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AbstractBackgroundTau PET provides continuous measurements of tau tangle pathology in the human brain. However, establishing cutoffs is crucial for selecting individuals for treatment in clinical trials or practice. In the absence of postmortem data, PET cutoffs must be established using statistical methods based on what is considered normal tracer uptake. In this study, we tested the impact of various methods to determine tau positivity using two different tau PET tracers in individuals scanned head‐to‐head.MethodsWe studied 147 individuals from Head‐to‐Head Harmonization of Tau Tracers in Alzheimer's Disease (HEAD) study with tau tangle PET scans with [18F]Flortaucipir and [18F]MK‐6240, and amyloid‐β (Aβ) PET. Tau deposition was measured with the standardized uptake value ratio (SUVR) of each agent in the Medial Temporal Lobe (MTL) and the Entorhinal Cortex (EC). To determine Tau positivity three different methods were used: >2.5 standard deviations (SD) than the mean of the young, >1.5 SD mean of the cognitively unimpaired (CU) and >1.5 SD mean of CUAβ‐.ResultsDemographic characteristics of the study population are reported in Table 1. Using the cutoff >2.5 SD mean of young, [18F]Flortaucipir was positive in 35 (23.8%) and 69 (46.9%) individuals in the EC and MTL, respectively. [18F]MK‐6240 was positive in 49 (33.3%) and 58 (39.5%) individuals in EC and in MTL. Using >1.5 SD mean of CUAβ‐, [18F]Flortaucipir was positive in 38 (25.9%) and 45 (30.6%) individuals in EC and MTL, while [18F]MK‐6240 was positive in 51 (34.7%) and 50 (34.0%) in EC and in MTL (Figure 1).ConclusionsOur findings indicate variations in tau positivity when employing different methods based on either the young or CUAβ‐. [18F]Flortaucipir exhibited a higher rate of positive results when the method based on young individuals was applied in the MTL. Conversely, [18F]MK‐6240 showed more consistent and generally higher positivity when other methods were used for cutoff determination and/or in the EC region. Further research with a larger sample size is required to gain a better understanding of the optimal cutoff determination methods for these tracers.

AbstractBackgroundIdentification of useful fluid biomarkers is expected to advance frontotemporal dementia spectrum disorders (FTD) care and therapeutic development. The clinical utility of emerging plasma Amyloid, Tau, and Neurodegeneration (ATN) biomarkers in FTD remains unexplored. This study analyzed ATN patterns by sporadic FTD phenotype, based on amyloid beta1‐42 (Aβ1‐42), amyloid beta1‐40 (Aβ1‐40), phospho‐tau217 (p‐tau217), and neurofilament (NfL) plasma concentrations.MethodThe present study included 625 participants enrolled in ALLFTD (46% female, median age 61 ± 12 years) for whom biomarker data were available. Plasma Aβ1‐42/Aβ1‐40 measures were determined by immunoprecipitation‐mass spectrometry whereas plasma p‐tau217 and NfL concentrations were respectively determined by electrochemiluminescence‐ and fluorescence‐based immunoassays. Biomarker concentrations were compared by phenotype and disease severity with ANOVA. Their diagnostic performance was tested with Receiver Operating Characteristic (ROC) curves, and biomarker relationships with measures of clinical severity were tested through linear regressions controlling for age, sex, and APOE genotype.ResultHigher plasma p‐tau217 and p‐tau217/Aβ1‐42, levels were observed in logopenic primary progressive aphasia (lvPPA) and amnestic dementia (AmD) cohorts, compared to other FTD groups. These biomarkers were not associated with disease severity but were affected by APOE4 genotype. High plasma NfL level was observed in fully symptomatic patients compared to controls, and as a function of disease severity. Plasma Aβ1‐42/Aβ1‐40 level was not different between phenotypes and was not affected by clinical severity. P‐tau217 level, but not Aβ1‐42/Aβ1‐40 or NfL levels showed high discrimination of lvPPA and AmD from other phenotypes (AUC = 0.87 (95% CI 0.77 – 0.96, p < 0.0001, 73% sensitivity, 92% specificity). The p‐tau217/Aβ1‐42 ratio improved this discrimination (AUC = 0.9, 95% CI 0.83 – 1, p < 0.0001, 77% sensitivity, 94% specificity). Plasma Aβ1‐42/Aβ1‐40, p‐tau217, NfL, p‐tau217/Aβ1‐42, p‐tau217/NfL, and (Aβ1‐42 x p‐tau217)/NfL were associated with cognitive (MoCA, CVLT), motor (UPDRS), and social behavior (RSMS) performances at baseline.ConclusionPlasma p‐tau217/Aβ1‐42 identifies, with high specificity, two phenotypes associated with Alzheimer’s disease: lvPPA and AmD. Pending molecular neuroimaging and neuropathological confirmation, this biomarker could be of value to identify primary Alzheimer’s disease pathology or co‐pathology associated with Alzheimer’s disease when sporadic FTD is suspected.

