Citations

Abstract Background In recent years, blood‐based biomarkers (BBMs) have transformed the diagnostic landscape of Alzheimer’s disease (AD), with some now approaching readiness for clinical implementation. This progress aligns with the growing importance of accurate early diagnostics and availability of anti‐Aβ therapies for the treatment of early symptomatic AD, reinforcing the need for more rapid and early diagnostic capabilities. To address this need, the Alzheimer’s Association convened a multidisciplinary panel of clinical experts, subject‐matter specialists, and guideline methodologists to conduct a systematic review and develop evidence‐based recommendations for the use of BBMs in the diagnostic evaluation of AD. The scope of this guideline is focused on individuals with cognitive impairment ‐ either MCI or dementia ‐ who are undergoing diagnostic assessment in secondary or tertiary care settings. Method The panel conducted a systematic review to assess BBMs' diagnostic test accuracy in detecting amyloid pathology for triaging (≥90% sensitivity, ≥75% specificity) and confirmatory (≥90% sensitivity and specificity) diagnostic workup. The BBMs of interest included plasma p ‐tau and Aβ tests measuring the following analytes: p ‐tau217, % p ‐tau217, p ‐tau181, p ‐tau231, and Aβ42/Aβ40 ratio. The reference standard tests included CSF, amyloid PET, or neuropathology examination. The panel applied the GRADE approach to assess the certainty of the evidence and the GRADE Evidence‐to‐Decision Framework to develop its recommendations. Result Across 49 observational studies meeting eligibility criteria, 31 different BBM tests were evaluated. Using predefined decision thresholds, the panel determined whether each test has 1) sufficient diagnostic test accuracy to be used as a triaging test where a positive test is to be confirmed by PET or CSF, 2) sufficient diagnostic test accuracy as a confirmatory test to replace PET or CSF, or 3) insufficient diagnostic test accuracy to recommend current use in clinical practice. Recommendations will be provided in case any BBMs met a priori DTA thresholds. Conclusion BBMs can improve early AD diagnosis and expand access to disease‐modifying therapies. Evidence‐based guidelines are key to standardizing their use and will be updated as new evidence and applications emerge.

Abstract Background Mitophagy constitutes a crucial cellular pathway in mitochondrial quality control, in which the enzymatic pair PINK1 and PRKN selectively decorates damaged mitochondria with phosphorylated‐ubiquitin (pS65‐Ub), facilitating their autophagic‐lysosomal removal. We previously demonstrated significant accumulation of pS65‐Ub labeled damaged mitochondria in Lewy body and Alzheimer’s disease autopsy brain. This mitophagy alteration was strongly associated with pre‐tangle tau pathology in neurons. The effects of distinct tau species on mitophagy across different cell types however remain largely unknown. We therefore expanded our analyses to primary tauopathies, exploring how specific tau forms and their aggregation influence mitophagy in selectively vulnerable brain regions and cell types. Method This study included 92 control and tauopathy cases with primary age‐related tauopathy (PART), Pick’s disease (PiD), progressive supranuclear palsy (PSP), or corticobasal degeneration (CBD). Immunohistochemistry for the mitophagy marker pS65‐Ub and phospho‐tau was performed in vulnerable brain regions (hippocampus for PART and PiD, primary motor cortex for PSP and CBD). A deep learning‐based image identification model was used to identify and quantify distinct tau inclusions. pS65‐Ub‐positive cells were manually counted and grouped based on signal intensity and morphology. Immunofluorescence co‐staining of pS65‐Ub and phospho‐tau was conducted to study their spatial relationship at single‐cell level. Result We observed marked increases in pS65‐Ub‐positive cells in the hippocampus of PART and PiD cases compared to controls. Although less prominent, significant increases were also found in the primary motor cortex of PSP, whereas only minor changes were observed in CBD. Single‐cell analyses revealed that the accumulation of pS65‐Ub was most abundant in hippocampal neurons from PART. Cells with lower signal intensity contained mostly granular pS65‐Ub deposits, while those with higher intensity harbored rather vacuolar inclusions. Across tau aggregate subtypes, pS65‐Ub levels were strongly correlated and often co‐resided with neurofibrillary tangles and Pick bodies. Such correlations were weaker for coiled bodies and tufted astrocytes, and absent for astrocytic plaques. Conclusion Our study highlights distinct mitophagy alterations in primary tauopathies that are associated with specific tau inclusions in neurons, oligodendrocytes, and astrocytes. The observed pathological heterogeneity and complex mitophagy disruption underscores the need for future studies to elucidate pathogenic mechanisms and advance targeted therapeutics for each tauopathy.

