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Females are disproportionately affected by dementia due to Alzheimer's disease. Despite a similar amyloid-β (Aβ) load, a higher load of neurofibrillary tangles (NFTs) is seen in females than males. Previous literature has proposed that Aβ and phosphorylated-tau (p-tau) synergism accelerates tau tangle formation, yet the effect of biological sex in this process has been overlooked. In this observational study, we examined longitudinal neuroimaging data from the TRIAD and ADNI cohorts from Canada and USA, respectively. We assessed 457 participants across the clinical spectrum of Alzheimer's disease. All participants underwent baseline multimodal imaging assessment, including MRI and PET, with radioligands targeting Aβ plaques and tau tangles, respectively. CSF data were also collected. Follow-up imaging assessments were conducted at 1- and 2-year intervals for the TRIAD cohort and 1-, 2- and 4-year intervals for the ADNI cohort. The upstream pathological events contributing to faster tau progression in females were investigated—specifically, whether the contribution of Aβ and p-tau synergism to accelerated tau tangle formation is modulated by biological sex. We hypothesized that cortical Aβ predisposes tau phosphorylation and tangle accumulation in a sex-specific manner. Findings revealed that Aβ-positive females presented higher CSF p-tau181 concentrations compared with Aβ-positive males in both the TRIAD (P = 0.04, Cohen's d = 0.51) and ADNI (P = 0.027, Cohen's d = 0.41) cohorts. In addition, Aβ-positive females presented faster NFT accumulation compared with their male counterparts (TRIAD: P = 0.026, Cohen's d = 0.52; ADNI: P = 0.049, Cohen's d = 1.14). Finally, the triple interaction between female sex, Aβ and CSF p-tau181 was revealed as a significant predictor of accelerated tau accumulation at the 2-year follow-up visit (Braak I: P = 0.0067, t = 2.81; Braak III: P = 0.017, t = 2.45; Braak IV: P = 0.002, t = 3.17; Braak V: P = 0.006, t = 2.88; Braak VI: P = 0.0049, t = 2.93). Overall, we report sex-specific modulation of cortical Aβ in tau phosphorylation, consequently facilitating faster NFT progression in female individuals over time. This presents important clinical implications and suggests that early intervention that targets Aβ plaques and tau phosphorylation may be a promising therapeutic strategy in females to prevent the further accumulation and spread of tau aggregates. [ABSTRACT FROM AUTHOR]

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: 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 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: 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]

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: 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: 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: 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: 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: Midlife hypertension (HTN) is a known risk factor for Alzheimer's Disease (AD) development. However, whether the same effect is observed in older individuals, at risk for AD, remains to be elucidated. Here, we aimed to assess whether late‐life HTN and the presence of amyloid‐β pathology (Aβ) interact to promote longitudinal cognitive decline in cognitively unimpaired (CU) individuals, and if systolic blood pressure (SBP) levels moderate this interaction. Interactive effect of disease pathophysiology is crucial for dementia prevention strategies. Method: We used two independent cohorts. We evaluated 475 CU individuals over 65 years of age from the ADNI cohort, with available baseline medical data and CSF Elecsys biomarkers (Aβ1‐42 and p‐tau181), as well as longitudinal clinical assessments with neuropsychological testing (up to 6 years); the individuals were classified as (A)+ or (A)‐ based on a previously proposed cut‐off of CSF p‐tau181/Aβ1‐42 lower than 0.025. We also evaluated 162 CU individuals over 65 years of age from the TRIAD cohort with baseline clinical data and Aβ‐PET, as well as longitudinal clinical assessments with neuropsychological testing (up to 3 years). The individuals were classified as (A)+ or (A)‐ based on Aβ‐PET positivity. All individuals were classified as positive or negative for hypertension based on medical history. For the ADNI cohort, we could also evaluate the SBP levels as a continuous variable. Result: Linear mixed‐effects (LME) models showed that HTN and Aβ acted together to promote longitudinal cognitive decline (ADNI: HTN X Aβ X Time, β = ‐0.44, p = 0.001, figure 1, TRIAD: HTN X Aβ X Time, β = ‐0.64, p < 0.001, figure 2). Also, we could see that higher SBP levels acted together with Aβ to promote further cognitive decline (SBP values X Aβ X Time, β = ‐0.011, p = 0.03, figure 3). Conclusion: Our results support a framework in which late‐life HTN is a modifiable risk factor for cognitive decline in CU individuals at increased risk for AD. This supports further investigation in determining the best target of SBP levels for individuals at risk for AD, in the context of offering precision medicine for this population. [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: 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]