function were mediated by hypometabolism in frontal, parietal, and temporal brain regions, which could point to a decrease resilience to AD pathology in individuals with ADHD. Clinically, our findings suggest that patients with comorbid ADHD and AD dementia have a more severe disease phenotype, with potential implications for prognosis and treatment. [ABSTRACT FROM AUTHOR]

Background: Age‐related memory decline is well associated with hippocampal atrophy, although the specific role of hippocampal subfields in memory function has not been thoroughly investigated yet. In this study, we investigated the associations between hippocampal subfield volumes and free recall and recognition performances in verbal and visual memory tasks in older adults without dementia. Method: We selected 97 right‐handed participants without dementia aged 60 to 85, 42 males, from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort. From T2‐weighted MRIs with 0.7*0.7*1mm voxel size, the hippocampi were segmented five subfields: 1) DG (DG); 2) CA2 with CA3 (CA2/CA3); 3) CA1; 4) strata radiatum, lacunosum and moleculare (SRLM); and 5) subiculum by using the MAGeT‐Brain algorithm. Memory was assessed with Rey Auditory Learning test for verbal free recall (RAVLT7) and recognition (RAVLT‐R), and with Aggie Figure Learning test for visual free recall (AFLT7) and recognition (AFLT‐R). Linear models were used to test the association between hippocampal subfield volumes and memory performances with age, sex and total intracranial volume as covariates (p<0.05, corrected for false discovery rate). Result: The volumes of bilateral CA1 and SRLM were significantly associated with RAVLT7 and RAVLT‐R. The right DG volume was significantly associated with AFLT7 and RAVLT7. AFLT7 was also associated with the volume of right CA2/CA3 while AFLT‐R was not associated with any hippocampal subfield (see Figures 1 & 2). Conclusion: Our results are consistent with the view that hippocampal subfields contribute differently to memory traces: CA1 for recollection that requires contextual information; the DG for pattern separation (creating new distinct representations of each stimulus); and CA3 for pattern completion (the recollection of a stimulus from a partial memory). Contextual information is more important when recollecting known words, while distinct and efficient encoding may be especially useful for search strategies inherent to free recall. Pattern completion could be particularly useful to efficiently recollect visual abstract figures based on visual features during free recall. Overall, this shows that hippocampal subfield segmentation offers a better understanding of their distinctive roles in cognition. [ABSTRACT FROM AUTHOR]

Background: Different methods have been proposed to assess vascular dysfunction in Alzheimer's disease (AD), mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects including quantitative susceptibility mapping (QSM), and recently oxygen extraction fraction (OEF). QSM is sensitive to the presence of iron and is a non‐invasive MR approach to measure local tissue susceptibility with high spatial resolution. OEF is a measure of the percentage of oxygen taken from the brain's blood supply and relates directly to brain oxygen metabolism. Here, we aimed to examine QSM (Figure1) and OEF (Figure1) potential in differentiating AD, MCI, and CU. Method: A cohort of 310 subjects with AD (n = 48), MCI (n = 80), and CU (n = 182) were recruited. All the patients received 3D gradient‐recalled echo sequence MRI. All QSM images were constructed by MEDI+0 and OEF images were constructed by QSM+qBOLD model with CCTV (Temporal clustering, tissue composition, and total variation). Eighty‐two various brain regions of interest (ROI) and 6 Braak ROI were investigated in the current study. ANOVA and Posthoc least significant difference (LSD) tests were implemented for each ROI over AD, MCI, and CU. Result: Over Braak ROI analysis, the ANOVA test showed that the average susceptibility of QSM with Braak 4 ROI is significantly different (p<0.001). LSD test showed that Braak 4 ROI of QSM is significantly different between AD vs. MCI and AD vs. CU (p<0.001). Braak 2 ROI of OEF was significantly different between AD vs. MCI and AD vs. CU (p = 0.21 and p = 0.32, respectively). Over brain ROI, the ANOVA test showed QSM right and left posterior cingulate and right Caudate were significantly different (p<0.001). Besides, these ROIs were significantly different between AD vs. MCI and AD vs. CU, according to the LSD test. Over brain ROI, OEF right Caudate was significantly different (p<0.001) followed by left Caudate and Right transverse temporal with p = 0.002 based on the ANOVA test. However, right and left Caudate were just significantly different between AD vs. CU (p<0.001) based on the LSD test (Table 1 and 2). Conclusion: QSM and OEF are affordable non‐invasive MR approaches to assess vascular dysfunction in the spectrum of clinical presentations of AD. [ABSTRACT FROM AUTHOR]

Background: To compare the post mortem uptake of [18F]MK6240 and [18F]PI2620 in the brain hemisphere, of A‐T‐ (CN) and A+T+ tissue Method: Autoradiography (ARG) imaging using [18F]MK6240 and [18F]PI2620 was used to assess uptake of CN and AD tissues. One study used sections of whole‐hemisphere of one CN tissue and one AD tissue. The uptake value of [18F]MK6240 was calculated from a regions of interest (ROI) drawn manually from the area of cortex gray matter (GM), cortex white matter (WM), and background (BG). Other study was 24 tissues from the prefrontal cortex (Pfc) (12CN, 12AD), hippocampus (Hip) (12 CN, 12 AD) and cerebellum (Cer) (12 CN, 12 AD) were evaluated with [18F]MK6240, and [18F]PI2620. The uptake value of [18F]MK6240 and [18F]PI2620 was calculated from ROI drawn manually from the area of pfc GM, hip GM and cer GM. Result: The ARG showed greater [18F]MK6240 uptake in AD than in CN tissue. There were significant differences between CN and AD tissue in terms of cortex gray GM distribution and cortex WM, but no significant differences in BG. These results also, there is no correlation between the specific activity of [18F]MK6240 and the uptake value in CN and AD tissues. In a second experiment, based on our findings there was a significant difference between CN and AD groups when assessed by [18F]MK6240 as well as [18F]PI2620 in the pfc_GM and hip_GM. Furthermore, we found no difference between AD and CN tissues when assessed by [18F]MK6240 and [18F]PI2620 in cerebellum GM. Ratio values Pfc/Cer and Hip/Cer assessed by [18F]MK6240 as well as there were significant differences between CN and AD. [18F]MK6240 and [18F]PI2620 were highly correlated in AD but not in CN brain tissues. Conclusion: We also found that [18F]PI2620 and [18F]MK6240 have similar brain uptake in post‐mortem tissue. [ABSTRACT FROM AUTHOR]