Abstract Background Individual environmental exposures, the exposome, can play an influential role in the development and progression of neurodegenerative diseases such as AD. The protein components of the main AD neuropathologies, amyloid (Aβ) plaques and tau neurofibrillary tangles (NFTs), can be measured using immunohistochemical and biochemical approaches. The main genetic risk factor for AD is the APOE e4 allele. APOE levels can also be measured in the brain. We hypothesize that social determinants of health, specifically alcohol and tobacco consumption, and the area deprivation index (ADI), could play a considerable role in severity of neuropathology and brain biochemical phenotypes of AD. Method We evaluated 478 neuropathologically characterized AD donors, from the Mayo Clinic Brain Bank. 469 samples had available biochemical measures (APOE, Aβ40, Aβ42, tau, and phospho‐tau (Thr231)), collected across three isolated fractions (soluble (TBS), membrane‐bound (TX), and insoluble (FA)) of the superior temporal gyrus. Exposome variables were extracted from the available records. ADI from the Neighborhood Atlas® version from the year 2015 was obtained from de‐identified addresses. ADI, smoking and alcohol were available for 263 of these donors and were examined for association with biochemical measures using linear regression adjusting for age at death and sex. Result Higher ADI levels representing neighborhoods with increased deprivation, were nominally associated (β=0.006, p =0.01) with soluble levels of APOE. Alcohol consumption was associated with lower levels of membrane bound APOE (β=‐0.23, p =0.04). Analysis with neuropathological variables including CAA ( N =454), Braak ( N =454), Thal ( N =454), Hippocampal sclerosis ( N =453), Lewy bodies ( N =455) will also be performed. Conclusion Different components of the social exposome may impact levels of core AD proteins. Additional analyses in expanded cohorts to further explore the associations with neuropathology are ongoing. We expect that understanding how the social exposome impacts specific aspects of neurodegenerative diseases will provide key insights into underlying, potentially modifiable, mechanisms of disease.

Abstract Background Alzheimer’s Disease (AD) progression is conceptualized as a cascade of sigmoidal (S‐shaped) curves, with biomarkers shifting sequentially from normal to abnormal states. Estimating these curves requires sophisticated modeling, with challenges including (1) selecting appropriate sigmoidal‐type functions, (2) achieving algorithmic convergence, and (3) producing clinically useful parameters. We compare commonly used sigmoidal‐functions and effects on novel estimates of early and late AD‐biomarker progression ages. Method Using 3,999 Amyloid PET (PiB SUVR) measurements from 1,367 Mayo Clinic Study of Aging participants, we compared model fits (AIC, BIC) between logistic and hyperbolic functions using Non‐Linear Mixed Effect (NLME) models with subject‐specific age‐shifts. We defined novel “initial” and “maximum” acceleration points (IAP and MAP) to compare clinically “earlier” and “later” AD‐progression ages between sigmoidal‐curves. The MAP uses the maximum second‐derivative point, reflecting the age of fastest acceleration in biomarker accumulation. The IAP is defined as the first point where the second derivative of the sigmoidal‐curve exceeds a clinical threshold (here, 5% velocity increase per year), indicating onset age of early accelerated biomarker accumulation. Estimates for C 2 N plasma p ‐tau217 were also attempted (1,306 measurements on 457 participants). Models adjusted for sex and APOE4‐positivity. Result In the larger PET sample, the hyperbola had lower AIC/BIC (better fit), but showed a decreasing early PiB‐SUVR trend (Table 1A, Figure 1). Ages of late MAP were comparable, as were sex and APOE4+ related PiB‐SUVR‐curve shifts. Age of early AD‐onset was estimated 1.5 years earlier for logistic (IAP=63.6) versus hyperbola (IAP=65.1) functions. The logistic function always converged, while the hyperbolic function did not converge for p ‐tau217 in the smaller C 2 N sample. Early‐onset and late‐progression ages were 7‐8 years later for p ‐tau217 versus PiB‐SUVR using logistic‐curves. Conclusion Late AD‐progression and covariate‐related shifts in progression were comparable across models, suggesting robustness to sigmoid choice. In contrast, estimates of early AD‐onset may be more dependent on functional form. While logistic‐sigmoids inherently increase monotonically, hyperbolas do not, here estimating a decline in initial amyloid trend. Future work may explore adding biologically informed constraints to flexible sigmoidal‐functions, and alternative Bayesian approaches, to optimize both model fit and estimation of early AD‐onset age.

