Diagnostic Accuracy of a Plasma Phosphorylated Tau 217 Immunoassay for Alzheimer Disease Pathology
Nicholas J. AshtonWagner S. BrumGuglielmo Di MolfettaAndréa Lessa BenedetBurak ArslanErin M. JonaitisRebecca E. LanghoughKarly Alex CodyRachael E WilsonCynthia M. CarlssonEugeen VanmechelenLaia Montoliu‐GayaJuan Lantero Rodrı́guezNesrine RahmouniCécile TissotJenna StevensonStijn ServaesJoseph TherriaultTharick A. PascoalAlberto LleóDaniel AlcoleaJuan ForteaPedro Rosa‐NetoSterling C. JohnsonAndreas JerominKaj BlennowHenrik Zetterberg
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Importance Phosphorylated tau (p-tau) is a specific blood biomarker for Alzheimer disease (AD) pathology, with p-tau217 considered to have the most utility. However, availability of p-tau217 tests for research and clinical use has been limited. Expanding access to this highly accurate AD biomarker is crucial for wider evaluation and implementation of AD blood tests. Objective To determine the utility of a novel and commercially available immunoassay for plasma p-tau217 to detect AD pathology and evaluate reference ranges for abnormal amyloid β (Aβ) and longitudinal change across 3 selected cohorts. Design, Setting, and Participants This cohort study examined data from 3 single-center observational cohorts: cross-sectional and longitudinal data from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort (visits October 2017–August 2021) and Wisconsin Registry for Alzheimer’s Prevention (WRAP) cohort (visits February 2007–November 2020) and cross-sectional data from the Sant Pau Initiative on Neurodegeneration (SPIN) cohort (baseline visits March 2009–November 2021). Participants included individuals with and without cognitive impairment grouped by amyloid and tau (AT) status using PET or CSF biomarkers. Data were analyzed from February to June 2023. Exposures Magnetic resonance imaging, Aβ positron emission tomography (PET), tau PET, cerebrospinal fluid (CSF) biomarkers (Aβ42/40 and p-tau immunoassays), and plasma p-tau217 (ALZpath pTau217 assay). Main Outcomes and Measures Accuracy of plasma p-tau217 in detecting abnormal amyloid and tau pathology, longitudinal p-tau217 change according to baseline pathology status. Results The study included 786 participants (mean [SD] age, 66.3 [9.7] years; 504 females [64.1%] and 282 males [35.9%]). High accuracy was observed in identifying elevated Aβ (area under the curve [AUC], 0.92-0.96; 95% CI, 0.89-0.99) and tau pathology (AUC, 0.93-0.97; 95% CI, 0.84-0.99) across all cohorts. These accuracies were comparable with CSF biomarkers in determining abnormal PET signal. The detection of abnormal Aβ pathology using a 3-range reference yielded reproducible results and reduced confirmatory testing by approximately 80%. Longitudinally, plasma p-tau217 values showed an annual increase only in Aβ-positive individuals, with the highest increase observed in those with tau positivity. Conclusions and Relevance This study found that a commercially available plasma p-tau217 immunoassay accurately identified biological AD, comparable with results using CSF biomarkers, with reproducible cut-offs across cohorts. It detected longitudinal changes, including at the preclinical stage.Keywords:
Tau protein
This article will summarize the results of recent years of exploration into deeper causes of Alzheimers disease with possible therapeutic strategies. The most popular pathological hypothesis for the causation of Alzheimers is the A cascade hypothesis. A has a dominant role in the pathophysiology of Alzheimers disease, according to genetic and pathological data. Another significant histological characteristic of Alzheimers disease brains is the presence of neurofibrillary tangles made of the protein tau, which is related with microtubules. In the brain, neuronal loss, neuroinflammation, and oxidative stress can result from the cascade consequences of tau toxicity. But as research has progressed, it has been found the A. The accumulation of protein and neurofibrillary tangles composed of phosphorylated tau are only manifestations of AD, not the result. This is also the reason why many drugs fail the phase III clinic. So people began to look for a way out of the problem, starting in the direction of the gene. How to diagnose AD early in the MCI stage, how to find markers for early diagnosis and how to inhibit the progression from the MCI stage to the dementia stage are all questions that need to be investigated in the future.