Background: There is a growing body of evidence suggesting changes in blood flow and metabolism as a trigger of the cascade of events leading to Alzheimer's disease (AD). Oxygen extraction fraction (OEF) MRI images provide valuable information about the brain's metabolism and blood flow in stroke, brain injury, and recently neurodegenerative diseases. However, OEF is a research tool, and its clinical utility is still being evaluated. Here, we aimed to assess brain oxygenation in aging and AD using OEF images. We test the hypothesis that OEF reduction is associated with the clinical stages of AD. Method: A cohort of 310 subjects with cognitively unimpaired (CU) (n = 182), mild cognitive impairment (MCI) (n = 80), and AD (n = 48), were recruited. All participants received 3D gradient‐recalled echo sequence MRI. All OEF images were constructed by QSM+qBOLD model with CCTV (Temporal clustering, tissue composition, and total variation) (Figure1). Eighty‐two various brain regions of interest (ROI) and 6 Braak ROI were investigated in the current study. Result: Our results demonstrated that the OEF mean value in almost all of the brain ROI encompassed the lowest amount of OEF in AD compared with MCI and CU. In most parts of the brain, the OEF mean value was AD

Background: To compare the performance of plasma p‐tau181, plasma‐NfL and FDG‐PET in the diagnosis of biological AD. Method: We included 787 participants (cognitively unimpaired (CU, n = 251), mild cognitive impairment (MCI, n = 412), and Alzheimer's dementia (AD, n = 124) from the ADNI database. Participants underwent measures of plasma p‐tau181, plasma‐NfL, CSF P‐tau181, [18F]FDG‐PET, amyloid‐PET and cognitive screening tests. ROC analyses were used to determine diagnostic accuracy of plasma P‐tau181, NfL and [18F]FDG‐PET using clinical diagnosis and core AD biomarkers (amyloid‐PET and CSF P‐tau181) as reference standards. We assessed whether plasma P‐tau181 and [18F]FDG‐PET diagnostic accuracies were significantly different with a bootstrap‐based test (pROC package in R). Second, correlations of [18F]FDG‐PET, plasma‐NfL, plasma P‐tau181 with P‐tau181 CSF, Aβ‐PET, and cognitive performance were evaluated. Correlation coefficients were compared via Cocor package, a statistical framework for comparing associations between intercorrelated measurements. Result: Across the total sample we observed that plasma P‐tau181, plasma‐NfL, and [18F]FDG‐PET were able to identify individuals with CSF evidence of AD as well as participants who are amyloid‐PET+. In the MCI group, plasma P‐tau181 outperformed [18F]FDG‐PET and plasma‐NfL in identifying AD pathophysiology measured via CSF P‐tau181 (p = 0.0007), and amyloid‐PET (p = 0.001). We also observed that both plasma P‐tau181, plasma‐NfL and [18F]FDG‐PET were associated with AD pathophysiology measured by core AD biomarkers (CSF and amyloid‐PET). However, [18F]FDG‐PET was more closely associated with cognitive outcomes than plasma P‐tau181 and plasma NfL (MoCA: p < 0.0001; MMSE: p< 0.0001; CDR‐SB: p < 0.0001) Conclusion: This study investigated the diagnostic properties of plasma P‐tau181 and two neurodegenerative biomarkers (plasma‐NfL and [18F]FDG‐PET) as well as their to identify biological AD. While plasma P‐tau181, plasma‐NfL concentrations and [18F]FDG‐PET were associated with AD pathophysiology measured by core AD biomarkers (CSF and amyloid‐PET), plasma P‐tau181 outperformed [18F]FDG‐PET and plasma‐NfL in identifying individuals with AD pathophysiology. However, we also observed that [18F]FDG‐PET was more strongly associated with neuropsychological assessments than plasma P‐tau181 and plasma‐NfL. Taken together, our study suggests that plasma P‐tau181 may aid in the evaluation of individuals by identifying those with underlying AD pathophysiology. [ABSTRACT FROM AUTHOR]

Background: The sequential model predicts synaptic depletion as a downstream of amyloid, tau and neuroinflammation. However, synaptic toxicity might be consequence of toxic forms of amyloid in the absence of tau. In this study we explore the role of synaptic depletion and neuroinflammation as determinants of tau pathology. GAP‐43 and neurogranin are pre‐ and post‐synaptic biomarkers known to be detected at elevated levels in cerebrospinal fluid (CSF) of patients with Alzheimer's disease (AD). Method: We included 126 individuals from TRIAD cohort. Brain inflammation, tau tangle and amyloid‐β (Aβ) deposition were assessed via [11C]PBR28‐PET, [18F]MK6240‐PET and [18F]AZD4694‐PET, respectively. All patients had plasma GFAP quantified, and a subset of 78 individuals had CSF GFAP, CSF Neurogranin and CSF GAP‐43 quantifications available. Voxel and region of interest regression models evaluated the relationship between PET tracers and the fluid biomarkers. Models with [18F]AZD4694‐PET as the outcome were adjusted for [18F]MK6240‐PET voxel‐wise and vice‐versa. A linear regression interaction model evaluated the interaction between CSF GFAP and both synaptic biomarkers with Aβ‐ and tau‐PET as the outcome. Result: Positive associations were found between CSF neurogranin and Aβ‐, tau‐ and brain inflammation‐PET in AD related regions; the strongest associations were found with tau‐pet and CSF neurogranin in the medial temporal lobe. GAP‐43 was also positively associated with tau‐ and TSPO‐PET, but no associations were found with Aβ‐PET. Both plasma and CSF GFAP were associated with Aβ‐, tau‐ and TSPO‐PET in AD related regions, with the strongest t‐values in the model including plasma GFAP and amyloid‐PET. A negative interaction was found between CSF GFAP and both synaptic biomarkers with amyloid‐ and tau‐PET as the outcomes. These associations were stronger with tau‐PET and were found in the temporal, occipital and parietal areas. Conclusion: This study supports the role of synaptic dysfunction and neuroinflammation on tau load. As both synaptic biomarkers and GFAP increase as a function of tangles load, tau accumulation is linked to the relationship between synaptic abnormalities and inflammation in early stages of the disease. Our results suggest that synaptic depletion is a phenomenon that might start prior to tau tangles. [ABSTRACT FROM AUTHOR]

Background: Accumulation of tau neurofibrillary tangles in Alzheimer's disease (AD) follows a stereotypical pattern, known as Braak staging. As individuals show different rates of tau accumulation, depending on their initial stage, this potentially biases drug effects on tau pathology over time. We hypothesized that amyloid would be a driving force behind tau deposition in later Braak regions for those that were at earlier stages of the disease. Method: 79 individuals (CU: 57, MCI: 21, AD: 1) were recruited from the Translational Biomarkers of Aging and Dementia (TRIAD) cohort. All individuals underwent baseline and 2‐year follow‐up amyloid ([18F]AZD4694) and tau ([18F]MK6240) PET imaging, as well as baseline plasma pTau181, pTau217, and pTau231 assessments. Differences in standardized uptake value ratios between the two timepoints in Braak I‐IV were used to separate fast accumulators of tau from slow accumulators, using the standard deviation of a Young group (n = 13). Pearson R's were calculated between tau deposition in Braak regions IV‐VI and the neocortical amyloid load at baseline, within both these groups. Furthermore, a voxelwise analysis was performed to identify specific regional differences in amyloid SUVR between slow and fast progressors, while correcting for age, sex, ApoE4 carriership, as well as the individual's Braak stage. In addition, an independent t‐test was performed to compare the levels of each pTau species at baseline. Result: Fast accumulators had stronger increases (p < 0.01) in tau accumulation in Braak region V and VI, compared to slower accumulators. Furthermore, the level of tau deposition in Braak region IV and V was positively associated (p < 0.01) with the amyloid load in the neocortex, but only in the fast accumulator group (figure 1). The voxelwise analysis confirmed that faster progressors had stronger associations with amyloid‐PET (figure 2). Furthermore, those who accumulated tau at a faster rate also had higher levels of plasma pTau at baseline (figure 3). Conclusion: Tau deposition in later Braak regions is associated with a higher amyloid load and increased values of pTau181, pTau217 and pTau231 at baseline in individuals in early Braak stages that accumulate tau at a more rapid rate. [ABSTRACT FROM AUTHOR]

