Implementation and Assessment of Tau Thresholds in Non-Demented Individuals as Predictors of Cognitive Decline in Tau Imaging Studies.

Background: Tau accumulation in Alzheimer's disease is associated with short term clinical progression and faster rates of cognitive decline in individuals with high amyloid-ß deposition. Defining an optimal threshold of tau accumulation predictive of cognitive decline remains a challenge. Objective: We tested the ability of regional tau PET sensitivity and specificity thresholds to predict longitudinal cognitive decline. We also tested the predictive performance of thresholds in the proposed new NIA-AA biological staging for Alzheimer's disease where multiple levels of tau positivity are used to stage participants. Methods: 18F-flortaucipir scans from 301 non-demented participants were processed and sampled. Four cognitive measures were assessed longitudinally. Regional standardized uptake value ratios were split into infra- and suprathreshold groups at baseline using previously derived thresholds. Survival analysis, log rank testing, and Generalized Estimation Equations assessed the relationship between the application of regional sensitivity/specificity thresholds and change in cognitive measures as well as tau threshold performance in predicting cognitive decline within the new NIA-AA biological staging. Results: The meta temporal region was best for predicting risk of short-term cognitive decline in suprathreshold, as compared to infrathreshold participants. When applying multiple levels of tau positivity, each subsequent level of tau identified cognitive decline at earlier timepoints. Conclusions: When using 18F-flortaucipir, meta temporal suprathreshold classification was associated with increased risk of cognitive decline, suggesting that abnormal tau deposition in the cortex predicts decline. Likewise, the application of multiple levels of tau clearly predicts the distinctive cognitive trajectories in the new NIA-AA biological staging framework.

Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer's disease.

Background: Blood-based biomarkers are gaining grounds for Alzheimer's disease (AD) detection. However, two key obstacles need to be addressed: the lack of methods for multi-analyte assessments and the need for markers of neuroinflammation, vascular, and synaptic dysfunction. Here, we evaluated a novel multi-analyte biomarker platform, NULISAseq CNS disease panel, a multiplex NUcleic acid-linked Immuno-Sandwich Assay (NULISA) targeting ~120 analytes, including classical AD biomarkers and key proteins defining various disease hallmarks. Methods: The NULISAseq panel was applied to 176 plasma samples from the MYHAT-NI cohort of cognitively normal participants from an economically underserved region in Western Pennsylvania. Classical AD biomarkers, including p-tau181 p-tau217, p-tau231, GFAP, NEFL, Aß40, and Aß42, were also measured using Single Molecule Array (Simoa). Amyloid pathology, tau pathology, and neurodegeneration were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and MRI, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA biomarkers and AD pathologies. Spearman correlations were used to compare NULISA and Simoa. Results: NULISA concurrently measured 116 plasma biomarkers with good technical performance, and good correlation with Simoa measures. Cross-sectionally, p-tau217 was the top hit to identify Aß pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878-0.983). Fourteen markers were significantly decreased in Aß-PET+ participants, including TIMP3, which regulates brain Aß production, the neurotrophic factor BDNF, the energy metabolism marker MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aß PET-dependent yearly increases in Aß-PET+ participants. Markers with tau PET-dependent longitudinal changes included the microglial activation marker CHIT1, the reactive astrogliosis marker CHI3L1, the synaptic protein NPTX1, and the cerebrovascular markers PGF, PDGFRB, and VEFGA; all previously linked to AD but only reliably measured in cerebrospinal fluid. SQSTM1, the autophagosome cargo protein, exhibited a significant association with neurodegeneration status after adjusting age, sex, and APOE ε4 genotype. Conclusions: Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

A Neuropathology Case Report of a Woman with Down Syndrome who Remained Cognitively Stable.

