Masdeu, Joseph C.

Abstract Background Neighborhood socio‐economic conditions may increase Alzheimer's disease (AD) risk through amyloid, tau, atrophy or vascular pathways, potentially varying by genetic risk, race or ethnicity and sex. We examined associations between Area Deprivation Index (ADI) and cognition, AD pathology, gray matter (GM) and white matter hyperintensities (WMH) volume in cognitively unimpaired older adults by race or ethnicity, sex and APOE4 status. Method We included 1896 non‐demented older adults from US POINTER study with available cognitive evaluations, ADI level – a census–based neighborhood disadvantage metric (categorized as low, moderate and high) and a subsample with neuroimaging data ( N = 806). Linear/logistic regressions were used to assess interactions of ADI with sex, race or ethnicity, and APOE4 on (1) the Preclinical Alzheimer's Cognitive Composite, (PACC‐5), (2) MRI‐derived GM volume meta‐ROI (entorhinal, fusiform, parahippocampal, mid‐temporal, inferior temporal), (3) WMH and (4) AD pathology (amyloid and tau PET), adjusted by age, sex and intracranial volume when appropriate. Result African American participants ( p <0.001) and females ( p <0.001) were more likely to live in deprived areas. African American participants had significantly lower PACC‐5 ( p <0.001) and GM volume ( p <0.001), “Other or multiple races” participants showed significantly higher amyloid burden ( p = 0.001), and White participants higher WMH ( p = 0.013). Females showed higher PACC‐5 scores ( p <0.001), while APOE4 carriers showed lower PACC‐5 ( p = 0.042) and higher amyloid burden ( p <0.001) (Table 1). Race and ADI interacted on PACC‐5 ( p = 0.043) and showed a borderline interaction on Tau ( p = 0.079), with higher ADI linked to lower PACC‐5 in White, Hispanic, and “Other or Multiple Races” and higher Tau in Hispanic. Further, there was a borderline ADI*APOE4 status interaction on WMH ( p = 0.059) and a significant ADI*APOE4 interaction on amyloid ( p = 0.001), where APOE4 carriers in deprived areas showed higher WMH and lower amyloid burden (Figure 1). No significant ADI*sex interactions were observed. Conclusion Neighborhood disadvantage was more commonly observed in African American participants, but interaction with cognition was primarily seen in other racial/ethnic groups. APOE4 carriers showed increased amyloid burden, but those from more disadvantaged neighborhoods exhibited a higher vascular component alongside lower amyloid‐driven pathology. These results may indicate varying effects of social disadvantages across groups or sample bias.

Abstract Background Neighborhood social and built environment characteristics may promote physical activity, a modifiable factor for dementia risk, which may in turn promote cognitive and brain health. We explored the associations of the U.S. National Walkability Index ‐ a nationwide geographic data resource that ranks block groups according to their relative walkability – with cognitive function, gray matter (GM) and white matter hyperintensities (WMH), neighborhood disadvantage, sedentary behavior and frequency of physical activity in cognitively unimpaired older adults at increased Alzheimer's disease (AD) risk. Method We included baseline data from 1398 older adults (68.3 ± 5.17 years, 68.3% females, 30.5% APOE4 carriers) from the U.S. POINTER trial. Walkability was classified as: least, below average, above average and most walkable. Neighborhood disadvantage (low, moderate and high) was assessed using Area Deprivation Index (ADI) ( N = 1384). Physical inactivity was assessed using the Sedentary Behavior Questionnaire (SBQ, hours sitting/week, N = 1106), and physical activity was measured with a composite score from the Community Health Activities Model Program for Seniors (CHAMPS) questionnaire. Linear and logistic regressions examined associations between walkability and (1) physical activity and sedentary behavior; and (2) cognition assessed with the Preclinical Alzheimer's Cognitive Composite (PACC‐5), GM volume and WMHs, Additionally, associations between neighborhood disadvantage and physical activity and sedentary behavior were explored. Models were adjusted for age, sex, race, APOE4 and total intracranial volume. Result More deprived areas were generally less walkable (β = ‐0.008, p < 0.001) (Figure 1). Higher walkability (Above average: β = 0.192, p = 0.011 and most walkable: β = 0.183, p = 0.032) was associated with higher PACC‐5 (Figure 2A) scores but not with more preserved GM or lower WMH. Walkability scores were not associated with physical activity and sedentary measurements, while greater neighborhood disadvantage was linked to sedentary behavior (β = 0.126, p < 0.001) (Figure 2B). Conclusion These results suggest that physical inactivity in more deprived areas is associated with neighborhood socioeconomic disadvantages rather than built environment. Additionally, walkability scores were not associated with any measure of physical activity or brain health, suggesting the need to further investigate other health effects of neighborhood disadvantage and walkability.