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Tau is a microtubule-associated protein th at can promote micortubulbe polymerization in vitro and can stabilize the asse mbled microtubules. Hyperphosphorylated form of tau comprises the main component of the paired helical filaments and neurofibrillary tangles found in Alzheimer s desease (AD). This paper reviews the functions and the prospects of tau in Al zheimers disease.
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Abstract Tau is a microtubule-binding protein that is critical to the organization and stabilization of microtubules physiologically. Cerebrospinal fluid (CSF) total tau (t-tau) and CSF phosphorylated tau (p-tau) have been recommended as diagnostic biomarkers for AD according to the 2018 National Institute on Aging and Alzheimer’s Association (NIA-AA) research framework. We performed a genome-wide association study (GWAS) of longitudinal change in CSF t-tau among 317 non-demented elders from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort in non-Hispanic Caucasians. We employed the linear regression model to identify novel variants associated with longitudinal change of t-tau in CSF. One single nucleotide polymorphism (SNP) (rs17149074) in the region of C9orf171 (CFAP77) gene was found to reach genome-wide significance associated with longitudinal change of CSF t-tau. Five SNPs (rs10916844, rs10916846, rs9425869, rs3744474, rs8078303) were identified as potential candidate loci associated with longitudinal change of CSF t-tau. Validation of the identified loci in larger samples and various races was needed in future research.
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A pathophysiologic model of Alzheimer's disease (AD) has been recently proposed in which beta amyloid accumulation occurs earlier (indexed by abnormal CSF Abeta42), followed by tau-mediated neuronal injury and dysfunction (abnormal CSF tau or FDG-PET) and lastly atrophic changes (abnormal hippocampal volume, HV). The aim of this study is to validate this model by comparing clinical features and conversion to AD and other dementias among groups of patients with mild cognitive impairment (MCI) with different abnormal biomarker profiles. The patients of this study were 58 with MCI in whom AD biomarkers (CSF Abeta42 and tau, temporoparietal hypometabolism on 18F-FDG PET, and hippocampal volume) were collected. Patients were divided into 3 groups of no abnormal biomarker, AD biomarker pattern (including 3 subgroups of early = only abnormal Abeta42, intermediate = abnormal Abeta42 and FDG-PET or tau, and late = abnormal Abeta42, FDG-PET or tau, and HV), and any other biomarker combination. MCI patients with AD biomarker pattern had lower behavioral disturbances than patients with any other biomarker combination (P <.0005) and lower performance on verbal and nonverbal memory than the other two groups (P = .07 and P = .004, respectively). Within the 3 subgroups with AD biomarker pattern there was a significant trend to higher rate of conversion to dementia (p for trend = .006). Moreover, AD was the type of incident dementia in 100% of patients with an AD biomarker pattern, but 0% and 27% in converters with no abnormal biomarker and any other biomarker combination, respectively (P = .002). Clinical cases representative of the three groups were also described. The results of this study provide evidence in favor of the dynamic biomarker model and support the use of biomarkers for the diagnosis of MCI due to AD according to the new recently published research criteria.
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Understanding the time-dependent changes of biomarkers related to AD is key to assessing disease progression and to measuring the outcomes of disease-modifying therapies. To better understand disease progression and the interrelationship of multiple biomarkers we demonstrate a technique to compute the time-dependent changes of biomarkers related to AD using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Multiple biomarkers are used to compute the progression of subjects in ADNI assuming that each subject has a different age of onset and rate of progression. The dynamic behavior of each biomarker is assumed to follow a common sigmoidal trajectory across the entire population, each having four parameters, which are also estimated. We then fit the time of onset, rate, and biomarker variances in a parametric statistical model based on these assumptions, yielding a normalized scale called the Alzheimer's disease progression scale (ADPS) against which each biomarker is visualized. Nine biomarkers from the ADNI dataset are chosen by experts for their importance in AD: MRI hippocampal volume divided by the intra-cranial volume (Hippo), thickness of the entorhinal cortex (EC), ADAS, MMSE, CSF protein concentration of tau and Aß42, CDRSB, RAVLT 30 minutes and the mean FDG PET uptake value of Left Angular, Right Angular, Left Temporal, Right Temporal and Bilateral Posterior The estimated sigmoid curves for each biomarker are standardized and plotted against the ADPS in Figure 1. RAVLT-30min is the earliest biomarker to become dynamic, as measured by the location of the inflexion point on the ADPS. A group of four biomarkers follows including Hippo, Aß42, tau, EC, followed by the FDG PET measurement. The cognitive biomarkers, including MMSE, SDR-SD, and ADAS, are maximally dynamic during the latter part of the disease. 75% confidence intervals for the ADPS value at which each biomarker is the most dynamic (inflexion point) are also provided below the sigmoid curves.