In this neuropathology case report, we present findings from an individual with Down syndrome (DS) who remained cognitively stable despite Alzheimer's disease (AD) neuropathology. Clinical assessments, fluid biomarkers, neuroimaging, and neuropathological examinations were conducted to characterize her condition. Notably, her ApoE genotype was E2/3, which is associated with a decreased risk of dementia. Neuroimaging revealed stable yet elevated amyloid profiles and moderately elevated tau levels, while neuropathology indicated intermediate AD neuropathologic change with Lewy body pathology and cerebrovascular pathology. Despite the presence of AD pathology, the participant demonstrated intact cognitive functioning, potentially attributed to factors such as genetic variations, cognitive resilience, and environmental enrichment. The findings suggest a dissociation between clinical symptoms and neuropathological changes, emphasizing the complexity of AD progression in DS. Further investigation into factors influencing cognitive resilience in individuals with DS, including comorbidities and social functioning, is warranted. Understanding the mechanisms underlying cognitive stability in DS could offer insights into resilience to AD neuropathology in people with DS and in the general population and inform future interventions.

Alzheimer blood biomarkers: practical guidelines for study design, sample collection, processing, biobanking, measurement and result reporting.

Alzheimer's disease (AD), the most common form of dementia, remains challenging to understand and treat despite decades of research and clinical investigation. This might be partly due to a lack of widely available and cost-effective modalities for diagnosis and prognosis. Recently, the blood-based AD biomarker field has seen significant progress driven by technological advances, mainly improved analytical sensitivity and precision of the assays and measurement platforms. Several blood-based biomarkers have shown high potential for accurately detecting AD pathophysiology. As a result, there has been considerable interest in applying these biomarkers for diagnosis and prognosis, as surrogate metrics to investigate the impact of various covariates on AD pathophysiology and to accelerate AD therapeutic trials and monitor treatment effects. However, the lack of standardization of how blood samples and collected, processed, stored analyzed and reported can affect the reproducibility of these biomarker measurements, potentially hindering progress toward their widespread use in clinical and research settings. To help address these issues, we provide fundamental guidelines developed according to recent research findings on the impact of sample handling on blood biomarker measurements. These guidelines cover important considerations including study design, blood collection, blood processing, biobanking, biomarker measurement, and result reporting. Furthermore, the proposed guidelines include best practices for appropriate blood handling procedures for genetic and ribonucleic acid analyses. While we focus on the key blood-based AD biomarkers for the AT(N) criteria (e.g., amyloid-beta [Aß]40, Aß42, Aß42/40 ratio, total-tau, phosphorylated-tau, neurofilament light chain, brain-derived tau and glial fibrillary acidic protein), we anticipate that these guidelines will generally be applicable to other types of blood biomarkers. We also anticipate that these guidelines will assist investigators in planning and executing biomarker research, enabling harmonization of sample handling to improve comparability across studies.

Novel ultrasensitive immunoassay for the selective quantification of tau oligomers and related soluble aggregates.

INTRODUCTION: Tau aggregation into paired helical filaments and neurofibrillary tangles is characteristic of Alzheimer's disease (AD) and related disorders. However, biochemical assays for the quantification of soluble, earlier-stage tau aggregates are lacking. We describe an immunoassay that is selective for tau oligomers and related soluble aggregates over monomers. METHODS: A homogeneous (single-antibody) immunoassay was developed using a novel anti-tau monoclonal antibody and validated with recombinant and brain tissue-derived tau. RESULTS: The assay signals were concentration dependent for recombinant tau aggregates in solution but not monomers, and recognized peptides within, but not outside, the aggregation-prone microtubule binding region. The signals in inferior and middle frontal cortical tissue homogenates increased with neuropathologically determined Braak staging, and were higher in insoluble than soluble homogenized brain fractions. Autopsy-verified AD gave stronger signals than other neurodegenerative diseases. DISCUSSION: The quantitative oligomer/soluble aggregate-specific assay can identify soluble tau aggregates, including oligomers, from monomers in human and in vitro biospecimens. HIGHLIGHTS: The aggregation of tau to form fibrils and neurofibrillary tangles is a key feature of Alzheimer's disease. However, biochemical assays for the quantification of oligomers/soluble aggregated forms of tau are lacking. We developed a new assay that preferentially binds to soluble tau aggregates, including oligomers and fibrils, versus monomers. The assay signal increased corresponding to the total protein content, Braak staging, and insolubility of the sequentially homogenized brain tissue fractions in an autopsy-verified cohort. The assay recognized tau peptides containing the microtubule binding region but not those covering the N- or C-terminal regions only.

