Plasma pTau181 predicts cortical brain atrophy in aging and Alzheimer’s disease

Tissot, Cécile; Benedet, Andréa Lessa; Therriault, Joseph; Lussier, Firoza Z; Saha‐Chaudhuri, Paramita; Chamoun, Mira; Savard, Mélissa; Mathotaarachchi, Sulantha; Bezgin, Gleb; Wang, Yiting; Arias, Jaime Fernández; Lantero‐Rodriguez, Juan; Snellman, Anniina; Ashton, Nicholas J.; Karikari, Thomas K.; Blennow, Kaj; Zetterberg, Henrik; Villers-Sidani, Étienne de; Gauthier, Serge; Rosa‐Neto, Pedro

doi:10.1186/s13195-021-00802-x

Cited by 50 publications

(48 citation statements)

References 36 publications

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“…This corroborates the framework in which pTau181 appears at later stages of the disease, when hippocampal atrophy is more prominent. 22 For the established AD biomarker CSF pTau181, rates of concordance and discordance differed widely between analyses. It is important to note that not all participants of the TRIAD cohort underwent a lumbar puncture, lowering the number of individuals in the above results.…”

Section: Discussionmentioning

confidence: 99%

“…This corroborates the framework in which pTau181 appears at later stages of the disease, when hippocampal atrophy is more prominent. 22 …”

Section: Discussionmentioning

confidence: 99%

“…We used the CUY as the healthy group as it is known that tau pathology is also related to aging, thus can be observed in CU elderlies. 21 , 22 CUY were not used in subsequent analyses. Exploratory analyses were also conducted using CU as the healthy group, contrasted with AD.…”

Section: Methodsmentioning

confidence: 99%

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Comparing tau status determined via plasma pTau181, pTau231 and [18F]MK6240 tau-PET

et al. 2022

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Background Tau in Alzheimer's disease (AD) is assessed via cerebrospinal fluid (CSF) and Positron emission tomography (PET). Novel methods to detect phosphorylated tau (pTau) in blood have been recently developed. We aim to investigate agreement of tau status as determined by [ 18 F]MK6240 tau-PET, plasma pTau181 and pTau231. MethodsWe assessed cognitively unimpaired young, cognitively unimpaired, mild cognitive impairment and AD individuals with [ 18 F]MK6240, plasma pTau181, pTau 231, [ 18 F]AZD4694 amyloid-PET and MRI. A subset underwent CSF assessment.We conducted ROC curves to obtain cut-off values for plasma pTau epitopes. Individuals were categorized as positive or negative in all biomarkers. We then compared the distribution among concordant and discordant groups in relation to diagnosis, Ab status, APOEe4 status, [ 18 F]AZD4694 global SUVR, hippocampal volume and CSF pTau181.Findings The threshold for positivity was 15.085 pg/mL for plasma pTau181 and 17.652 pg/mL for plasma pTau231. Most individuals had concordant statuses, however, 18% of plasma181/PET, 26% of plasma231/PET and 25% of the pTau231/pTau181 were discordant. Positivity to at least one biomarker was often accompanied by diagnosis of

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Section: Discussionmentioning

confidence: 99%

“…This corroborates the framework in which pTau181 appears at later stages of the disease, when hippocampal atrophy is more prominent. 22 …”

Section: Discussionmentioning

confidence: 99%

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Comparing tau status determined via plasma pTau181, pTau231 and [18F]MK6240 tau-PET

et al. 2022

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“…This finding was subsequently verified by others ( Lewczuk et al, 2004 ; Fagan et al, 2011 ; Tang et al, 2014 ) and later also was confirmed with the measurement of serum ptau181 ( Shekhar et al, 2016 ). Recent studies demonstrated that blood ptau181 can predict cortical brain atrophy ( Llibre-Guerra et al, 2019 ; Tissot et al, 2021 ), tau and amyloid-beta pathology ( Lantero Rodriguez et al, 2020 ; Clark et al, 2021 ; Moscoso et al, 2021b ), differentiate AD from other neurodegenerative diseases ( Mielke et al, 2018 ; Thijssen et al, 2020 ; Grothe et al, 2021 ) and identify AD across the clinical continuum ( Janelidze et al, 2020a ; Karikari et al, 2020b , 2021 ). In familial AD, plasma ptau181 levels may rise as early as 16 years before clinical onset ( O’connor et al, 2020 ).…”

Section: Properties Of Neurofilaments Relevant To Their Use As Biomarkersmentioning

confidence: 99%

Neurofilament Proteins as Biomarkers to Monitor Neurological Diseases and the Efficacy of Therapies