AbstractBackgroundUtilizing PET amyloid‐beta (Aβ) and tau for staging Alzheimer’s Disease (AD) has demonstrated potential in identifying individuals with varying degrees of disease severity, applicable to both clinical trials and practice. However, the diverse binding characteristics of tau tracers pose challenges to the application of this staging across different ligands. In this study, we evaluate a novel staging framework proposed by the AA working group, employing Aβ PET and either [18F]MK6240 or [18F]Flortaucipir in individuals participating in a head‐to‐head study of tau PET tracers. This will provide insights into the robustness of this framework when applied across different tau ligands.MethodWe evaluated 154 people (89 CU and 65 CI), with MK6240, Flortaucipir, Aβ‐PET, MRI. SUVRs used inferior cerebellar gray as reference. Participants were categorized in Aβ positivity(A), using visual read, and tau positivity (T) in medial temporal lobe(MTL) and neocortex(NEO). Tau MTL(TMTL) and NEO positivity were defined as average +3 SD of youngs. NEO moderate(TMOD) and high(THIGH) was defined as below and above median SUVR of NEO‐positive individuals. Individuals were grouped as: A‐T‐, A+T‐, A+TMTL+(early), A+TMOD+(intermediary), and A+THIGH+(advanced).ResultsWe found that the distribution of the number of individuals across the five AD stages was comparable when using both MK6240 and Flortaucipir tracers (Figure 1). However, upon examining the concordance within each group, we found a low concordance of 30% (3/10) between tracers in the early tau stage (A+TMTL+). Concordance increased in the intermediary stage A+TMOD+, with a rate of 65% (9/14), and further increased to 79% (11/14) in the advanced stage (A+THIGH+). The A+T‐ group exhibited the highest concordance between tracer results, at 95% (21/22).ConclusionOur findings indicate that the application of this framework across different populations using different tau tracers can lead to a compatible distribution among groups. However, in a direct comparison, we noted that this did not necessarily result in high concordance between groups, as group shifts were observed that depended on the cutoffs used (data not shown). These results emphasize the necessity for harmonization of tau PET tracers prior to their utilization for stage determination.

AbstractBackgroundIdentifying individuals’ levels of tau PET pathology could prove to be beneficial in clinical settings, given that emerging therapies aimed reducing Aβ seem to be most effective in these individuals. Here, we present the cases of four patients who visited the memory clinic at the University of Pittsburgh Medical Center between June and December 2023 and underwent both Aβ and tau‐PET scans.MethodThese individuals had standard clinical and cognitive outcomes, typical blood tests order in patients with memory impairment, MRI, and, as part of the HEAD study, PET PIB Aβ and two tau PET tracers (MK6240 and Flortaucipir).ResultAll patients presented a primary complaint of progressive memory decline. The results of their blood count, metabolic panel, TSH, and vitamin B12 tests were unremarkable. Patient 1 had a normal MRI, while Patients 2‐4 exhibited mild ischemic changes (Table 1). All patients tested positive for amyloid PET, supporting the diagnosis of AD. However, their tau PET results varied. Patients 1 (MOCA = 6) and 3 (MOCA =19), who were demented with impaired Instrumental Activities of Daily Living (IADL), tested positive for tau PET with Braak stages V‐VI using both tau PET tracers. Conversely, the mild cognitive impairment (MCI) Patients 2 (MOCA = 19) and 5 (MOCA = 29, neuropsych evaluation indicating MCI), who exhibited low tau levels, tested positive for MK6240 but negative for FTP.ConclusionAll four patients exhibited symptoms and signs consistent with typical amnestic AD presentation, which were confirmed using Aβ PET. Aβ PET presented a similar pattern across all individuals (Figure 1). Tau PET demonstrated greater variability, with lower levels in less cognitively impaired patients. Although tau PET can potentially be useful in identifying individuals in the earliest stages that would benefit from Aβ‐lowering therapies, this small series did not exclude that a low MOCA score combined with Aβ PET could achieve a similar outcome. Larger samples in real‐world clinical contexts are necessary to identify individuals with discrepancies between tau PET uptake and cognitive tests. This would reinforce the need for tau PET markers as complementary tests, singularly capable of identifying individuals across various biological stages to indicate therapies in clinical practice.