Background: Aβ accumulation precedes tau pathology in cognitively unimpaired (CU) individuals, which is an event closely related to the development of cognitive symptoms. However, Aβ leads to tau pathology in some individuals, but not in others, suggesting the presence of other processes triggering the deleterious effects of Aβ in the early Alzheimer's disease (AD) stages. It was already demonstrated that glial fibrillary acidic protein (GFAP)‐positive astrocytes can internalize tau and might contribute to its propagation. Here we investigated whether astrocyte reactivity is a key element to determining the longitudinal accumulation and spreading of tau pathology in CU individuals. Method: We assessed 147 CU individuals from the TRIAD cohort with available baseline Aβ‐ and Tau‐PET, plasma p‐tau181 and GFAP. Individuals were classified as positive (Ast+) or negative (Ast‐) for astrocyte reactivity using a cutoff based on plasma GFAP levels of younger Aβ‐ individuals. Longitudinal analysis included 71 CU individuals with Tau‐PET (mean follow‐up:2.3 years).Voxel‐wise associations between biomarkers were tested using linear regressions accounting for age, sex, and adjusting for multiple comparisons. We measured the annual rate of progression in Tau uptake as the difference between follow‐up and baseline uptakes divided by the time between scans. To assess individuals' percentage of abnormal regions, we used composite brain regions corresponding to Braak histopathological stages. Result: Tau‐PET deposition occurred as a function of Aβ burden only in CU Ast+ and in regions expected to present the earliest tau deposition (Fig.1a). In the longitudinal analysis, we observed that the annual rate of tau‐PET accumulation was higher in the CU Ast+ group (Fig.2a) and predicted by baseline Aβ burden only in CU Ast+ (Fig.2b). Interestingly, while the baseline association was confined to the mesial temporal cortex, the longitudinal tau‐PET accumulation as a function of Aβ/Ast presented initial tau spread over the neocortex in Braak III‐IV regions (Fig.2c). Conclusion: We observed that Tau‐PET accumulation was highest in CU Ast+ individuals, with astrocyte reactivity being necessary for the spreading of tau over the neocortex. Our findings suggest that astrocyte reactivity is an important upstream event to unleash tau accumulation and spread in CU individuals, which might have implications for selecting individuals for early preventive clinical trials. [ABSTRACT FROM AUTHOR]

Background: While in healthy conditions, synaptic function is sustained by the interplay between neurons and glial cells. The presence of Alzheimer's disease (AD) pathophysiology might impose synaptic alterations via neuroinflammatory responses from microglia and astrocytes. Imaging and fluid biomarkers allow for testing this conceptual framework in living individuals. Here, we aim at uncovering the associations between neuroinflammatory and synaptic markers in aging and AD. Method: Participants were recruited from the Translational Biomarker for aging and dementia cohort (TRIAD). We analyzed cognitively unimpaired young (CUY, N = 20), cognitively unimpaired older adults (CU; N = 48) individuals, patients with mild cognitive impairment (MCI; N = 24), AD dementia patients (ADD; N = 16) participants. The endpoints were CSF 14‐3‐3 sigma‐delta (ζδ), CSF Growth Associated Protein 43 (GAP43) and neurogranin as markers of synaptic dysfunction. We modeled associations between these synaptic biomarkers with astrocyte and microglial related inflammatory markers as well as tau, amyloid and neurodegeneration biomarkers. Result: According to ROC analyses contrasting CSF biomarkers, 14‐3‐3 ζδ was able to discriminate amyloid‐ β pathology in cognitively impaired individuals (AUC = 0.88) (Figure 1). The amyloid‐induced 14‐3‐3 ζδ increase was mediated by inflammatory astrogliosis measured by GFAP, whereas the tau induced 14‐3‐3 ζδ pathology was mediated by anti‐inflammatory cytokines (Figure 2). CSF levels of GAP43 and neurogranin were positively associated with TSPO measured with [11C]PBR28(Figure 3.1) and GFAP (Figure 3.2). [11C]PBR28 cluster‐based SUVRs was positively correlated to concentrations of (A) Neurogranin (p = 5.8e‐08) and (B) GAP‐43(p = 6.8e‐10). Conclusion: The present observations support the framework in which glia‐related neuroinflammatory responses contribute to synaptic alterations in carriers of AD pathophysiology. We will discuss the implications and limitations of this framework. [ABSTRACT FROM AUTHOR]

Background: Glial contributions to Alzheimer's disease (AD) etiology and progression have been increasingly recognized. Experimental and clinical evidence suggests that both microglial activation and astrocyte reactivity have pivotal roles in the progression of tau pathology. However, it remains to be elucidated how microglia and astrocytes complement each other in AD pathogenesis. Here, we tested the associations between reactive astrogliosis and microglial activation with tau phosphorylation and aggregation in individuals across the aging and AD spectrum. Method: We studied 95 participants (14 cognitively unimpaired [CU] young, 46 CU elderly, and 35 cognitively impaired [CI]) from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort. Individuals had available positron emission tomography (PET) for Aβ ([18F]AZD4694), tau tangles ([18F]MK6240), and microglial activation ([11C]PBR28). We further assessed reactive astrogliosis (plasma glial fibrillary acidic protein [GFAP]) and pathological tau phosphorylation (plasma phosphorylated tau [p‐tau] at threonine 231, 181, and 217). Result: Demographic characteristics of the study population are reported in Table 1. Regression analyses revealed that reactive astrogliosis and microglial activation were synergistically associated with higher plasma phosphorylated tau levels independently of Aβ pathology in CU but not CI individuals (Figure 1A). On the other hand, no interactive effects of reactive astrogliosis and microglial activation on tau‐PET burden were observed in either CU or CI individuals (Figure 1B). Similar findings were observed in sensitivity analyses excluding CU young participants. Conclusion: Our results suggest that reactive astrocytes and activated microglia have Aβ‐independent synergistic effects on the early progression of AD pathophysiology by contributing to tau phosphorylation but not tau aggregation. These findings can help to better understand the complementary roles of glial cells in neurodegenerative diseases, as well as provide insights for the development novel therapeutic strategies for AD targeting the interplay between astrocyte and microglial reactivity. [ABSTRACT FROM AUTHOR]