Abstract Background In aging and dementia research, the relationships between modifiable risk factors and biomarkers often vary across cohorts. We aim to evaluate the consistency and generalizability of the association between the area deprivation index (ADI) ‐ an indicator of socioeconomic/resource disadvantage within a specific area ‐ and cognition and Alzheimer's disease (AD) biomarkers across three distinct research cohorts, two from the U.S. (U.S. POINTER Imaging, ADNI) and one from Spain (UBRAIN). Method We included a total of 3091 participants without dementia, 1879 from U.S. POINTER Imaging, 282 from ADNI and 930 from UBRAIN (Table 1). The ADI was calculated using validated indices from the US and Spain, with neighborhood deprivation ranked as Low, Moderate, and High in the US, and Low and High in Spain. Outcomes of interest included (1) the preclinical Alzheimer's cognitive composite (PACC‐5), (2) MRI‐derived gray matter volumes in AD‐vulnerable regions and (3) PET or CSF‐derived amyloid status (+/‐). We ran logistic regression models with ADI level as predictor adjusted by sex, age, and APOE4 and considered education and race or ethnicity (only US cohorts) in our analyses. Result Participants in more deprived areas had lower education across cohorts and were more likely to be female (U.S. POINTER) and African American (U.S. POINTER and ADNI, Table 1). Independent of age, sex and APOE4 status, higher ADI was associated with lower PACC in U.S. POINTER (Moderate: β=‐0.165, p <0.001; High β=‐0.412, p <0.001) and UBRAIN (ADI: High β=‐0.096, p = 0.022) but not in ADNI (Figure 1). After controlling for education, this association remained significant in U.S. POINTER (Moderate: β=‐0.166, p = 0.002; High: β=‐0.248, p = 0.007) but not in UBRAIN (β=‐0.021, p = 0.66). In U.S. POINTER, there was a trend towards an association of higher ADI with lower GM volume in AD‐vulnerable regions (β=‐0.126, p = 0.089) that was not observed in ADNI or UBRAIN (Figure 2). No associations between ADI and amyloid status were found across cohorts. Conclusion We observed differing associations in neighborhood deprivation and cognition across US cohorts, as well as between the US and Spain cohorts. Country‐specific factors and sample representativeness must be considered when investigating the pathophysiologic mechanisms underlying social determinants and disparities in AD.

Abstract Background In vivo Braak staging stratifies patients across the AD spectrum and has the potential to harmonize tau PET tracer staging. This study aims to compare and test harmonization procedures for Braak staging individuals using MK6240 and Flortaucipir tau PET tracers. Methods We assessed 437 participants across the AD spectrum (245 cognitively unimpaired (CU) and 192 cognitively impaired; mean age 68.5 ± 8.6) using head‐to‐head MK6240 and Flortaucipir scans. We computed SUVRs in Braak regions of interest (ROIs) and assessed four cut‐off methods for Braak positivity: (a) mean + 2.5 SD of young controls (age <28 years), (b) mean + 2.5 SD of elderly CU Aβ−, (c) Gaussian mixture modeling (GMM), and (d) the Youden index. Braak stages were assigned using seven (0 to VI) or four (0, I–II, III–IV, V–VI) categories. We evaluated inter‐ and intra‐tracer concordance (intra‐tracer, i.e., whether it follows the sequential Braak pattern). Results The intra‐tracer seven‐class Braak staging concordance ranged from 63% to 94%. With the highest intra‐tracer Braak concordance being achieved when using GMM cutoffs: 94% (MK6240) and 89% (Flortaucipir; Figure 1). Inter‐tracer agreement concordance ranged from 56% to 76%. The highest concordance emerged from the CU Elderly Aβ– cutoff optimizing the Braak II region for spill‐off (Figure 2). Using the Braak staging simplified version improved intra‐tracer concordance in both tracers (MK6240as well as inter‐tracer agreement (86.5%). Most inter‐tracer discrepancies were observed at Braak stages II–IV. Despite showing staging discordances, the distribution of cognitive status across the Braak stages is similar for both tracers (Figure 3). Conclusion These preliminary findings reveal some discrepancies in Braak staging when comparing MK6240 and Flortaucipir. Our results also suggest that adjustments in cutoffs and regions of interest can partially mitigate both inter‐ and intra‐tracer divergences. Finally, our analysis suggests robust concordance after adjustment and using 4 classes (0, I‐II, III‐IV, V‐VI).