Abstract Background Multi‐site aging and dementia studies are challenged by data heterogeneity caused by scanner (manufacturer and protocol) related variability. Quantitative analysis of white matter hyperintensity (WMH) from pooled samples, a traditional marker of cerebrovascular disease, is sensitive to machine variability. Our goal was to compare Deming (symmetric) linear regression and ComBat, a common method used for harmonizing neuroimaging measurements, for harmonizing WMH measurements on participants scanned on two different scanners (GE and Siemens) and protocols (2D FLAIR on GE and 3D FLAIR on Siemens). Methods Cognitively unimpaired (CU) ( n =53, age (mean (SD))=67 (15) years) and cognitively impaired (CI; MCI/dementia) ( n =34, age (mean (SD))=82 (9) years) participants from the Mayo Clinic Study of Aging and Mayo ADRC were scanned on GE and Siemens scanners within a week of each other (typically done on the same day). WMH as a percentage of total intracranial volume was measured using multispectral segmentation of FLAIR and T1‐weighted images (Gunter et. al. AAIC 2023). We used Deming regression as it includes errors in both input dimensions. ComBat was applied using age, sex, and diagnosis as covariates, with scanner vendor and protocol as the batch variable. We assessed the inter‐scanner similarity using Maximum Mean Discrepancy (MMD) analysis, where a value of zero indicates identical distributions, p >0.05 indicates no significant difference under H 0 . (Gretton et al., JMLR 2012). Results Our results showed that the distribution of WMH measurements, which were different in the CU group, were more similar after Deming than ComBat harmonization (Figure 1). This was confirmed by the lower MMD in the CU group from Deming regression (‐0.01 ( p =0.76) from 0.03 ( p =0.05)) compared to ComBat (0.04 ( p =0.02))(Figure 1). However, ComBat and Deming were unable to create distributions as compatible as original measurements for the CI group. Conclusion Using a unique dataset of the same participants scanned close in time on different scanner and protocols, we found that (1) MMD measurements were useful to test the need/improvement due to harmonization; (2) ComBat was not substantially better at creating compatible distributions between scanner vendors than Deming regression, and both were sometimes inadequate. Future work will require alternative models to linear scale and shift transformations.

