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Record W4390195359 · doi:10.1002/alz.076691

Plasma p‐tau231 and p‐tau217 provides information on tau tangle deposition in symptomatic Alzheimer’s disease individuals

2023· article· en· W4390195359 on OpenAlexaffabout
Pâmela C.L. Ferreira, Bruna Bellaver, Guilherme Povala, Joseph Therriault, João Pedro Ferrari‐Souza, Cécile Tissot, Douglas Teixeira Leffa, Wagner S. Brum, Andréa Lessa Benedet, Firoza Z Lussier, Arlec Cabrera, Hussein Zalzale, Carolina Soares, Cristiano Schaffer Aguzzoli, Gleb Bezgin, Stijn Servaes, Jenna Stevenson, Gallen Triana‐Baltzer, Hartmuth C. Kolb, Nesrine Rahmouni, Vanessa Pallen, Nina Margherita Poltronetti, Dana Tudorascu, William E. Klunk, Victor L. Villemagne, Ann D. Cohen, Serge Gauthier, Eduardo R. Zimmer, Nicholas J. Ashton, Henrik Zetterberg, Kaj Blennow, Thomas K. Karikari, Pedro Rosa‐Neto, Tharick A. Pascoal

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsBiomarkerGlial fibrillary acidic proteinDemographicsPsychologyCohortTau proteinInternal medicineAmyloid (mycology)PathologyNeurofibrillary tangleOncologyAlzheimer's diseaseDiseaseMedicineNuclear medicineNeuroscienceChemistrySenile plaquesBiochemistryImmunohistochemistry

Abstract

fetched live from OpenAlex

Abstract Background It has been suggested that phosphorylated tau(p‐tau) at threonine 231 and 217 are markers of amyloid‐β(Aβ) rather than tau pathology. However, most of the studies were conducted in cohorts composed mainly of preclinical Alzheimer Disease(AD) individuals. It remains to be elucidated if p‐tau is still more associated with Aβ than tau pathology in symptomatic individuals. Here, we evaluate the contribution of each plasma biomarker to the demographics in identifying brain Aβ and tau pathologies across the AD spectrum. Method We evaluated 138 cognitively unimpaired(CU) and 87 cognitively impaired(CI) individuals with available Aβ[18F]AZD4694 PET and tau[18F]MK‐6340 PET, plasma Aβ42/40, p‐tau (at threonine 181, 217, and 231), neurofilament light chain, and glial fibrillary acidic protein(GFAP), from the McGill TRIAD cohort(Table1). The performance of plasma biomarkers in predicting Aβ‐ and tau‐PET abnormalities over that one provided by demographics‐only(age and sex) was evaluated using logistic and linear regression, receiver operating characteristic analysis, and goodness‐of‐fit metrics. Using voxel‐wise linear regression models, we assessed the brain regions where plasma biomarker contribution to the demographic‐only model overlapped to detect Aβ and tau‐PET signals. Result Our results demonstrated that in the CU only plasma p‐tau231 and p‐tau217+ significantly added to the demographics‐only model to detect Aβ pathology(Figure 1A), while no plasma biomarker added information to identify tau pathology(Figure 1B). In the CI, plasma p‐tau217+ and GFAP significantly added to the demographic‐only model to identify tau and Aβ pathology(Figure 1C), while p‐tau231 only added to detect tau deposition(Figure 1D). P‐tau181, Aβ42/40, and NfL did not significantly add to the demographics‐only in CU or CI group (Figure 1). Voxel‐wise analysis demonstrated that in CU, p‐tau231 and p‐tau217+ were only regionally associated with Aβ‐PET(Figure 2A‐C). In CI, p‐tau231 provided additional information on tau tangle accumulation in AD‐related regions(Figure 2D). On the other hand, for p‐tau217+, 3% of brain regions were only associated with Aβ‐PET, 35% with only tau‐PET, and 39% overlapped with both(Figure 2E). Conclusion Our results support plasma p‐tau231 and p‐tau217+ as state markers of Aβ deposition in preclinical AD. In CI, plasma p‐tau231 is mainly related to tau pathology, and p‐tau217+ appears to be linked to both Aβ and tau pathology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.299
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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