Association Between ß-Amyloid Accumulation and Incident Dementia in Individuals 80 Years or Older Without Dementia.

BACKGROUND AND OBJECTIVES: While the highest prevalence of dementia occurs in individuals older than 80 years, most imaging studies focused on younger populations. The rates of ß-amyloid (Aß) accumulation and the effect of Alzheimer disease (AD) pathology on progression to dementia in this age group remain unexplored. In this study, we examined the relationship between changes in Aß deposition over time and incident dementia in nondemented individuals followed during a period of 11 years. METHODS: We examined 94 participants (age 85.9 + 2.8 years) who had up to 5 measurements of Pittsburgh compound-B (PiB)-PET and clinical evaluations from 2009 to 2020. All 94 participants had 2 PiB-PET scans, 76 participants had 3 PiB-PET scans, 18 participants had 4 PiB-PET scans, and 10 participants had 5 PiB-PET scans. The rates of Aß deposition were compared with 120 nondemented individuals younger than 80 years (69.3 ± 5.4 years) from the Australian Imaging, Biomarker, and Lifestyle (AIBL) study who had 3 or more annual PiB-PET assessments. RESULTS: By 2020, 49% of the participants developed dementia and 63% were deceased. There was a gradual increase in Aß deposition in all participants whether they were considered Aß positive or negative at baseline. In a Cox model controlled for age, sex, education level, APOE-4 allele, baseline Mini-Mental State Examination, and mortality, short-term change in Aß deposition was not significantly associated with incident dementia (HR 2.19 (0.41-11.73). However, baseline Aß burden, cortical thickness, and white matter lesions volume were the predictors of incident dementia. Aß accumulation was faster (p = 0.01) in the older cohort (5.6%/year) when compared with AIBL (4.1%/year). In addition, baseline Aß deposition was a predictor of short-term change (mean time 1.88 years). DISCUSSION: There was an accelerated Aß accumulation in cognitively normal individuals older than 80 years. Baseline Aß deposition was a determinant of incident dementia and short-term change in Aß deposition suggesting that an active Aß pathologic process was present when these participants were cognitively normal. Consequently, age may not be a limiting factor for the use of the emergent anti-Aß therapies.

Application of robust regression in translational neuroscience studies with non-Gaussian outcome data.

Linear regression is one of the most used statistical techniques in neuroscience, including the study of the neuropathology of Alzheimer's disease (AD) dementia. However, the practical utility of this approach is often limited because dependent variables are often highly skewed and fail to meet the assumption of normality. Applying linear regression analyses to highly skewed datasets can generate imprecise results, which lead to erroneous estimates derived from statistical models. Furthermore, the presence of outliers can introduce unwanted bias, which affect estimates derived from linear regression models. Although a variety of data transformations can be utilized to mitigate these problems, these approaches are also associated with various caveats. By contrast, a robust regression approach does not impose distributional assumptions on data allowing for results to be interpreted in a similar manner to that derived using a linear regression analysis. Here, we demonstrate the utility of applying robust regression to the analysis of data derived from studies of human brain neurodegeneration where the error distribution of a dependent variable does not meet the assumption of normality. We show that the application of a robust regression approach to two independent published human clinical neuropathologic data sets provides reliable estimates of associations. We also demonstrate that results from a linear regression analysis can be biased if the dependent variable is significantly skewed, further indicating robust regression as a suitable alternate approach.