ImportanceThe recent proliferation of phosphorylated tau (p-tau) biomarkers has raised questions about their preferential association with the hallmark pathologies of Alzheimer disease (AD): amyloid-β plaques and tau neurofibrillary tangles.ObjectiveTo determine whether cerebrospinal fluid (CSF) and plasma p-tau biomarkers preferentially reflect cerebral β-amyloidosis or neurofibrillary tangle aggregation measured with positron emission tomography (PET).Design, Setting, and ParticipantsThis was a cross-sectional study of 2 observational cohorts: the Translational Biomarkers in Aging and Dementia (TRIAD) study, with data collected between October 2017 and August 2021, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), with data collected between September 2015 and November 2019. TRIAD was a single-center study, and ADNI was a multicenter study. Two independent subsamples were derived from TRIAD. The first TRIAD subsample comprised individuals assessed with CSF p-tau (p-tau181, p-tau217, p-tau231, p-tau235), [18F]AZD4694 amyloid PET, and [18F]MK6240 tau PET. The second TRIAD subsample included individuals assessed with plasma p-tau (p-tau181, p-tau217, p-tau231), [18F]AZD4694 amyloid PET, and [18F]MK6240 tau PET. An independent cohort from ADNI comprised individuals assessed with CSF p-tau181, [18F]florbetapir PET, and [18F]flortaucipir PET. Participants were included based on the availability of p-tau and PET biomarker assessments collected within 9 months of each other. Exclusion criteria were a history of head trauma or magnetic resonance imaging/PET safety contraindications. No participants who met eligibility criteria were excluded.ExposuresAmyloid PET, tau PET, and CSF and plasma assessments of p-tau measured with single molecule array (Simoa) assay or enzyme-linked immunosorbent assay.Main Outcomes and MeasuresAssociations between p-tau biomarkers with amyloid PET and tau PET.ResultsA total of 609 participants (mean [SD] age, 66.9 [13.6] years; 347 female [57%]; 262 male [43%]) were included in the study. For all 4 phosphorylation sites assessed in CSF, p-tau was significantly more closely associated with amyloid-PET values than tau-PET values (p-tau181 difference, 13%; 95% CI, 3%-22%; P = .006; p-tau217 difference, 11%; 95% CI, 3%-20%; P = .003; p-tau231 difference, 15%; 95% CI, 5%-22%; P < .001; p-tau235 difference, 9%; 95% CI, 1%-19%; P = .02) . These results were replicated with plasma p-tau181 (difference, 11%; 95% CI, 1%-22%; P = .02), p-tau217 (difference, 9%; 95% CI, 1%-19%; P = .02), p-tau231 (difference, 13%; 95% CI, 3%-24%; P = .009), and CSF p-tau181 (difference, 9%; 95% CI, 1%-21%; P = .02) in independent cohorts.Conclusions and RelevanceResults of this cross-sectional study of 2 observational cohorts suggest that the p-tau abnormality as an early event in AD pathogenesis was associated with amyloid-β accumulation and highlights the need for careful interpretation of p-tau biomarkers in the context of the amyloid/tau/neurodegeneration, or A/T/(N), framework.

INTRODUCTIONAmyloid‐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.METHODSWe 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.RESULTSPlasma 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.DISCUSSIONThese 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.

Amyloid-β plaques and neurofibrillary tangles (NFTs) are the 2 histopathologic hallmarks of Alzheimer disease (AD). On the basis of the pattern of NFT distribution in the brain, Braak and Braak proposed a histopathologic staging system for AD. Braak staging provides a compelling framework for staging and monitoring of NFT progression in vivo using PET imaging. Because AD staging remains based on clinical features, there is an unmet need to translate neuropathologic staging to a biologic clinical staging system. Such a biomarker staging system might play a role in staging preclinical AD or in improving recruitment strategies for clinical trials. Here, we review the literature regarding AD staging with the Braak framework using tau PET imaging, here called PET-based Braak staging. Our aim is to summarize the efforts of implementing Braak staging using PET and assess correspondence with the Braak histopathologic descriptions and with AD biomarkers. Methods: We conducted a systematic literature search in May 2022 on PubMed and Scopus combining the terms “Alzheimer” AND “Braak” AND (“positron emission tomography” OR “PET”). Results: The database search returned 262 results, and after assessment for eligibility, 21 studies were selected. Overall, most studies indicate that PET-based Braak staging may be an efficient method to stage AD since it presents an adequate ability to discriminate between phases of the AD continuum and correlates with clinical, fluid, and imaging biomarkers of AD. However, the translation of the original Braak descriptions to tau PET was done taking into account the limitations of this imaging technique. This led to important interstudy variability in the anatomic definitions of Braak stage regions of interest. Conclusion: Refinements in this staging system are necessary to incorporate atypical variants and Braak-nonconformant cases. Further studies are needed to understand the possible applications of PET-based Braak staging to clinical practice and research. Furthermore, there is a need for standardization in the topographic definitions of Braak stage regions of interest to guarantee reproducibility and methodologic homogeneity across studies.

INTRODUCTIONPhosphorylated 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).METHODSWe assessed 138 CU and 87 CI with available plasma p‐tau231, 217+, and 181, Aβ42/40, GFAP and Aβ‐ and tau‐PET.RESULTSIn 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.DISCUSSIONOur 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.