Background: CSF total tau (t‐tau) is postulated as a marker of neuronal degeneration in the ATN criteria. Here, we hypothesize that CSF t‐tau is more closely related to synaptic loss than over neurodegeneration. Method: We evaluated 128 older individuals (75 CU,33 MCI,20 AD) in the TRIAD cohort (McGill, Canada) with CSF t‐tau, SNAP25long and Neurofilament Light (NfL), Amyloid‐beta (Aβ) positron emission tomography and magnetic resonance imaging, and clinical assessments. We used increased SNAP25long as an index of synaptic dysfunction (S+) and reduced hippocampal volume as neurodegeneration (N+). S and N positivity were determined using a cutoff anchored in young CU individuals. Linear regression models were used to test the association between CSF t‐tau, SNAP25long and NfL levels and hippocampal volume, adjusted for age, sex, and cognitive status. ANCOVA was employed to inspect CSF t‐tau levels across the groups 1) S‐N‐; 2) S+N‐; 3) S‐N+; 4) S+N+, adjusted for age, sex, cognitive status, and Aβ burden. Result: We found that a model with the addition of SNAP25long to the covariates explained 65% of the variance (p‐value<0.0001) of CSF t‐tau. Alternatively, hippocampal volume (R2 = 0.2187; p‐value<0.0001) and NfL (R2 = 0.3671; p‐value<0.0001) alone or in combination explained less than 40% of the variance of CSF t‐tau (R2 = 0.3597; p‐value<0.0001) (Figure 1). Interestingly, we observed an increase of CSF t‐tau in the S+N‐ group but not in the S‐N+, further supporting that CSF t‐tau is abnormal in the presence of synaptic dysfunction rather than neurodegeneration. Additionally, Loess regression lines confirm a larger magnitude of change of synaptic dysfunction as a function of t‐tau, compared with neurodegeneration (Figure 3). Conclusion: Our results suggest that CSF t‐tau may reflect synaptic loss rather than neuronal degeneration. This has implications for the interpretability of results obtained with CSF t‐tau in biomarker studies. [ABSTRACT FROM AUTHOR]

Background: Biomarkers of astrocyte reactivity have the potential to improve diagnostic precision, disease monitoring, and treatment efficacy. Glial fibrillary acidic protein (GFAP) protein is expressed in astrocytes, which is important to synaptic plasticity, cell communication, and reactive gliosis. Method: Cerebrospinal fluid (CSF) biomarkers were measured in participants of the McGill TRIAD cohort. 75 individuals (>50 years old, 44 cognitively unimpaired (CU), and 31 with cognitive impairment (CI)), had available Aβ and tau‐PET. We measured CSF GFAP and synaptic markers (growth‐associated protein 43 (GAP‐43), neurogranin (Ng), synaptotagmin 1 (SYT1), presynaptic protein synaptosomal‐associated protein 25 (SNAP‐25)). Linear regressions adjusted for age, sex, clinical diagnosis, and Aβ/tau‐PET were used to test the associations between astrocyte reactivity and synaptic function. Result: Demographic information is shown in Table 1. We found an association between CSF GFAP and presynaptic markers (GAP‐43: p<0.0001, β = 0.1076; SYT1: p<0.0001, β = 0.0024; SNAP‐25 long: p<0.0001, β = 0.0008) as well as postsynaptic markers (Ng: p<0.05, β = 0.0066) independently of Aβ and tau burden (Figure 1). Conclusion: Our biomarker results support experimental literature suggesting that astrocyte reactivity plays a role in downstream synaptic dysfunction independent of the brain levels of Aβ and tau tangles pathologies. This supports in vitro literature suggesting that therapeutic interventions targeting astrocyte reactivity can contribute to halting AD progression. [ABSTRACT FROM AUTHOR]

Background: Growing evidence suggests an increased prevalence of Alzheimer's disease (AD) in females compared to males. Elucidating how disease biomarkers correlate in females and males is critical to understanding the basis of sex differences in AD. Our aim was to evaluate sex‐related differences in the association of plasma Glial fibrillary acidic protein (GFAP) levels, a biomarker of astrocyte reactivity, with downstream neurodegeneration in Alzheimer's disease (AD) pathophysiology. Method: We cross‐sectionally assessed participants from TRIAD cohorts. Unpaired t‐test compare the difference in plasma GFAP levels between females and males. We performed linear regression with an interaction term for sex to compare the association of plasma GFAP with neurodegeneration measured with hippocampal volume (HCV) between cognitively impaired females and males. Result: We assessed 308 participants (MCI = 63, AD = 45 CN = 200, mean age = 69.8 (8)). Females showed significantly higher GFAP levels than males (Fig 1). We found a significant interaction term and strong correlation between plasma GFAP and neurodegeneration (HCV atrophy) (R‐squared = 0.13; P = 0.003) only in females. No association was found between GFAP and AD biomarkers in females or males without cognitive impairment (Fig 2). Conclusion: Our results suggest astrocyte reactivity is highly associated with neurodegeneration, a process known to play a key role in AD pathophysiology, in females than males. This may have important implications for elucidating the basis of the higher prevalence of AD in females. Yet, these results were generated with a limited number of subjects and using a cross‐sectional design. Therefore, replication in larger, independent longitudinal datasets is needed to better understand the association of GFAP levels, sex, and AD pathophysiology. [ABSTRACT FROM AUTHOR]

Background: Developing evidence highlight the importance of neuroinflammation in the progression of Alzheimer's disease (AD) pathophysiology. It has been shown that inflammatory mechanisms affect both amyloid and tau deposition in the brain. However, given the complexity of the immune cascades and the unknown interactions between the peripheral and central nervous systems in the disease, it has been challenging to identify biomarkers that reflect a given inflammatory state in this context. Thus we aim here to further explore inflammation‐related proteins for potential biomarker utility in AD. Method: Participants from the TRIAD cohort incorporating within the AD spectrum, and with available TSPO PET ([11C]PBR28) imaging data, had their cerebrospinal fluid (CSF) (n = 100) and plasma (n = 169) samples analyzed using Proximity Extension Assay (PEA) technology targeting 368 inflammation‐related proteins (Olink). LIMMA models evaluated the differential protein expression according to TSPO PET status (positivity attributed by 3 SD above the mean ROI‐SUVR of young participants), adjusting for age and brain amyloid load (PET). Associations were FDR corrected for multiple comparisons. Gene ontology (GO) was used for enrichment analysis on relevant results. Result: Preliminary results identified several proteins that are in higher concentration in the CSF of TSPO positive in comparison with TSPO negative participants (Figure 1). The enrichment analysis (Figure 2) unveiled terms related with cytokine and chemokine signaling pathways as well as with the regulation of mitogen‐activated protein kinase (MAPK) cascade, which is an interesting finding given the frequently reported association between MAPK cascade and AD. Conclusion: The initial assessment of the data revealed encouraging preliminary findings. However, more detailed analyses will be performed in both CSF and plasma to further investigate potential biomarkers and how they coverage between amyloid and tau deposition. [ABSTRACT FROM AUTHOR]