2021

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Biomarkers of neurodegeneration and neuronal injury have the potential to improve diagnostic accuracy, disease monitoring, prognosis, and measure treatment efficacy. Neurofilament proteins (NfPs) are well suited as biomarkers in these contexts because they are major neuron-specific components that maintain structural integrity and are sensitive to neurodegeneration and neuronal injury across a wide range of neurologic diseases. Low levels of NfPs are constantly released from neurons into the extracellular space and ultimately reach the cerebrospinal fluid (CSF) and blood under physiological conditions throughout normal brain development, maturation, and aging. NfP levels in CSF and blood rise above normal in response to neuronal injury and neurodegeneration independently of cause. NfPs in CSF measured by lumbar puncture are about 40-fold more concentrated than in blood in healthy individuals. New ultra-sensitive methods now allow minimally invasive measurement of these low levels of NfPs in serum or plasma to track disease onset and progression in neurological disorders or nervous system injury and assess responses to therapeutic interventions. Any of the five Nf subunits – neurofilament light chain (NfL), neurofilament medium chain (NfM), neurofilament heavy chain (NfH), alpha-internexin (INA) and peripherin (PRPH) may be altered in a given neuropathological condition. In familial and sporadic Alzheimer’s disease (AD), plasma NfL levels may rise as early as 22 years before clinical onset in familial AD and 10 years before sporadic AD. The major determinants of elevated levels of NfPs and degradation fragments in CSF and blood are the magnitude of damaged or degenerating axons of fiber tracks, the affected axon caliber sizes and the rate of release of NfP and fragments at different stages of a given neurological disease or condition directly or indirectly affecting central nervous system (CNS) and/or peripheral nervous system (PNS). NfPs are rapidly emerging as transformative blood biomarkers in neurology providing novel insights into a wide range of neurological diseases and advancing clinical trials. Here we summarize the current understanding of intracellular NfP physiology, pathophysiology and extracellular kinetics of NfPs in biofluids and review the value and limitations of NfPs and degradation fragments as biomarkers of neurodegeneration and neuronal injury.

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“…Ji et al (14) used MRI to extract the voxel features of the brainstem for research and confirmed that early AD is caused by brainstem atrophy. Tissot et al (15) used voxelbased morphometry to detect the association between plasma phosphorylation-tau181 (p-tau181) and neurodegeneration.…”

Section: Introductionmentioning

confidence: 99%

Using Three-dimensional Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

Lin¹,

Lin²

2021

Sensors and Materials

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Alzheimer's disease (AD) is an irreversible neurodegenerative disease. Pathology shows atrophy of brain tissue, senile plaques, neurofibrillary tangles, and so forth. Magnetic resonance imaging (MRI) is the most sensitive brain imaging method in a clinic, which provides detailed anatomical structure information of the brain and is commonly studied with pattern recognition methods for AD diagnosis. Most existing methods extract hand-crafted imaging features or brain region-of-interest images to train a classifier to recognize AD. In this study, a threedimensional convolutional neural network (3D-CNN) is implemented to detect AD. The proposed network is trained with 3D magnetic resonance (MR) images to extract the spatial features. A uniform experimental design is used to optimize the network parameters and improve the 3D-CNN performance. To ensure satisfactory performance on a small amount of training data, a transfer learning technology is proposed to improve the recognition rate of the 3D-CNN. In addition, the uniform experimental design (UED) method is used to determine the optimal parameter combination of the network and improve the 3D-CNN performance. The validation data in this study are from the Open Access Series of Imaging Studies (OASIS), where the OASIS-1 data set is used as the original data set and the 3D-CNN is trained as the pretraining model. Experimental results show that when 10, 30, 60, and 90% of the OASIS-2 data set are used to train the pre-trained 3D-CNN, the average accuracy reaches 74.66, 86.99, 94.58, and 97.02%, respectively. In addition, compared with the original manual design parameters, the proposed 3D-CNN with the best parameter combination improves the recognition rate by 2.07 percentage points.

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Plasma pTau181 predicts cortical brain atrophy in aging and Alzheimer’s disease

Cited by 50 publications

References 36 publications

Comparing tau status determined via plasma pTau181, pTau231 and [18F]MK6240 tau-PET

Comparing tau status determined via plasma pTau181, pTau231 and [18F]MK6240 tau-PET

Neurofilament Proteins as Biomarkers to Monitor Neurological Diseases and the Efficacy of Therapies

Using Three-dimensional Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

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