Abstract Background Tau‐PET provides critical topographic information on tau deposition in the living brain. Integrating research‐based practices into clinical settings through qualitative methods, such as visual reads, is becoming increasingly important. This study aims to compare the performance of existing visual reading methods for [ 18 F]Flortaucipir (FTP) and [ 18 F]MK‐6240 (MK) and provide preliminary data toward a unified visual reading approach for all tau‐PET tracers using a head‐to‐head dataset. Methods The study design and development plans are illustrated in Figure 1. To evaluate previously published visual reading methods, two blinded raters conducted FTP and MK visual reads on 340 participants from the HEAD study. A unified visual reading method was developed and tested on 101 participants, using a composite of adjusted Braak regions to determine 3 stages of severity while harmonizing between tracers. For external validation, the method will be tested on four additional cohorts, each using one of the following tracers: FTP, MK, PI, or RO. Results Inter‐rater agreement using previous methods (Figure 2A) showed high Cohen’s kappa values (0.71‐0.77) for the low and high tau burden categories in both FTP and MK visual reads. However, agreement was substantially lower in the non‐AD‐like category, leading to overall agreement rates of 0.55 for FTP and 0.69 for MK. In contrast, the method developed here improved inter‐rater agreement across all categories (0.87‐0.96) and introduced a moderate tau (0.79‐0.83) burden group (Figure 2B), leading to higher overall agreement rates of 0.81 for FTP and 0.87 for MK (Figure 2C). For inter‐tracer agreement (Figure 3), previous methods resulted in discordant visual reads for FTP and MK in 34.4% of cases for rater 1 and 24.7% for rater 2. The developed method reduced this disagreement to 11.9% (rater 1) and 15.8% (rater 2), demonstrating improved consistency across different tracers. Conclusion This study demonstrates that previously published visual reading methods produced varying classifications depending on the tracer used. The method proposed here improved inter‐rater and inter‐tracer visual read agreements by developing a unified, tracer‐agnostic approach easy to be used. Importantly, this clinician‐friendly method has the potential for widespread adoption, offering a single harmonized visual reading technique for all tau‐PET tracers.

Abstract Background One of the hallmarks of Alzheimer’s disease (AD) is the progressive spread of tau pathology. However, heterogeneity in the spatial patterns of tau deposition at the individual level may contribute to distinct clinical presentations. Here, we investigated the different patterns of tau deposition across disease progression and the different cognitive profiles linked to these patterns. Method We investigated 106 tau PET positive individuals (16% cognitively unimpaired, 84% amnestic cognitively impaired) from the HEAD study using head‐to‐head Flortaucipir and MK6240 tau PET. For each tracer, we extracted the SUVR values from seven major brain networks: Limbic, Visual (Vis), Default Mode (Default), Dorsal Attention (DorsAttn), Frontoparietal, Somatomotor (SomMot) and Salience Ventral Attention (SalVentAttn) as well as the Entorhinal cortex. Pathways were identified by ranking regions from highest to lowest SUVR, with the region displaying the highest SUVR value being defined as the peak region. Individuals in late stages of tau deposition displaying distinct pathways were compared. Image averages and voxel‐wise group comparisons using tau negative individuals as the reference were generated for each pathway and tau PET tracer. Cognitive differences were assessed using ANOVA for MoCA score and domain‐specific composite scores for Memory, Attention and Visuospatial skills. Result In all individuals and tracers, tau in the Entorhinal cortex progressed to the Limbic network, supporting a common starting point for tau propagation. Beyond these initial stages, tau propagation followed three main distinct pathways following either the Vis, Default or DorsAttn networks (Figure 1). Voxel‐wise comparison between individuals across these three pathways captured clear and similar differences in tau PET uptake as measured with Flortaucipir and MK6240. (Figure 2). While MoCA and Memory scores did not differ between groups, individuals following the Vis network exhibited significantly lower Attention scores compared to those in the Default and DorsAttn networks. Additionally, individuals following the Vis network exhibited lower Visuospatial scores compared to the Default network group (Figure 3). Conclusion Our results indicate a model in which, although tau progresses hierarchically, its deposition peaks mainly follow specific networks, which drive tau deposition and, consequently, the clinical manifestations of AD.