AbstractBackgroundTau PET tracers are employed to measure the accumulation of tau in vivo in the brain. Each tau tracer possesses unique characteristics, including binding affinity, sensitivity, and specificity to tau aggregates. This study leverages the HEAD study dataset, which is currently performing baseline tau PET tracers and conducting multiple clinical and cognitive assessments. The objective of this study is to elucidate the relationship between tau PET tracers [18F]Flortaucipir and [18F]MK6240 and commonly used tests of general cognitive performance.MethodsWe assessed 170 individuals (107 cognitively unimpaired, and 63 cognitively impaired) with available PET [18F]Flortaucipir [18F]MK6240 and cognitive assessments (MoCA, MMSE, and CDR‐SB). We calculated tau PET SUVR for [18F]Flortaucipir and [18F]MK6240 using the inferior cerebellar grey matter as a reference. Pearson correlations were performed between SUVR values for each Braak region and MoCA, MMSE, and CDR‐SB. In addition, linear regression models accounting for age, sex, years of education, HEAD site, and diagnosis were used to estimate the relationships. The Akaike Information Criterion (AIC) was utilized to evaluate the models, with smaller AIC values signifying better performance.ResultsPerson correlation coefficients (r), which were calculated without considering any covariates, demonstrated higher numerical values between [18F]MK6240 and the scores of MoCA, MMSE, and CDR‐SB across the Braak regions (Figure 1). Similarly, when we performed linear regressions that accounted for relevant covariates, we found that the [18F]MK6240 SUVR in Braak 1 and Braak 4 regions exhibited a stronger association with cognitive tests than in other Braak regions or any region with [18F]Flortaucipir SUVR (Figure 2).ConclusionsBoth [18F]Flortaucipir and [18F]MK6240 SUVR demonstrated a robust association with tests of general cognitive performance commonly used in AD research and clinical practice. Generally, Braak regions 1 and 4 exhibited a stronger association with general cognitive performance using all tests, whereas Braak regions 5‐6 showed the weakest association.

AbstractBackgroundParkinson’s disease and dementia with Lewy bodies are characterized by abnormal iron deposition in the substantia nigra, which can be measured with quantitative susceptibility mapping (QSM) on MRI. However, neuropathologic validation of the increased QSM in the substantia nigra associated with Lewy body disease (LBD) is lacking. We compared substantia nigra QSM on antemortem MRI between patients with and without Lewy‐related pathology which was confirmed by autopsy in all cases.MethodWe studied cases with antemortem QSM who underwent autopsy from the Mayo Clinic Alzheimer’s Disease Research Center and Mayo Clinic Study of Aging (n=54). Immunohistochemistry was performed with rabbit NACP antibody against the amino acids 98‐115 of the α‐synuclein protein to detect Lewy‐related pathology. After neuropathologic evaluation, cases were classified as LBD‐present (n=13) if they had Lewy‐related pathology and LBD‐absent (n=42) if they did not have Lewy‐related pathology. Alzheimer’s disease co‐pathology was present in 9 of the LBD‐present cohort (70%). QSM was calculated for the whole substantia nigra and the two sub‐regions: pars compacta and pars reticulata. Non‐parametric Wilcoxon rank‐sum test was used to compare substantia nigra QSM values between LBD‐present and LBD‐absent cases. Area under the curve (AUC) analyses tested the accuracy of QSM to distinguish the two groups.ResultsLBD‐present group had higher QSM values in the substantia nigra than LBD‐absent group. Furthermore, sub‐regional analyses showed that the LBD‐present group had higher QSM in the substantia nigra pars compacta (p=0.008) but not in the pars reticulata than the LBD‐absent group (Figure 1). QSM of the substantia nigra pars compacta distinguished LBD‐present and LBD‐absent cases with good accuracy (AUC=0.74; p=0.006; Figure 2).ConclusionThis study provides neuropathologic insight into the relevance of QSM of substantia nigra pars compacta as an in‐vivo biomarker of autopsy‐confirmed LBD. These findings indicate that imaging evidence of abnormal iron deposition in the substantia nigra may serve as a biomarker of Lewy‐related pathology in patients with primary LBD (i.e., Parkinson disease, dementia with Lewy bodies) and patients with potential LBD co‐pathology (e.g., patients with Alzheimer disease).