Abstract Background Exposure to repetitive head impacts (RHI) is associated with developing chronic traumatic encephalopathy (CTE) neuropathology. Cognitive and behavioral symptoms have been associated with CTE neuropathology, but efforts to define the specific clinical syndrome are ongoing. This study characterizes the current use of centrally acting medications (CAMs), chronic pain, and number of orthopedic surgeries in former American football players and evaluates for associations with cognitive and behavioral symptoms. Methods This study analyzed data from the DIAGNOSE CTE Research Project, which includes 120 former professional football players, 60 collegiate football players, and 56 asymptomatic men without history of RHI. Medical history was obtained by self‐report, and participants completed the Montreal Cognitive Assessment (MoCA), Brief Pain Inventory, Barratt Impulsiveness Scale‐11 (BIS‐11), BRIEF‐A Behavioral Regulation Index (BRI), Beck Depression Inventory‐II (BDI‐II), Beck Anxiety Index (BAI), and Brown‐Goodwin Lifetime History of Aggression adulthood score (BGLHA). Logistic regression assessed the association of CAMs, average pain score, and orthopedic surgeries on the cognitive impairment and neurobehavioral dysregulation components of the NINDS traumatic encephalopathy syndrome (TES) research criteria, in former American football players. Linear regression assessed the associations with MoCA and behavioral scales. Models were adjusted for age, education, race, and years of football played. Results Tables 1 and 2 describe the sample and CAM burden. More orthopedic surgeries, CAMs, and higher average pain were observed in former American football players. Number of CAMs was associated with neurobehavioral dysregulation (OR = 2.18), lower MoCA (β = ‐0.47), and higher BIS‐11 (β = 2.23), BDI‐II (β = 2.01), BRI (β = 3.71), BAI (β = 2.04), and BGLHA (β = 0.68) scores. Higher average pain was associated with neurobehavioral dysregulation (OR = 1.57) and higher BIS‐11 (β = 2.20), BDI‐II (β = 1.98), BRI (β = 2.20), BAI (β = 1.84), and BGLHA (β = 0.52) scores. No associations were observed with orthopedic surgeries. Conclusions In former American football players, the number of CAMs and higher average pain were associated with neurobehavioral dysregulation. No associations were observed with diagnosis of cognitive impairment by TES criteria, though the number of CAMs was associated with decreased MoCA.

Abstract Background G 4 C 2 repeat expansions in C9orf72 gene are the most common genetic cause of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). Mounting evidence indicates that dipeptide repeat (DPR) proteins translated from the expanded repeats, in particular poly(GR) and poly(PR), play a significant role. We and others have shown that these positively‐charged arginine‐rich DPR proteins induce the assembly of stress granules (SGs) and impair their disassembly. While distinct from SGs, processing bodies (PBs) are also cytoplasmic messenger ribonucleoprotein condensates that assemble upon stress. However, the impacts of arginine‐rich DPR proteins on PB assembly and dynamics have yet to be investigated. Method We conducted immunofluorescence staining for arginine‐rich DPR proteins, SG‐ and PB‐associated markers in cultured cells and mouse brain. Additionally, we performed in vitro liquid‐liquid phase separation (LLPS) using recombinant proteins and assessed molecular dynamics of SG and PB through fluorescence recovery (FRAP) after photobleaching in living cells. Result Here, we observed that both GFP‐(GR) 100 and GFP‐(PR) 100 formed cytosolic inclusions immunopositive PB proteins. Unlike GFP‐(GR) 100 and GFP‐(PR) 100 inclusions, cellular cytoplasmic GFP‐(GA) 100 inclusions did not contain PB proteins. We subsequently examined whether the phenomena observed in cultured cells occur in vivo. We observed that PB proteins were recruited to poly(GR) inclusions in cells with aggregated poly(GR). Next, we examined the dynamics of poly(GR)‐induced PBs in cultured cells using the FRAP technique. We observed that the recovery of stress‐induced PBs was more rapid than that of PBs in HEK293T cells expressing GFP‐(GR) 100 . Lastly, mechanistic studies revealed that poly(GR) interacts with PB proteins to form condensates in vitro via LLPS. Conclusion Our results provide both in vitro and in vivo evidence that arginine‐rich DPRs induce the spontaneous formation of PBs via LLPS. Our results also indicate that poly(GR)‐induced PBs are more stable than the transient and dynamic conventional PBs. Given that PBs uniquely enriched with mRNA degradation and decay factors, the consequences of impairing these processes are expected to contribute to abnormal RNA metabolism in c9FTD/ALS.

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.