Background: Several recent studies have provided evidence for high diagnostic performance of plasma biomarkers for AD, with Area Under the Curve values sometimes exceeding 90%. However, positive and negative predictive values are rarely reported yet are critical for interpreting individual‐level screening test results. Method: We determined positive and negative predictive values of plasma biomarkers (p‐tau181, p‐tau217, p‐tau231, GFAP and NfL) for biological AD (A+T+, determined either with PET or CSF) in the TRIAD, WRAP, ALFA+, BIODEGMAR, ADNI and McGill memory clinic cohorts (total n = 2219). We also determined positive and negative predictive values for specific clinical scenarios: MCI and mild AD – two populations that may benefit from disease‐modifying therapies in AD. Prevalence estimates of biological AD in these groups were taken from the Mayo Clinic Study of Aging. Result: P‐tau217 had the highest positive and negative predictive values of all biomarkers in all cohorts, with above 80% positive and negative predictive values. In individuals with MCI, an etiologically heterogeneous syndrome, plasma p‐tau217 had sufficiently high performance to rule out biological AD in differential diagnosis. In mild AD dementia, plasma p‐tau217 could rule in biological AD, but follow‐up with PET/CSF will likely be needed to confirm the absence of AD. Conclusion: Plasma biomarkers have the potential to be used as stand‐alone diagnostic tools in specific clinical scenarios. In MCI, a negative plasma p‐tau217 could rule out AD as a cause of cognitive impairment, but a positive result should be followed up with PET/CSF. In mild AD dementia, a positive plasma p‐tau217 result can rule in biological AD, but a negative result should be followed up with PET/CSF. [ABSTRACT FROM AUTHOR]

Background: Recent studies have suggested that plasma p‐tau biomarkers are associated with cross‐sectional brain amyloid(Aβ) rather than tau tangle pathology. However, it is still unclear if plasma tau biomarkers are closely related to changes in AD pathophysiology over time. In this work, we aimed to determine if cross‐sectional measures of plasma tau biomarkers are associated with longitudinal changes in Aβ‐PET, tau‐PET, and cognition across the AD spectrum. Method: We evaluated 157 individuals(96 cognitively unimpaired(CU) and 61 cognitively impaired (CI)) with available baseline measures of plasma Aβ42/40, p‐tau(at threonine 181, 217+, and 231), N‐terminal tau fragments(NTA, a new brain derived tau marker), and glial fibrillary acidic protein(GFAP) and with longitudinal [18F]AZD4694 Aβ‐PET, [18F]MK6340 tau‐PET and Clinical Dementia Rating sum of boxes(CDR‐SB) score from the TRIAD cohort. We also included 321(118 CU and 203 CI) individuals from the ADNI cohort with baseline plasma p‐tau181 and [18F]florbetapir Aβ‐PET and longitudinal CDR‐SB. Using linear regressions adjusted for age and sex we tested the associations between plasma biomarkers and longitudinal changes in Aβ‐PET, tau‐PET, and CDR‐SB. Result: In CU, changes in tau‐PET were significantly associated only with plasma p‐tau217+(β = 0.331, p<0.01,Figure1A). Changes in Aβ‐PET were significantly associated with p‐tau181(β = 0.594, p<0.01,Figure2A), p‐tau217+(β = 0.311, p = 0.02) and NTA(β = 0.435, p<0.01), while no plasma biomarker was associated with changes in cognition in CU(Figure3A). In CI, changes in tau‐PET were significantly associated with plasma p‐tau181(β = ‐0.338, p = 0.03,Figure1B) and NTA(β = ‐0.379, p = 0.03), while no plasma biomarker was significantly associated with changes in Aβ‐PET. Changes in cognition were significantly associated with plasma p‐tau181(β = 0.340, p = 0.01, Figure3B), p‐tau217+(β = 0.54, p<0.01), and NTA(β = 0.530, p<0.01). Similarly, in the ADNI cohort, plasma p‐tau181 was associated with longitudinal changes in cognition in CI (β = 0.247, p<0.01,Figure3D). Conclusion: We demonstrate that cross‐sectional measures of plasma p‐tau and NTA biomarkers were associated with longitudinal Aβ deposition and tau accumulation in CU, while in CI they were only associated with tau accumulation. Interestingly, plasma tau biomarkers were also associated with changes in cognition in CI individuals. The fact that abnormal levels of plasma tau biomarkers are associated with longitudinal changes in AD pathophysiology has implications for the use of these markers in clinical trials and practice. [ABSTRACT FROM AUTHOR]

Background: The 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. Method: We 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. Result: We 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. Conclusion: Our 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. [ABSTRACT FROM AUTHOR]

Introduction: [18F]AZD4694 is an amyloid beta (Aβ) imaging agent used in several observational studies and clinical trials. However, no studies have yet published data on longitudinal Aβ accumulation measured with [18F]AZD4694. Methods: We assessed 146 individuals who were evaluated with [18F]AZD4694 at baseline and 2‐year follow‐up. We calculated annual rates of [18F]AZD4694 change for clinically defined and biomarker‐defined groups Results: Cognitively unimpaired (CU) older adults displayed subtle [18F]AZD4694 standardized uptake value ratio (SUVR) accumulation over the follow‐up period. In contrast, Aβ positive CU older adults displayed higher annual [18F]AZD4694 SUVR increases. [18F]AZD4694 SUVR accumulation in Aβ positive mild cognitive impairment (MCI) and dementia was modest across the neocortex Discussion: Larger increases in [18F]AZD4694 SUVR were observed in CU individuals who had abnormal amyloid positron emission tomography levels at baseline. [18F]AZD4694 can be used to monitor Aβ levels in therapeutic trials as well as clinical settings, particularly prior to initiating anti‐amyloid therapies. [ABSTRACT FROM AUTHOR]

Following the development of magnetic resonance imaging (MRI) methods to assay the integrity of catecholamine nuclei, including the locus coeruleus (LC), there has been an effort to develop automated methods that can accurately segment this small structure in an automated manner to promote its widespread use and overcome limitations of manual segmentation. Here we characterize an automated LC segmentation approach (referred to as the funnel-tip [FT] method) in healthy individuals and individuals with LC degeneration in the context of Alzheimer's disease (AD, confirmed with tau-PET imaging using [18F]MK6240). The first sample included n = 190 individuals across the AD spectrum from cognitively normal to moderate AD. LC signal assayed with FT segmentation showed excellent agreement with manual segmentation (intraclass correlation coefficient [ICC] = 0.91). Compared to other methods, the FT method showed numerically higher correlation to AD status (defined by presence of tau: Cohen's d = 0.64) and AD severity (Braak stage: Pearson R = -.35, cognitive function: R = .25). In a separate sample of n = 12 control participants, the FT method showed excellent scan--rescan reliability (ICC = 0.82). In another sample of n = 30 control participants, we found that the structure of the LC defined by FT segmentation approximated its expected shape as a contiguous line: <5% of LC voxels strayed >1 voxel (0.69 mm) from this line. The FT LC segmentation shows high agreement with manual segmentation and captures LC degeneration in AD. This practical method may facilitate larger research studies of the human LC-norepinephrine system and has potential to support future use of neuromelanin-sensitive MRI as a clinical biomarker. [ABSTRACT FROM AUTHOR]