AbstractBackgroundPatients with rapid progressive Alzheimer disease and related dementias (rpAD/ADRD) develop dementia within 1 year or incapacitation within 2 years of symptom onset. We previously showed that selected CSF biomarkers of neuronal injury (NfL, VILIP‐1), AD neuropathology (p‐tau181), and neuroinflammation (GFAP, MCP‐1, sTREM2) measured at presentation associated with etiologic diagnoses and reliably differentiated patients with treatment‐responsive causes of rapid progressive dementia. However, no differences were identified between CSF biomarkers in patients with rapid and typical progressive forms of AD/ADRD, leaving key questions unanswered concerning the mechanisms that drive rpAD/ADRD. Blood‐based biomarkers may inform the systemic factors that contribute to rpAD/ADRD, including the systemic inflammation, metabolic function, and vascular injury.MethodsBiomarkers of systemic inflammation (Eotaxin‐3, IFN‐γ, IL‐1b, IL‐2, IL‐4, IL‐6, IL‐8, IL‐10, IL‐12p70, IL‐13, TARC, TNF‐α), metabolic function (A2M, B2M, FABP‐3, FVII, I‐309, IL‐18, PPY, TNC, TPO), and vascular injury (CRP, SAA, sICAM‐1, sVCAM‐1) were measured in plasma obtained at presentation from patients with rpAD/ADRD (n=29) and age‐ and sex‐ similar participants with typical progressive AD/ADRD (n=39) enrolled in prospective studies of memory and aging at Mayo Clinic in Florida. Biomarker measures were completed at the University of North Texas Institute for Translational Research using Meso Scale Discovery multiplexed assays. Biomarker concentrations were compared between groups using univariate analyses; potential predictors (p<0.05) were further evaluated via step‐wise multivariate logistic regression.ResultsMarkers of vascular injury (SAA, CRP, sICAM‐1), metabolic function (I‐309, TPO), and systemic inflammation (Eotaxin‐3, TARC, TNF‐α) were increased in patients with rapid versus typical progressive AD/ADRD (p<0.05). Eotaxin‐3, I‐309, and SAA were independently associated with rpAD/ADRD in multivariable model after controlling for age and sex. The overall model correctly characterized 86.8% of patients, with an area under the curve of 0.864 (95%CI: 0.777‐0.950; p<0.001).ConclusionBlood‐based biomarkers of systemic inflammation, metabolic function, and vascular injury were elevated at presentation in patients with rapid vs typical AD/ADRD. These findings may provide additional insights into the mechanisms that drive rpAD/ADRD. Longitudinal measures from larger cohorts are needed to decipher the relationship between candidate biomarkers and rpAD/ADRD.

Abstract Background The HEAD study aims to collect a large dataset of multiple tau‐PET tracers to provide robust anchor values for tau‐PET harmonization. Here, we tested the hypothesis that anchoring two tau tracer uptake values using head‐to‐head measurements has the potential to generate an accurate universal tau‐PET scale, named Uniτ(tau). Methods We assessed 200 individuals across the aging and AD spectrum (Training: HEAD data freeze 2.0, n=185; Testing: UPitt dataset (Gogola et al.), n=15) with [ 18 F]Flortaucipir and [ 18 F]MK‐6240 tau‐PET. SUVRs were processed to a common 8mm FWHM, with inferior cerebellar gray matter as the reference region (Pascoal et al.). Uniτ explored two anchoring/harmonization methods. First, we examined within‐tracer anchoring by creating anchor values based on the mean SUVR of Youngs (<25 years) and 95th percentile voxels from cognitively impaired individuals. Second, we explored within‐ plus between‐tracer anchoring, employing linear (e.g., piecewise) and non‐linear regressions. To address the inherent problem of discontinuity of piecewise transformations, we implemented two smoothing methods at the inflection point between equations, transforming them into a continuous function. Results Uniτ scale anchoring within‐tracer resulted in similar estimates for high values, but less accurate in the lower range (Figure 1,2). Anchoring within‐ plus between‐tracer improved estimate consistency, with the piecewise transformation generating the best results. The piecewise smoothing equation yielded estimates comparable to those obtained from the piecewise method without smoothing. This allowed for the use of a single formula. In addition, this leads to more robust results when the goal is to study longitudinal changes in the scale (data not shown). UPitt testing dataset showed similar results to the training set (Figure 3). Conclusion Our preliminary findings suggest that anchoring tau‐PET values both within and between tracers has the potential to harmonize tau‐PET tracers, while preserving their characteristics. Currently, piecewise smoothing is the preferred method for Uniτ, but we are continuously fine‐tuning scale parameters as we acquire more data. The final scale parameters will be determined based on extensive training and testing data from multiple tracers. This cautious methodology holds the promise of delivering reliable, robust, and reproducible results, ensuring safe usage of the scale in clinical trials, and potentially paving the way for future use in clinical practice.

