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Record W4312087425 · doi:10.1002/alz.064811

Longitudinal Blood Biomarker Trajectories in Preclinical Alzheimer’s Disease

2022· article· en· W4312087425 on OpenAlexaff
Yara Yakoub, Nicholas J. Ashton, Thomas K. Karikari, Cherie Strikwerda‐Brown, Laia Montoliu‐Gaya, Pierre‐François Meyer, Frédéric St‐Onge, Michael Schöll, John C.S. Breitner, Henrik Zetterberg, Kaj Blennow, Judes Poirier, Sylvia Villeneuve

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsBiomarkerPositron emission tomographyMedicineCohortOncologyInternal medicinePathologicalStandardized uptake valueEntorhinal cortexDiseasePathologyNuclear medicineHippocampusBiology

Abstract

fetched live from OpenAlex

Abstract Background Blood‐biomarkers of Alzheimer’s disease (AD) pathology have been investigated cross‐sectionally in heterogenous AD cohorts and have been shown to detect underlying AD pathology, even at preclinical stages. However, longitudinal studies, including serial blood‐biomarker measurements, are needed to better understand whether these markers could help monitor disease progression. We aimed to assess blood‐biomarkers temporal trajectories in cognitively unimpaired older adults at different pathological stages as assessed by positron emission tomography (PET). This may provide insight into dynamic changes of these biomarkers beginning prior to abnormality on PET. Method We included a subset of 126 cognitively unimpaired older adults from the Prevent‐AD cohort. Blood was drawn from baseline up to four‐year follow‐up visits. We measured Aβ42/Aβ40 ratio, pTau181 and pTau231 using novel single molecular array (Simoa). All participants completed Aβ (18F‐NAV4694) and tau (18F‐flortaucipir) PET scans, which were mostly performed at the latest blood collection timepoint. Aβ positivity was defined by global neocortical Aβ‐PET retention (SUVR cut‐off = 1.29), and tau‐PET positivity by entorhinal cortex flortaucipir binding (SUVR cut‐off = 1.23). Using these thresholds, 82 subjects were classified as A‐T‐, 29 as A+T‐, and 15 as A+T+. Linear mixed effects models were used to assess differences in the longitudinal rate of change in plasma biomarkers between the groups. Result Amyloid‐PET‐positive individuals showed elevated levels of pTau181, and pTau231 and lower levels of Aβ42/40 when compared with amyloid‐PET‐negative participants. Plasma pTau181 levels showed a greater increase over time in the A+T+ group when compared with the A‐T‐ group (p = 0.01; Figure 1). Overall, the A+T‐ and A+T+ groups had lower levels of plasma Aβ42/40 compared with the A‐T‐ group (p = 0.05, p = 0.0189; Figure 2), and the A+T‐ group had higher pTau231 compared with A‐T‐ participants (p = 0.0006; Figure 3). Longitudinal rate of change in Aβ42/40 and pTau231 did not differ between the groups. Conclusion We observed increased pTau181 levels and rate of change in those with both amyloid and tau pathology on PET. The slopes between PET groups did not differ across time when using pTau231 and Aβ biomarkers despite group differences in the overall level of 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)0.001
metaresearch head score (Gemma)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.366
Teacher spread0.287 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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