Introduction: The optimal combination of amyloid‐β/tau/neurodegeneration (A/T/N) biomarker profiles for the diagnosis of Alzheimer's disease (AD) dementia is unclear. Methods: We examined the discriminative accuracy of A/T/N combinations assessed with neuroimaging biomarkers for the differentiation of AD from cognitively unimpaired (CU) elderly and non‐AD neurodegenerative diseases in the TRIAD, BioFINDER‐1 and BioFINDER‐2 cohorts (total n = 832) using area under the receiver operating characteristic curves (AUC). Results: For the diagnosis of AD dementia (vs. CU elderly), T biomarkers performed as well as the complete A/T/N system (AUC range: 0.90–0.99). A and T biomarkers in isolation performed as well as the complete A/T/N system in differentiating AD dementia from non‐AD neurodegenerative diseases (AUC range; A biomarker: 0.84–1; T biomarker: 0.83–1). Discussion: In diagnostic settings, the use of A or T neuroimaging biomarkers alone can reduce patient burden and medical costs compared with using their combination, without significantly compromising accuracy. [ABSTRACT FROM AUTHOR]

Background: Although it has been alredy demonstrated that plasma amyloid‐β (Aβ), phosphorylated tau (p‐tau), and glial fibrillar protein (GFAP) can predict with high accuracy Alzheimer's disease (AD) pathophysiology, no previous study has compared their performance in the same set of individuals. Here, we compare the performance of plasma Aβ42/40, p‐tau, GFAP, and NfL against Aβ and tau PET across the AD spectrum. Method: We used the ROC curve to test the predictive performance of Simoa plasma Aβ42/40, p‐tau (at threonine 181 and 231), NfL, and GFAP to identify Aβ and Pau PET positivity in 138 cognitive unimpaired (CU) and 87 cognitive impaired (CI) from the McGill TRIAD cohort. Pearson correlation and linear regression tested the association between markers. Result: We showed that plasma p‐tau231, p‐tau181, GFAP, and NfL correlated with each other (Figure 1), while Aβ42/40 did not. In CU, voxel‐wise linear regressions (Figure 2A) showed that p‐tau231, p‐tau181, and GFAP concentrations were significantly associated with Aβ‐PET (Figure 2A). While for Tau‐PET (Figure 2B), there was a significant association only with p‐tau231 and p‐tau181 (Figure 2B). P‐tau231 outperformed the other plasma biomarkers to identify both Aβ‐ and Tau‐PET positivity (AUC 0.877 and 0.796, respectively) in CU individuals. In CI, Aβ‐ and Tau‐PET were significantly associated with p‐tau231, p‐tau181, and GFAP, whereas NfL was only associated with Tau‐PET. The discriminative accuracy of GFAP in identifying both Aβ‐PET and Tau positivity (AUC 0.936 and 0.944, respectively) outperformed the other plasma biomarkers in CI individuals (Figure 3, Table 1). Conclusion: We showed that plasma p‐tau231, a novel biomarker of early AD, best depicted AD pathophysiology in CU individuals. Interestingly, GFAP, an astrocyte reactivity marker, was better associated with brain Aβ and tau pathologies than plasma p‐tau and Aβ markers in CI individuals. Our results highlight that the performance of the novel plasma biomarkers of amyloid, tau and neuroinflammation to detect brain AD pathophysiology is disease stage specific. [ABSTRACT FROM AUTHOR]

Background: Gold standard diagnostic methods for AD rely on staging systems to measure disease severity, which have not yet been incorporated into the in vivo biological research framework for AD. The topographical information conferred by tau‐PET offers the potential to translate the gold standard histopathological Braak staging system to living individuals. Method: Using the topographical information from [18F]MK6240 tau‐PET, we applied the Braak tau staging system to 324 living individuals. We used PET‐based Braak stage to model the trajectories of amyloid‐b, phosphorylated tau in cerebrospinal fluid (pTau181, pTau217, pTau231, pTau235) and plasma (pTau181, pTau231), neurodegeneration and cognitive symptoms. Result: PET‐based Braak stages were We identified nonlinear AD biomarker trajectories corresponding to the spatial extent of tau‐PET, with modest biomarker changes detectable by Braak stage II and significant changes occurring at stages III‐IV, followed by plateaus. Early Braak stages were associated with isolated memory impairment, while Braak stages V‐VI were incompatible with normal cognition. Follow up tau‐PET scans indicated sequential progression of in vivo Braak stages over time, with progression beyond Braak stage III requiring the presence of amyloid‐beta abnormality. Conclusion: Our results support PET‐based Braak staging as a framework to model the natural history of AD, as well as monitor AD severity from presymptomatic to clinical dementia phases. [ABSTRACT FROM AUTHOR]

Background and purpose: Abnormal mitochondrial metabolism has been described in the Alzheimer's disease (AD) brain. However, the relationship between AD pathophysiology and key mitochondrial processes remains elusive. The purpose of this study was to investigate whether mitochondrial complex I dysfunction is associated with amyloid aggregation or glucose metabolism and brain atrophy in patients with mild AD using positron emission tomography (PET). Methods: Amyloid‐ and tau‐positive symptomatic AD patients with clinical dementia rating 0.5 or 1 (N = 30; mean age ± standard deviation: 71.8 ± 7.6 years) underwent magnetic resonance imaging and PET scans with [18F]2‐tert‐butyl‐4‐chloro‐5–2H‐pyridazin‐3‐one (BCPP‐EF), [11C]Pittsburgh Compound‐B (PiB) and [18F]fluorodeoxyglucose (FDG) to assess brain atrophy, mitochondrial complex I dysfunction, amyloid deposition, and glucose metabolism, respectively. Local cortical associations among these biomarkers and gray matter volume were evaluated with voxel‐based regressions models. Results: [18F]BCPP‐EF standardized uptake value ratio (SUVR) was positively correlated with [18F]FDG SUVR in the widespread brain area, while its associations with gray matter volume were restricted to the parahippocampal gyrus. Reductions in [18F]BCPP‐EF SUVR were associated with domain‐specific cognitive performance. We did not observe regional associations between mitochondrial dysfunction and amyloid burden. Conclusions: In symptomatic cases, although mitochondrial complex I reduction is linked to a wide range of downstream neurodegenerative processes such as hypometabolism, atrophy, and cognitive decline, a link to amyloid was not observable. The data presented here support [18F]BCPP‐EF as an excellent imaging tool to investigate mitochondrial dysfunction in AD. [ABSTRACT FROM AUTHOR]