AbstractBackgroundHarmonization of the two most commonly used Tau PET tracers, 18F‐Flortaucipir and 18F‐MK6240 has proven to be complex. Unlike the centiloid scale for amyloid tracers, Tau PET SUVRs of the two tracers are not linearly comparable and vary markedly in dynamic range and sensitivity.MethodTau PET SUVRs for Braak stage (1‐6) in 18F‐MK6240 and 18F‐Flortaucipir were obtained from the Longitudinal multicenter head‐to‐head harmonization of tau‐PET tracers (HEAD) project. For training, we used 13 amyloid confirmed AD, 24 amyloid confirmed MCI and 66 amyloid negative controls. To assess linearity and correlations with neuropsychological tests, we included the training data as well as 74 subjects of unknown or inconsistent amyloid status. Two methods of generating outcome measures were used. (1) As a stand‐in for Tau load, similar to the centiloid scale, a score of 100, 50 or 0 was given to participants of the training set by multiplying CDR score by 100. Hierarchical and ridge regression were performed for each tracer using PET‐Braak SUVRs as predictive variables. Regression coefficients were used to calculate an outcome measure for all participants. (2) Principal component analysis (PCA) was applied for each tracer to the vectors of PET‐Braak regional SUVR of the training subset. Projections along the principal axis were calculated for both modalitiesResultHierarchical, ridge regression and PCA all yielded composite measures with good linear dependence between the two tracers (r2 for hierarchical, ridge and PCA were 0.84, 0.80, 0.80, respectively). Regions remaining in the hierarchical regression analyses varied between tracers: predictive regions for 18F‐MK6240 were PET‐Braak 1, 4 and 5, while 18F‐Flortaucipir used only PET‐Braak 1 and 5. Composite measures correlated well with MMSE and total MOCA score with all composite measures r < ‐0.61.ConclusionWhile both tracers may have different sensitivities to regional Tau distributions and disease stages, weighting multiple regions differently for each tracer gives a composite score linearly comparable across tracers that correlates with neuropsychological measures. This study is limited by the low total tau load and impairment level of all participants and would benefit from the inclusion of patients with more disease progression.

AbstractBackgroundAlzheimer disease (AD) related cognitive decline occurs at relatively young ages in individuals with Down syndrome (DS, early‐mid 50s) and in those with autosomal dominant mutations (ADAD, 40‐50s). Both groups show similar patterns of amyloid accumulation. We examined if brain volumes are similarly affected by AD pathology in individuals with DS and ADAD.MethodData for cognitively stable and declining participants was obtained from the Alzheimer Biomarker Consortium‐Down Syndrome (ABC‐DS) and the Dominantly Inherited Alzheimer Network (DIAN). Stability/decline was identified based on cognitive testing and interview of individuals and caregivers by trained assessors. Cognitively stable family members without DS/ADAD mutations were recruited as controls from both studies. Participants underwent MRI and amyloid positron emission tomography (PET) scans from which brain volumes and amyloid (centiloids) were derived, respectively. Participants from DIAN had Pittsburgh Compound‐B (PIB) scans, ABC‐DS had PIB or florbetapir. Nonlinear cross‐sectional associations between regional brain volumes and estimated years to onset of cognitive decline (EYO, negative values before onset, positive after) and centiloid were evaluated using generalized additive models while controlling for sex and random effects of family. EYO was set to 52 for all participants with DS and based on parental decline/mutation type for participants with ADAD. EYO for controls was based on the EYO of their family member.ResultData from 239 participants with DS (47 declining), and 340 participants with ADAD (122 declining), and 263 familial controls were included. Higher EYO and centiloid values were associated with lower brain volumes in almost all regions. At earlier EYOs, individuals with DS typically had smaller regional volumes than ADAD or sibling controls, with volume declining linearly across the EYO range. By contrast, ADAD mutation carriers had similar volumes to non‐carriers at early EYOs, with volumes diverging as early as 10 years before decline. Brain volumes and centiloid values were inversely related in ADAD and DSAD. Volume in key cortical regions were similar by the expected year of onset in both groups.ConclusionADAD and DSAD demonstrated different temporal patterns of regional neurodegeneration prior to cognitive change despite being similarly affected by early onset amyloid.