Abstract Background HEAD is a longitudinal, multi‐site, non‐randomized study aiming to harmonize different tau PET tracers onto a common scale for enrichment and monitoring of anti‐tau clinical trials. To achieve this, the Uniτ scale is being developed using data from participants scanned head‐to‐head with Flortaucipir and MK6240, with a subset also scanned with PI2620 and RO948. However, access to the Uniτ scale has been limited to the study community and collaborators. To address this limitation, we have developed the Uniτ Ecosystem – a set of tools for tau PET harmonization designed to support research and clinical trials ( www.unitau.app ). Method The Uniτ Ecosystem was developed for multiple operating systems (Web/macOS/Windows/Ubuntu) (Figure 1) and currently include four tools. The Uniτ harmonization is an online ROI harmonization module that allows the user to harmonize SUVRs from spreadsheets. The Uniτ packages (R, Python and MATLAB) facilitates the transformation of SUVR values to the Uniτ scale using the most used programming languages in research settings. The Uniτ calculator app allows rapid and convenient harmonization. The Uniτ desktop app is a 3D harmonization module for voxel‐wise harmonization. The Uniτ parameters were calculated using a training set of 200 participants with head‐to‐head MK6240 and Flortaucipir, and 90 individuals with additional PI2620 and RO948. Researchers can use their own datasets and convert tau PET data to the Uniτ scale. Result The online ROI harmonization module allows users to harmonize tau PET SUVRs uploaded from spreadsheets and offers interactive visualizations such as scatter plots and histograms for tau positivity thresholds (Figure 2). The calculator app enables users to harmonize single SUVR values effortlessly. The desktop application for 3D tau PET harmonization leverages the functionalities of the previous modules to harmonize entire 3D images, eliminating the need for predefined ROIs. Additionally, this module provides visualization tools for the parametric Uniτ images in 2D and 3D (Figure 3). Uniτ tools never store any data on any external servers. Conclusion The Uniτ Ecosystem represents a significant advance in tau PET harmonization, providing a user‐friendly platform to harmonize and visualize tau PET data for research and clinical trials.

Abstract Background We showed that astrocyte reactivity, as measured by plasma GFAP levels, influences Aβ mediated tau PET pathology in cognitively unimpaired (CU) Aβ‐positive individuals. However, the link between GFAP and tau PET in individuals without detectable Aβ pathology remains elusive. The aim of the current study is to investigate the association between plasma GFAP and tau PET in CU Aβ PET‐negative individuals. Method We studied 147 CU Aβ PET‐negative participants from the HEAD cohort with plasma GFAP and p ‐tau217, as well as tau PET Flortaucipir and MK6240 data. Aβ positivity was determined by Aβ PET visual reading and Centiloid 12. Voxel‐wise linear regression models adjusted for age and sex tested the association of plasma GFAP and p ‐tau217 with tau PET. Further, the associations of plasma GFAP with peak tau PET SUVR values extracted from voxel‐wise association were fitted with a linear regression model adjusted for age and sex. Result Voxel‐wise analysis showed that plasma GFAP levels, but not plasma p ‐tau217 levels, were associated with tau PET in the medial temporal lobe (e.g., amygdala, entorhinal cortex, hippocampus) predominantly for Flortaucipir tau PET [Figure 1A, B, C, D]. The association between plasma p ‐tau217 and tau PET was weak in Aβ‐negative individuals. These results were similar when Aβ positivity was defined based on Centiloid 12. Furthermore, plasma GFAP and peak tau PET SUVR of Flortaucipir showed stronger association than that of MK6240 [Flortaucipir: β=0.4017, p <0.0001; MK6240: β=0.194, p = 0.0424; Figure 2A, B]. Conclusion We found an association between GFAP levels and tau PET uptake in individuals not expected to exhibit high levels of tau tangle‐related tracer uptake. Further analysis will be designed to elucidate the underpinning of this association, which could represent low levels of tau pathology, astrogliosis, or other factors.