Background: Episodic memory decline is a hallmark of cognitive impairment due to Alzheimer's Disease (AD). Literature has consistently indicated that delayed recall is compromised early in the disease. Recognition memory seems disrupted at late stages, although findings are mixed. Recent PET studies have demonstrated that the presence of tau in the medial temporal lobe (MTL) relates to early deterioration of delayed recall, but no PET studies have assessed how recognition memory relates to tau. We sought to investigate this relationship across the Braak staging continuum. Method: Tau PET ([18F]‐MK6240) was acquired for 140 cognitively unimpaired elderly (CU), 54 MCI patients and 33 AD patients. Participants were segregated into Braak stages, based on in vivo PET neuroimaging of tau. Episodic memory was assessed using RAVLT delayed recall and recognition tests. MRI were segmented into probabilistic grey (GM) and white (WM) maps, non‐linearly registered to the ADNI template using Dartel and smoothed with an 8mm FWHM gaussian kernel. Independent samples t‐tests or their nonparametric counterparts were conducted to evaluate differences in memory scores by Braak stage. Voxel‐wise linear regression models were applied, using VoxelStats, with either delayed recall or recognition scores as dependent variables and tau PET as a predictor. Global amyloid, age, sex, education and APOE genotype were entered as covariates. Results: Pairwise comparisons revealed that recognition memory is spared in early Braak stages. Voxel‐wise analyses in the whole sample unveiled that delayed recall and recognition memory relate to tau in virtually the same cortical areas. However, the strength of the relationships widely varied by memory type. Tau‐PET in anterior MTL and posterior hippocampus shows stronger associations with delayed recall, while tau binding in lateral temporal, temporooccipital and posterior areas exhibits stronger associations with recognition memory. Conclusion: Our findings support the differentiation between delayed recall and recognition across the AD spectrum. Relationships between delayed recall and tau in the MTL are in concordance with previous research. Moreover, the contrast in the strength of the associations between memory and tau aggregation further supports the notion that delayed recall is already impaired in early Braak stages while recognition is damaged in later Braak stages. [ABSTRACT FROM AUTHOR]

Background: Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by the emergence of persistent non‐cognitive neuropsychiatric symptoms (NPS) in older adults, representing an at‐risk state for dementia and a potential marker of Alzheimer's disease (AD). However, few studies have investigated associations between MBI and plasma biomarkers of AD pathophysiology, specifically tau hyperphosphorylation. Method: Individuals across the AD spectrum (cognitively unimpaired (CU) and impaired (CI)) were selected from the Translational Biomarkers of Aging and Dementia (TRIAD) cohort. MBI was assessed using the MBI‐Checklist (MBI‐C), which assesses NPS severity in five subdomains: decreased motivation (apathy), emotional dysregulation (mood/anxiety symptoms), impulse dyscontrol (agitation, impulsivity, abnormal reward salience), social inappropriateness (impaired social cognition), and abnormal thoughts/perception (psychosis). Plasma levels of p‐tau181 were quantified in all individual, and log‐transformed due to right skew. Spearman correlation analysis investigated cross‐sectional associations between plasma p‐tau181 and MBI‐C scores, and between baseline plasma p‐tau181 and annual change in MBI‐C. Multivariable linear regression analyses assessed the ability of cross‐sectional and longitudinal MBI‐C scores to predict plasma p‐tau181, adjusting for age, sex, education, and diagnostic group (CU/CI, Aβ +/‐). Result: Cross‐sectional MBI data were available for 211 individuals, with longitudinal data available for 128 individuals (). Significant correlations were found between plasma p‐tau181 and MBI‐C total and apathy scores in the cross‐sectional sample (Figure A). Further, significant correlations were found with plasma p‐tau181 and the annual change in MBI‐C total and apathy scores (Figure B). These associations were corroborated by significant standardized and adjusted regression coefficients from multivariate regression models, which showed that baseline and longitudinal MBI‐C total and apathy score predicted plasma level of p‐tau181 (Figure C). Conclusion: Our study provides novel findings on the association between MBI and tau hyperphosphorylation assessed using plasma biomarkers in an AD cohort. These results add to the existing evidence for MBI as a clinical manifestation of AD pathology and support the use of the MBI‐C as an enrichment tool for clinical trial enrollment and therapeutic intervention. [ABSTRACT FROM AUTHOR]

Background: [18F]MK6240 tau‐PET can detect changes in the early and late stages of tau tangles accumulation. However, off‐target binding, often observed in the meninges and neuromelanin‐containing cells, can interfere with longitudinal tracer quantification. Here, we investigated the association of longitudinal changes in off‐target and target signals using [18F]MK6240. Method: We assessed individuals from the TRIAD cohort with [18F]MK6240 tau‐PET and clinical evaluation. Longitudinal analyses included 83 cognitively unimpaired (CU) and 37 cognitively impaired (CI) individuals. Age‐related off‐target binding was estimated comparing 36 CU young individuals (<25 y.o) and 28 CU elderly amyloid/tau negative (40‐65 y.o). A t‐test was used to compare both groups. The ratio between baseline and follow‐up SUVR measured changes in off‐target and target regions. Pearson correlations tested the associations between regions, and Bonferroni analysis corrected for multiple comparisons. Result: Although averaged images did not present a striking visual difference in [18F]MK6240 uptake between CU young and elderlies, t‐test revealed significant differences between groups in the cerebellar white matter and subcortical regions (Figure 1). Table 1 depicts the percentage of area of selected regions overlapping with the age‐related off‐target binding. Notably, we did not observe a significant association between longitudinal changes in age‐related or meningeal off‐target binding with longitudinal change in target regions (Braak‐II, BraakIII‐IV, BraakV‐VI), whereas changes in target regions were highly correlated (Figure 2). Conclusion: Despite not being visually perceptible, [18F]MK6240 presents age‐related off‐target binding in subcortical regions, similar to regions reported using [18F]Flortaupicir. The overlap between age‐related off‐target and Braak IV region (∼3%) may lead to the confounding results in quantifying this region. The lack of correlation between off‐target and target [18F]MK6240 changes suggests little influence of the off‐target binding on longitudinal tracer quantification. Our results suggest that although off‐target uptake appears to have a modest influence on longitudinal quantification, it is necessary to consider both age‐related and meningeal off‐target signals for an accurate tracer assessment. [ABSTRACT FROM AUTHOR]