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Different CSF biomarker combinations can provide conflicting diagnostic information in Alzheimer′s disease (AD). This is often attributed to differences in sensitivity and specificity, at the cohort level, between CSF markers (Aβ42, t-Tau, p-Tau181, t-Tau/Aβ42, and p-Tau181/Aβ42). When these biomarkers are analyzed against the same gold standard independently, conflicting biomarker information can also result from biomarker substructures not obvious to investigators. Previous studies have not examined conflicting biomarker information at the individual level (e.g., a profile showing normal Aβ42 levels but abnormal t-Tau/Aβ42 ratio may be interprted as AD-like even though the normal Aβ42 level argues against amyloid pathology). The prevalence of these conflicts and ways to resolve them are unknown. We measured CSF AD biomarker levels in one consecutive series (n=431) from Emory University using the multiplex AlzBio3 assay and surveyed the concordance rates between CSF biomarkers at the individual level. We also compared these results with those from clinical testing through a comparable ELISA. To resolve the issue of differential sensitivity and biomarker substructure, we then analyzed CSF AD biomarker levels through two-step clustering to identify naturally existing subgroups of biomarker profiles. Finally, to determine if the cluster membership or the combination of independent biomarker information confers greater information on prognosis, we analyzed if either predicted longitudinal cognitive changes in the Alzheimer’s Disease Neuro-Imaging Initiative (ADNI, n=409). Conflicting CSF biomarker information was very common: 59% of the Emory subjects and 37% of ADNI subjects had at least one biomarker providing diagnostic information distinct from the other biomarkers. Clustering analysis revealed three groupings: one characterized by p-Tau181/Aβ42>0.131 and longitudinal cognitive decline in MCI, and two others (including one characterized by Aβ42>258.5pg/mL) associated with cognitive stability. Within each cluster, concordant or discordant biomarker findings did not further distinguish rates of longitudinal cognitive decline. Conflicting information from different CSF AD biomarkers was common. A data-driven strategy accounting for all biomarker combinations identified naturally existing groupings each characterized by similar biochemical and prognostic profiles.
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Here, we review progress by the Penn Biomarker Core in the Alzheimer's Disease Neuroimaging Initiative (ADNI) toward developing a pathological cerebrospinal fluid (CSF) and plasma biomarker signature for mild Alzheimer's disease (AD) as well as a biomarker profile that predicts conversion of mild cognitive impairment (MCI) and/or normal control subjects to AD. The Penn Biomarker Core also collaborated with other ADNI Cores to integrate data across ADNI to temporally order changes in clinical measures, imaging data, and chemical biomarkers that serve as mileposts and predictors of the conversion of normal control to MCI as well as MCI to AD, and the progression of AD. Initial CSF studies by the ADNI Biomarker Core revealed a pathological CSF biomarker signature of AD defined by the combination of Aβ1‐42 and total tau (T‐tau) that effectively delineates mild AD in the large multisite prospective clinical investigation conducted in ADNI. This signature appears to predict conversion from MCI to AD. Data fusion efforts across ADNI Cores generated a model for the temporal ordering of AD biomarkers which suggests that Aβ amyloid biomarkers become abnormal first, followed by changes in neurodegenerative biomarkers (CSF tau, F‐18 fluorodeoxyglucose‐positron emission tomography, magnetic resonance imaging) with the onset of clinical symptoms. The timing of these changes varies in individual patients due to genetic and environmental factors that increase or decrease an individual's resilience in response to progressive accumulations of AD pathologies. Further studies in ADNI will refine this model and render the biomarkers studied in ADNI more applicable to routine diagnosis and to clinical trials of disease modifying therapies.
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