Abstract Background Standardizing tau pathology quantification in vivo is challenged by inherent characteristics of tau‐PET tracers. The HEAD study aims to generate a leading, longitudinal head‐to‐head dataset of MK‐6240, Flortaucipir, RO948, and PI‐2620 tau‐PET to harmonize tracers' outcomes and develop tools to generalize findings across studies and trials. Here, we provide an update on the progression of the HEAD study. Method HEAD is a multicentric study comprising nine performance sites. Recruitment aimed for 620 individuals between 18‐28 or 50‐90 years, classified as Young/CU/MCI/Dementia. The HEAD protocol involves clinical assessment utilizing the NACC Uniform Data Set, blood collection for banking of plasma/serum/buffy coat/whole blood, and MRI acquisition based on ADNI4. All participants undergo amyloid‐PET with either PiB/NAV4694/Florbetaben/Flutemetamol. All undergo head‐to‐head tau‐PET with at least two tracers, including MK‐6240 (90‐110), Flortaucipir (80‐100), PI‐2620 (45‐75), and RO948 (70‐90). PET data is reconstructed and processed uniformly similarly to ADNI4. The Laboratory of Neuroimaging (LONI) provides centralized databasing, and the National Centralized Repository for ADRD (NCRAD) provides the blood biorepository for all samples. All study procedures are repeated at 18 months. Result Over 26 months, N = 679 participants were enrolled into HEAD, exceeding our proposed enrollment by 9.5%. Mean age of older adults is 72.1 years, female distribution is 54%, and 24% of individuals are from underrepresented groups (race/ethnicity/rurality). Progression in data collection has led to N = 551 (81%) of enrolled participants having a completed initial timepoint, and 1,489 total acquired head‐to‐head tau‐PET scans (mean acquisition window=34.9 days). Clinical characteristics including group distribution, APOEε4 carriership, plasma biomarker distribution (Aβ42/40 ratio/NfL/GFAP/PTau217), consensus visual rating of amyloid‐PET, and Braak stage classification are summarized in Figure 1. Two representative cases (CU/AD) of head‐to‐head tau‐PET with four tau tracers are shown in Figure 2. Longitudinal data collection has been initiated in N = 95 participants. Figure 3. demonstrates two cases (CU/MCI) of 18‐month longitudinal head‐to‐head tau‐PET with MK‐6240 and Flortaucipir. Conclusion The HEAD study cohort represents a continued effort in the optimization of AD imaging biomarkers. Cross‐sectional and longitudinal data collection in HEAD are ongoing, in addition to comprehensive plasma biomarker measurements. Generation of findings from HEAD cohort data will provide novel and crucial guidance on the use of tau‐PET tracers.

Abstract Background Quantifying tau aggregates in the human brain can be achieved using Positron Emission Tomography (PET) techniques, which can potentially be affected by binding competition due to medication use. Patients with dementia often have high rates of comorbidities and polypharmacy. Therefore, this study aims to investigate the potential influence of multiple medications on the uptake of the tau tracers MK6240 (MK) and Flortaucipir (FTP). Method Five classes of medications were evaluated: Anti‐Hypertensives, Statins, Anti‐Diabetics, Psychoactive drugs, and NSAIDs (Table 1). We included 292 individuals [170 cognitively unimpaired (CU) Aβ‐negative and 122 cognitively impaired (CI) Aβ‐positive] from the HEAD study (Table 2). We compared MK and FTP SUVR in the Medial Temporal Lobe (MTL) and Neotemporal Cortex (NTC) in individuals on and off medications. The linear regressions that tested associations were corrected for confounding factors, including age, sex, education, and MoCA score. Correction for multiple comparisons was applied using the Bonferroni method (adjusted p ‐value at 0.00125). Result Among CI Aβ‐positive individuals, Anti‐Diabetics were associated with lower SUVR in the NTC for both FTP and MK. However, these associations did not remain significant after correction for multiple comparisons. (Table 3). Conclusion Our findings indicate that there are no significant associations between the use of the medications studied and MK or FTP uptake when accounting for covariates and applying multiple comparison corrections.