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. We studied 104 individuals across the aging and AD spectrum who underwent clinical assessments, APOE genotyping, magnetic resonance imaging, positron emission tomography (PET) for Aβ ([18F]AZD4694) and tau ([18F]MK-6240) at baseline, as well as a 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). We found that APOEε4 carriership potentiates Aβ effects on longitudinal tau tangle accumulation over two years. The APOEε4-potentiated Aβ effects on tangles were mediated by longitudinal plasma p-tau217+ increase. This longitudinal tau accumulation as measured by PET was accompanied by brain atrophy and clinical decline. 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.

BackgroundWhile fluid phosphorylated tau (pTau) epitopes are interpreted to be biomarkers of tau pathology according to the A/T/(N) framework, it is unclear to what extent they are preferentially associated with the defining histopathological hallmarks of Alzheimer’s Disease (AD): amyloid‐β plaques and tau neurofibrillary tangles.MethodWe studied 171 individuals, including young adults (n=27), cognitively unimpaired elderly (n=85), individuals with mild cognitive impairment (n=36) and individuals with Alzheimer’s clinical syndrome (n=23), who were evaluated with [18F]AZD4694 amyloid‐PET, [18F]MK6240 tau‐PET, four tau phosphorylation sites in CSF (pTau181, pTau217, pTau231, pTau235) and two phosphorylation sites in plasma (pTau181, pTau231). To better understand the role of soluble biomarkers in AD diagnostic and research purposes, we evaluated associations between soluble pTau sites and cerebral amyloid‐PET and tau‐PET concentrations. Voxel‐wise linear regressions between fluid and imaging biomarkers were performed. Results for plasma and CSF pTau181 were replicated in an independent sample of 258 individuals included in the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI).ResultWe observed that for all plasma and CSF epitopes, pTau is more closely associated with amyloid‐PET than with tau‐PET. Squared correlation coefficients (Spearman’s R2) between CSF pTau epitopes and neocortical [18F]AZD4694 SUVR range from 0.48 to 0.63, while those for the temporal meta‐ROI [18F]MK6240 SUVR vary between 0.33 and 0.45 (p<0.001 for all phosphorylation sites). Using the R package “Cocor”, for CSF pTau181, pTau217 and pTau231 the difference in correlation coefficients between both measures of imaging biomarkers appear significant (p<0.01). Voxel‐wise linear regression analyses further support these results, in particular showing sizable associations for CSF pTau231 with amyloid‐PET. In addition, soluble pTau is more closely correlated with medial temporal than with neocortical tau. All soluble pTau concentrations rise significantly with increasing amyloid‐β plaque load. In contrast, soluble pTau plateau as tau‐PET concentrations increase, starting at Braak stage III. These findings were replicated using plasma and CSF pTau181 in the ADNI cohort.ConclusionPhosphorylated tau epitopes measured in CSF and plasma better reflect cerebral amyloidosis than neurofibrillary tangles in the brain. The current findings support careful interpretation of fluid pTau concentrations when implementing the A/T/(N) framework.

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.MethodWe 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.ResultAlthough 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).ConclusionDespite 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.

BackgroundWhile fluid phosphorylated tau (pTau) epitopes are interpreted to be biomarkers of tau pathology according to the A/T/(N) framework, it is unclear to what extent they are preferentially associated with the defining histopathological hallmarks of Alzheimer’s Disease (AD): amyloid‐β plaques and tau neurofibrillary tangles.MethodWe studied 171 individuals, including young adults (n=27), cognitively unimpaired elderly (n=85), individuals with mild cognitive impairment (n=36) and individuals with Alzheimer’s clinical syndrome (n=23), who were evaluated with [18F]AZD4694 amyloid‐PET, [18F]MK6240 tau‐PET, four tau phosphorylation sites in CSF (pTau181, pTau217, pTau231, pTau235) and two phosphorylation sites in plasma (pTau181, pTau231). To better understand the role of soluble biomarkers in AD diagnostic and research purposes, we evaluated associations between soluble pTau sites and cerebral amyloid‐PET and tau‐PET concentrations. Voxel‐wise linear regressions between fluid and imaging biomarkers were performed. Results for plasma and CSF pTau181 were replicated in an independent sample of 258 individuals included in the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI).ResultsWe observed that for all plasma and CSF epitopes, pTau is more closely associated with amyloid‐PET than with tau‐PET. Squared correlation coefficients (Spearman’s R2) between CSF pTau epitopes and neocortical [18F]AZD4694 SUVR range from 0.48 to 0.63, while those for the temporal meta‐ROI [18F]MK6240 SUVR vary between 0.33 and 0.45 (p<0.001 for all phosphorylation sites). Using the R package “Cocor”, for CSF pTau181, pTau217 and pTau231 the difference in correlation coefficients between both measures of imaging biomarkers appear significant (p<0.01). Voxel‐wise linear regression analyses further support these results, in particular showing sizable associations for CSF pTau231 with amyloid‐PET. In addition, soluble pTau is more closely correlated with medial temporal than with neocortical tau. All soluble pTau concentrations rise significantly with increasing amyloid‐β plaque load. In contrast, soluble pTau plateau as tau‐PET concentrations increase, starting at Braak stage III. These findings were replicated using plasma and CSF pTau181 in the ADNI cohort.ConclusionPhosphorylated tau epitopes measured in CSF and plasma better reflect cerebral amyloidosis than neurofibrillary tangles in the brain. The current findings support careful interpretation of fluid pTau concentrations when implementing the A/T/(N) framework.