Background: Recent evidence suggests that microglial activation sets the stage for tau spread in Alzheimer's disease (AD). However, the underpinnings of microglial activation in early regions of tau accumulation are poorly understood. The apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for sporadic AD and influences microglial response in animal models of AD. Here, we tested the hypothesis that APOE ε4 carriership is associated with microglial activation in early Braak regions independently of amyloid‐β (Aβ) and tau pathologies. Method: We assessed 118 participants [79 cognitively unimpaired (CU), 23 with mild cognitive impairment (MCI), and 16 with AD dementia] with 50 years of age or older from the McGill TRIAD cohort. Individuals had available positron emission tomography (PET) for Aβ ([18F]AZD4694), tau tangles ([18F]MK6240), and microglial activation ([11C]PBR28), as well as magnetic resonance imaging (MRI) and APOE genotyping. Braak I (transentorhinal) and Braak II (entorhinal and hippocampus) were defined as early regions of tau tangles accumulation. Result: Voxel‐wise analysis revealed that APOE ε4 carriership was associated with microglial activation mainly in regions corresponding to early Braak stages (e.g., transentorhinal, entorhinal, and hippocampus; Figure 1). In these regions, we observed that APOE ε4 carriership was associated with microglial activation independently of Aβ and tau pathologies (Braak I: β = 0.087, P < 0.001; Braak II: β = 0.053, P = 0.026; Table 1). Furthermore, structural equation modeling demonstrated that microglial activation mediated the Aβ‐independent effect of APOE ε4 carriership on tau accumulation in regions corresponding to Braak I‐II (mediation effect size = 34.1%; Figure 2). Conclusion: Our results suggest a deleterious effect of APOE ε4 carriership on AD progression by contributing to microglial activation in regions of early tau accumulation (transentorhinal, entorhinal, and hippocampus). These findings can help to understand the influence of the APOE ε4 allele on AD pathogenesis, as well as provide insights for the development of drugs targeting the interplay between APOE ε4 and microglial activation. [ABSTRACT FROM AUTHOR]

Background: The presence of p‐tau in biofluids has previously been proposed to be a response to neurofibrillary tangle pathology, one of the hallmarks of Alzheimer's disease (AD). However, the increase of p‐tau in cerebrospinal fluid (CSF) precedes detectable neurofibrillary tangle pathology, as indexed by tau Positron Emission Tomography (PET), by up to a decade, suggesting that soluble tau could be an indication of early tau pathology. With this study, we investigated the heterogeneity of p‐tau species in CSF to assess the disease status of participants of the Translational Biomarkers of Aging and Dementia (TRIAD) cohort. Methods: Support vector machines were used to determine cutoff values of p‐tau181, p‐tau217, p‐tau231 and p‐tau235 in CSF, identifying a group of participants that were amyloid positive (58 from a total of 165 participants). Amyloid positivity was determined by using an [18F]AZD4694 SUVR threshold value of 1.55 in the neocortex. Using these cutoff values, signatures were calculated on an individual level to identify the number of phosphorylated sites. Based on the number of phosphorylated sites, [18F]MK6240 SUVR maps and [18F]AZD4694 SUVR maps were calculated. Results: When combining different CSF p‐tau species, the largest contribution in identifying amyloid positivity came from p‐tau217, followed by p‐tau231, p‐tau181 and p‐tau235. Achieving the cutoff for multiple p‐tau species was associated with more tau pathology particularly in the later Braak stages (fig 1) and increased amyloid‐β plaque accumulation (fig 2). Conclusion: Our findings suggest that heterogeneity in p‐tau species carries predictive power in the identification of disease severity in incipient Alzheimer's Disease. [ABSTRACT FROM AUTHOR]

Background: Inflammation has long been pointed out as a key feature in Alzheimer's disease (AD). However, the heterogeneity of AD pathology might trigger different aspects of the immune response. Here, we aimed to study the association of the newly proposed concept of inflammatory composite indexes with Aβ and tau pathologies across the aging and AD spectra. We hypothesize that distinct inflammatory pathways are associated with the different hallmarks of AD. Method: A panel of 92 inflammation‐related markers was measured in the CSF of 60 individuals (35 cognitively unimpaired and 25 cognitively impaired) from the McGill TRIAD cohort. These 92 proteins were clustered by ontogenetic similarity and physical interaction using STRING. A z‐scored composite value representing each cluster was created for each individual. Individuals were classified as Aβ‐ and tau‐positive using global [18F]AZD4694 and temporal meta‐ROI [18F]MK6240 PET, respectively. Linear regressions (corrected by diagnosis, age, sex, and APOEɛ4 status) and receiver operating characteristic (ROC) analyses evaluated clusters' performance. Result: Of the six clusters identified in our protein network analysis, three positively correlated with global Aβ‐PET (C4,C5, and C6) and three with meta‐ROI tau PET (C2,C4, and C5;Fig.1). However, controlling for Aβ PET, tau PET was only associated with C2, indicating that the association with the other two clusters (C4 and C5) was dependent of Aβ pathology. The cluster exclusively associated with tau (C2), represents tumor necrosis factor‐mediated signaling pathways. On the other hand, C6, related to the stimulation of interferon‐gamma production, and C5, related to glial cell‐derived neurotrophic receptor binding, are exclusively associated with Aβ. ROC analysis showed that all clusters presented a better performance predicting Aβ‐PET positivity than tau‐PET positivity (Fig.2A,B). Additionally, overall the clusters showed a better performance than sTREM2, a classical inflammatory biomarker. Conclusion: Our novel clustering approach supports that Aβ and tau proteinopathies are associated with distinct aspects of the inflammatory cascade. Our results suggest that a composite of inflammatory proteins better identifies AD pathology than a single marker. Finally, this suggested cluster approach helps to understand the inflammatory response to Aβ and tau and can uncover inflammation‐related AD pathways. [ABSTRACT FROM AUTHOR]

Background: It has already been shown that longitudinal changes in plasma phosphorylated tau 181 (p‐tau181) correlate with longitudinal Alzheimer's disease (AD) progression. However, it is unclear whether longitudinal plasma p‐tau181 is a suitable measure to be used as a surrogate variable in clinical trials. This study aims to evaluate the utility of using longitudinal changes in plasma p‐tau181 to monitor drug effects in AD clinical trials. Method: We evaluated 715 individuals (227 cognitively unimpaired (CU) amyloid‐beta (Aβ) negative, 103 CU Aβ positive, 265 mild cognitive impairment (MCI) Aβ positive, and 120 Alzheimer's disease (AD) dementia Aβ positive) from the Alzheimer's Disease Neuroimaging Initiative. Individuals diagnosed with AD or MCI were classified as cognitive impaired (CI). The subjects had [18F]florbetapir Aβ PET at baseline and longitudinal plasma p‐tau181 (up to 4‐year follow‐up duration). We determined the longitudinal rates of change in plasma p‐tau181 concentrations and its effect sizes, calculated as mean change divided by the standard deviation. Logistic regression tested whether the longitudinal change in p‐tau181 status was associated with individuals' clinical deterioration. Result: Plasma p‐tau181 slope of change was positively associated with baseline plasma p‐tau concentrations (Fig 1). Longitudinal increase in plasma p‐tau181 was correlated with longitudinal worsening in cognition. Long‐term increase in plasma p‐tau181 values was more consistent in CU Aβ negative (48‐month effects size=0.4) and CU Aβ positive (48‐month effects size=0.6) than in CI Aβ positive (48‐month effects size=0.2) individuals (Fig 2). Conclusion: Monitoring change in plasma p‐tau181 status from negative to positive can be helpful to identify individuals on the verge of cognitive deterioration in clinical practice. Longitudinal changes in plasma p‐tau181 values can open a new frontier in AD research offering a cost‐effective and scalable alternative to be used as a surrogate variable in large‐scale trials designed to test the effects of population interventions on the natural history of tau pathology in elderly populations. [ABSTRACT FROM AUTHOR]